Impact of Design Factors for ESA CCI Satellite Soil Moisture Data Assimilation over Europe

Abstract In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) product are assimilated into Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance data assimilation (DA) system is evaluated by comparing it with model-only experiment (at in situ sites) assessing statistics innovations increments as DA diagnostics (over entire domain). For both assessments, we explore impact three design choices, resulting following insights. 1) magnitude assumed observation errors strongly affects skill improvements against stations internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching bias correction method only has marginal effect on system. However, suggest more robust parameterization if observations rescaled monthly. 3) choice atmospheric reanalysis dataset to force improvements. higher input from MERRA-2 than ERA5 reanalysis, larger for latter. Additionally, show that added value depends quality satellite cover, most substantial occurring croplands degradation densely forested areas.