Fairbairn, D.; Rosnay, P.D.; Weston, P. Evaluation of an Adaptive Soil Moisture Bias Correction Approach in the ECMWF Land Data Assimilation System. Remote Sens.2024, 16, 493.
Fairbairn, D.; Rosnay, P.D.; Weston, P. Evaluation of an Adaptive Soil Moisture Bias Correction Approach in the ECMWF Land Data Assimilation System. Remote Sens. 2024, 16, 493.
Fairbairn, D.; Rosnay, P.D.; Weston, P. Evaluation of an Adaptive Soil Moisture Bias Correction Approach in the ECMWF Land Data Assimilation System. Remote Sens.2024, 16, 493.
Fairbairn, D.; Rosnay, P.D.; Weston, P. Evaluation of an Adaptive Soil Moisture Bias Correction Approach in the ECMWF Land Data Assimilation System. Remote Sens. 2024, 16, 493.
Abstract
Satellite-derived soil moisture observations typically rely on bias-correction (BC) prior to assimilation in land surface models. Current techniques include rescaling or machine learning approaches to map the observations to the modelled soil moisture climatology. However, these approaches do not allow for non-stationary biases and recalibrations require a long training period, which is not always feasible. In this study we evaluate a two-stage filter to dynamically correct soil moisture biases from satellite-derived active ASCAT C-band and passive L-band SMOS surface soil moisture observations in the European Centre for Medium Range Weather Forecasts (ECMWF) land data assimilation system. This adaptive soil moisture BC approach is designed to complement the operational seasonal rescaling of the ASCAT observations and the SMOS neural network retrieval used at ECMWF, while allowing the assimilation to correct sub-seasonal scale errors. Over a 3-year test period, the adaptive BC reduces the seasonal-scale first guess-observation departures by 20-30% for ASCAT and SMOS. The adaptive BC leads to (i) slight improvements in soil moisture performance against in situ data; and (ii) moderate but statistically significant reductions in the 1-5 day relative humidity forecast errors in the boundary layer of the northern hemisphere midlatitudes.
Keywords
satellite; soil moisture; data assimilation; bias-correction
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
