Abstract: Techniques for dynamically forecasting high spatial resolution air temperature in real-time using a multiplicity of data sources and advanced machine learning models are provided. In one aspect, a system for generating air temperature forecasts includes: a spatial downscaling module for providing air temperature data from a multiplicity of sources as input to a generative adversarial network, conditioning the input to the generative adversarial network on at least one conditioning variable, and generating high resolution air temperature maps; and a temporal forecasting module for providing the high resolution air temperature maps along with real-time air temperature data from a sensor network as input to a physics-informed neural network with extended Kalman filter, and generating high temporal and spatial resolution air temperature forecasts. A method for generating air temperature forecasts using the present system is also provided.

Abstract: Techniques for dynamically forecasting high spatial resolution air temperature in real-time using a multiplicity of data sources and advanced machine learning models are provided. In one aspect, a system for generating air temperature forecasts includes: a spatial downscaling module for providing air temperature data from a multiplicity of sources as input to a generative adversarial network, conditioning the input to the generative adversarial network on at least one conditioning variable, and generating high resolution air temperature maps; and a temporal forecasting module for providing the high resolution air temperature maps along with real-time air temperature data from a sensor network as input to a physics-informed neural network with extended Kalman filter, and generating high temporal and spatial resolution air temperature forecasts. A method for generating air temperature forecasts using the present system is also provided.