Create the ensemble sea surface temperature using the Bayesian model averaging

Kwangjin Kim; Yang-Won Lee

doi:10.1117/12.2067324

14 October 2014 Create the ensemble sea surface temperature using the Bayesian model averaging

Kwangjin Kim, Yang-Won Lee

Proceedings Volume 9240, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2014; 924011 (2014) https://doi.org/10.1117/12.2067324
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands

Abstract

Measuring accurately Sea Surface temperature is important for many marine applications and monitoring the global climate system. Many instruments are used for the measuring the SST. The SST delivered from satellite have the advantages that are a broad scope and consistent detection. But SST products show the different value because of different of retrieval algorithm and sensor. To reduce the uncertain, SST data ensemble is carried out using the Bayesian model averaging(BMA). BMA is method of the weighted average using the posterior probability distribution. And the means and variances of the posterior probabilities are estimated using Expectation-Maximization(EM) algorithm. The estimated mean of the posterior probability is used as the weight for the weighted average. SST data of Aqua/MODIS, Terra/MODIS and NOAA/AVHRR was used as ensemble member. SST data Envisat/AATSR was used as reference data for calculating the posterior probability and validation data. To make the monthly ensemble SST, their provided monthly SST data was used. one-leave-out-cross validation that is one of the statistical validation method is used for validating the ensemble SST. The 12 cases, except for the data of one month per the case, was made and excepted month was used validation period. And we compared with the ensemble mean and median. As the result, ensemble BMA showed the lowest RMSE.

Citation Download Citation

Kwangjin Kim and Yang-Won Lee "Create the ensemble sea surface temperature using the Bayesian model averaging", Proc. SPIE 9240, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2014, 924011 (14 October 2014); https://doi.org/10.1117/12.2067324

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KEYWORDS

Expectation maximization algorithms

Satellites

Data modeling

Climatology

Temperature metrology

Coastal modeling

Sensors

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