Impact of ASCAT Level-2 Soil Moisture Assimilation Using a Simplified Extended Kalman Filter in the AROME Model

This study investigates the impact of assimilating Advanced Scatterometer (ASCAT) Level-2 surface soil moisture retrievals into the Application of Research to Operations at Mesoscale (AROME) model, the operational numerical weather prediction system of the Hungarian Meteorological Service. The Level-2 retrievals are geophysical soil moisture estimates derived from satellite radar backscatter observations and represent the uppermost soil layer (approximately 0–5 cm). Data assimilation is performed using a Simplified Extended Kalman Filter (SEKF) within the SURFEX surface modeling platform. In the reference configuration (REF), the same SEKF framework is applied, as used operationally for the assimilation of 2 m temperature and relative humidity observations. A second experiment (ASCAT) extends this configuration by additionally assimilating ASCAT surface soil moisture retrievals. The experimental period covers May–October 2023. The objective of the study is to quantify the added value of ASCAT soil moisture assimilation relative to the REF experiment, which does not assimilate ASCAT retrievals. Results indicate a systematic improvement in root-zone soil moisture and soil temperature, suggesting that the assimilation of surface soil moisture observations propagates beneficially to deeper soil layers. Verification against in situ and model-derived diagnostics shows a positive impact on near-surface atmospheric variables, particularly for 2 m temperature and humidity during nighttime conditions. Furthermore, precipitation verification reveals a measurable improvement, suggesting a beneficial influence of improved land–atmosphere coupling on short-range forecasts.