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Incorporating Sentinel-derived products into numerical weather models: the ESA STEAM project
Abstract
The STEAM (SaTellite Earth observation for Atmospheric Modelling) project, funded by the European Space Agency,
aims at investigating new areas of synergy between high-resolution numerical weather prediction (NWP) models and
data from spaceborne remote sensing sensors. An example of synergy is the incorporation of high-resolution remote
sensing data products in NWP models. The rationale is that NWP models are presently able to produce forecasts with a
spatial resolution in the order of 1 km, but unreliable surface information or poor knowledge of the initial state of the
atmosphere may imply an inaccurate simulation of the weather phenomena. It is expected that forecast inaccuracies
could be reduced by ingesting high resolution Earth Observation derived products into models operated at cloud
resolving grid spacing. In this context, the Copernicus Sentinel satellites represent an important source of data, because
they provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature,
wind speed, columnar water vapor) used NWP models runs. This paper presents the first results of the experiments
carried out in the framework of the STEAM project, regarding the ingestion/assimilation of surface information derived
from Sentinel data into a NWP model. The experiments concern a flood event occurred in Tuscany (Central Italy) in
September 2017. Moreover, in view of the assimilation of water vapor maps obtained by applying the SAR
Interferometry technique to Sentinel-1 data, the results of the assimilation of Zenith total delay data derived from global
navigation satellite system (GNSS) are also presented.
In the field of Earth observation, the importance of in situ data was recognized by the Group on Earth Observations (GEO) in the Canberra Declaration in 2019. The GEO community focuses on three global priority engagement areas: the United Nations 2030 Agenda for Sustainable Development, the Paris Agreement, and the Sendai Framework for Disaster Risk Reduction. While efforts have been made by GEO to open and disseminate in situ data, GEO did not have a general way to capture in situ data user requirements and drive the data provider efforts to meet the goals of its three global priorities. We present a requirements data model that first formalizes the collection of user requirements motivated by user-driven needs. Then, the user requirements can be grouped by essential variable and an analysis can derive product requirements and parameters for new or existing products. The work was inspired by thematic initiatives, such as OSCAR, from WMO, OSAAP (formerly COURL and NOSA) from NOAA, and the Copernicus In Situ Component Information System. The presented solution focuses on requirements for all applications of Earth observation in situ data. We present initial developments and testing of the data model and discuss the steps that GEO should take to implement a requirements database that is connected to actual data in the GEOSS platform and propose some recommendations on how to articulate it.
In this study, a technique developed to retrieve integrated water vapor from interferometric synthetic aperture radar (InSAR) data is described, and a three-dimensional variational assimilation experiment of the retrieved precipitable water vapor into the mesoscale weather prediction model MM5 is carried out. The InSAR measurements were available in the framework of the European Space Agency (ESA) project for the “Mitigation of electromagnetic transmission errors induced by atmospheric water vapor effects” (METAWAVE), whose goal was to analyze and possibly predict the phase delay induced by atmospheric water vapor on the spaceborne radar signal. The impact of the assimilation on the model forecast is investigated in terms of temperature, water vapor, wind, and precipitation forecast. Changes in the modeled dynamics and an impact on the precipitation forecast are found. A positive effect on the forecast of the precipitation is found for structures at the model grid scale or larger (1 km), whereas a negative effect is found on convective cells at the subgrid scale that develops within 1 h time intervals. The computation of statistical indices shows that the InSAR assimilation improves the forecast of weak to moderate precipitation ( ).
The representation of land-atmosphere interactions in weather forecast
models has a strong impact on the Planetary Boundary Layer (PBL) and, in
turn, on the forecast. Soil moisture is one of the key variables in land
surface modelling, and an inadequate initial soil moisture field can
introduce major biases in the surface heat and moisture fluxes and have
a long-lasting effect on the model behaviour. Detecting the variability
of soil characteristics at small scales is particularly important in
mesoscale models because of the continued increase of their spatial
resolution. In this paper, the high resolution soil moisture field
derived from ENVISAT/ASAR observations is used to derive the soil
moisture initial condition for the MM5 simulation of the Tanaro flood
event of April 2009. The ASAR-derived soil moisture field shows
significantly drier conditions compared to the ECMWF analysis. The
impact of soil moisture on the forecast has been evaluated in terms of
predicted precipitation and rain gauge data available for this event
have been used as ground truth. The use of the drier, highly resolved
soil moisture content (SMC) shows a significant impact on the
precipitation forecast, particularly evident during the early phase of
the event. The timing of the onset of the precipitation, as well as the
intensity of rainfall and the location of rain/no rain areas, are better
predicted. The overall accuracy of the forecast using ASAR SMC data is
significantly increased during the first 30 h of simulation. The impact
of initial SMC on the precipitation has been related to the change in
the water vapour field in the PBL prior to the onset of the
precipitation, due to surface evaporation. This study represents a first
attempt to establish whether high resolution SAR-based SMC data might be
useful for operational use, in anticipation of the launch of the
Sentinel-1 satellite.
