Dynamically forecasting high resolution air temperature in real-time using multiple sources

A computer system comprising:

• a processor set;
• one or more computer readable storage media;
• and
• program instructions stored on the one or more computer readable storage media to cause the processor set to perform operations comprising:
• providing air temperature data from a multiplicity of sources as first input to a generative adversarial network;
• conditioning the first input to the generative adversarial network on at least one conditioning variable;
• training the generative adversarial network with the air temperature data and the conditioning the first input to the generative adversarial network on the at least one conditioning variable;
• generating high resolution air temperature maps having a spatial resolution of 100 meters×100 meters or finer as a first output from the generative adversarial network, wherein the high resolution air temperature maps are generated at a higher spatial resolution than the air temperature data from the multiplicity of sources;
• providing the high resolution air temperature maps along with real-time air temperature data from a sensor network as a second input to a physics-informed neural network with extended Kalman filter;
• and
• generating high temporal and spatial resolution air temperature forecasts as a second output from the physics-informed neural network with the extended Kalman filter, wherein a loss function of the physics-informed neural network computes loss L as L=Lb+Lo+Le+Li, wherein Le is a residual loss, Lb is a boundary condition loss, Lo is an initial condition loss, and Li is a loss term defined by an innovation vector and Kalman gain in the extended Kalman filter.

A method for generating air temperature forecasts, the method comprising:

• providing air temperature data from a multiplicity of sources as a first input to a generative adversarial network;
• conditioning the first input to the generative adversarial network on at least one conditioning variable;
• training the generative adversarial network with the air temperature data and the conditioning the first input to the generative adversarial network on the at least one conditioning variable;
• generating high resolution air temperature maps having a spatial resolution of 100 meters×100 meters or finer as a first output from the generative adversarial network, wherein the high resolution air temperature maps are generated at a higher spatial resolution than the air temperature data from the multiplicity of sources;
• providing the high resolution air temperature maps along with real-time air temperature data from a sensor network as a second input to a physics-informed neural network with extended Kalman filter;
• and
• generating high temporal and spatial resolution air temperature forecasts as a second output from the physics-informed neural network with the extended Kalman filter, wherein a loss function of the physics-informed neural network computes loss L as L=Lb+Lo+Le+Li, wherein Le is a residual loss, Lb is a boundary condition loss, Lo is an initial condition loss, and Li is a loss term defined by an innovation vector and Kalman gain in the extended Kalman filter.