Article

Observing-system impact assessment using a data assimilation ensemble technique: Application to the ADM - Aeolus wind profiling mission

Quarterly Journal of the Royal Meteorological Society

March 2007
133(623):381 - 390

DOI:10.1002/qj.43

Authors:

Erik Andersson

European Center For Medium Range Weather Forecasts

Ensembles of parallel 4D-Var data assimilation cycles have been used to assess the impact of two observing systems: the existing network of radiosonde and wind profilers, and the future spaceborne ADM–Aeolus wind-profiling LIDAR. We demonstrate that this new technique for impact assessment provides a practical alternative to the traditional observing system simulation experiments (OSSEs), with the particular advantage that real existing observations are assimilated exactly as in operational practice, and do not need to be simulated artificially. It is only the future observing system under test (ADM–Aeolus in our case) that is generated through simulation. Furthermore, in contrast with OSSEs, there is no need to generate an artificial reference atmosphere (‘proxy truth’ or ‘nature run’), and the problems normally associated with identical-twin experiments are thus avoided. Our results, based on detailed simulation of the ADM–Aeolus wind-measuring capabilities and expected data quality, show that ADM–Aeolus will provide benefits comparable to the radiosonde and wind-profiler network, with analysis impact particularly over ocean and in the tropics. The impact is retained up to the medium range of forecast (around day 5). Our results for radiosonde and wind-profiler impact agree qualitatively with those obtained with the well-established observing system experiment (OSE) technique; this agreement gives some confidence in the usefulness of the ensemble-based technique for impact assessment. Copyright © 2007 Royal Meteorological Society

Assessment for operational assimilation of horizontal line of sight winds from the European Space Agency's Aeolus at the Met Office

Article

Full-text available

May 2024
Q J ROY METEOR SOC

The European Space Agency's Aeolus satellite was launched in August 2018 and began delivering horizontal line‐of‐sight (HLOS) wind observations in early September 2018. In early 2019, the Met Office began assessing the suitability of the HLOS winds for operational assimilation into its global numerical weather prediction system. We performed a number of assimilation experiments to assess the impact of HLOS wind observations on our global forecasts. We have found that assimilating HLOS winds changes the zonal winds in the analysis fields predominantly in the Tropics and Southern Hemisphere, with the largest changes being in the upper troposphere and lower stratosphere. This has a positive impact on the accuracy of the global weather forecasts, with improvements in the root‐mean‐square error seen throughout the troposphere. Assimilation of Aeolus HLOS winds improves the standard deviation of the observation minus background (a 6 hr forecast) of almost all other observation types, suggesting that the numerical weather prediction model analysis is improved, which consequently improves the 6 hr forecast. In a set of short‐period observation denial experiments, we found that assimilating Aeolus has an impact similar in magnitude to assimilating surface winds from scatterometers. Assimilating winds from the Rayleigh channel has approximately three times the impact that assimilating HLOS winds from the Mie channel does. Both channels contribute a measureable improvement to the global forecast, and we therefore started operational assimilation of winds from the Mie channel in December 2020 and the Rayleigh channel operationally in May 2022.

Assessing forecast benefits of future constellations of microwave sounders on small satellites using an ensemble of data assimilations

Article

Full-text available

Feb 2025
Q J ROY METEOR SOC

The ensemble of data assimilations (EDA) method is employed to evaluate the expected impact of a wide range of potential future constellations of passive microwave (MW) sounders on small satellites for global numerical weather prediction. Such constellations are expected to become an important component of the future observing system, complementing a backbone of larger, high‐performance platforms and allowing unprecedented temporal sampling. Twelve constellations are investigated to probe key aspects of the constellation design: number of satellites (ranging from 8 to 20), types of orbits (sun synchronous, mid‐inclination), and channel complement (183 GHz humidity‐sounding capabilities only versus combining these with temperature‐sounding capability around 50 GHz). The small‐satellite data and accompanying errors are simulated, using an all‐sky framework, and the relative benefits from adding the different constellations to the observing system are measured by the reduction in the spread of the ensemble members, reflecting improvement to the forecast uncertainties. Results suggest continued benefit from adding MW‐sounding observations beyond currently available observations. The EDA spread reduction for different variables (e.g., wind and geopotential height) and different pressure levels is already significant using the smallest constellation considered, adding eight satellites to a four‐satellite baseline of existing MW‐sounding instruments. The EDA spread reduction continues as further observations are added, although the rate of reduction slows significantly, especially where scenarios use only humidity‐sounding channels. Use of temperature‐sounding channels gives significant added benefit over humidity sounding only, generally 2–3 and 1.5–2 times larger in the extratropics and Tropics respectively. Different behaviour in the relative magnitudes and rates of EDA spread reduction is seen between the extratropics and Tropics, which can be attributed to different physical processes and different error growth. This study was unable to provide conclusive results on the choice of polar orbits only, mid‐inclination orbits only, or a mix of orbit types.

Operational assimilation of Aeolus winds in the Météo‐France global NWP model ARPEGE

Article

Full-text available

Jun 2022
Q J ROY METEOR SOC

The European Space Agency's Aeolus satellite was launched in August 2018. Measurements of wind profiles are provided for the first time from space using an onboard Doppler wind lidar. The quality of Aeolus Level‐2B (L2B) wind products has been found suitable for data assimilation in the Météo‐France global model ARPEGE since April 2020, in particular, when applying a suitable bias correction method. This article describes a series of Observing System Experiments (OSEs) conducted in April–May 2020 to assess the impact of Aeolus horizontal line‐of‐sight winds (HLOSW) on Météo‐France's global numerical weather prediction analyses and forecasts. Innovation statistics and a posteriori diagnostics from a period of July–August 2019 were used to scale the random observation errors provided by the L2B processor (mostly for Rayleigh‐clear winds). Although the HLOSW data represent only 0.42% of the total amount of all observations assimilated in ARPEGE, their contribution to the reduction of the global analysis variance is up to 2.3% (measured by the Degree of Freedom for Signal). The assimilation of HLOSW showed improvement in 6 hr short‐range forecasts which is demonstrated by an overall reduction of innovations statistics for various operational observing systems. From a Forecast Sensitivity to Observations impact (FSOi) study Aeolus is found to be the third most effective observing system (per individual observation) at reducing global 24‐hour forecast errors. For longer forecast ranges, the largest positive impacts are noticed over the tropics, particularly in the lower stratosphere up to 102 hr ahead (with up to 2% root‐mean‐square error reduction for wind and temperature), but also in the troposphere up to 72 hr ahead. To a lesser extent, a similar improvement is observed over the Southern Hemisphere. This positive impact of Aeolus HLOSW in OSEs has led to their operational assimilation at Météo‐France starting in June 2020.

Assimilation of individual components of radiosonde winds: An investigation to assess the impact of single-component winds from space-borne measurements on NWP

Article

Full-text available

Jun 2021
J EARTH SYST SCI

Wind observations are critical for the better atmospheric analysis for Numerical Weather Prediction (NWP), particularly over the tropics. High-resolution direct wind observations are essential for defining smaller scales and deeper atmospheric structures. Recently launched Aeolus satellite delivers wind profiles that mostly satisfy these requirements, suitable for NWP assimilation. The main product from Aeolus is the horizontally projected Line of Sight wind component, a single component of wind, approximately zonal in nature over the tropics and more meridional over the Polar region, and the main limitation of this observation. Observing system experiments are conducted with the assimilation of individual components of radiosonde and pilot balloon winds to assess the impact of a single component of wind compared to the assimilation of full wind vector in the NCMRWF global assimilation and forecast system. Denial of the zonal component of wind in the assimilation system produced a larger observation increment (observation – model background) in the meridional wind than the full vector assimilation. In contrast, the observation increment of the zonal wind remains nearly the same, even after removing the meridional wind component from the assimilation system. Assimilation of both zonal and meridional components produced changes in the analysis fields of various meteorological variables; however, the zonal component plays a significant role in the tropics. Both wind components play an important role in controlling the humidity field, whereas only zonal components of wind impact the temperature field in the upper troposphere and lower stratosphere. Though the full vector wind assimilation produces a larger impact in the forecast fields of various meteorological variables, the zonal component has more impact than the meridional component. Verification of analysis and forecast wind against the satellite-derived atmospheric motion vectors clearly show the importance of both the horizontal components of winds in the lower troposphere. In contrast, the zonal component of wind alone has a high impact on the upper troposphere and lower stratosphere. https://www.ias.ac.in/article/fulltext/jess/130/0089

Assimilation of spaceborne Doppler wind lidar observations in a mesoscale model

Thesis

Aug 2019

Matic Šavli

A continuous improvement of weather prediction is the most important activity of the most of meteorological research. Numerical Weather Prediction (NWP) is the initial value problem, in addition dependent on the quality of numerical model. The NWP improvements rely substantially on the quality atmospheric observations. They are needed in the process of data assimilation that prepares initial conditions for the model forecast. The lack of observations of wind profiles is currently the main shortcoming of the Global Observing System (GOS). The wind information is crucial in the tropics and for small-scale processes in the extra-tropics. On 22 August 2018, a long awaited ESA’s mission, the Aeolus satellite has been launched, which marks the beginning of the new era of measuring winds using lidars from space. Aeolus will measure the so-called horizontal line-of-sight (HLOS) winds below about 30 km. This is the wind component measured in the direction of the pointing lidar and projected horizontally. The line of sight is defined by the azimuth angle from the north which is in the midlatitudes around 60o. Winds are retrieved from the light scattered on the air molecules (Rayleigh winds) and on the air particles such as aerosols and cloud particulates (Mie winds). The HLOS Aeolus winds are expected to improve the forecast skill in global models. The potential of HLOS winds in limited area models (LAMs), the main objective of this thesis, has not been yet addressed. As LAMs simulate small-scale processes, their initialization requires higher resolution observations compared to global models. Even though the Aeolus data with its default horizontal resolution of 90 km can not provide many profiles for the use in a LAM domain, they may be valuable due to the lack of wind profiles. In addition, it is possible to increase the HLOS horizontal resolution at the expense of the data accuracy. The main goal of the thesis is to assess the potential of the HLOS winds in comparison to the zonal and meridional wind components and the full wind information in a LAM domain over Europe and northern Atlantic. As a single HLOS observation contains some information on both the zonal and meridional wind components, its impact in the assimilation will project on both components depending on the azimuth and data assimilation modelling, especially the covariances of the background errors which define the spreading of observed information in the model space. The impact of HLOS profiles in a LAM was addressed using the ensemble data assimilation that provides flow-dependent background error covariance. A novel system built for the thesis is based on the 50-member ensemble using the Weather Research and Forecasting (WRF) model and the Ensemble Adjustment Kalman Filter (EAKF), nested in the state-of-the-art operational ensemble prediction system of the European Centre for medium-Range Weather Forecasts (ECMWF). The flow-dependent representation of the background-error covariances has been shown crucial for the assimilation of HLOS. This was demonstrated on the case of a cold front in the North Atlantic. It was also shown that the assimilation of HLOS winds in special cases with the EAKF may be more useful than the assimilation of full wind vector. An average potential of HLOS winds was investigated using a series of Observing System Simulation Experiments (OSSEs) that compared the impact of simulated HLOS data with the impact of full wind and its two wind components as well as temperature observations. Results show that the impact of HLOS winds is linearly distributed between the zonal and meridional wind components as defined by the applied azimuth of 30◦ from the zonal direction. The multivariate coupling has been found on average weak. Despite a weak multivariate impact, the HLOS winds have been shown promising as they provide better analysis in the zonal wind component compared to the case when only meridional winds are assimilated, and a better impact on the meridional wind compared to the assimilation of the zonal wind component only. The impact of increased resolution of Aeolus observations was addressed using sensitivity experiments with the Aeolus simulator and a global high resolution (T3999) 10-day forecast of ECMWF coupled with the CALIPSO satellite observations of optical properties of the atmosphere. It is found that the Mie winds are less sensitive on the changes in the accumulation length used to prepare a single HLOS profile then the Rayleigh winds. In particular, the Mie wind observation error is found rather constant with amplitude 1-1.2 ms−1 for the range of the accumulation lengths between 30 km and 90 km. These results suggest a significant tuning potential of the Aeolus retrieval for the need of weather prediction with high-resolution LAMs. Available at: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=104358

Calibrations and Wind Observations of an Airborne Direct-Detection Wind LiDAR Supporting ESA’s Aeolus Mission

Article

Full-text available

Dec 2018

The Aeolus satellite mission of the European Space Agency (ESA) has brought the first wind LiDAR to space to satisfy the long-existing need for global wind profile observations. Until the successful launch on 22 August 2018, pre-launch campaign activities supported the validation of the measurement principle, the instrument calibration, and the optimization of retrieval algorithms. Therefore, an airborne prototype instrument has been developed, the ALADIN Airborne Demonstrator (A2D), with ALADIN being the Atmospheric Laser Doppler Instrument of Aeolus. Two airborne campaigns were conducted over Greenland, Iceland and the Atlantic Ocean in September 2009 and May 2015, employing the A2D as the first worldwide airborne direct-detection Doppler Wind LiDAR (DWL) and a well-established coherent 2-µm wind LiDAR. Both wind LiDAR instruments were operated on the same aircraft measuring Mie backscatter from aerosols and clouds as well as Rayleigh backscatter from molecules in parallel. This paper particularly focuses on the instrument response calibration method of the A2D and its importance for accurate wind retrieval results. We provide a detailed description of the analysis of wind measurement data gathered during the two campaigns, introducing a dedicated aerial interpolation algorithm that takes into account the different resolution grids of the two LiDAR systems. A statistical comparison of line-of-sight (LOS) winds for the campaign in 2015 yielded estimations of the systematic and random (mean absolute deviation) errors of A2D observations of about 0.7 m/s and 2.1 m/s, respectively, for the Rayleigh, and 0.05 m/s and 2.3 m/s, respectively, for the Mie channel. In view of the launch of Aeolus, differences between the A2D and the satellite mission are highlighted along the way, identifying the particular assets and drawbacks.

