Analyses of the Positive Bias of Remotely Sensed SST Retrievals in the Coastal Waters of Rio de Janeiro

This paper analyzes the large positive bias of sea surface temperature (SST) retrievals of selected remotely sensed algorithms recorded during the simultaneous occurrence of upwelling and atmospheric subsidence along the coastal waters of Rio de Janeiro, Brazil. The optimal estimator (OE) for retrie...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 55; číslo 11; s. 6344 - 6353
Hlavní autoři: Peres, Leonardo F., Franca, Gutemberg B., Paes, Rosa C. O. V., Sousa, Rodrigo C., Oliveira, Antonio N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.11.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Atmospheric measurements
Bias
Bias correction
Buoys
Climatology
Coastal environments
Coastal waters
Compression
Errors
Mathematical model
Ocean circulation
Ocean temperature
optimal estimator (OE)
Profiles
Radiometers
Remote sensing
Satellites
Sea measurements
Sea surface
Sea surface temperature
sea surface temperature (SST)
Sensitivity analysis
Subsidence
Surface temperature
Temperature inversion
Temperature inversions
Temperature measurement
Upwelling
Water vapor
Water vapour
Brazil
Rio de Janeiro Brazil
ISSN:0196-2892, 1558-0644
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Shrnutí:This paper analyzes the large positive bias of sea surface temperature (SST) retrievals of selected remotely sensed algorithms recorded during the simultaneous occurrence of upwelling and atmospheric subsidence along the coastal waters of Rio de Janeiro, Brazil. The optimal estimator (OE) for retrieving SST and the multichannel (MCSST) and nonlinear (NLSST) estimators are compared using Advanced Very High Resolution Radiometer-3 data. The in situ SST (SST buoy ) data set used to validate the remotely sensed SST retrievals was collected from five moored buoys (four in the open sea and one in coastal waters). The principal results of this paper are as follows. First, the sensitivity analyses show that OE is quite susceptible to the first-guess SST rather than to the humidity profiles. Second, the comparison between the SST OE and 365 cloud-free SST buoy measurements in open sea waters presents an root mean squared error (RMSE), bias, and standard deviation (STD) with the intervals of [0.5, 0.6], [−0.51, 0.13], and [0.27, 0.48], respectively. Third, the MCSST, NLSST, and OE SST produce a positive bias that can reach 5 K during simultaneous upwelling and atmospheric subsidence in coastal waters. Such unexpected errors are due to low SST values and water vapor compression in the lower layer of the atmosphere related to a temperature inversion. Fourth, an alternative approach using SST buoy obtained on the previous day as a first guess instead of the climatological SST significantly improves the errors (SST OE -SST buoy ) by reducing RMSE, bias, and STD by 58% (from 3.30 to 1.39 K), 73% (from 3.00 to 0.80 K), and 19% (from 1.38 to 1.12 K), respectively.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2017.2726344