Enhancing the GEOS Land Analysis with the Assimilation of CYGNSS Soil Moisture Retrievals

Abstract Soil moisture (SM) retrievals from the NASA Cyclone Global Navigation Satellite System (CYGNSS) are assimilated into the NASA Goddard Earth Observing System (GEOS) land data assimilation system from August 2018 to June 2024. The system uses a 24-member ensemble Kalman filter to integrate CYGNSS 6-hourly SM retrievals alongside, or in place of, established datasets from the Advanced Scatterometer (ASCAT), the Soil Moisture Active Passive (SMAP) mission, and the Soil Moisture Ocean Salinity (SMOS) mission. Assimilating CYGNSS alone (CYG_DA) reduces the standard deviation of CYGNSS observation-minus-forecast (OmF) residuals by about 6% relative to a model-only control simulation (CNTL). OmF residuals for monitored-only (that is, not assimilated) observations (ASCAT, SMAP, and SMOS) are also smaller in CYG_DA, indicating that assimilating CYGNSS improves the model background more broadly. When all four sensors are assimilated jointly (ALL_DA), OmF standard deviations are reduced by up to 11%, indicating enhanced cross-sensor consistency. Validation against independent in situ measurements confirms that CYG_DA improves surface-layer SM correlations and reduces unbiased RMSE relative to CNTL, with the largest skill gains seen in the multi-sensor ALL_DA experiment. A quasi-global triple-collocation analysis of CYG_DA using SMOS and ASCAT SM products shows widespread skill increases (median ΔR 2 ≈ 0.04) and reductions in error variance (mean ≈ −22%) relative to CNTL, particularly across semi-arid and seasonally vegetated regions. These results demonstrate that CYGNSS provides complementary SM information that enhances the temporal realism and stability of GEOS land-surface analyses. CYGNSS SM should thus be included in future multi-sensor reanalysis systems.