Feasibility Study for Future Space-Borne Coherent Doppler Wind Lidar, Part 3: Impact Assessment Using Sensitivity Observing System Simulation Experiments

Article

Feb 2018

This study evaluated the impact of a future space-borne Doppler wind lidar (DWL) on a super-low-altitude orbit by using an observing system simulation experiment (OSSE) based on a sensitivity observing system experiment (SOSE) approach. Realistic atmospheric data, including wind and temperature, was provided as “pseudo-truth” (PT) to simulate DWL observations. Hourly aerosols and clouds that are consistent with PT winds were also created for the simulation. A full-scale lidar simulator, which is described in detail in the companion paper, simulated realistic line-of-sight wind measurements and observation quality information, such as signal-to-noise ratio (SNR)and measurement error. Quality control (QC) procedures in the data assimilation system were developed to select high-quality DWL observations on the basis of the averaged SNR from strong backscattering in the presence of aerosols or clouds. Furthermore, DWL observation errors used in the assimilation were calculated using the measurement error estimated by the lidar simulator. The forecast impacts of DWL onboard polar- and tropical-orbiting satellites were assessed using the operational global data assimilation system. Data assimilation experiments were conducted in January and August in 2010 to assess overall impact and seasonal dependence. It is found that DWL on either polar- or tropical-orbiting satellites is overall beneficial for wind and temperature forecasts, with greater impacts for the January experiments. The relative forecast error reduction reaches almost 2 % in the tropics. An exception is degradation in the southern hemisphere in August, thus suggesting a need to further refine observation error assignment and QC. A decisive conclusion cannot be drawn with regard to the superiority of polar- or tropical-orbiting satellites because of their mixed impacts. This is probably related to the characteristics of error growth in the tropics. The limitations and possible underestimation of the DWL impacts, for example, due to a simple observation error inflation setting, in the SOSE-OSSE are also discussed.

The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)

Article

Full-text available

Jan 2025
GMD

The Modular and Integrated Data Assimilation System (MIDAS) software (version 3.9.1) is described in terms of its range of functionality, modular software design, parallelization strategy, and current uses within real-time operational and experimental systems. MIDAS is developed at Environment and Climate Change Canada for both operational and research applications, including all atmospheric data assimilation (DA) elements of the Canadian operational numerical weather prediction systems. The described version of MIDAS is part of the Canadian prediction systems that became operational in June 2024. The software is designed to be sufficiently general to enable other DA applications, including atmospheric constituents (e.g. ozone), sea ice, and sea surface temperature. In addition to describing the current MIDAS applications, a sample of the results from these systems is presented to demonstrate their performance in comparison with either systems from before the switch to using MIDAS software or similar systems at other numerical weather prediction (NWP) centres. The modular software design also allows the code that implements high-level components (e.g. observation operators, error covariance matrices, state vectors) to easily be used in many different ways depending on the application, such as for both variational and ensemble DA algorithms, for estimating the observation impact on short-term forecasts, and for performing various observation pre-processing procedures. The use of a single common DA software package for multiple components of the Earth system provides both practical and scientific benefits, including the facilitation of future research on DA approaches that explicitly include the coupled connections between multiple Earth system components. To this end, work is currently underway to allow the use of MIDAS DA algorithms for initializing both deterministic and ensemble three-dimensional ocean model forecasts.

The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)

Preprint

Full-text available

Apr 2024

The Modular and Integrated Data Assimilation System (MIDAS) software (version 3.9.1) is described in terms of its range of functionality, modular software design, parallelization strategy, and current uses within real-time operational and experimental systems. MIDAS is developed at Environment and Climate Change Canada for both operational and research applications, including all atmospheric data assimilation (DA) elements of the numerical weather prediction systems. The software is designed to be sufficiently general to enable other DA applications, including atmospheric constituents (e.g. ozone), sea ice, and sea surface temperature. In addition to describing the current MIDAS applications, a sample of the results from these systems is presented to demonstrate their performance in comparison with either systems from before the switch to using MIDAS software or similar systems at other NWP centres. The modular software design also allows the code that implements high-level components (e.g. observation operators, error covariance matrices, state vectors) to easily be used in many different ways depending on the application, such as for both variational and ensemble DA algorithms; for estimating the observation impact on short-term forecasts; and for performing various observation pre-processing procedures. The use of a single common DA software for multiple components of the Earth system provides both practical and scientific benefits, including the facilitation of future research on DA approaches that explicitly include the coupled connections between multiple Earth system components. To this end, work is currently underway to allow the use of MIDAS DA algorithms for initializing both deterministic and ensemble three-dimensional ocean model forecasts.

Observing System Simulation Experiments (OSSEs) in Support of Next-Generation NOAA Satellite Constellation

Article

Mar 2024

Between 2014 and 2018 the National Oceanic and Atmospheric Administration conducted the NOAA Satellite Observing System Architecture (NSOSA) study to plan for the next generation of operational environmental satellites. The study generated some important questions that could be addressed by Observing System Simulation Experiments (OSSEs). This paper describes a series of OSSEs in which benefits to numerical weather prediction from existing observing systems are combined with enhancements from potential future capabilities. Assessments include the relative value of the quantity of different types of thermodynamic soundings for global numerical weather applications. We compare the relative impact of several sounding configuration scenarios for infrared (IR), microwave (MW), and radio occultation (RO) observing capabilities. The main results are: (1) increasing the revisit rate for satellite radiance soundings produces the largest benefits, but at a significant cost by requiring an increase of the number of polar orbiting satellites from two to twelve; (2) a large positive impact is found when the number of RO soundings/day is increased well beyond current values and other observations are held at current levels of performance; (3) RO can be used as a mitigation strategy for lower MW/IR sounding revisit rates, particularly in the tropics; and (4) smaller benefits result from increasing the horizontal resolution along the track of the satellites of MW/IR satellite radiances. Furthermore, disaggregating IR and MW instruments into six evenly distributed sun-synchronous orbits is slightly more beneficial than when the same instruments are combined and collocated on three separate orbits.

Sampled Together: Assessing the Value of Simultaneous Co-located Measurements for Optimal Satellite Configurations

Article

Full-text available

Dec 2023

This work applies a quantitative metric in order to capture the relative representativeness of non-simultaneous or non-co-located observations and quantify how these observations decorrelate in both space and time. This methodology allows for the effective determination of thresholding decisions for representative matchup conditions, and is especially useful for informing future network designs and architectures. Future weather and climate satellite missions must consider a range of architectural trades to meet observing requirements. Frequently, fundamental decisions such as the number of observatories, the instruments manifested, and orbit parameters are determined based upon assumptions about the characteristic temporal and spatial scales of variability of the target observation. With the introduced methodology, representativity errors due to separations in space and time can be quantified without prior knowledge of instrument performance, and errors driven by constellation design can be estimated without model ingest or analysis.

Impact of assimilating Aeolus observations in the global model ICON: A global statistical overview

Article

Full-text available

Aug 2023
Q J ROY METEOR SOC

Global wind profiles provided by the satellite mission Aeolus are an important recent supplement to the Global Observing System. This study investigates the impact of Aeolus horizontal line‐of‐sight wind observations in the operational global assimilation and forecasting system of Deutscher Wetterdienst that is based on the Icosahedral Nonhydrostatic (ICON) model. For this purpose, an observing system experiment was conducted and evaluated for a 3‐month period from July 2020 to October 2020. The Aeolus Rayleigh clear and Mie cloudy data quality and consistency were derived from observation minus background statistics. To correct for an altitude‐dependent bias, a model‐based bias correction scheme has been implemented. Comparisons of the systematic changes in the analysis and the respective forecasts provide an overview of the overall impact of the Aeolus horizontal line‐of‐sight wind assimilation in ICON. Increased influence of Aeolus wind profiles is found in jet regimes (e.g., amplification of the zonal wind component), around large‐scale circulation systems, and convectively active areas in the Tropics. The reduction in forecast error is largest in the tropical upper troposphere and stratosphere, as well as in the mid and upper troposphere of the Southern Hemisphere. The Northern Hemisphere shows a somewhat smaller but still beneficial impact of Aeolus observations. The verification with other conventional observations shows a mean relative reduction in short‐range forecast error between 0.1% and 0.6% in the Northern Hemisphere and up to 1.6% in the Tropics and the Southern Hemisphere. When verifying against the European Centre for Medium‐Range Weather Forecast Reanalysis v5, forecast errors of zonal wind, temperature, and geopotential up to 5 days lead time are reduced by 2–4% on global average and up to 5–8% around the tropical tropopause.

Impacts of Marine Surface Pressure Observations from a Spaceborne Differential Absorption Radar Investigated with an Observing System Simulation Experiment

Article

Full-text available

Jun 2023

A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multi-channel differential absorption radar (DAR) to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration (NASA) for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system. Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root mean square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-hour forecast temperatures for spaceborne surface pressures on par with rawinsondes and aircraft, and greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the southern hemisphere extratropics.

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

Article

Full-text available

Dec 2022
AMT

During the first 3 years of the European Space Agency's Aeolus mission, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) performed four airborne campaigns deploying two different Doppler wind lidars (DWL) on board the DLR Falcon aircraft, aiming to validate the quality of the recent Aeolus Level 2B (L2B) wind data product (processor baseline 11 and 12). The first two campaigns, WindVal III (November–December 2018) and AVATAR-E (Aeolus Validation Through Airborne Lidars in Europe, May and June 2019), were conducted in Europe and provided first insights into the data quality at the beginning of the mission phase. The two later campaigns, AVATAR-I (Aeolus Validation Through Airborne Lidars in Iceland) and AVATAR-T (Aeolus Validation Through Airborne Lidars in the Tropics), were performed in regions of particular interest for the Aeolus validation: AVATAR-I was conducted from Keflavik, Iceland, between 9 September and 1 October 2019 to sample the high wind speeds in the vicinity of the polar jet stream; AVATAR-T was carried out from Sal, Cape Verde, between 6 and 28 September 2021 to measure winds in the Saharan dust-laden African easterly jet. Altogether, 10 Aeolus underflights were performed during AVATAR-I and 11 underflights during AVATAR-T, covering about 8000 and 11 000 km along the Aeolus measurement track, respectively. Based on these collocated measurements, statistical comparisons of Aeolus data with the reference lidar (2 µm DWL) as well as with in situ measurements by the Falcon were performed to determine the systematic and random errors of Rayleigh-clear and Mie-cloudy winds that are contained in the Aeolus L2B product. It is demonstrated that the systematic error almost fulfills the mission requirement of being below 0.7 m s-1 for both Rayleigh-clear and Mie-cloudy winds. The random error is shown to vary between 5.5 and 7.1 m s-1 for Rayleigh-clear winds and is thus larger than specified (2.5 m s-1), whereas it is close to the specifications for Mie-cloudy winds (2.7to2.9 m s-1). In addition, the dependency of the systematic and random errors on the actual wind speed, the geolocation, the scattering ratio, and the time difference between 2 µm DWL observation and satellite overflight is investigated and discussed. Thus, this work contributes to the characterization of the Aeolus data quality in different meteorological situations and allows one to investigate wind retrieval algorithm improvements for reprocessed Aeolus data sets.

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

Article

Aug 2022

Fabian Weiler

During the first three years of European Space Agency’s Aeolus mission, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) performed four airborne campaigns deploying two different Doppler wind lidars (DWL) on-board the DLR Falcon aircraft, aiming to validate the quality of the recent Aeolus Level 2B (L2B) wind data product (processor baseline 11 and 12). The first two campaigns, WindVal III (Nov/Dec 2018) and AVATAR-E (Aeolus Validation Through Airborne Lidars in Europe, May/Jun 2019) were conducted in Europe and provided first insights in the data quality at the beginning of the mission phase. The two later campaigns, AVATAR-I (Aeolus Validation Through Airborne Lidars in Iceland) and AVATAR-T (Aeolus Validation Through Airborne Lidars in the Tropics), were performed in regions of particular interest for the Aeolus validation: AVATAR-I was conducted from Keflavik, Iceland between 9 September and 1 October 2019 to sample the high wind speeds in the vicinity of the polar jet stream. AVATAR-T was carried out from Sal, Cape Verde between 6 September and 28 September 2021 to measure winds in the Saharan dust-laden African easterly jet. Altogether, 10 Aeolus underflights were performed during AVATAR-I and 11 underflights during AVATAR-T, covering about 8000 km and 11000 km along the Aeolus measurement track, respectively. Based on these collocated measurements, statistical comparisons of Aeolus data with the reference lidar (2-µm DWL) as well as with in-situ measurements by the Falcon were performed to determine the systematic and random error of Rayleigh-clear and Mie-cloudy winds that are contained in the Aeolus L2B product. It is demonstrated that the systematic error almost fulfills the mission requirement of being below 0.7 m s−1 for both Rayleigh-clear and Mie-cloudy winds. The random error is shown to vary between 5.5 m s−1 and 7.1 m s−1 for Rayleigh-clear winds and is thus larger than specified (2.5 m s−1 ), whereas it is close to the specifications for Mie-cloudy winds (2.7 to 2.9 m s−1). In addition, the dependency of the systematic and random errors on the actual wind speed, the geolocation, the scattering ratio and the time difference between 2-µm DWL observation and satellite overflight is investigated and discussed. Thus, this work contributes to the characterization of the Aeolus data quality in different meteorological situations and allows to investigate wind retrieval algorithm improvements for reprocessed Aeolus data sets.

Investigation of dynamical scenarios leading to particularly high impact of Aeolus on NWP forecasts

Preprint

Full-text available

Nov 2022

This study discusses the dynamical scenarios underlying the overall beneficial impact of Aeolus wind observations in the ICOsahedral Nonhydrostatic (ICON) global model of Deutscher Wetterdienst (DWD). Therefore, it is focused on time periods and regions with particularly large forecast error reduction. The phase shift of large-scale tropical circulation systems, namely the quasi-biennial oscillation (QBO) and the El Niño–Southern Oscillation (ENSO), and the interaction of tropical cyclones undergoing extratropical transition (ET) with the midlatitude waveguide have been found to constitute potential pathways for significant improvement.

An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space

Article

Full-text available

Oct 2022

The task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi‐global coverage, are non‐intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short‐term numerical weather and sub‐seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources.

