SMOS Neural Network Soil Moisture Data Assimilation

Abstract

A set of Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) data assimilation (DA) experiments are presented. The SMOS soil moisture dataset used in this study was produced training a neural network (NN) using SMOS brightness temperatures as input and ECMWF H-TESSEL SM fields as reference for the training. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. SMOS NN SM DA experiments were compared to Advanced Scat-terometer (ASCAT) SM DA. In both cases, experiments with and without 2 metre air temperature and relative humidity DA are discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives similar results to the model open loop with no assimilation. The effect of the soil moisture analysis on the Numerical Weather Prediction (NWP) was evaluated using the analysed surface fields to perform atmospheric forecast experiments. In the Northern Hemisphere both with ASCAT and SMOS, the experiments using 2m air temperature and relative humidity improve the forecast in April-September. SMOS alone has a significant positive effect in July-September. Maps of the forecast skill with respect to the open loop experiment show that SMOS improves the forecast in North America and to a lesser extent in Northern Asia for up to 72 hours.