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Operational GOES-SST and MSG-SEVIRI-SST Products
for GOES-R Risk Reduction

Eileen Maturi, NOAA/NESDIS/STAR/SOCD
Andrew Harris, and Jon Mittaz, CICS, University of Maryland

SOCD shield

Sea Surface Temperature map

Introduction

Sea surface temperature is a key climate parameter, and can serve as a powerful diagnostic tool for many aspects of instrument and processing chain performance. These include calibration, cloud detection, instrument characterization and radiative transfer modeling (RTM).

Current GOES-Imager

The current GOES-Imager possesses a relatively small subset of the Advanced Baseline Imager (ABI) capabilities. This current imager is more useful for certain aspects of anticipated ABI performance (e.g. calibration due to the 3-axis stabilization and concomitant thermal cycling). An improved sun-shield was fitted to the MT-SAT 1R, which is likely to be more representative of the situation on the ABI.

Radiance bias a critical aspect of physical retrieval methodology

There is a trend in radiance bias with brightness temperature (far left plot below). However, the variation is best predicted by atmospheric correction (i.e. the "net" effect of the atmosphere) implying a radiative transfer problem (e.g. SRF). Also, bias should →0 as atmospheric correction →0. There is still a significant cycle of bias in throughout the day (far right plot below).

Radiance Bias chart 1Radiance Bias chart 2Radiance Bias chart 3

 

Methodology

The careful analysis of both proxy data sets, combined with simulated ABI data, will be used. The findings will be extrapolated on the basis of instrument and retrieval physics in order to build a best-estimate model of the ABI performance. Tools will also be developed to quickly diagnose on-orbit problems in terms of physical instrument parameters.

MSG-SEVIRI Instrument

The physical SST retrieval methodology developed for the operational MSG-SEVIRI SSTs are similar to the techniques that will be employed for the ABI. The extension from 5 (GOES Imager) to 12 channels (SEVIRI) allows the refining and testing of methodologies that will be required in the GOES-R era. Certain aspects are not well-matched (e.g. spin-scan vs. 3-axis stabilized, and the "over-broad" 3.8 micron channel of the former).

Bayesian Cloud Detection

Bayesian cloud detection is our method for discriminating cloudy and clear radiances (see Merchant et al., QJRMS, Oct. 2005). The current operational scheme uses a static global prior PDF for cloudy radiances. Here, we examine the variation with location, season, and view angle at the "sharp end", i.e. where BTs are close to those of clear-sky.

Southern Hemisphere High Zenith Angle plotSouthern Hemisphere Low Zenith Angle plot
Southern Hemisphere January plotNorthern Hemisphere January plot

This page excerpted from a presentation at the 88th AMS Annual Meeting, New Orleans, Louisiana, January 20-24, 2008.


Data, algorithms, and images presented on STAR websites are intended for experimental use only and are not supported on an operational basis.  More information

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