Global Observing System Experiments within the Météo-France 4D-Var Data Assimilation System

Article

Oct 2022

Observing System Experiments were undertaken within the 4D-Var data assimilation of the Météo-France global Numerical Weather Prediction (NWP) model. A six-month period was chosen (October 2019 - March 2020) where 40 millions of observations per day were assimilated. The importance of in-situ observations provided by aircraft, radiosondes and surface weather stations, despite their small fractional amount (7 %), has been confirmed particularly in the Northern Hemisphere. Moreover, the largest impact over Europe in terms of Root Mean Square Error (RMSE) scores comes from surface observations. Satellite data play a dominant role over tropical regions and the Southern Hemisphere. Microwave radiances have a more pronounced impact on the long range and on the humidity field than infrared radiances, despite being less numerous (10 % versus 80 %). Bending angles impact significantly the quality of the upper troposphere / lower stratosphere temperature of the tropics and Southern Hemisphere. Atmospheric Motion Vectors (AMVs) are beneficial in wind forecasts at low and high levels in the tropics and the Southern Hemisphere, but also in the humidity field. Such impacts are only significant during the first 48 hours of the forecasts. Scatterometer winds have an impact restricted to low levels which is kept at longer ranges. A comparison with Forecast Sensitivity - Observation Impact studies over a 3 month period using the same measure of short-range (24 h) forecast errors reveals that the ranking between the major observing systems is kept between these two ways of measuring observation impact in NWP. From our conclusions, recommendations are provided on possible evolutions of the global observing system for NWP.

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

Preprint

Full-text available

Aug 2022

During the first three years of European Space Agency’s Aeolus mission, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) performed four airborne campaigns deploying two different Doppler wind lidars (DWL) on-board the DLR Falcon aircraft, aiming to validate the quality of the recent Aeolus Level 2B (L2B) wind data product (processor baseline 11 and 12). The first two campaigns, WindVal III (Nov/Dec 2018) and AVATAR-E (Aeolus Validation Through Airborne Lidars in Europe, May/Jun 2019) were conducted in Europe and provided first insights in the data quality at the beginning of the mission phase. The two later campaigns, AVATAR-I (Aeolus Validation Through Airborne Lidars in Iceland) and AVATAR-T (Aeolus Validation Through Airborne Lidars in the Tropics), were performed in regions of particular interest for the Aeolus validation: AVATAR-I was conducted from Keflavik, Iceland between 9 September and 1 October 2019 to sample the high wind speeds in the vicinity of the polar jet stream. AVATAR-T was carried out from Sal, Cape Verde between 6 September and 28 September 2021 to measure winds in the Saharan dust-laden African easterly jet. Altogether, 10 Aeolus underflights were performed during AVATAR-I and 11 underflights during AVATAR-T, covering about 8000 km and 11000 km along the Aeolus measurement track, respectively. Based on these collocated measurements, statistical comparisons of Aeolus data with the reference lidar (2-µm DWL) as well as with in-situ measurements by the Falcon were performed to determine the systematic and random error of Rayleigh-clear and Mie-cloudy winds that are contained in the Aeolus L2B product. It is demonstrated that the systematic error almost fulfills the mission requirement of being below 0.7 m s-1 for both Rayleigh-clear and Mie-cloudy winds. The random error is shown to vary between 5.5 m s-1 and 7.1 m s-1 for Rayleigh-clear winds and is thus larger than specified (2.5 m s-1), whereas it is close to the specifications for Mie-cloudy winds (2.7 to 2.9 m s-1). In addition, the dependency of the systematic and random errors on the actual wind speed, the geolocation, the scattering ratio and the time difference between 2-µm DWL observation and satellite overflight is investigated and discussed. Thus, this work contributes to the characterization of the Aeolus data quality in different meteorological situations and allows to investigate wind retrieval algorithm improvements for reprocessed Aeolus data sets.

Connecting Hazard and Impact: A Partnership between Physical and Human Science

Chapter

Full-text available

Jun 2022

The bridge from a hazard to its impact is at the heart of current efforts to improve the effectiveness of warnings by incorporating impact information into the warning process. At the same time, it presents some of the most difficult and demanding challenges in contrasting methodology and language. Here we explore the needs of the impact scientist first, remembering that the relevant impacts are those needed to be communicated to the decision maker. We identify the challenge of obtaining historical information on relevant impacts, especially where data are confidential, and then of matching suitable hazard data to them. We then consider the constraints on the hazard forecaster, who may have access to large volumes of model predictions, but cannot easily relate these to the times and locations of those being impacted, and has limited knowledge of model accuracy in hazardous situations. Bridging these two requires an open and pragmatic approach from both sides. Relationships need to be built up over time and through joint working, so that the different ways of thinking can be absorbed. This chapter includes examples of partnership working in the Australian tsunami warning system, on health impact tools for dispersion of toxic materials in the UK and on the health impacts of heatwaves in Australia. We conclude with a summary of the characteristics that contribute to effective impact models as components of warning systems, together with some pitfalls to avoid.

Early Warning Systems and Their Role in Disaster Risk Reduction

Chapter

Full-text available

Jan 2022

In this chapter, we introduce early warning systems (EWS) in the context of disaster risk reduction, including the main components of an EWS, the roles of the main actors and the need for robust evaluation. Management of disaster risks requires that the nature and distribution of risk are understood, including the hazards, and the exposure, vulnerability and capacity of communities at risk. A variety of policy options can be used to reduce and manage risks, and we emphasise the contribution of early warnings, presenting an eight-component framework of people-centred early warning systems which highlights the importance of an integrated and all-society approach. We identify the need for decisions to be evidence-based, for performance monitoring and for dealing with errors and false information. We conclude by identifying gaps in current early warning systems, including in the social components of warning systems and in dealing with multi-hazards, and obstacles to progress, including issues in funding, data availability, and stakeholder engagement.

Connecting Forecast and Warning: A Partnership Between Communicators and Scientists

Chapter

Full-text available

Jan 2022

In this chapter, we examine the ways that warning providers connect and collaborate with knowledge sources to produce effective warnings. We first look at the range of actors who produce warnings in the public and private sectors, the sources of information they draw on to comprehend the nature of the hazard, its impacts and the implications for those exposed and the process of drawing that information together to produce a warning. We consider the wide range of experts who connect hazard data with impact data to create tools for assessing the impacts of predicted hazards on people, buildings, infrastructure and business. Then we look at the diverse ways in which these tools need to take account of the way their outputs will feed into warnings and of the nature of partnerships that can facilitate this. The chapter includes examples of impact prediction in sport, health impacts of wildfires in Australia, a framework for impact prediction in New Zealand, and communication of impacts through social media in the UK.

Pulling It All Together, End-to-End

Chapter

Full-text available

Jan 2022

Brian Golding

In this concluding chapter, we emphasise the performance of the whole chain. We look at fire warnings as an effective system to see what we can learn from them. We see that each step in the chain is important but that the bridges are at least as important as each of the steps. Partnership is key to the effectiveness of each bridge and is also crucial for the overall chain. Understanding the way in which value propagates up and down the chain can enable improvements to be targeted in the most beneficial places. We conclude with a summary of some attributes of the “perfect” warning chain.

Predicting the Weather: A Partnership of Observation Scientists and Forecasters

Chapter

Full-text available

Jan 2022

Weather forecasts are the foundation of much of the information needed in the warnings we have been considering. To be useful, they require knowledge of the current atmospheric state as a starting point. In this chapter, we first look at the methods used to predict the weather and the resulting demands for observations. Then, we explore the wide variety of sensors and platforms used to obtain this information. There has been a long history of close working between sensor and platform designers and meteorologists that has produced spectacular advances in forecast accuracy. However, the latest high-resolution models require new approaches to obtaining observations that will require different collaborations. Examples are presented of partnerships in space observing and in aviation, a demonstration system from Canada, and the use of testbeds and observatories as environments for progress.

Connecting Weather and Hazard: A Partnership of Physical Scientists in Connected Disciplines

Chapter

Full-text available

Jun 2022

Achieving consistency in the prediction of the atmosphere and related environmental hazards requires careful design of forecasting systems. In this chapter, we identify the benefits of seamless approaches to hazard prediction and the challenges of achieving them in a multi-institution situation. We see that different modelling structures are adopted in different disciplines and that these often relate to the user requirements for those hazards. We then explore the abilities of weather prediction to meet the requirements of these different disciplines. We find that differences in requirement and language can be major challenges to seamless data processing and look at some ways in which these can be resolved. We conclude with examples of partnerships in flood forecasting in the UK and wildfire forecasting in Australia.

Impact of the Aeolus Level‐2B horizontal line‐of‐sight winds in the Environment and Climate Change Canada global forecast system

Article

Full-text available

May 2022
Q J ROY METEOR SOC

The European Space Agency Aeolus mission was launched in August 2018. This satellite carries the first Doppler lidar able to provide global measurements of wind profiles. Aeolus Level‐2B products have been generated and monitored by the European Centre for Medium‐Range Weather Forecasts (ECMWF) in near real‐time since a few weeks after the launch. These products include the horizontal line‐of‐sight (HLOS) winds that are suitable for data assimilation in numerical weather prediction systems. This article presents a series of observing system experiments conducted over summer 2019 to assess the value of the Level‐2B HLOS winds and their impact on the Environment and Climate Change Canada global forecasts. The impact of atmospheric motion vectors (AMVs) on forecasts is also examined and compared with the impact of HLOS winds. Two datasets are used: the HLOS winds produced in near real‐time at ECMWF and those reprocessed later in fall 2020. It is found that the near real‐time data are significantly biased and should be corrected. A look‐up table bias correction based on observation minus background departures is applied to this dataset as initially proposed by ECMWF. The reprocessed data are of better quality and bias corrected using the telescope's primary mirror temperature variations as predictor. The impacts of the near real‐time and reprocessed HLOS winds on forecasts are generally positive for both temperature and wind. The impacts are largest in the troposphere over the Tropics and polar regions. The positive impacts on forecasts are larger with the reprocessed data, particularly in the stratosphere, where a significant degradation over the Southern Hemisphere is found from assimilating the near real‐time data. The normalized forecast error reductions at days 1 and 2 for the wind are ∼1.25% over the Tropics and Southern Hemisphere. The positive impact of the HLOS winds on forecasts is enhanced by ∼40% when the AMVs are not assimilated in the control experiment. The forecast error reduction from assimilating AMVs is, however, two times larger than from assimilating HLOS winds in the extratropics. Conversely, the impact of HLOS winds on forecasts is generally larger in the Tropics.

Spectral performance analysis of the Aeolus Fabry–Pérot and Fizeau interferometers during the first years of operation

Article

Full-text available

Mar 2022
AMT

In August 2018, the European Space Agency (ESA) launched the first Doppler wind lidar into space, which has since then been providing continuous profiles of the horizontal line-of-sight wind component at a global scale. Aeolus data have been successfully assimilated into several numerical weather prediction (NWP) models and demonstrated a positive impact on the quality of the weather forecasts. To provide valuable input data for NWP models, a detailed characterization of the Aeolus instrumental performance as well as the realization and minimization of systematic error sources is crucial. In this paper, Aeolus interferometer spectral drifts and their potential as systematic error sources for the aerosol and wind products are investigated by means of instrument spectral registration (ISR) measurements that are performed on a weekly basis. During these measurements, the laser frequency is scanned over a range of 11 GHz in steps of 25 MHz and thus spectrally resolves the transmission curves of the Fizeau interferometer and the Fabry–Pérot interferometers (FPIs) used in Aeolus. Mathematical model functions are derived to analyze the measured transmission curves by means of non-linear fit procedures. The obtained fit parameters are used to draw conclusions about the Aeolus instrumental alignment and potentially ongoing drifts. The introduced instrumental functions and analysis tools may also be applied for upcoming missions using similar spectrometers as for instance EarthCARE (ESA), which is based on the Aeolus FPI design.

Uncertainties in Kelvin Waves in ECMWF Analyses and Forecasts: Insights From Aeolus Observing System Experiments

Article

Full-text available

Nov 2021
GEOPHYS RES LETT

Plain Language Summary The tropics are the region with the largest uncertainties in the initial states for numerical weather prediction, called analyses. Analysis uncertainties are largest in the tropical upper troposphere and the lower stratosphere (UTLS). One of the reasons is a lack of wind profiles which are more useful than temperature profiles in the tropics. This classical effect was described by Smagorinsky as “Not all data are equal in their information‐yielding capacity. Some are more equal than others.” ESA's ongoing Aeolus mission provides the first global wind profile observations from space. Despite their small number and relatively large random error, Aeolus winds have a positive impact on the quality of global weather forecasts, especially in the UTLS. In this paper, we discuss the impact of the Aeolus winds in UTLS focusing on the vertically propagating Kelvin waves, which are a major contributor to tropical variability. Several case studies are presented using the ECMWF model and data assimilation with and without Aeolus winds. The studied period May to September 2020 was characterized by a weakening easterly phase of the quasi‐biennial oscillation (QBO). Results suggest that a stronger impact of Aeolus winds in May than later in summer was associated with the QBO and the background flow.

On the addition of microwave sounders and numerical weather prediction skill

Article

Full-text available

Aug 2021
Q J ROY METEOR SOC

The utility of microwave sounding for numerical weather prediction (NWP) is well established. However, the volume of sounder data assimilated in NWP systems may change substantially in upcoming years, as there is an ageing constellation of satellites but also the prospect of proliferating small satellite constellations. This study examines the addition of temperature and humidity sounders from a baseline that includes no microwave sounders at all, aiming to elucidate the incremental benefit gained from adding sounders to the assimilation system. Framed as a series of observing‐system experiments (OSEs), large improvements in forecast skill and background fits to independent observations are gained from the first sounders added. Significant further benefit is observed from additional sounders, with the impacts largest at higher latitudes and no saturation evident in the maximal setup. In the second part of this study, the relative importance of updating background errors for OSEs is investigated to give greater context to the sounder addition results. The ensemble of data assimilations (EDA) underlying the background‐error specification is rerun for this purpose to be consistent with the observation usage in the OSE. Comparing experiments with updated and unchanged background errors shows that the effect of updating background errors is secondary to that caused by the observing‐system change itself. Rerunning the EDA can reduce some of the forecast skill apparently lost in a data‐denial experiment or gained when assessing data addition, but this is roughly 10% of the overall change and should not alter conclusions resulting from typical OSEs, in which background errors are not tailor‐made.

