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Ingestion of Sentinel-Derived Remote Sensing Products in Numerical Weather Prediction Models: First Results of the ESA Steam Project
Abstract
The European Space Agency (ESA) STEAM (SaTellite Earth
observation for Atmospheric Modelling) project aims at
investigating new areas of synergy between high-resolution
numerical atmosphere models and data from spaceborne
remote sensing sensors, with focus on Copernicus Sentinels
1, 2 and 3 satellites. An example of synergy is the ingestion
of surface information derived from Sentinel data in
numerical weather prediction models. The rationale is that
Sentinels 1, 2 and 3 are able to provide high spatio-temporal
resolution information on the surface boundary (as well as the
atmosphere column) and that an inaccurate representation of
the boundary conditions represents a major source of
uncertainty for weather forecasts.
For a profitable ingestion of EO data in numerical weather
prediction models, a critical aspect is the choice of a suitable
model. Once the numerical model is chosen, the problem of
the selection of the Sentinel-derived surface variables that
have to be ingested in the model has to be tackled. While
some data, such as sea and land surface temperature, are
directly available, other surface data, such as soil moisture,
have to be retrieved.
Being STEAM currently in its initial phase, this paper gives
a general overview of the project and focuses on the first
activities performed in its framework. In particular, it
describes the rationale behind the choice of the Numerical
Weather Prediction Model and the multi-temporal approach
designed to retrieve soil moisture from Sentinel-1 data.
Moreover, the first results of the ingestion of Sentinel derived
soil moisture, land surface temperature and sea surface
temperature data into the selected model are shown. These
results concern an extreme weather event that occurred in
Tuscany (central Italy) in September 2017.
Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state‐of‐the‐art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security.
This article is categorized under:
• Energy Infrastructure > Systems and Infrastructure
• Wind Power > Systems and Infrastructure
• Photovoltaics > Systems and Infrastructure
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.
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
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.