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Open AccessArticle

SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

1
European Centre for Medium Range Weather Forecasts, Shinfield Road, Reading RG2 9AX, UK
2
Centre 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, France
3
Centre National de Recherches Météorologiques, Meteo France, Université de Toulouse, Centre National de la Recherche Scientifique (CNRS), 42 Avenue Gaspard Coriolis, 31057 Toulouse, France
4
Observatoire de Paris, 77 Av. Denfert Rochereau, 75014 Paris, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1334; https://doi.org/10.3390/rs11111334
Received: 17 April 2019 / Revised: 23 May 2019 / Accepted: 28 May 2019 / Published: 3 June 2019
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, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m 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. View Full-Text
Keywords: soil moisture; L-band; passive radiometry; data assimilation; numerical weather prediction soil moisture; L-band; passive radiometry; data assimilation; numerical weather prediction
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MDPI and ACS Style

Rodríguez-Fernández, N.; de Rosnay, P.; Albergel, C.; Richaume, P.; Aires, F.; Prigent, C.; Kerr, Y. SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact. Remote Sens. 2019, 11, 1334.

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