The impact of Aeolus wind retrievals on ECMWF global weather forecasts

Article

Full-text available

Aug 2021
Q J ROY METEOR SOC

Aeolus is the world's first spaceborne Doppler Wind Lidar, providing profiles of horizontal line‐of‐sight (HLOS) wind retrievals. Numerical weather prediction (NWP) impact and error statistics of Aeolus Level‐2B (L2B) wind statistics have been assessed using the European Centre for Medium‐range Weather Forecasts (ECMWF) global data assimilation system. Random and systematic error estimates were derived from observation minus background departure statistics. The HLOS wind random error standard deviation is estimated to be in the range 4.0–7.0 m·s⁻¹ for the Rayleigh‐clear and 2.8–3.6 m·s⁻¹ for the Mie‐cloudy, depending on atmospheric signal levels which in turn depend on instrument performance, atmospheric backscatter properties and the processing algorithms. Complex systematic HLOS wind error variations on time‐scales less than one orbit were identified, most strongly affecting the Rayleigh‐clear winds. NWP departures and instrument housekeeping data confirmed that it is caused by temperature gradients across the primary mirror. A successful bias correction scheme was implemented in the operational processing chain in April 2020. In Observing System Experiments (OSEs), Aeolus provides statistically significant improvement in short‐range forecasts as verified by observations sensitive to temperature, wind and humidity. Longer forecast range verification shows positive impact that is strongest at the day two to three forecast range: ∼2% improvement in root‐mean‐square error for vector wind and temperature in the tropical upper troposphere and lower stratosphere, and polar troposphere. Positive impact up to 9 days is found in the tropical lower stratosphere. Both Rayleigh‐clear and Mie‐cloudy winds provide positive impact, but the Rayleigh accounts for most tropical impact. The Forecast Sensitivity Observation Impact (FSOI) metric is available since 9 January 2020, when Aeolus was operationally assimilated, which confirms Aeolus is a useful contribution to the global observing system, with the Rayleigh‐clear and Mie‐cloudy winds providing similar overall short‐range impact in 2020.

Sensitivity of Aeolus HLOS winds to temperature and pressure specification in the L2B processor

Article

Full-text available

Jun 2021
AMT

The retrieval of wind from the first Doppler wind lidar of European Space Agency (ESA) launched in space in August 2018 is based on a series of corrections necessary to provide observations of a quality useful for numerical weather prediction (NWP). In this paper we examine the properties of the Rayleigh–Brillouin correction necessary for the retrieval of horizontal line-of-sight wind (HLOS) from a Fabry–Pérot interferometer. This correction is taking into account the atmospheric stratification, namely temperature and pressure information that are provided by a NWP model as suggested prior to launch. The main goal of the study is to evaluate the impact of errors in simulated atmospheric temperature and pressure information on the HLOS sensitivity by comparing the Integrated Forecast System (IFS) and Action de Recherche Petite Echelle Grande Echelle (ARPEGE) global model temperature and pressure short-term forecasts collocated with the Aeolus orbit. These errors are currently not taken into account in the computation of the HLOS error estimate since its contribution is believed to be small. This study largely confirms this statement to be a valid assumption, although it also shows that model errors could locally (i.e. jet-stream regions, below 700 hPa over both earth poles and in stratosphere) be significant. For future Aeolus follow-on missions this study suggests considering realistic estimations of errors in the HLOS retrieval algorithms, since this will lead to an improved estimation of the Rayleigh–Brillouin sensitivity uncertainty contributing to the HLOS error estimate and better exploitation of space lidar winds in NWP systems.

The Development of a Four Tier-Based Diagnostic Test Diagnostic Assessment on Science Concept Course

Article

Full-text available

Mar 2021

A learning success could be seen from the material mastery and conceptual understanding inculcation obtained by the students. To find out how students’ misconceptions occur, it could be done by developing a diagnostic assessment. This research aims to develop and describe the instrument validity of the four-tier diagnostic test for diagnosing misconceptions by the Primary School Teacher Education program of Universitas Muria Kudus on Science Concept course. This research applies the Research and Development design. It consists of a preliminary study, diagnostic test development, and diagnostic test validation. This article discusses the development and four-tier diagnostic test instrument validation results. The developed instrument consisted of 40 question items. They were Physics and Biology materials. The validation stage involved content and face validities. The expert judgment results showed an average score of 93.09, with very valid criterion. Thus, the four�tier based diagnostic test was valid and could be implemented. The reliability calculation obtained the r-count score = 0.698, while the r-table score = 0.514. The r-count is higher than r�table, then the instrument was reliable. It showed that the instrument was valid and reliable and could be implemented for the students of Primary School Teacher Education. Based on the validation result, the Four-Tier based diagnostic test was valid and reliable to apply for diagnosing the students’ misconceptions in science concept course.

Sensitivity of Aeolus HLOS winds to temperature and pressure specification in the L2B processor

Preprint

Full-text available

Feb 2021

The retrieval of wind from the first Doppler wind lidar of Europen Space Agency (ESA) launched in space in August 2018 is based on a series of corrections necessary to provide observations of a quality useful for Numerical Weather Prediction (NWP). In this paper we examine properties of the Rayleigh-Brillouin correction necessary for the retrieval of horizontal line-of-sight wind (HLOS) from a Fabry-Perot interferometer. This correction is taking into account the atmospheric stratification, namely temperature and pressure information that are provided by a NWP model as suggested prior launch. Since NWP models contain errors the main goal in the study is to evaluate the impact of these errors on the HLOS sensitivity by comparing the Integrated Forecast System (IFS) and Action de Recherche Petite Echelle Grande Echelle (ARPEGE) global model temperature and pressure short term forecasts collocated with the Aeolus orbit. The model error is currently not taken into account in the computation of the HLOS error estimate since its contribution is believed small. This study largely confirms this statement to be a valid assumption, although it also shows that model errors could locally (i.e. jet-stream regions, below 700 hPa over both earth poles and as well in stratosphere) be significant. For a future Aeolus follow-on missions this study suggests to consider realistic estimations of model errors in the HLOS retrieval algorithms, since this will lead to an improved estimation of the Rayleigh-Brillouin sensitivity uncertainty contributing to the HLOS error estimate and better exploitation of space lidar winds in NWP systems.

First validation of Aeolus wind observations by airborne Doppler wind lidar measurements

Article

Full-text available

May 2020

Soon after the launch of Aeolus on 22 August 2018, the first ever wind lidar in space developed by the European Space Agency (ESA) has been providing profiles of the component of the wind vector along the instrument’s line of sight (LOS) on a global scale. In order to validate the quality of Aeolus wind observations, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR) recently performed two airborne campaigns over central Europe deploying two different Doppler wind lidars (DWLs) on board the DLR Falcon aircraft. The first campaign – WindVal III – was conducted from 5 November 2018 until 5 December 2018 and thus still within the commissioning phase of the Aeolus mission. The second campaign – AVATARE (Aeolus Validation Through Airborne Lidars in Europe) – was performed from 6 May 2019 until 6 June 2019. Both campaigns were flown out of the DLR site in Oberpfaffenhofen, Germany, during the evening hours for probing the ascending orbits. All together, 10 satellite underflights with 19 flight legs covering more than 7500 km of Aeolus swaths were performed and used to validate the early-stage wind data product of Aeolus by means of collocated airborne wind lidar observations for the first time. For both campaign data sets, the statistical comparison of Aeolus horizontal line-of-sight (HLOS) observations and the corresponding wind observations of the reference lidar (2 µm DWL) on board the Falcon aircraft shows enhanced systematic and random errors compared with the bias and precision requirements defined for Aeolus. In particular, the systematic errors are determined to be 2.1 m s−1 (Rayleigh) and 2.3 m s−1 (Mie) for WindVal III and −4:6 m s−1 (Rayleigh) and −0:2 m s−1 (Mie) for AVATARE. The corresponding random errors are determined to be 3.9 m s−1 (Rayleigh) and 2.0 m s−1 (Mie) for WindVal III and 4.3 m s−1 (Rayleigh) and 2.0 m s−1 (Mie) for AVATARE. The Aeolus observations used here were acquired in an altitude range up to 10 km and have mainly a vertical resolution of 1 km (Rayleigh) and 0.5 to 1.0 km (Mie) and a horizontal resolution of 90 km (Rayleigh) and down to 10 km (Mie). Potential reasons for those errors are analyzed and discussed.

First validation of Aeolus wind observations by airborne Doppler wind lidar measurements

Article

Full-text available

May 2020
AMT

Soon after the launch of Aeolus on 22 August 2018, the first ever wind lidar in space developed by the European Space Agency (ESA) has been providing profiles of the component of the wind vector along the instrument's line of sight (LOS) on a global scale. In order to validate the quality of Aeolus wind observations, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR) recently performed two airborne campaigns over central Europe deploying two different Doppler wind lidars (DWLs) on board the DLR Falcon aircraft. The first campaign – WindVal III – was conducted from 5 November 2018 until 5 December 2018 and thus still within the commissioning phase of the Aeolus mission. The second campaign – AVATARE (Aeolus Validation Through Airborne Lidars in Europe) – was performed from 6 May 2019 until 6 June 2019. Both campaigns were flown out of the DLR site in Oberpfaffenhofen, Germany, during the evening hours for probing the ascending orbits. All together, 10 satellite underflights with 19 flight legs covering more than 7500 km of Aeolus swaths were performed and used to validate the early-stage wind data product of Aeolus by means of collocated airborne wind lidar observations for the first time. For both campaign data sets, the statistical comparison of Aeolus horizontal line-of-sight (HLOS) observations and the corresponding wind observations of the reference lidar (2 µm DWL) on board the Falcon aircraft shows enhanced systematic and random errors compared with the bias and precision requirements defined for Aeolus. In particular, the systematic errors are determined to be 2.1 m s-1 (Rayleigh) and 2.3 m s-1 (Mie) for WindVal III and -4.6 m s-1 (Rayleigh) and -0.2 m s-1 (Mie) for AVATARE. The corresponding random errors are determined to be 3.9 m s-1 (Rayleigh) and 2.0 m s-1 (Mie) for WindVal III and 4.3 m s-1 (Rayleigh) and 2.0 m s-1 (Mie) for AVATARE. The Aeolus observations used here were acquired in an altitude range up to 10 km and have mainly a vertical resolution of 1 km (Rayleigh) and 0.5 to 1.0 km (Mie) and a horizontal resolution of 90 km (Rayleigh) and down to 10 km (Mie). Potential reasons for those errors are analyzed and discussed.

Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China

Article

Sep 2020
ATMOS RES

Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China.

First validation of Aeolus wind observations by airborne Doppler Wind Lidar measurements

Preprint

Full-text available

Jan 2020

Abstract. Soon after the launch of Aeolus on 22 August 2018, the first ever wind lidar in space developed by the European Space Agency (ESA) has been providing profiles of the component of the wind vector along the instrument's line-of-sight (LOS) on a global scale. In order to validate the quality of Aeolus wind observations, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR) recently performed two airborne campaigns over Central Europe deploying two different Doppler wind lidars (DWL) on-board the DLR Falcon aircraft. The first campaign – WindVal III – was conducted from 5 November 2018 until 5 December 2018 and thus, still within the commissioning phase of the Aeolus mission. The second campaign – AVATARE (Aeolus Validation Through Airborne Lidars in Europe) – was performed from 6 May 2019 until 6 June 2019. Both campaigns were flown out of the DLR site in Oberpfaffenhofen, Germany. All together, 10 satellite underflights with 19 flight legs covering more than 7500 km of Aeolus swaths were performed and used to validate the early stage wind data product of Aeolus by means of collocated airborne wind lidar observations for the first time. For both campaign data sets, the statistical comparison of Aeolus data and the data of the reference lidar (2-µm DWL) on-board the Falcon aircraft shows enhanced systematic and random errors compared with the bias and precision requirements defined for Aeolus. In particular, the systematic errors are determined to be 2.1 m/s (Rayleigh) and 2.3 m/s (Mie) for WindVal III and −4.6 m/s (Rayleigh) and −0.2 m/s (Mie) for AVATARE. The corresponding random errors are determined to be 4.0 m/s (Rayleigh) and 2.2 m/s (Mie) for WindVal III, and 4.4 m/s (Rayleigh) and 2.2 m/s (Mie) for AVATARE. Potential reasons for those errors are analyzed and discussed.

The prospects for increasing the horizontal resolution of the Aeolus horizontal line‐of‐sight wind profiles

Article

Full-text available

Sep 2019
Q J ROY METEOR SOC

This article evaluates the prospects for increasing the horizontal resolution of the Aeolus horizontal line‐of‐sight (HLOS) wind profiles at the expense of their accuracy. The evaluation is performed by combining a 10‐day atmosphere simulation by the ECMWF model at T3999 horizontal resolution with the CALIPSO observations of atmospheric composition as inputs to the Aeolus simulator. The validation shows that the ECMWF model represents the location and the vertical structure of the observed cloud systems well. At the nominal accumulation length of L ≈ 90 km (from the Aeolus measurement scale of ∼3 km), the Mie‐cloudy retrieval provides 1–4 times fewer observations than Rayleigh‐clear but the Mie‐cloudy HLOS winds have the highest quality with estimated error standard deviation of about 1 m/s in the troposphere and no bias. The experiments with reduced L reveal that neither the observation error standard deviation nor bias of the Mie‐cloudy winds are significantly affected when the accumulation length L varies in the range between 100 and 10 km. At the same time, the number of observations significantly increases as L reduces. This suggests that mesoscale NWP may profit from the Aeolus Mie‐cloudy HLOS profiles with the accumulation lengths as small as 10 km.

