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NEAR SURFACE AIR TEMPERATURE
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In order to develop a solution to estimate near-ground air temperature, GlobVision with the Canadian Centre for Remote Sensing has developed methodologies and architecture, using artificial intelligence analysis and integration of remote sensing data and in-situ measurements.
Through the systematic exploration of data mining and visualization methodologies the GlobVision team increased its understanding of the relationship between in situ measurements of air temperature at specific points and air temperature estimates based on remote sensing imagery and developed the most promising architecture.
Data mining methodologies were exploited to integrate and increase the understanding of the relationship between in-situ measurements of near surface temperature at meteorological stations and remotely sensed data. Results were obtained in terms of the best combinations of input remote sensing parameters for optimum performance in estimating temperature, as well as in terms of different spatial and temporal cases involving grouped and single meteorological stations.
Considering the need for detailed data for climate modelling, near-ground air temperature is one of the most critical parameters of meteorological models. The approach of using remote sensing data to complement ground information, or even to compensate for missing data, offers clear advantages to meteorologists. Near-ground air temperature is one of the most critical parameters of meteorological models.
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