A Blended Satellite - In situ Land Surface Temperature Product

The current network of in situ stations is inadequate for monitoring regional temperature 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). This presentation explains how we refined a set of equations that identify various surface types and made corresponding dynamic emissivity adjustments, in order to estimate the shelter height temperatures from the seven channel measurements flown on the SSMI instrument. We used first order in situ stations over the eastern half of the U.S. for the time of overpass calibration and inter-satellite adjustment. Results show that the instantaneous bias is about 2.0 C, and the error characteristics are largely independent of surface type. Over the same study area we performed an independent validation of the product, using high resolution monthly mean anomalies, generated from the U.S. cooperative network. The validation study shows that the standard error of the monthly mean anomalies in a 1x1 degree grid box is 0.76 C. 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 a sea surface temperature anomaly field from the same base period. We demonstrate our success in reaching this goal and the value of the global product, which has extremely valuable applications to the climate modeling community, since it can serve as a validation tool and/or direct input to the surface parameterization, allowing the radiation feedback to be realistically grounded on surface temperature observations.