A framework for solving meta inverse problems: experimental design and application to an acoustic source problem

Article

Full-text available

Jun 2019

The selection of optimal measurement locations in remote sensing or imaging algorithms is of large practical interest in many applications. The target is usually to choose a measurement setup that best resolves some particular quantity of interest. This work describes a general framework for selecting such an optimal setup within a given set Q of possible setups for the formulation and solution of the meta inverse problem. The work shows that it is crucial to incorporate the basic ingredients which are usually part of the inversion process. In particular, it takes care of the nature and the size of the measurement error, the choice of the regularization scheme which is employed for the inverse problem, and the prior knowledge on solutions. The basic idea of the framework is to minimize the errors associated with the reconstruction of a given quantity of interest. Five functional layers which reflect the structure of the meta inverse problem are introduced. Further, with framework adaption, an iterative algorithm is formulated to solve the meta inverse problem at each iterative step in order to obtain improved reconstructions of the inverse problem. Using the initial reconstructions as input for meta inversion, the framework adaption algorithm does not require prior knowledge of the source distribution. The feasibility of the framework adaption algorithm is illustrated by using it to solve the inverse acoustic source problem.

Assimilation of horizontal line‐of‐sight winds with a mesoscale EnKF data assimilation system

Article

Jun 2018

This paper compares the horizontal line‐of‐sight (HLOS) wind observations with a single wind component and full wind information in a limited‐area domain over Europe and the North Atlantic. The motivation for the study is the forthcoming Aeolus mission of the European Space Agency which will provide vertical profiles of HLOS winds. A new observing system simulation experiment framework was developed using the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter (EAKF) data assimilation with the Weather Research and Forecasting (WRF) model at a horizontal resolution of 15 km. The 50‐member EAKF/WRF is nested in the operational 50‐member ensemble prediction system of ECMWF (ENS) using model‐level data available twice per day. The ensemble spread at lateral boundaries provided by ENS, especially in the North Atlantic, is shown to be sufficient to carry out experiments without covariance inflation. Results show that the information content of HLOS winds is on average divided linearly between the zonal and meridional wind components depending on the observation azimuth. In areas of significant covariances such as fronts in the Atlantic, multivariate covariance information provides significant useful analysis increments from the HLOS wind observations, especially if observations are aligned along the front. The application of the spatially and temporally adaptive prior inflation improved all scores compared with the case without inflation.

Extraction of wind and temperature information from hybrid 4D-Var assimilation of stratospheric ozone using NAVGEM

Article

Full-text available

Mar 2018
ATMOS CHEM PHYS

Extraction of wind and temperature information from stratospheric ozone assimilation is examined within the context of the Navy Global Environmental Model (NAVGEM) hybrid 4-D variational assimilation (4D-Var) data assimilation (DA) system. Ozone can improve the wind and temperature through two different DA mechanisms: (1) through the “flow-of-the-day” ensemble background error covariance that is blended together with the static background error covariance and (2) via the ozone continuity equation in the tangent linear model and adjoint used for minimizing the cost function. All experiments assimilate actual conventional data in order to maintain a similar realistic troposphere. In the stratosphere, the experiments assimilate simulated ozone and/or radiance observations in various combinations. The simulated observations are constructed for a case study based on a 16-day cycling truth experiment (TE), which is an analysis with no stratospheric observations. The impact of ozone on the analysis is evaluated by comparing the experiments to the TE for the last 6 days, allowing for a 10-day spin-up. Ozone assimilation benefits the wind and temperature when data are of sufficient quality and frequency. For example, assimilation of perfect (no applied error) global hourly ozone data constrains the stratospheric wind and temperature to within ∼ 2 m s-1 and ∼ 1 K. This demonstrates that there is dynamical information in the ozone distribution that can potentially be used to improve the stratosphere. This is particularly important for the tropics, where radiance observations have difficulty constraining wind due to breakdown of geostrophic balance. Global ozone assimilation provides the largest benefit when the hybrid blending coefficient is an intermediate value (0.5 was used in this study), rather than 0.0 (no ensemble background error covariance) or 1.0 (no static background error covariance), which is consistent with other hybrid DA studies. When perfect global ozone is assimilated in addition to radiance observations, wind and temperature error decreases of up to ∼ 3 m s-1 and ∼ 1 K occur in the tropical upper stratosphere. Assimilation of noisy global ozone (2 % errors applied) results in error reductions of ∼ 1 m s-1 and ∼ 0.5 K in the tropics and slightly increased temperature errors in the Northern Hemisphere polar region. Reduction of the ozone sampling frequency also reduces the benefit of ozone throughout the stratosphere, with noisy polar-orbiting data having only minor impacts on wind and temperature when assimilated with radiances. An examination of ensemble cross-correlations between ozone and other variables shows that a single ozone observation behaves like a potential vorticity (PV) “charge”, or a monopole of PV, with rotation about a vertical axis and vertically oriented temperature dipole. Further understanding of this relationship may help in designing observation systems that would optimize the impact of ozone on the dynamics.

On methods for assessment of the value of observations in convection‐permitting data assimilation and numerical weather forecasting

Article

Full-text available

Feb 2025
Q J ROY METEOR SOC

In numerical weather prediction (NWP), a large number of observations are used to create initial conditions for weather forecasting through a process known as data assimilation. An assessment of the value of these observations for NWP can guide us in the design of future observation networks, help us to identify problems with the assimilation system, and allow us to assess changes to the assimilation system. However, assessment can be challenging in convection‐permitting NWP. This is because verification of convection‐permitting forecasts is not easy, the forecast model is strongly nonlinear, a limited‐area model is used, and the observations used often contain complex error statistics and are often associated with nonlinear observation operators. We compare methods that can be used to assess the value of observations in convection‐permitting NWP and discuss operational considerations when using these methods. We focus on their applicability to ensemble forecasting systems, as these systems are becoming increasingly dominant for convection‐permitting NWP. We also identify several future research directions, which include comparing results from different methods, comparing forecast validation using analyses versus using observations, applying flow‐dependent covariance localization, investigating the effect of ensemble size on the assessment, and generating and validating the nature run in observing‐system simulation experiments.

Assimilation of horizontal line-of-sight winds in National Centre for Medium Range Weather Forecasting – Global Forecast System

Article

Sep 2024
J EARTH SYST SCI

European Space Agency (ESA) launched its first space-based Doppler Wind Lidar (DWL) mission called Atmospheric Dynamic Mission (ADM) – Aeolus. Onboard the Aeolus mission is the Atmospheric LAser Doppler Instrument (ALADIN) which measures the horizontal line-of-sight (HLOS) winds. Aeolus Level-2B wind observations in Rayleigh clear and Mie cloudy channels are evaluated for implementation in the NCMRWF (National Centre for Medium Range Weather Forecasting) Global Forecast System (NGFS). The GSI (grid-point statistical interpolation) analysis scheme has been modified and updated to assimilate the HLOS wind information. Quality control criteria are applied during observation processing and during minimization of cost function for computation of the initial condition. An observation system experiment (OSE) is performed by employing the GSI-3DVar (3-Dimensional Variation) approach and involving HLOS data. In addition to assimilation and forecast diagnostics, two case studies of very severe cyclonic storms are investigated to assess the impact of this new wind information on a severe weather event. Statistically, significant improvement is observed mostly over the Southern Hemisphere, Tropics, and RSMC (Regional Specialized Meteorological Centre, 29°–120°E and 21°S–46°N) region in terms of reduction in wind root mean square error. Assimilation of HLOS winds shows a reduction in direct positional error (DPE) for both cyclonic systems. Improvement in the 6-hourly analysis of minimum sea level pressure and maximum 10 m wind speed is also observed.

Wind Observations

Chapter

Dec 2023

Verification of different Fizeau fringe analysis algorithms based on airborne wind lidar data in support of ESA’s Aeolus mission

Article

Full-text available

Oct 2023

The Aeolus mission by the European Space Agency was launched in August 2018 and stopped operations in April 2023. Aeolus carried the direct-detection Atmospheric LAser Doppler INstrument (ALADIN). To support the preparation of Aeolus, the ALADIN Airborne Demonstrator (A2D) instrument was developed and applied in several field campaigns. Both ALADIN and A2D consist of so-called Rayleigh and Mie channels used to measure wind from both molecular and particulate backscatter signals. The Mie channel is based on the fringe-imaging technique, which relies on determining the spatial location of a linear interference pattern (fringe) that originated from multiple interference in a Fizeau spectrometer. The accuracy of the retrieved winds is among others depending on the analytic algorithm used for determining the fringe location on the detector. In this paper, the performance of two algorithms using Lorentzian and Voigt fit functions is investigated by applying them to A2D data that were acquired during the AVATAR-I airborne campaign. For performance validation, the data of a highly accurate heterodyne detection wind lidar (2-µm DWL) that was flown in parallel are used as a reference. In addition, a fast and non-fit-based algorithm based on a four-pixel intensity ratio approach (

{{\rm R}_4}

R 4 ) is developed. It is revealed that the Voigt-fit-based algorithm provides 50% more data points than the Lorentzian-based algorithm while applying a quality control that yields a similar random error of about 1.5 m/s. The

{{\rm R}_4}

R 4 algorithm is shown to deliver a similar accuracy as the Voigt-fit-based algorithms, with the advantage of a one to two orders of magnitude faster computation time. Principally, the

{{\rm R}_4}

R 4 algorithm can be adapted to other spectroscopic applications where sub-pixel knowledge of the location of measured peak profiles is needed.

Investigation of links between dynamical scenarios and particularly high impact of Aeolus on numerical weather prediction (NWP) forecasts

Article

Full-text available

Mar 2023

Global wind profiles from the Aeolus satellite mission provide an important source of wind information for numerical weather prediction (NWP). Data assimilation experiments show large mean changes in the analysis and a significant reduction in forecast errors. At Deutscher Wetterdienst (DWD), a 3-month observing system experiment (OSE), from July 2020 to October 2020, was performed to evaluate the impact of the Aeolus horizontal line-of-sight (HLOS) wind observations in the operational data assimilation system of the ICOsahedral Nonhydrostatic (ICON) global model. To better understand the underlying dynamics leading to the overall beneficial impact, specific time periods and regions with a particularly high impact of Aeolus are investigated. In this study, we illustrate three examples of atmospheric phenomena that constitute dynamical scenarios for significant forecast error reduction through the assimilation of Aeolus: the phase shift of large-scale tropical circulation systems, namely the Quasi-Biennial Oscillation (QBO) and the El Niño–Southern Oscillation (ENSO), and the interaction of tropical cyclones undergoing extratropical transition (ET) with the midlatitude waveguide.

Assimilation of satellite data in numerical weather prediction. Part II: Recent years

Article

Full-text available

Jan 2022
Q J ROY METEOR SOC

Developments in the assimilation of satellite data in numerical weather prediction (NWP), from the first experiments in the late 1960s to the present day, are presented in a two‐part review article. This part, Part II, reviews the progress in recent years, from about 2000. It includes summaries of advances in the relevant satellite remote‐sensing technologies and in methods to assimilate observations from these instruments into NWP systems. It also summarises impacts on forecast skill. Continued progress has been made on the assimilation of passive infrared (IR) sounding data and microwave (MW) sounding and imaging data. This has included data from hyperspectral IR sounders, which first became available during this period. Advances in the use of cloud‐affected radiances, from both IR and MW instruments, have been made. In support of this progress, further developments have been made in fast radiative transfer models and in bias correction techniques, and work has continued to improve understanding and representation of observation uncertainties. Continued progress has also been made on the use of wind information from satellites, including atmospheric motion vectors and scatterometer data. A new source of temperature and humidity information, from radio occultation observations, has become available during the period and has been exploited by many NWP centres. The impact of satellite data on NWP accuracy is continually assessed using a range of methods and metrics. Some results from recent Observing System Experiments (OSEs) and Forecast Sensitivity to Observation Impact (FSOI) assessment are presented and other methods are discussed. The role of satellite data in NWP‐based atmospheric reanalysis systems is also described.

Spectral performance analysis of the Aeolus Fabry-Pérot and Fizeau interferometers during the first years of operation

Article

Nov 2021

In August 2018, the European Space Agency (ESA) launched the first Doppler wind lidar into space which has since then been providing continuous profiles of the horizontal line-of-sight wind component at a global scale. Aeolus data has been successfully assimilated into several NWP models and demonstrated a positive impact on the quality of the weather forecasts. In order to provide valuable input data for NWP models, a detailed characterization of the Aeolus instrumental performance as well as the realization and minimization of systematic error sources is crucial. In this paper, Aeolus interferometer spectral drifts and their potential as systematic error sources for the aerosol and wind product are investigated by means of instrument spectral registration (ISR) measurements that are performed on a weekly basis. During these measurements, the laser frequency is scanned over a range of 11 GHz in steps of 25 MHz and thus spectrally resolves the transmission curves of the Fizeau interferometer and the Fabry-Pérot interferometers (FPIs) used in Aeolus. Mathematical model functions are derived in order to analyze the measured transmission curves by means of non-linear fit procedures. The obtained fit parameters are used to draw conclusions about the Aeolus instrumental alignment and potentially ongoing drifts. The introduced instrumental functions and analysis tools may also be applied for the upcoming missions using similar spectrometers as for instance EarthCARE (ESA) which is based on the Aeolus FPI design.

Spectral performance analysis of the Aeolus Fabry-Pérot and Fizeau interferometers during the first years of operation

Preprint

Full-text available

Nov 2021

In August 2018, the European Space Agency (ESA) launched the first Doppler wind lidar into space which has since then been providing continuous profiles of the horizontal line-of-sight wind component at a global scale. Aeolus data has been successfully assimilated into several NWP models and demonstrated a positive impact on the quality of the weather forecasts. In order to provide valuable input data for NWP models, a detailed characterization of the Aeolus instrumental performance as well as the realization and minimization of systematic error sources is crucial. In this paper, Aeolus interferometer spectral drifts and their potential as systematic error sources for the aerosol and wind product are investigated by means of instrument spectral registration (ISR) measurements that are performed on a weekly basis. During these measurements, the laser frequency is scanned over a range of 11 GHz in steps of 25 MHz and thus spectrally resolves the transmission curves of the Fizeau interferometer and the Fabry-Perot interferometers (FPIs) used in Aeolus. Mathematical model functions are derived in order to analyze the measured transmission curves by means of non-linear fit procedures. The obtained fit parameters are used to draw conclusions about the Aeolus instrumental alignment and potentially ongoing drifts. The introduced instrumental functions and analysis tools may also be applied for the upcoming missions using similar spectrometers as for instance EarthCARE (ESA) which is based on the Aeolus FPI design.

