Using the Special Sensor Microwave Imager to Monitor Surface Wetness and Temperature

Abstract

The current network of in situ stations is inadequate for monitoring regional temperature and moisture anomalies across the land surface, leaving the climate monitoring community insufficient information to identify spatial structure and variations over many areas of the world. Therefore, we need to blend satellite observations with in situ data to obtain global coverage. In order to accomplish this task, we have calibrated and independently validated an algorithm that derives land surface temperatures from the Special Sensor Microwave Imager (SSMI). The goal of this exercise is to blend both the in situ and satellite data sets into one superior product, then merge this product with an sea surface temperature anomaly field form the same base period. The value of the global product has extremely valuable applications to climate modeling community, since it can serve as a validation tool and/or direct input to the surface parameterization, allowing the radiation feed back to be realistically grounded on surface temperature and humidity observations.