Ground station temperature data are not commonly used simultaneously with the Advanced Very High Resolution Radiometer (AVHRR) to model and predict air temperature or land surface temperature. Technology was developed to acquire near-synchronous datasets over a 1 000 000 km2 region with the goal of improving the measurement of air temperature at the surface. This study compares several statistical approaches that combine a simple AVHRR split window algorithm with ground meterological station observations in the prediction of air temperature. Three spatially dependent (kriging) models were examined, along with their non-spatial counterparts (multiple linear regressions). Cross-validation showed that the kriging models predicted temperature better (an average of 0.9°C error) than the multiple regression models (an average of 1.4°C error). The three different kriging strategies performed similarly when compared to each other. Errors from kriging models were unbiased while regression models tended to give biased predicted values. Modest improvements seen after combining the data sources suggest that, in addition to air temperature modelling, the approach may be useful in land surface temperature modelling.
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Document Type: Research Article
The Johns Hopkins University Applied Physics Laboratory Johns Hopkins Road Laurel MD 20723-6099 USA
University of Alberta Department of Mathematical Sciences
IBM Watson Research Center
The Johns Hopkins University Bloomberg School of Hygiene Public Health
Publication date: 2004-08-01
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