Shallow Circulations: Relevance and Strategies for Satellite Observation

Chapter

Jan 2017

Shallow circulations are central to many tropical cloud systems. We investigate the potential of existing and upcoming data to document these circulations. Different methods to observe or constrain atmospheric circulations rely on satellite-borne instruments. Direct observations of the wind are currently possible at the ocean surface or using tracer patterns. Satellite-borne wind lidar will soon be available, with a much better coverage and accuracy. Meanwhile, circulations can be constrained using satellite observations of atmospheric diabatic heating. We evaluate the commonalities and discrepancies of these estimates together with reanalysis in systems that include shallow circulations. It appears that existing datasets are in qualitative agreement, but that they still differ too much to provide robust evaluation criteria for general circulation models. This state of affairs highlights the potential of satellite-borne wind lidar and of further work on current satellite retrievals.

Atmospheric Observations and Experiments to Assess Their Usefulness in Data Assimilation (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice)

Article

Full-text available

Mar 1997

Robert Atlas

Atmospheric observations consist of a mixture of in situ, visual, and remotely sensed observations. These provide an extensive database for research and numerical weather prediction. However, significant data deficiencies still exist, and new observing systems are continually being proposed. Observing system experiments (OSE's) are conducted to assess the usefulness of different types of existing atmospheric observations. Observing system simulation experiments (OSSE's) are conducted to evaluate the potential impact of proposed observing systems, as well as to determine tradeoffs in their design, and to evaluate data assimilation methodology. This paper contains a review of the development of the global atmospheric observing system, a description of the principal types of data, an overview of OSE and OSSE methodology, and results from recent experiments to evaluate the relative utility of the principal atmospheric observing systems and the potential for new observing systems. These experiments show the critical contributions being made by both conventional and space-based observations, and indicate considerable potential for future satellite observing systems to improve data assimilation.

Atmospheric observations and experiments to assess their usefulness in data assimilation

Article

Full-text available

Jan 1997

Robert Atlas

Comments on "The Impact of Doppler Lidar Wind Observations on a Single-Level Meteorological Analysis"

Article

Full-text available

Feb 2005

The Impact of Doppler Lidar Wind Observations on a Single-Level Meteorological Analysis

Article

Full-text available

May 2004

Through the use of observation operators, modern data assimilation systems can ingest observations of quantities that are not themselves model variables, but are mathematically related to those variables. An example of this are the LOS (line of sight) winds that Doppler wind lidars provide. The model - or data assimilation system - needs information about both components of the horizontal wind vectors, whereas the individual observations in this case only provide the projection of the wind vector onto a given direction. In order to assess the expected impact of such an observing system, it is important to examine the extent to which a meteorological analysis can be constrained by the LOS winds. A single-level wind analysis system designed to explore these issues has been built at the NASA Data Assimilation Office. In this system, simulated wind observations can be evaluated in terms of their impact on the analysis quality under various assumptions about their spatial and angular distributions as well as the observation error characteristics. The basic design of the system will be presented along with experimental results obtained with it. In particular, the value of measuring LOS winds along two different directions for a given location will be discussed.

The Atmospheric Dynamics Mission for Global Wind Field Measurement

Article

Full-text available

Jan 2005

The prime aim of the Atmospheric Dynamics Mission is to demonstrate measurements of vertical wind profiles from space. Extensive studies conducted by the European Space Agency over the past 15 years have culminated in the selection of a high-performance Doppler wind lidar based on direct-detection interferometric techniques. Such a system, with a pulsed laser operating at 355-nm wavelength, would utilize both Rayleigh scattering from molecules and Mie scattering from thin cloud and aerosol particles; measurement of the residual Doppler shift from successive levels in the atmosphere provides the vertical wind profiles. The lidar would be accommodated on a satellite flying in a sun-synchronous orbit, at an altitude of 400 km, providing near-global coverage; target date for launch is in 2007. Processing of the backscatter signals will provide about 3000 globally distributed wind profiles per day, above thick clouds or down to the surface in clear air, at typically 200-km separation along the satellite track. Such improved knowledge of the global wind field is crucial to many aspects of climate research and weather prediction. Knowledge over large parts of the Tropics and major oceans is presently quite incomplete-leading to major difficulties in studying key processes in the climate system and in improving numerical simulations and predictions; progress in climate modeling is indeed intimately linked to progress in numerical weather prediction. The background studies, potential impact on climate and weather prediction, choice of measurement specifications, and the lidar technology are discussed.

A simulated future atmospheric observation database including ATOVS, ASCAT, and DWL

Article

Full-text available

Oct 1996

A database for study of the impact of Doppler wind lidar data on numerical weather prediction in Observation System Simulation Experiments was created. Five Doppler wind lidar scenarios, TIROS Operational Vertical Sounder, Advanced TIROS Operational Vertical Sounder, Advanced Scatterometer, and all conventional observation types with a realistic distribution in time and space have been successfully simulated. A 30-day run of the ECMWF forecast model was used as a physically sound reference state. This "true" atmospheric state was sampled at the observation positions and times. The simulated true variables were mapped onto the "measured" variables, and a mix of random and gross errors with realistic statistical characteristics was added. The simulated observations were validated by comparison with existing data where available.

Atmospheric Data Assimilation with an Ensemble Kalman Filter: Results with Real Observations

Article

Full-text available

Mar 2005

An ensemble Kalman filter (EnKF) has been implemented for atmospheric data assimilation. It assimilates observations from a fairly complete observational network with a forecast model that includes a standard operational set of physical parameterizations. To obtain reasonable results with a limited number of ensemble members, severe horizontal and vertical covariance localizations have been used. It is observed that the error growth in the data assimilation cycle is mainly due to model error. An isotropic parameterization, similar to the forecast-error parameterization in variational algorithms, is used to represent model error. After some adjustment, it is possible to obtain innovation statistics that agree with the ensemble-based estimate of the innovation amplitudes for winds and temperature. Currently, no model error is added for the humidity variable, and, consequently, the ensemble spread for humidity is too small. After about 5 days of cycling, fairly stable global filter statistics are obtained with no sign of filter divergence. The quality of the ensemble mean background field, as verified using radiosonde observations, is similar to that obtained using a 3D variational procedure. In part, this is likely due to the form chosen for the parameterized model error. Nevertheless, the degree of similarity is surprising given that the background-error statistics used by the two procedures are rather different, with generally larger background errors being used by the variational scheme. A set of 5-day integrations has been started from the ensemble of initial conditions provided by the EnKF. For the middle and lower troposphere, the growth rates of the perturbations are somewhat smaller than the growth rate of the actual ensemble mean error. For the upper levels, the perturbation patterns decay for about 3 days as a consequence of diffusive model dynamics. These decaying perturbations tend to severely underestimate the actual error that grows rapidly near the model top.

Initial Testing of a Massively Parallel Ensemble Kalman Filter with the Poseidon Isopycnal Ocean General Circulation Model

Article

Full-text available

Dec 2002

A multivariate ensemble Kalman filter (MvEnKF) implemented on a massively parallel computer architecture has been developed for the Poseidon ocean circulation model and tested with a Pacific basin model configuration. There are about 2 million prognostic state-vector variables. Parallelism for the data assimilation step is achieved by regionalization of the background-error covariances that are calculated from the phase-space distribution of the ensemble. Each processing element (PE) collects elements of a matrix measurement functional from nearby PEs. To avoid the introduction of spurious long-range covariances associated with finite ensemble sizes, the background-error covariances are given compact support by means of a Hadamard (element by element) product with a three-dimensional canonical correlation function. The methodology and the MvEnKF implementation are discussed. To verify the proper functioning of the algorithms, results from an initial experiment with in situ temperature data are presented. Furthermore, it is shown that the regionalization of the background covariances has a negligible impact on the quality of the analyses. Even though the parallel algorithm is very efficient for large numbers of observations, individual PE memory, rather than speed, dictates how large an ensemble can be used in practice on a platform with distributed memory.

Assimilation of Equatorial Waves by Line-of-Sight Wind Observations

Article

Full-text available

Aug 2004

Nedjeljka Žagar

This paper investigates the potential of line-of-sight (LOS) wind information from a spaceborne Doppler wind lidar to reduce uncertainties in the analysis fields of equatorial waves. The benefit of LOS winds is assessed by comparing their impact to that of a single wind component, full wind field information, and mass field data in three- and four-dimensional variational data assimilation. The dynamical framework consists of nonlinear shallow-water equations solved in spectral space and a back- ground error term based on eigenmodes derived from linear equatorial wave theory. Based on observational evidence, simulated wave motion fields contain equatorial Kelvin, Rossby, mixed Rossby-gravity, and the lowest two modes of the westward-propagating inertio-gravity waves. The same dynamical structures are included, entirely or partially, into the background error covariance matrix for the multivariate analysis. The relative usefulness of LOS data is evaluated by carrying out ''identical twin'' observing system simulation experiments and assuming a perfect model. Results from the experiments involving a single observation or an imperfect background error covariance matrix illustrate that the assimilation increments due to LOS wind information rely more on the background error term specification than the full wind field information. This sensitivity is furthermore transferred to the balanced height field increments. However, all assimilation experiments suggest that LOS wind observations have a capability of being valuable and need supplemental information to the existing satellite mass field measurements in the Tropics. Although the new wind information is incomplete, it has a potential to provide reliable analysis of tropical wave motions when it is used together with the height data.

Observing-Systems Simulation Experiments: Past, Present, and Future

Article

Full-text available

May 1986

A summary of the history of observing-systems simulation experiments (OSSEs) is presented together with a description of current methodology, its capabilities and limitations, and considerations for the design of future experiments. These experiments are defined as a type of sensitivity study and are contrasted with real-data experiments otherwise known as observing-systems experiments (OSEs), data-impact, or data-denial experiments, which form a related type of sensitivity study. Simulation is presented as a means by which an a priori evaluation of proposed remote-sensing systems ran be made.

Comments on "The Impact of Doppler Lidar Wind Observations on a Single-Level Meteorological Analysis"

Article

Full-text available

Aug 2005

The paper by Riishøjgaard et al. (2004) investigates the assimilation and impact of prospective Doppler wind lidar (DWL) line-of-sight (LOS) single-perspec-tive winds in meteorological analysis. It is argued that single-component wind observations are far less effec-tive in reducing wind analysis error than vector wind information. This work has relevance because the pros-pects are good that space-based DWL instruments will provide accurate wind profiles of single-perspective LOS wind profile measurements in the future. Riishøj-gaard et al. rightly argue that the usefulness of such winds needs to be well addressed in the design phase of space missions. The forthcoming European Space Agency Atmospheric Dynamics Mission (ADM), called Aeolus, is referred to in this context. The Riishøjgaard et al. study is carried out in an idealized and very simplified framework. Our concerns are 1) that the simple framework poorly represents the characteristics of a state-of-the-art global data assimi-lation system for numerical weather prediction (NWP) and 2) that the DWL scenarios that are discussed have abundant and unrealistic coverage and quality. As such, their conclusions may be misleading for, and contribute little toward, the critical design consider-ations for an affordable space-based DWL. The results (and the quality of the analyzed wind fields) could be far more realistic and, in our view, far more favorable for LOS winds in a more carefully designed experiment. The NWP analysis problem would be severely under-determined if it were based on the observations alone. To overcome this problem, data assimilation typically combines the information provided by the relatively sparse observations with a short-range forecast on a dense grid (Daley 1991). Because the NWP model state is poorly observed, it is critical that local observation increments are carefully distributed spatially in a wider area. This process is done based on statistical knowl-edge of the background error structures. In a four-dimensional variational data assimilation (4DVAR) analysis system, information on the temporal evolution of the model state is also exploited. Around any local observation, information on the multivariate spatial correlation of the background errors, as represented in the background-error covariance matrix B, is used to provide a spatially coherent update of the model atmo-spheric state. For LOS wind analysis, the B covariance structures are crucial in both spatially interpolating the observed wind component and inferring the spa-tial pattern of the unobserved component of wind as well as the associated temperature and pressure incre-ments. The design of the B matrix and the sampling strategy of the DWL space mission are the two most important factors that determine the impact of the data, both in real application and within the simplified framework of Riishøjgaard et al. In the case in which B is poor, this would generally result in spatially poor analyses, espe-cially when the observations are sparse or when one or several analysis variables are unobserved. In a rela-tively dense observation network, on the other hand, the multivariate spatial structures associated with many observations will overlap and the effect of an imperfect B will diminish (by oversampling). Our specific comments are in two areas. The first is that the Riishøjgaard et al. paper uses a synthetic vortex

A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation

Article

Full-text available

Jan 2001

An ensemble Kalman filter may be considered for the 4D assimilation of atmospheric data. In this paper, an efficient implementation of the analysis step of the filter is proposed. It employs a Schur (elementwise) product of the covariances of the background error calculated from the ensemble and a correlation function having local support to filter the small (and noisy) background-error covariances associated with remote observations. To solve the Kalman filter equations, the observations are organized into batches that are assimilated sequentially. For each batch, a Cholesky decomposition method is used to solve the system of linear equations. The ensemble of background fields is updated at each step of the sequential algorithm and, as more and more batches of observations are assimilated, evolves to eventually become the ensemble of analysis fields. A prototype sequential filter has been developed. Experiments are performed with a simulated observational network consisting of 542 radiosonde and 615 satellite-thickness profiles. Experimental results indicate that the quality of the analysis is almost independent of the number of batches (except when the ensemble is very small). This supports the use of a sequential algorithm. A parallel version of the algorithm is described and used to assimilate over 100 000 observations into a pair of 50-member ensembles. Its operation count is proportional to the number of observations, the number of analysis grid points, and the number of ensemble members. In view of the flexibility of the sequential filter and its encouraging performance on a NEC SX-4 computer, an application with a primitive equations model can now be envisioned.

