Investigating sea surface temperature diurnal variation over the Tropical Warm Pool using MTSAT-1R data

Investigating sea surface temperature diurnal variation over the Tropical Warm Pool using... Diurnal variation (DV) of sea surface temperature (SST) plays an important role in air–sea interaction. We have validated four months (January to April 2010) of the version 3 Australian Bureau of Meteorology reprocessed Multifunction Transport SATellite-1R (v3 MTSAT-1R) SST data over the Tropical Warm Pool (TWP) region (90°E to 170°E, 25°S to 15°N) against both drifting buoy and Advanced Along-Track Scanning Radiometer (AATSR) SST data. Validation against collocated point measurements from drifting buoys, under conditions where the surface ocean is well-mixed, shows that overall the v3 MTSAT-1R SSTs perform well with an average bias of 0.00°C and a 0.73°C standard deviation (STD). The average daytime and night-time mean bias is −0.06°C and 0.08°C, respectively. For all hours of the diurnal cycle, the mean biases are within ±0.25°C, indicating the consistency between day and night v3 MTSAT-1R data. However, on average, the v3 MTSAT-1R SSTs are overestimated at cold SSTs and underestimated at warm SSTs. Similar results are obtained from validation against the AATSR satellite SSTs but with smaller STD (0.48°C) and smaller average daytime and night-time mean biases (−0.04°C and 0.06°C, respectively). These results indicate that the v3 MTSAT-1R data set is suitable for SST DV investigations and validation of DV models. Using the validated v3 MSTAT-1R data, together with surface wind speed and solar shortwave insolation (SSI) outputs from the Australian Community Climate and Earth-System Simulator – Regional (ACCESS-R) numerical prediction model, we investigate SST DV events over the TWP region. Good correlation is found between DV events and low wind and high SSI conditions. The dominant role of wind speed in SST DV events over the SSI is also revealed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing of Environment Elsevier

Investigating sea surface temperature diurnal variation over the Tropical Warm Pool using MTSAT-1R data

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier Inc.
ISSN
0034-4257
D.O.I.
10.1016/j.rse.2016.05.002
Publisher site
See Article on Publisher Site

Abstract

Diurnal variation (DV) of sea surface temperature (SST) plays an important role in air–sea interaction. We have validated four months (January to April 2010) of the version 3 Australian Bureau of Meteorology reprocessed Multifunction Transport SATellite-1R (v3 MTSAT-1R) SST data over the Tropical Warm Pool (TWP) region (90°E to 170°E, 25°S to 15°N) against both drifting buoy and Advanced Along-Track Scanning Radiometer (AATSR) SST data. Validation against collocated point measurements from drifting buoys, under conditions where the surface ocean is well-mixed, shows that overall the v3 MTSAT-1R SSTs perform well with an average bias of 0.00°C and a 0.73°C standard deviation (STD). The average daytime and night-time mean bias is −0.06°C and 0.08°C, respectively. For all hours of the diurnal cycle, the mean biases are within ±0.25°C, indicating the consistency between day and night v3 MTSAT-1R data. However, on average, the v3 MTSAT-1R SSTs are overestimated at cold SSTs and underestimated at warm SSTs. Similar results are obtained from validation against the AATSR satellite SSTs but with smaller STD (0.48°C) and smaller average daytime and night-time mean biases (−0.04°C and 0.06°C, respectively). These results indicate that the v3 MTSAT-1R data set is suitable for SST DV investigations and validation of DV models. Using the validated v3 MSTAT-1R data, together with surface wind speed and solar shortwave insolation (SSI) outputs from the Australian Community Climate and Earth-System Simulator – Regional (ACCESS-R) numerical prediction model, we investigate SST DV events over the TWP region. Good correlation is found between DV events and low wind and high SSI conditions. The dominant role of wind speed in SST DV events over the SSI is also revealed.

Journal

Remote Sensing of EnvironmentElsevier

Published: Sep 15, 2016

References

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