Parameterization of land surface processes and consideration of surface inhomogeneities are very important to mesoscale meteorological modeling applications, especially those that provide information for air quality modeling. To provide crucial, reliable information on the diurnal evolution of the planetary boundary layer (PBL) and its dynamic characteristics, it is necessary in a mesoscale model to include a land surface parameterization that simulates the essential physics processes and is computationally efficient.A land surface model is developed and implemented in the Fifth-Generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) to enable MM5 to respond to changing soil moisture and vegetation conditions. This land surface model includes explicit soil moisture, which is based on the Interactions between Soil, Biosphere, and Atmosphere model and three pathways for evaporation, including soil evaporation, canopy evaporation, and vegetative evapotranspiration. The stomatal conductance, leaf-to-canopy scaling, and surface moisture parameterizations are newly developed based on a variety of sources in the current literature. Also, a processing procedure for gridding soil and vegetation parameters and simulating seasonal growth has been developed. MM5 with the land surface model is tested and evaluated against observations and the `standard' MM5, which uses a simple surface moisture availability scheme to estimate the soil wetness and then the latent heat flux, for two cases from the First International Satellite Land Surface Climatology Project Field Experiment. The evaluation analysis focuses primarily on surface fluxes of heat and moisture, near-surface temperature, soil temperature, PBL height, and vertical temperature profiles. A subsequent article will describe extensions of this model to simulate chemical dry deposition.
This first paper of the two-part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah-MP). The Noah-MP's performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long-term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture-groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local-scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah-MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah-MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah-MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah-MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework.
The authors use a procedure called the method for object-based diagnostic evaluation, commonly referred to as MODE, to compare forecasts made from two models representing separate cores of the Weather Research and Forecasting (WRF) model during the 2005 National Severe Storms Laboratory and Storm Prediction Center Spring Program. Both models, the Advanced Research WRF (ARW) and the Nonhydrostatic Mesoscale Model (NMM), were run without a traditional cumulus parameterization scheme on horizontal grid lengths of 4 km (ARW) and 4.5 km (NMM). MODE was used to evaluate 1-h rainfall accumulation from 24-h forecasts valid at 0000 UTC on 32 days between 24 April and 4 June 2005. The primary variable used for evaluation was a “total interest” derived from a fuzzy-logic algorithm that compared several attributes of forecast and observed rain features such as separation distance and spatial orientation. The maximum value of the total interest obtained by comparing an object in one field with all objects in the comparison field was retained as the quality of matching for that object. The median of the distribution of all such maximum-interest values was selected as a metric of the overall forecast quality.
Results from the 32 cases suggest that, overall, the configuration of the ARW model used during the 2005 Spring Program performed slightly better than the configuration of the NMM model. The primary manifestation of the differing levels of performance was fewer false alarms, forecast rain areas with no observed counterpart, in the ARW. However, it was noted that the performance varied considerably from day to day, with most days featuring indistinguishable performance. Thus, a small number of poor NMM forecasts produced the overall difference between the two models.
ABSTRACT A mesoscale atmospheric forecast model configured in a hybrid isentropic-sigma vertical coordinate and used in the NOAA Rapid Update Cycle (RUC) for operational numerical guidance is presented. The RUC model is the only quasi-isentropicforecast model running operationally in the world and is distinguished fromother hybrid-isentropic models by its application at fairly high horizontal resolution (10-20 km) and a generalized vertical coordinate formulation that allows model levels to remain continuous and yet be purely isentropic well into the middle and even lower troposphere. The RUC model is fully described in its 2003 operational version, including numerics and a set of fairly advanced physical parameterizations. The use of these parameterizations, including mixed-phase cloud microphysics and an ensemble-closure-basedcum ulus parameterization, is fully consistent with the RUC vertical coordinate without any loss of generality. A series of experiments confirmthat the RUC hybrid θ-σcoordinate reduces cross-coordinate
Very high-resolution Precipitable Water Vapor (PWV) maps obtained by the Sentinel-1 A Synthetic Aperture Radar (SAR), using the SAR interferometry (InSAR) technique, are here shown to have a positive impact on the performance of severe weather forecasts. A case study of deep convection which affected the city of Adra, Spain, on 6-7 September 2015, is successfully forecasted by the Weather Research and Forecasting (WRF) model initialized with InSAR data assimilated by the three-dimensional variational (3D-Var) technique, with improved space and time distributions of precipitation, as observed by the local weather radar and rain gauge. This case study is exceptional because it consisted of two severe events 12 hours apart, with a timing that allows for the assimilation of both the ascending and descending satellite images, each for the initialization of each event. The same methodology applied to the network of Global Navigation Satellite System (GNSS) observations in Iberia, at the same times, failed to reproduce observed precipitation, although it also improved, in a more modest way, the forecast skill. The impact of PWV data is shown to result from a direct increment of convective available potential energy, associated with important adjustments in the low-level wind field, favoring its release in deep convection. It is suggested that InSAR images, complemented by dense GNSS data, may provide a new source of water vapor data for weather forecasting, since their sampling frequency could reach the sub-daily scale by merging different SAR platforms, or when future geosynchronous radar missions become operational.
