Intelligent Monitoring of Air Temperature by the DATA of Satellites and Meteorological Stations
Keywords:climate models, intelligence monitoring, thermal imagery, Landsat satellites, machine-learning, group method of data handling, temperature
Climate models are the primary tools for investigating the response of the climate system to various forcings and for climate predictions. The combined use of the data from remote sensors and meteostations allows taking into account the spatial and temporal components of monitoring. In this study the temperature forecasting technique was improved by using the data from thermal imaging satellites and weather stations. This technique uses for this purpose the model of dependence of temperature received from satellite imagery on the temperature obtained from existing meteorological stations. During the investigation of the variables selected from the input data array, it was shown that satellite imagery data can be used in regional models of temperature prediction, and temperature traces obtained from satellite imagery and weather stations at similar points show similar dynamics. The effectiveness of the group method of data handling using multi-row algorithm for forecasting temperature for areas with no meteorological stations is shown.
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