Toward an improvement of surface identification in land precipitation retrieval algorithm

The non-ancillary data approach of surface identification in land precipitation algorithm is not without false identifications. The approach uses only brightness temperatures information from different channels in assigning surface types. False identifications most likely occur over certain land surface types possessing similar microwave scattering signatures as rain. Here we propose to use available ancillary datasets of global land surface types to overcome the misidentification problems. The proposed approach would be a generic approach for all sensors. This presentation is a progress report of the proposed approach.