This paper presents MULESME, a software designed for the systematic mapping of surface soil moisture using Sentinel-1 SAR data. MULESME implements a multi-temporal algorithm that uses time series of Sentinel-1 data and ancillary data, such as a plant water content map, as inputs. A secondary software module generates the plant water content map from optical data provided by Landsat-8, or Sentinel-2, or MODIS. Each output of MULESME includes another map showing the level of uncertainty of the soil moisture estimates. MULESME was tested by using both synthetic and actual data. The results of the tests showed that root mean square error is in the range between 0.03 m 3 /m 3 (synthetic data) and 0.06 m 3 /m 3 (actual data) for bare soil. The accuracy decreases in the presence of vegetation (root mean square in the range 0.08e0.12 m 3 /m 3), as expected.
In this study a technique developed to retrieve integrated water vapor from Interferometric Synthetic Aperture Radar (InSAR) data is described and a three dimensional variational assimilation experiment of the retrieved precipitable water vapor into the mesoscale weather prediction model MM5 is carried out. The InSAR measurements were available in the framework of the European Space Agency (ESA) project for the “Mitigation of Electromagnetic Transmission errors induced by Atmospheric WAter Vapour Effects” (METAWAVE), whose goal was to analyze and possibly predict the phase delay induced by atmospheric water vapor on the space borne radar signal. The impact of the assimilation on the model forecast is investigated in terms of temperature, water vapor, wind and precipitation forecast. Changes in the modeled dynamics and an impact on the precipitation forecast are found. A positive effect on the forecast of the precipitation is found for structures at the model grid scale or larger (1 km), whereas a negative effect is found on convective cells at the sub-grid scale that develop within one hour time intervals. The computation of statistical indices shows that the InSAR assimilation improves the forecast of small precipitation amounts ( 15 mm/12h).
The Sentinel-1 mission will offer the opportunity to obtain C-band radar data characterized by short revisit time, thus allowing for the generation of frequent soil moisture maps. This work presents a prototype software implementing a multitemporal approach to the problem of soil moisture retrieval using Synthetic Aperture Radar (SAR) data. The approach exploits the short revisit time of Sentinel-1 data by assuming the availability of a time series of SAR images that is integrated within a retrieval algorithm based on the Bayesian maximum a posteriori probability statistical criterion. The paper focuses on the combination of on-line and off-line processing that has been designed in order to decrease the time necessary to produce a soil moisture map, which may be a critical aspect of multitemporal approaches. It describes also the optimization of the algorithm carried out to find the set of algorithm parameters that allow obtaining the best tradeoff between accuracy of the estimates and computational efficiency. A set of simulations of C-band SAR data, produced by applying a well-established radar-backscattering model, is used to perform the optimization. The designed system is tested on a series of ERS-1 SAR data acquired on February-April 1994 in Central Italy with a revisit time of three days. The results indicate that the temporal trend of estimated soil moisture is consistent with the succession of rain events occurred throughout the period of ERS-1 acquisitions over the observed geographic area.
Because the microwave dielectric constant of dry vegetative matter is
much smaller (by an order of magnitude or more) than the dielectric
constant of water, and because a vegetation canopy is usually composed
of more than 99% air by volume, it is proposed that the canopy can be
modeled as a water cloud whose droplets are held in place by the
vegetative matter. Such a model was developed assuming that the canopy
"cloud" contains identical water droplets randomly distributed within
the canopy. By integrating the scattering and attenuation cross-section
contributions of N droplets per unit volume over the signal pathlength
through the canopy, an expression was derived for the backscattering
coefficient as a function of three target parameters: volumetric
moisture content of the soil, volumetric water content of the
vegetation, and plant height. Regression analysis of the model
predictions against scattering data acquired over a period of four
months at several angles of incidence (0°-70°) and frequencies
(8-18 GHz) for HH and VV polarizations yields correlation coefficients
that range from .7 to .99 depending on frequency, polarization, and crop
type. The corresponding standard errors of estimate range from 1.1 to
2.6 dB.
A limited-area three-dimensional variational data assimilation (3DVAR) system applicable to both synoptic and mesoscale numerical weather prediction is described. The system is designed for use in time-critical real-time applications and is freely available to the data assimilation community for general research. The unique features of this implementation of 3DVAR include (a) an analysis space represented by recursive filters and truncated eigenmodes of the background error covariance matrix, (b) the inclusion of a cyclostrophic term in 3DVAR's explicit mass–wind balance equation, and (c) the use of the software architecture of the Weather Research and Forecast (WRF) model to permit efficient performance on distributed-memory platforms. The 3DVAR system is applied to a multiresolution, nested-domain forecast system. Resolution and seasonal-dependent background error statistics are presented. A typhoon bogusing case study is performed to illustrate the 3DVAR response to a single surface pressure observation and its subsequent impact on numerical forecasts of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Results are also presented from an initial real-time MM5-based application of 3DVAR.