Abstract:
The NASA Global Modeling and Assimilation Office (GMAO) at NASA GSFC, is working to develop and implement a full suite of land data assimilation components for the water and carbon cycles, including satellite remote sensing observations of soil moisture, snow, land surface temperature, and vegetation, with a focus on supporting the Goddard Earth Observing System (GEOS) reanalysis.

We are building on the existing Soil Moisture Active Passive (SMAP) Level-4 soil moisture ensemble data assimilation algorithm as used in GEOS land data assimilation system (GEOSldas) with the Catchment Land Surface Model and its weak coupling to the GEOS weather analysis system by adding new capabilities with respect to using additional observation types. Desiring to include multi-decadal records of land remote sensing observations in the reanalysis, an initial target has been to assess the use of ASCAT soil moisture (SM) retrievals along with the more recent record of SMAP brightness temperature (Tb) observations.

Here we briefly describe how we have added capability to assimilate multiple observation types to GEOSldas, including our approach to bias correction. We then present results showing the impact on global soil moisture analysis of assimilating the 17-year long time series of ASCAT SM retrievals from Metop-A, -B and -C, including at longer timescales relevant for reanalysis, with validation against a network of in-situ observations. The latter half of the ASCAT SM retrieval record overlaps with the availability of SMAP Tb observations that are routinely assimilated in GEOSldas. Our updates to GEOSldas allow us to compare assimilating the two observation types either independently or together, and we attempt an initial quantification of their relative impacts. Finally, we outline the way ahead for land DA in upcoming reanalysis, incorporating recent GEOSldas developments in coupling with the atmosphere model and assimilating snow cover observations.