There is an inadequate network of in situ stations for monitoring the global distribution of land surface temperature anomalies. Therefore, we are using satellite observations to help identify this global distribution. The Special Sensor Microwave Imager (SSMI), flown by the Defense Meteorological Satellite Program, is used to derive near surface temperatures over land, as it flies 14 times around the globe each day. The over flights correspond with 6 A.M. and 6 P.M. local equatorial crossing times, and the period of record for this suite of satellite instruments is 1987 - present. We have developed a series of algorithms, using the SSMI seven channel measurements, to calculate the near surface temperature for various surface conditions. The algorithm uses the unique signal of each surface type to make dynamic surface emissivity adjustments under conditions such as vegetation, wet ground, snow cover and deserts. The in situ data from the time of over flight serve as ground truth for the calibration these equations. We used the best high density network in the world to independently validate the accuracy of the final algorithm. Over the validation region the mean standard error is less than 1°C in a one degree grid box.
The satellite and in situ derived mean monthly anomalies are blended into a global product. The satellite derived temperatures begin at 1/3° resolution, before they are averaged into monthly mean anomalies at 1° resolution. Then in situ anomalies from the same base period are merged with these satellite values. Smoothing of the in situ and satellite values allows us to use the strength of the in situ observations, when they are present, with the spatial coverage provided by the satellite field. In this talk we will show how this blended product provides better spatial coverage of the monthly mean anomalies than either of the stand alone data sets. Numerous examples of the success of this project will be demonstrated. The data set is freely available to the public at large in near real time, through a web site located at the National Climatic Data Center.