Correlation of Warm Season Crowdsourced Temperature with Satellite-Derived Temperature within the City of Atlanta and Its Application to Localized Prediction

Abstract This study was an attempt to correlate crowdsourced temperature with satellite-derived temperature during the warm season within the City of Atlanta of the Atlanta, Georgia (GA) metropolitan region (AMR). If crowdsourced and satellite-derived temperature examined during the evaluated warm season were found to have a significant correlation, an application to localized temperature prediction was to be addressed. Results suggests crowdsourced temperature could be more or just as accurate as official meteorological stations in simulating temperature calculated from remote sensors over a 7-year, 6-month warm season analyzing 15 imagery scenes. To see how localized variation in predicted temperatures could be visualized at the scale surrounding the City of Atlanta, regression equations were applied to scenes representing 7-monthly seasonal snapshots from April to October and based upon a mean scene temperature computed using averaged temperature values from 4 core AMR counties and the City of Atlanta. A significant finding is that mean scene temperature range between crowdsourced and official regression equations decreased incrementally from a maximum in April to a minimum in July to a maximum again during October and points toward equalized temperature readings at the localized scale during the height of the warm season when thermal anomalies are highest.