Observing system simulation experiments at NCEP

Technical Report

Full-text available

Sep 2006

A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems

Article

Full-text available

May 2005

The present paper summarizes the methodologies used at the European Centre for Medium-Range Weather Forecasts (ECMWF), the Meteorological Service of Canada (MSC), and the National Centers for Environmental Prediction (NCEP) to simulate the effect of initial and model uncertainties in ensemble forecasting. The characteristics of the three systems are compared for a 3-month period between May and July 2002. The main conclusions of the study are the following: • the performance of ensemble prediction systems strongly depends on the quality of the data assimilation system used to create the unperturbed (best) initial condition and the numerical model used to generate the forecasts; • a successful ensemble prediction system should simulate the effect of both initial and model-related uncertainties on forecast errors; and • for all three global systems, the spread of ensemble forecasts is insufficient to systematically capture reality, suggesting that none of them is able to simulate all sources of forecast uncertainty.

Data Assimilation Using an Ensemble Kalman Filter Technique

Article

Full-text available

Mar 1998

The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a three-level, quasigeostrophic, T21 model and simulated observations, experiments are performed in a perfect-model context. By using forward interpolation operators from the model state to the observations, the ensemble Kalman filter is able to utilize nonconventional observations. In order to maintain a representative spread between the ensemble members and avoid a problem of inbreeding, a pair of ensemble Kalman filters is configured so that the assimilation of data using one ensemble of short-range forecasts as background fields employs the weights calculated from the other ensemble of short-range forecasts. This configuration is found to work well: the spread between the ensemble members resembles the difference between the ensemble mean and the true state, except in the case of the smallest ensembles. A series of 30-day data assimilation cycles is performed using ensembles of different sizes. The results indicate that (i) as the size of the ensembles increases, correlations are estimated more accurately and the root-mean-square analysis error decreases, as expected, and (ii) ensembles having on the order of 100 members are sufficient to accurately describe local anisotropic, baroclinic correlation structures. Due to the difficulty of accurately estimating the small correlations associated with remote observations, a cutoff radius beyond which observations are not used, is implemented. It is found that (a) for a given ensemble size there is an optimal value of this cutoff radius, and (b) the optimal cutoff radius increases as the ensemble size increases.

Assimilation of Geosat Altimeter Data for the Agulhas Current using the Ensemble Kalman Filter with a Quasi-Geostrophic Model

Article

Full-text available

Jun 1995

The ring-shedding process in the Agulhas Current is studied using the ensemble Kalman filter to assimilate Geosat altimeter data into a two layer quasi-geostrophic ocean model. The properties of the ensemble Kalman filter are further explored with focus on the analysis scheme and the use of gridded data. The Geosat data consist of 10 fields of gridded sea-surface height anomalies separated 10 days apart which are added to a climatic mean field. This corresponds to a huge number of data values and a data reduction scheme must be applied to increase the efficiency of the analysis procedure. Further, it is illustrated how one can resolve the rank problem occurring when a too large data set or a small ensemble is used. 1 Introduction The Agulhas Current is a western-boundary current flowing along the east coast of South Africa. Its water originates from the Mozambique channel (see e.g. Saetre and da Silva, 1984) and from east of Madagascar (e.g. Lutjeharms et al., 1981) as part of...

A reformulation of the background error covariance in the ECMWF global data assimilation system

Article

Mar 1999

The background error covariance plays an important role in modern data assimilation andanalysis systems by determining the distribution of the information in the data in space andbetween variables. A new formulation has been developed for use in the ECMWF system. Thenon-separable structure functions depend on the horizontal and vertical scales and a generalizedlinear balance operator to imply multivariate structure functions. The balance operator isincorporated into the definition of the analysis variables to ensure good preconditioning of theproblem. The formulation and structure of the background error covariance are presented, andthe implications for the analysis increments are examined. This reformulation became the operationalECMWF formulation in 3D-Var in May 1997 and in 4D-Var in November 1997. DOI: 10.1034/j.1600-0870.1999.t01-2-00003.x

Observing system experiments of all main data types in the ECMWF operational system

Article

Jan 2004

G. Kelly

Using improved background-error covariances from an ensemble Kalman filter for adaptive observations

Article

Jun 2002
MON WEATHER REV

A method for determining adaptive observation locations is demonstrated. This method is based on optimal estimation (Kalman filter) theory; it determines the observation location that will maximize the expected improvement, which can be measured in terms of the expected reduction in analysis or forecast variance. This technique requires an accurate model for background error statistics that vary both in space and in time. Here, these covariances are generated using an ensemble Kalman filter assimilation scheme. A variant is also developed that can estimate the analysis improvement in data assimilation schemes where background error statistics are less accurate. This approach is demonstrated using a quasigeostrophic channel model under perfect-model assumptions. The algorithm is applied to find the supplemental rawinsonde location to add to a regular network of rawinsondes that will reduce analysis errors the most. The observation network is configured in this experiment so there is a data void in the western third of the domain. One-hundred-member ensembles from three data assimilation schemes are tested as input to the target selection procedure, two variants of the standard ensemble Kalman filter and a third perturbed observation (3DVAR) ensemble. The algorithm is shown to find large differences in the expected variance reduction depending on the observation location, the flow of the day, and the ensemble used in the adaptive observation algorithm. When using the two variants of the ensemble Kalman filter, the algorithm defined consistently similar adaptive locations to each other, and assimilation of the adaptive observation typically reduced analysis errors significantly. When the 3DVAR ensemble was used, the algorithm picked very different observation locations and the analyses were not improved as much. The amount of improvement from assimilating a supplemental adaptive observation instead of a fixed observation in the middle of the void depended on whether the observation was assimilated sporadically or during every analysis cycle. For sporadic assimilation, the adaptive observation provided a dramatic improvement relative to the supplemental fixed observation. When an adaptive observation was regularly assimilated every cycle, the improvement was smaller. For the sporadic assimilation of an adaptive observation, targeting based simply on the maximum spread in background forecasts provided similar target locations and similar analysis improvements to those generated with the full algorithm. The improvement from the regular assimilation of an adaptive observation based on the spread algorithm was no larger than when observations from a fixed target in the middle of the void were regularly assimilated.

Overview of Observing System Experiments

Article

Jan 2003

Richard Dumelow

Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observation

Article

Jun 2002

A method for determining adaptive observation locations is demonstrated. This method is based on optimal estimation (Kalman filter) theory; it determines the observation location that will maximize the expected im- provement, which can be measured in terms of the expected reduction in analysis or forecast variance. This technique requires an accurate model for background error statistics that vary both in space and in time. Here, these covariances are generated using an ensemble Kalman filter assimilation scheme. A variant is also developed that can estimate the analysis improvement in data assimilation schemes where background error statistics are less accurate. This approach is demonstrated using a quasigeostrophic channel model under perfect-model assumptions. The algorithm is applied here to find the supplemental rawinsonde location to add to a regular network of rawinsondes that will reduce analysis errors the most. The observation network is configured in this experiment so there is a data void in the western third of the domain. One-hundred-member ensembles from three data assimilation schemes are tested as input to the target selection procedure, two variants of the standard ensemble Kalman filter and a third perturbed observation (3DVAR) ensemble. The algorithm is shown to find large differences in the expected variance reduction depending on the observation location, the flow of the day, and the ensemble used in the adaptive observation algorithm. When using the two variants of the ensemble Kalman filter, the algorithm defined consistently similar adaptive locations to each other, and assimilation of the adaptive obser- vation typically reduced analysis errors significantly. When the 3DVAR ensemble was used, the algorithm picked very different observation locations and the analyses were not improved as much. The amount of improvement from assimilating a supplemental adaptive observation instead of a fixed ob- servation in the middle of the void depended on whether the observation was assimilated sporadically or during every analysis cycle. For sporadic assimilation, the adaptive observation provided a dramatic improvement relative to the supplemental fixed observation. When an adaptive observation was regularly assimilated every cycle, the improvement was smaller. For the sporadic assimilation of an adaptive observation, targeting based simply on the maximum spread in background forecasts provided similar target locations and similar analysis improvements to those generated with the full algorithm. The improvement from the regular assimilation of an adaptive observation based on the spread algorithm was no larger than when observations from a fixed target in the middle of the void were regularly assimilated.

The Era-40 Re-analysis

Article

Oct 2005
Q J ROY METEOR SOC

Atlantic atmospheric aerosol studies: 2. Compendium of airborne backscatter measurements at 10.6μm

Article

Jan 1995

The airborne backscatter measurements at 10.6 mum, from the South Atlantic Backscattering Lidar Experiment (SABLE) and the Global Atmospheric Backscatter Lidar Experiment (GABLE) programs over the Atlantic, are gathered together and presented. Plots of backscatter versus altitude are shown for 80 flight sequences. These data comprising about 4×105 measurements from over 180 flight hours is statistically analyzed. The material is distributed in altitude (1 km) and backscatter (half decade) intervals and presented in tabular and histogram form for six principal regions and seasons: South Atlantic summer, South Atlantic winter, far-North Atlantic spring, mid-Atlantic spring/summer, and Northeast Atlantic winter and summer. Several features of the results are discussed, including the incidence of low backscatter and drop-outs and the remarkably high incidence of thin cirrus in the southern tropical winter season. The compendium should provide a climatology of atmospheric backscatter at 10.6 mum for regions of the Atlantic during the relaively clean atmospheric period 1988-1990.

Use and Impact of Automated Aircraft Data in a Global 4DVAR Data Assimilation System

Article

Aug 2003

The use of automated aircraft data [Aircraft Meteorological Data Relay (AMDAR) and Aircraft Communication Addressing and Reporting System (ACARS)] has recently been extended in ECMWF's operational 4DVAR data assimilation system. Herein, a modified data selection procedure is reported on that allows the use of more aircraft profiling data during the aircraft's ascending and descending phase, and more of the most frequent reports at cruise level. It is shown that the accuracy of analyzed jet streams is improved through these changes, as verified against independent (non-real time) aircraft data that had not been used in the experiments. The modifications are shown to have a clear positive impact on the short- and medium-range forecast performance. The revised aircraft usage was implemented operationally in January 2002. The impact in 4DVAR of profiles from American and European automated aircraft in ascending and descending phase has been tested in a data denial impact study, for January and July 2001. This particular impact study was run partly on the request of the WMO/Commission for Basic Systems (CBS) Expert Team on data requirements and the redesign of the global observing system. Their interest is in testing whether a modern data assimilation system (such as 4DVAR) obtains substantial benefit from the aircraft profiles, which sample very irregularly in space and time, given that America and Europe are relatively well covered by radiosondes and wind profilers. The results show a substantial positive impact of the profiling aircraft data on analysis and forecast accuracy. The short-range forecast performance is improved over North America, the North Atlantic, and Europe, In the medium range a clear positive impact is found in the North Atlantic, the European, and Arctic areas in the winter period, and beyond day 6 in the summer period. These results are statistically significant and support the ongoing WMO initiative for further expansion of the AMDAR/ACARS coverage. The results also illustrate the effectiveness of 4DVAR with respect to observations that are irregularly distributed in space and time.

Assimilation of geostat altimeter data for the Agulhas Current using the ensemble Kalman Filter

Article

Jan 1996
MON WEATHER REV

The ring-shedding process in the Agulhas Current is studied using the ensemble Kalman filter to assimilate Geosat altimeter data into a two-layer quasigeostrophic ocean model The properties of the ensemble Kalman filter are further explored with focus on the analysis scheme and the use of gridded data. The Geosat data consist of 10 fields of gridded sea surface height anomalies separated 10 days apart that are added to a climatic mean field This corresponds to a huge number of data values, and a data reduction scheme must be applied to increase the efficiency of the analysis procedure. Further, it is illustrated how one can resolve the rank problem occurring when a too large dataset or a small ensemble is used.

Stochastic representation of model uncertainties in the ECMWF EPS

Article

Oct 1999
Q J ROY METEOR SOC

A stochastic representation of random error associated with parametrized physical processes ('stochastic physics') is described, and its impact in the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF EPS) is discussed. Model random errors associated with physical parametrizations are simulated by multiplying the total parametrized tendencies by a random number sampled from a uniform distribution between 0.5 and 1.5. A number of diagnostics are described and a choice of parameters is made. It is shown how the scheme increases the spread of the ensemble, and improves the skill of the probabilistic prediction of weather parameters such as precipitation. A choice of stochastic parameters is made for operational implementation. The scheme was implemented successfully in the operational ECMWF EPS on 21 October 1998.

Diagnosis and tuning of observational error in a quasi-operational data assimilation setting

Article

Dec 2006
Q J ROY METEOR SOC

Desroziers and Ivanov proposed a method to tune error variances used for data assimilation. The implementation of this algorithm implies the computation of the trace of certain matrices which are not explicitly known. A method proposed by Girard, allowing an approximate estimation of the traces without explicit knowledge of the matrices, was then used. This paper proposes a new implementation of the Desroziers and Ivanov algorithm, including a new computation scheme for the required traces. This method is compared to Girard's in two aspects: its use in the implementation of the tuning algorithm, and the computation of a quantification of the observation impacts on the analysis known as Degrees of Freedom for Signal. Those results are illustrated by studies utilizing the French data assimilation/numerical weather-prediction system ARPEGE. The impact of a first quasi-operational tuning of variances on forecasts is shown and discussed. Copyright © 2006 Royal Meteorological Society

A reformulation of the background error covariance in the ECMWF global data assimilation

Article

Sep 2002
TELLUS A

The background error covariance plays an important role in modern data assimilation and analysis systems by determining the distribution of the information in the data in space and between variables. A new formulation has been developed for use in the ECMWF system. The non-separable structure functions depend on the horizontal and vertical scales and a generalized linear balance operator to imply multivariate structure functions. The balance operator is incorporated into the definition of the analysis variables to ensure good preconditioning of the problem. The formulation and structure of the background error covariance are presented, and the implications for the analysis increments are examined. This reformulation became the operational ECMWF formulation in 3D-Var in May 1997 and in 4D-Var in November 1997.

