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

Nemesio Rodríguez-Fernández,Filipe Aires,Clement Albergel,Philippe Richaume,Catherine Prigent,Yann Kerr,Patricia De Rosnay

doi:10.3390/rs11111334

Abstract

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.

Highlights

The amount of moisture in the soil is an important variable to understand the coupling of the continental surface and the atmosphere [1,2]
The neural network soil moisture (NNSM) map is similar to the H-TESSEL map, but it shows small differences coming from the Soil Moisture and Ocean Salinity (SMOS) Tbs and the trained neural network (NN)
Some differences can be seen for example in India and Australia in JFM, as they are in agreement for NNSM and RH2m but not T2m in India and they are in agreement for T2m and RH2m but not for NNSM in Australia

Summary

Introduction

The amount of moisture in the soil is an important variable to understand the coupling of the continental surface and the atmosphere [1,2]. The launch of the Soil Moisture and Ocean Salinity (SMOS) satellite [8] in November 2009 has offered global L-band (1.4 GHz) observations in polarimetry mode with multi-angular capabilities and a revisit time of three days or less. The assimilation of Tbs requires the instrument noise, and the radiative transfer uncertainties which can be very hard to specify [11]. Another advantage is that Tbs can be available in NRT, which makes their use possible in an operational system. A neural network approach was implemented recently by the European Space Agency to develop the SMOS NRT SM product [12]

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