Comprehensive Approach of Land Data Assimilation Based on Multi-remote Sensing Datasets

Abstract

This study investigates whether assimilating soil moisture (SM) observations from multiple sensors can increase spatiotemporal coverage and improve SM estimates. A global SM data assimilation (DA) system based on the Local Ensemble Transform Kalman Filter is used to merge SM retrievals from the Soil Moisture Active Passive (SMAP) mission, the Soil Moisture and Ocean Salinity (SMOS) mission, the Advanced Scatterometer (ASCAT), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) into the Joint UK Land Environment Simulator (JULES) land model. The SM retrievals are assimilated both separately and jointly over the boreal warm seasons (May–September) of 2015– 2021. The resulting SM estimates are validated using an Instrumental Variable approach globally, and against in-situ measurements in North America, Europe, and East Asia. The four single-sensor DA experiments result in a global average improvement of 0.045 in the anomaly correlation coefficient (R) compared to the model-only (Openloop) simulation. The assimilation of SMAP retrievals yields the best R over 48 % of the global land area, followed by ASCAT (24%), AMSR2 (16%), and SMOS (12%). Furthermore, validation against in-situ measurements shows that all single-sensor DA experiments improve surface and root-zone SM estimates relative to the Openloop. Again, SMAP provides superior single-sensor benefit. The multi-sensor assimilation achieves additional gains by merging complementary observations. These benefits are also evident at the sub-daily timescale, with skill improvement both during daytime and nighttime. The skill improvement of the multi-sensor DA over single-sensor DA at each sub-daily time step is associated with the overpass times of the individual sensors and their respective performance. This study utilizes a multi-sensor data assimilation system to investigate the improvement in heatwave prediction skill driven by land initialization, interpreting the results from the perspective of land-atmosphere interactions. Furthermore, this study quantitatively assesses the pivotal role of soil moisture-temperature coupling. This study quantitatively evaluates the spatiotemporal improvements in heatwave prediction skill resulting from enhanced land initial conditions, specifically focusing on the 2022 compound heatwave-drought event in South China. Our results demonstrate that land initialization via data assimilation improves the spatial representation of soil moisture over East Asia. Consequently, this yields a statistically significant improvement in simulating land-atmosphere coupling regimes compared to control runs based on climatological soil moisture. This enhanced coupling fidelity leads to better temperature and heatwave forecasts. Crucially, these gains are attributed to the model's improved capability to capture the 'hyper-sensitive' regime where soil moisture-temperature coupling sensitivity is strengthened since heatwave predictability is shown to be significantly amplified within this regime. Overall, this study demonstrates that expanding the spatiotemporal coverage of satellite observations through multi-sensor assimilation provides better SM estimates and suggests the advantage of using multi-sensor in terms of land initialization and land- atmosphere interaction in subseasonal to seasonal forecasts. Keywords: soil moisture, data assimilation, land surface model, local ensemble transform kalman filter, multi-sensor, land initialization, land-atmosphere interaction