Results from Global Land-surface Data Assimilation Methods

Realistic representation of the land surface is crucial in global climate modeling (GCM). Recently, the Mosaic Land-surface Model (LSM) has been driven off-line using GEOS DAS (Goddard Earth Observing System Data Assimilation System) atmospheric forcing, forming the Off-line Land-surface Global Assimilation (OLGA) system. This system provides a computationally efficient test bed for land surface data assimilation. Here, we validate the OLGA simulation of surface processes and the assimilation of ISCCP surface temperatures.

Another component of this study was the incorporation of the Physical-space Statistical Analysis System (PSAS) into OLGA, in order to assimilate surface temperature observations from the International Satellite Cloud Climatology Project (ISCCP). To counteract the subsequent forcing of the analyzed skin temperature back to the initial state following the analysis, incremental bias correction was included in the assimilation. In addition, quality control of the ISCCP data and anisotropic temperature correction were implemented in PSAS. The results showed a substantial impact from the inclusion of PSAS that was visible in the surface meteorology fields and energy budget. The surface temperature assimilation technique developed in OLGA was also incorporated into the coupled land-atmosphere GEOS Data Assimilation System. Several simulations were conducted and preliminary results will be presented.