Influence-matrix diagnostic of a data assimilation system

Article

Dec 2006
Q J ROY METEOR SOC

The influence matrix is used in ordinary least‐squares applications for monitoring statistical multiple‐regression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis—the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub‐set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the self‐sensitivities) has been developed for a large‐dimension variational data assimilation system (the four‐dimensional variational system of the European Centre for Medium‐Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short‐range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface‐based observing systems, and 75% by satellite systems. Low‐influence data points usually occur in data‐rich areas, while high‐influence data points are in data‐sparse areas or in dynamically active regions. Background‐error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation‐error covariance matrices can be identified, interpreted and better understood by the use of influence‐matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system. Copyright © 2004 Royal Meteorological Society

The ERA-40 re-analysis

Article

Oct 2005

ERA-40 is a re-analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with many institutions. The observing system changed considerably over this re-analysis period, with assimilable data provided by a succession of satellite-borne instruments from the 1970s onwards, supplemented by increasing numbers of observations from aircraft, ocean-buoys and other surface platforms, but with a declining number of radiosonde ascents since the late 1980s. The observations used in ERA-40 were accumulated from many sources. The first part of this paper describes the data acquisition and the principal changes in data type and coverage over the period. It also describes the data assimilation system used for ERA-40. This benefited from many of the changes introduced into operational forecasting since the mid-1990s, when the systems used for the 15-year ECMWF re-analysis (ERA-15) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis were implemented. Several of the improvements are discussed. General aspects of the production of the analyses are also summarized. A number of results indicative of the overall performance of the data assimilation system, and implicitly of the observing system, are presented and discussed. The comparison of background (short-range) forecasts and analyses with observations, the consistency of the global mass budget, the magnitude of differences between analysis and background fields and the accuracy of medium-range forecasts run from the ERA-40 analyses are illustrated. Several results demonstrate the marked improvement that was made to the observing system for the southern hemisphere in the 1970s, particularly towards the end of the decade. In contrast, the synoptic quality of the analysis for the northern hemisphere is sufficient to provide forecasts that remain skilful well into the medium range for all years. Two particular problems are also examined: excessive precipitation over tropical oceans and a too strong Brewer-Dobson circulation, both of which are pronounced in later years. Several other aspects of the quality of the re-analyses revealed by monitoring and validation studies are summarized. Expectations that the ‘second-generation’ ERA-40 re-analysis would provide products that are better than those from the firstgeneration ERA-15 and NCEP/NCAR re-analyses are found to have been met in most cases.

Simulation of the yield and accuracy of wind profile measurements from the Atmospheric Dynamics Mission (ADM‐Aeolus)

Article

Apr 2005
Q J ROY METEOR SOC

The European Space Agency's ADM-Aeolus mission will for the first time provide wind profile measurements from space. In this study we carry out global simulations to predict the yield (i.e. quantity) and accuracy of future ADM-Aeolus wind profile measurements, using realistic clouds and climatological aerosol distributions. Based on an assessment of wind analysis accuracy, we argue that the main Aeolus impacts on global data assimilation are to be expected (i) in the jet streams over the oceans, especially away from main air traffic routes, and in the African/Asian subtropical jet, (ii) in the lower troposphere, e.g. western parts of the N. Pacific and N. Atlantic oceans, if cloud gaps are sufficient, and (iii) in the tropics, where mass–wind balance is weak and hence temperature information is not effective for inferring wind. These expectations are supported by the simulations of the yield and accuracy of ADM-Aeolus data. Observational data from the 1994 Lidar In-space Technology Experiment (LITE) are used to provide realistic profiles of cloud cover as input to such simulations. Profiles of model cloud cover are also used and these are validated against the LITE data. While the overall occurrence of model cloud agrees well with LITE-inferred cloud, model cloud cover underestimates the observations by around 20%. The use of model cloud cover for simulating Aeolus data thus results in a modest overestimation of Aeolus penetration altitude. Probability distributions of Aeolus instrument error show good agreement between simulations with observed and model cloud, but the worst 10% of errors are underestimated when model cloud cover is used. The mission-specified accuracy requirement for the free troposphere (instrument error below 2 m s−1) is met by more than 90% of the simulated data. The corresponding requirement for the boundary layer (instrument error below 1 m s−1) is met by two thirds of simulated data from the Mie channel, even when substantial cloud is encountered and under conservative assumptions about aerosol backscatter. It is shown that, after accounting for errors of representativeness, such data can be expected to receive appreciable weight in global data-assimilation systems. Copyright © 2005 Royal Meteorological Society

Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System

Article

Oct 2007
Q J ROY METEOR SOC

A stochastic representation of random error associated with parametrized physical processes (‘stochastic physics’) is described, and its impact in the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF EPS) is discussed. Model random errors associated with physical parametrizations are simulated by multiplying the total parametrized tendencies by a random number sampled from a uniform distribution between 0.5 and 1.5. A number of diagnostics are described and a choice of parameters is made. It is shown how the scheme increases the spread of the ensemble, and improves the skill of the probabilistic prediction of weather parameters such as precipitation. A choice of stochastic parameters is made for operational implementation. the scheme was implemented successfully in the operational ECMWF EPS on 21 October 1998.

Simulation of wind profiles from a space‐borne Doppler wind lidar

Article

Oct 2003
Q J ROY METEOR SOC

The importance of wind observations for meteorological analysis has been recognized for many years. The current global observing system lacks a uniform distribution of tropospheric wind measurements, especially in the tropics and southern hemisphere, and over the northern-hemisphere oceans. A Doppler wind lidar (DWL) mounted on a space-borne platform has the potential to provide a global three-dimensional coverage of wind data. The European Space Agency has decided to fly in 2007 a DWL on a polar-orbiting satellite platform as part of the Atmospheric Dynamics Mission, now called Aeolus. The proposed DWL is a non-scanning single-perspective instrument, operating in the ultraviolet part of the electromagnetic spectrum, providing profiles of line-of-sight (LOS) wind components from detected light backscattered from the illuminated atmospheric volume. The concept has been simulated and was used in observation-system simulation experiments to assess its potential impact on numerical weather prediction and climate processes. This paper describes the simulation of Aeolus LOS wind-component profiles and their expected quality in cloud-free and cloudy conditions. Copyright © 2003 Royal Meteorological Society

ADM‐Aeolus Doppler wind lidar Observing System Simulation Experiment

Article

Jul 2006
Q J ROY METEOR SOC

Within the Atmospheric Dynamics Mission Aeolus (ADM-Aeolus), the European Space Agency (ESA) has approved a Doppler wind lidar (DWL) to fly on a dedicated platform orbiting dawn to dusk at 400 km altitude, planned for launch in 2008. Rigorous design trade-offs have resulted in a lidar concept capable of delivering high-quality wind component profiles, but with a limited coverage. A companion paper describes the realistic simulation of this DWL, whereas this paper sets out to assess the impact of such a lidar in meteorological analyses and forecasts. To this end, an Observing System Simulation Experiment (OSSE) is run. The superior conventional observation coverage of 1993 is used to simulate all conventional observations, although a limited set of satellite observations is simulated. As a consequence, only the northern hemisphere DWL impact in the OSSE is assumed realistic. Here, over a 15-day period with variable weather, out of 15 daily forecasts, 14 show beneficial impact of the DWL. Although the experiment is limited, it corroborates other practical and theoretical evidence that the ADM DWL will demonstrate a beneficial impact in meteorological analyses and forecasts. Copyright © 2006 Royal Meteorological Society

Balanced tropical data assimilation based on study of equatorial waves in ECMWF short-range forecast errors

Article

Apr 2005
Q J ROY METEOR SOC

This paper seeks to represent the tropical short-range forecast error covariances of the European Centre for Medium-RangeWeather Forecasts (ECMWF) model in terms of equatorial waves. The motivation for undertaking this investigation is increasing observational evidence indicating that a substantial fraction of the tropical largescale variability can be explained by equatorially trapped wave solutions known from shallow-water theory. Shortrange forecast differences from a data-assimilation ensemble were taken to serve as a proxy for background errors.

Some challenges of middle atmosphere data assimilation

Article

Oct 2005
Q J ROY METEOR SOC

The assimilation of measurements from the stratosphere and mesosphere is becoming increasingly common as the lids of weather prediction and climate models rise into the mesosphere and thermosphere. However, the dynamics of the middle atmosphere pose specific challenges to the assimilation of measurements from this region. Forecast-error variances can be very large in the mesosphere and this can render assimilation schemes very sensitive to the details of the specification of forecast error correlations. An example is shown where observations in the stratosphere are able to produce increments in the mesosphere. Such sensitivity of the assimilation scheme to misspecification of covariances can also amplify any existing biases in measurements or forecasts. Since both models and measurements of the middle atmosphere are known to have biases, the separation of these sources of bias remains a issue. Finally, well-known deficiencies of assimilation schemes, such as the production of imbalanced states or the assumption of zero bias, are proposed explanations for the inaccurate transport resulting from assimilated winds. The inability of assimilated winds to accurately transport constituents in the middle atmosphere remains a fundamental issue limiting the use of assimilated products for applications involving longer time-scales. Copyright © 2005 Royal Meteorological Society

Sequential Data Assimilation With a Nonlinear Quasi-Geostrophic Model Using Monte-Carlo Methods to Forecast Error Statistics

Article

May 1994

Geir Evensen

A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error covariance equation are avoided because storage and evolution of the error covariance matrix itself are not needed. The results are also better than what is provided by the extended Kalman filter since there is no closure problem and the quality of the forecast error statistics therefore improves. The method should be feasible also for more sophisticated primitive equation models. The computational load for reasonable accuracy is only a fraction of what is required for the extended Kalman filter and is given by the storage of, say, 100 model states for an ensemble size of 100 and thus CPU requirements of the order of the cost of 100 model integrations. The proposed method can therefore be used with realistic nonlinear ocean models on large domains on existing computers, and it is also well suited for parallel computers and clusters of workstations where each processor integrates a few members of the ensemble.

Estimation of entropy reduction and degrees of freedom for signal for large variational analysis systems

Jan 2003

Fisher M

Observing System Simulation Experiments at NCEP The challenges of middle atmosphere data assimilation. Q

Jan 2005
3513-3528

M Masutani
Js Wollen
Lord
Sj
Tj
Emmitt
Gd
H Sun
Wood
Sa
Terry S J Greco
R Treadon
Campana
Ks

Masutani M, Wollen JS, Lord SJ, Kleespies TJ, Emmitt GD, Sun H, Wood SA, Greco S, Terry J, Treadon R, Campana KS. 2006. Observing System Simulation Experiments at NCEP. Office Note 451, US Department of Commerce, EMC/NCEP/NWS/NOAA, Camp Springs, Maryland 20746, USA. Polavarapu S. 2005. The challenges of middle atmosphere data assimilation. Q. J. R. Meteorol. Soc. 131: 3513–3528.

Estimation of entropy reduction and degrees of freedom for signal for large variational analysis systems Using improved background error covariances from an ensemble Kalman filter for adaptive observations

Jan 2003
1552-1572

M Fisher

Fisher M. 2003b. Estimation of entropy reduction and degrees of freedom for signal for large variational analysis systems. ECMWF Tech. Memo. 397. Hamill TM, Snyder C. 2002. Using improved background error covariances from an ensemble Kalman filter for adaptive observations. Mon. Weather Rev. 130: 1552–1572.

Assimilation of high-resolution satellite data

Jan 2003
6-12

J-N Thé
Andersson

Thé J-N, Andersson E. 2003. Assimilation of high-resolution satellite data. ECMWF Newsletter 97: 6–12.

OSEs on all main data types in the ECMWF operational system

Apr 2004
9-12

Kelly G T Mcnally
J-N Thé
Szyndel

Kelly G, McNally T, Thé J-N, Szyndel M. 2004. OSEs on all main data types in the ECMWF operational system. In Proceedings of the 3rd WMO Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction, 9–12 March 2004, Alpbach, Austria, 63–94.

DOI: 10.1002/qj Evensen G, van Leeuwen PJ. 1996. Assimilation of Geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasi-geostrophic model

Jan 2007
381-390

Copyright  2007 Royal Meteorological Society Q. J. R. Meteorol. Soc. 133: 381–390 (2007) DOI: 10.1002/qj Evensen G, van Leeuwen PJ. 1996. Assimilation of Geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasi-geostrophic model. Mon. Weather Rev. 124: 85–96.

OSEs on all main data types in the ECMWF operational system'. Pp 63–94 in Proceedings of the 3rd WMO Workshop on The impact of various observing systems on numerical weather prediction

Apr 2004
9-12

G Kelly
T Mcnally
J.-N Thépaut
M Szyndel

Kelly, G., McNally, T., Thépaut, J.-N. and Szyndel, M., 2004: 'OSEs on all main data types in the ECMWF operational system'. Pp 63–94 in Proceedings of the 3rd WMO Workshop on The impact of various observing systems on numerical weather prediction, 9–12 March 2004, Alpbach, Austria.

The Four Candidate Earth Explorer Core Missions – Atmospheric Dynamics Mission ESA Report for Mission Selection Sequential data assimilation with a nonlinear quasigeostrophic model using Monte-Carlo methods to forecast statistics

Jan 1994
1233-99

G European Space Agency Pp Evensen

European Space Agency, 1999: The Four Candidate Earth Explorer Core Missions – Atmospheric Dynamics Mission. ESA Report for Mission Selection, ESA SP-1233(4), 157 pp Evensen, G., 1994: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte-Carlo methods to forecast statistics. J. Geophys. Res., 99(C5), 10143–10162

Overview of observing system experiments Pp 95–111 in Proceedings of the ECMWF Seminar on Recent developments in data assimilation for atmosphere and ocean

Oct 2003
8-12

R Dumelow

Dumelow, R., 2003: Overview of observing system experiments. Pp 95–111 in Proceedings of the ECMWF Seminar on Recent developments in data assimilation for atmosphere and ocean, 8-12 September, 2003, Reading, UK.

Background error covariance modelling

Fisher M

Observing System Simulation Experiments at NCEP. Office Note 451

M Masutani
Js Wollen
Sj Lord
Tj Kleespies
Gd Emmitt
H Sun
Sa Wood
S Greco
J Terry
R Treadon
Ks Campana

The Four Candidate Earth Explorer Core Missions-Atmospheric Dynamics Mission

InProceedings of the ECMWF Seminar on Recent developments in Data Assimilation for Atmosphere and Ocean 8-12

Dumelowr

Observing-system impact assessment using a data assimilation ensemble technique: Application to the ADM - Aeolus wind profiling mission

Abstract

No full-text available

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