Abstract:
The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level-4 Soil Moisture (L4_SM) product. The L4_SM data are published with a mean latency of ~2.5 days from the time of observation and provide global, three-hourly, 9-km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. The L4_SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF).

In 2018, the L4_SM algorithm was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high-quality, space-borne remote sensing observations. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of surface soil moisture from below under non-equilibrium conditions, which brings the model’s surface soil moisture more in line with the SMAP Level-2 and in situ soil moisture. Moreover, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4, and the “catchment deficit” model variable was removed from the EnKF state vector to avoid degrading the model’s groundwater estimates.

Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation-minus-forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4_SM product and the lessons learned from the effort.