SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact
The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically b...
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doaj-cf15d62e39c04076a8a14ea6fcfbda152020-11-25T00:25:27ZengMDPI AGRemote Sensing2072-42922019-06-011111133410.3390/rs11111334rs11111334SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric ImpactNemesio Rodríguez-Fernández0Patricia de Rosnay1Clement Albergel2Philippe Richaume3Filipe Aires4Catherine Prigent5Yann Kerr6European Centre for Medium Range Weather Forecasts, Shinfield Road, Reading RG2 9AX, UKEuropean Centre for Medium Range Weather Forecasts, Shinfield Road, Reading RG2 9AX, UKCentre National de Recherches Météorologiques, Meteo France, Université de Toulouse, Centre National de la Recherche Scientifique (CNRS), 42 Avenue Gaspard Coriolis, 31057 Toulouse, FranceCentre d’Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse, Centre National d’Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Dévelopement (IRD), Université Paul Sabatier, 18 av. Edouard Belin, bpi 2801, 31401 Toulouse, FranceObservatoire de Paris, 77 Av. Denfert Rochereau, 75014 Paris, FranceObservatoire de Paris, 77 Av. Denfert Rochereau, 75014 Paris, FranceCentre d’Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse, Centre National d’Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Dévelopement (IRD), Université Paul Sabatier, 18 av. Edouard Belin, bpi 2801, 31401 Toulouse, FranceThe assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if local biases can remain. Experiments performing joint data assimilation (DA) of NNSM, 2 m air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T<sub>2m</sub> and RH<sub>2m</sub> DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T<sub>2m</sub> and RH<sub>2m</sub> but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T<sub>2m</sub> and RH<sub>2m</sub> DA improves the forecast in April−September, while NNSM alone has a significant positive effect in July−September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 h lead time.https://www.mdpi.com/2072-4292/11/11/1334soil moistureL-bandpassive radiometrydata assimilationnumerical weather prediction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nemesio Rodríguez-Fernández Patricia de Rosnay Clement Albergel Philippe Richaume Filipe Aires Catherine Prigent Yann Kerr |
spellingShingle |
Nemesio Rodríguez-Fernández Patricia de Rosnay Clement Albergel Philippe Richaume Filipe Aires Catherine Prigent Yann Kerr SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact Remote Sensing soil moisture L-band passive radiometry data assimilation numerical weather prediction |
author_facet |
Nemesio Rodríguez-Fernández Patricia de Rosnay Clement Albergel Philippe Richaume Filipe Aires Catherine Prigent Yann Kerr |
author_sort |
Nemesio Rodríguez-Fernández |
title |
SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact |
title_short |
SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact |
title_full |
SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact |
title_fullStr |
SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact |
title_full_unstemmed |
SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact |
title_sort |
smos neural network soil moisture data assimilation in a land surface model and atmospheric impact |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-06-01 |
description |
The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if local biases can remain. Experiments performing joint data assimilation (DA) of NNSM, 2 m air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T<sub>2m</sub> and RH<sub>2m</sub> DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T<sub>2m</sub> and RH<sub>2m</sub> but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T<sub>2m</sub> and RH<sub>2m</sub> DA improves the forecast in April−September, while NNSM alone has a significant positive effect in July−September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 h lead time. |
topic |
soil moisture L-band passive radiometry data assimilation numerical weather prediction |
url |
https://www.mdpi.com/2072-4292/11/11/1334 |
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