Sea surface temperature from a geostationary satellite by optimal estimation

2009 ◽  
Vol 113 (2) ◽  
pp. 445-457 ◽  
Author(s):  
C.J. Merchant ◽  
P. Le Borgne ◽  
H. Roquet ◽  
A. Marsouin
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Optimal Estimation ◽  
Geostationary Satellite ◽  
Sea Surface

scholarly journals Sea Surface Temperature Retrieval from the First Korean Geostationary Satellite COMS Data: Validation and Error Assessment

2018 ◽  
Vol 10 (12) ◽  
pp. 1916 ◽  
Author(s):  
Hye-Jin Woo ◽  
Kyung-Ae Park ◽  
Xiaofeng Li ◽  
Eun-Young Lee
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Geostationary Satellite ◽  
Sea Surface ◽  
Surface Drifter ◽  
Temperature Retrieval ◽  
Buoy Data ◽  
Continuous Use ◽  
Meteorological Satellite ◽  
Surface Drifting Buoy

Korea’s first geostationary satellite, the “Communication, Ocean, and Meteorological Satellite” (COMS), has been operating since 2010. The Meteorological Imager (MI), an sensor on-board the COMS, has observed sea-surface radiances for the estimation of sea surface temperature (SST) in the western Pacific Ocean and eastern Indian Ocean. To derive the SST coefficients of COMS, quality-controlled surface drifting buoy data were collected for the period of April 2011 to March 2015. A collocation procedure between COMS/MI data and the surface drifter data produced a matchup database for 4 years from 2011 to 2015. The coefficients for the COMS/MI SST were derived by applying appropriate algorithms, i.e., the Multi-channel SST (MCSST) and Non-linear SST (NLSST) algorithms, for daytime and nighttime data using a regression method. Validation results suggest the possibility of the continuous use of the coefficients as representative SST coefficients of COMS. The estimated SSTs near the edge of a full disk with high satellite zenith angles over 60° revealed relatively large errors compared to drifter temperatures. Most of NLSST formulations exhibited overestimation of SSTs at low SSTs (<10 °C). This study suggests an approach by which SST can be measured accurately in order to contribute to tracking climate change.

Advanced sea surface temperature retrieval using the Japanese geostationary satellite, Himawari-6

2010 ◽  
Vol 66 (6) ◽  
pp. 855-864 ◽  
Author(s):  
Hiroshi Kawamura ◽  
Huiling Qin ◽  
Kohtaro Hosoda ◽  
Futoki Sakaida ◽  
Chunhua Qiu
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Geostationary Satellite ◽  
Sea Surface ◽  
Temperature Retrieval

scholarly journals Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature

2020 ◽  
Vol 12 (6) ◽  
pp. 1048 ◽  
Author(s):  
Christopher J. Merchant ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
Owen Embury ◽  
Jonathan P. D. Mittaz ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Land Surface ◽  
Optimal Estimation ◽  
Sea Surface ◽  
Observation Error ◽  
Climate Data ◽  
Water Vapor Path ◽  
Along Track ◽  
Reference Sensor

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.

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