Abstract:
Sea surface temperature (SST) maps with a high horizontal resolution (at around 1 km) are becoming available and showing impacts in such applications as regional weather forecasting. These SST maps are typically produced by merging measurements from multiple satellite sensors to facilitate horizontal and temporal coverage. In particular, the microwave (MW) sensors have typically coarser 25-km resolutions than the infra-red (IR) sensors which can resolve down to a 1-km scale. On the other hand, the IR-based measurements are prone to data-voids due to cloud contamination, which does not affect MW sensors nearly as much. Combination of these data sets is thus complementary, contributing to accuracy of the blended SST maps. The root-mean-squares (RMS) error of an SST map produced by merging the MODIS and AMSRE sensor data sets at 1-km resolution is estimated to be in the range of 0.3 to 0.5 degrees Celsius, for example. Satellite-based SST values from all sensors to date, however, can be biased due to atmospheric conditions such as aerosol concentration as well as calibration issues unique to each sensor. Moored and drifting buoy SST (in addition to in-situ IR radiometer data where available) are often used for reduction of bias in the satellite data. Here we report on the use of such buoy data to calibrate each satellite sensor data set before blending and present an assessment of the quality of the final blended maps through satellite-to-buoy match-up statistics. We show, through our multi-resolution analysis, that the RMS difference between the buoy values and the map values analyzed at various resolutions is nearly constant over a wide range of horizontal resolutions. This indicates that the traditional buoy match-up approach may have some limitations when used for validation of the high-resolution information in the blended maps, because of relatively sparse buoy locations. Several additional validation methods are thus suggested, including independent satellite measurements, a set of other blended products (ensemble uncertainty), and use of simulated high-resolution SST fields to quantify the errors introduced by the blending procedure (an observation system simulation experiment).
Reference:
Estimation of analysis errors in satellite-based, high-resolution SST maps. T. M. Chin; J. Vazquez; E. Armstrong; A. Mariano; E. H. Ryan; G. Jedlovec; F. LaFontaine; J. Shafer, 2010 Ocean Sciences Meeting, 22-26 February 2010, PO54F-07.