HR: 1330h
AN: OS42A-0827    [PDF]
TI: Identification of Errors and Uncertainties in Sea Surface Temperature Retrievals by Comparison of Microwave and Infrared Observations from TRMM
AU: * Ricciardulli, L
EM: Ricciardulli@Remss.com
AF: Remote Sensing Systems, 438 First St., Suite 200, Santa Rosa, CA 95401 United States
AU: Wentz, F J
EM: Wentz@Remss.com
AF: Remote Sensing Systems, 438 First St., Suite 200, Santa Rosa, CA 95401 United States
AB: The availability of both infrared (VIRS) and microwave (TMI) sensors onboard the Tropical Rainfall Measurement Mission (TRMM) provided an opportunity to perform a detailed intercomparison of Sea Surface Temperature (SST) retrievals from two distinct but perfectly co-located sensors. Infrared (IR) retrievals provide good spatial resolution but they are affected by clouds and atmospheric water vapor, and are therefore limited to clear sky conditions. Microwave (MW) observations provide supplemental SST information in cloudy areas, but they are subject to other limitations like poor spatial resolution and possible wind biases. A rigorous study of the uncertainties and potential errors in IR and MW SST retrievals by satellite is described here. One year of co-located VIRS and TMI SSTs are analyzed, together with other co-located ancillary datasets including surface winds and water vapor. Results from the IR/MW intercomparison and the analysis of the correlation between SST differences and potential sources of error are presented. In particular, we focus on the effects on SST retrievals due to: high winds, land contamination, atmospheric water vapor, undetected clouds, choice of IR SST algorithm, satellite maneuvers and calibration errors. In addition, both the datasets are used to investigate the amplitude of the diurnal variation of skin SST. The identification of error characteristics can lead to improvements in both IR and MW SST algorithms. Moreover, the results of this intercomparison are valuable for the development of a future generation of a blended IR/MW SST dataset, which combines the strengths of each observational method
DE: 3337 Numerical modeling and data assimilation
DE: 4275 Remote sensing and electromagnetic processes (0689)
DE: 4294 Instruments and techniques
SC: Ocean Sciences [OS]
MN: 2003 Fall Meeting