Methods and systems for climate forecasting using artificial neural networks

A system for generating a neural network (NN)-based climate forecasting model, comprising:

• at least one processor;
• and
• a non-transitory physical storage medium for storing program code and accessible by the processor, the program code when executed by the processor causes the processor to:
• select a first global climate simulation dataset from a plurality of candidate simulation datasets, wherein each candidate simulation dataset is generated from a different global climate simulation model (GCM);
• train the NN-based climate forecasting model on the selected global climate simulation dataset, wherein the NN-based climate forecasting model comprises a predictive neural network, and wherein each input and corresponding desired output used to train the NN-based climate forecasting model are elements of the selected first global climate simulation dataset;
• tune the NN-based climate forecasting model on reanalysis data by freezing one or more layers of the predictive neural network and training the NN-based climate forecasting model on the reanalysis data, wherein the reanalysis data is generated from assimilating a first set of observational historical data with one or more physical dynamical models;
• validate the NN-based climate forecasting model using a second set of observational historical climate data to generate a first validation result;
• in response to the first validation result, select a second global climate simulation dataset from the plurality of candidate simulation datasets;
• train the NN-based climate forecasting model on the selected second global climate simulation dataset, wherein each input and corresponding desired output used in the training are elements of the selected second global climate simulation dataset;
• validate the NN-based climate forecasting model on the second set of observational historical climate data to generate a second validation result;
• in response to the second validation result, forecast a primary target climate variable at a target lead time using the NN-based climate forecasting model;
• forecast a secondary climate variable at a plurality of geographical locations using a second predictive neural network, wherein an input of the second predictive neural network is connected to the output of the NN-based climate forecasting model to intake the primary target climate variable;
• and
• generate for display, a map showing the secondary climate variable at the plurality of geographical locations.

A non-transitory physical storage medium for generating a neural network (NN)-based climate forecasting model, the storage medium comprising program code stored thereon, that when executed by a processor causes the processor to:

• select a first global climate simulation dataset from a plurality of candidate simulation datasets, wherein each candidate simulation dataset is generated from a different global climate simulation model (GCM);
• train the NN-based climate forecasting model on the selected global climate simulation dataset, wherein the NN-based climate forecasting model comprises a predictive neural network, and wherein each input and corresponding desired output used to train the NN-based climate forecasting model are elements of the selected first global climate simulation dataset;
• tune the NN-based climate forecasting model on reanalysis data by freezing one or more layers of the predictive neural network and training the NN-based climate forecasting model on the reanalysis data, wherein the reanalysis data is generated from assimilating a first set of observational historical data with one or more physical dynamical models;
• validate the NN-based climate forecasting model using a second set of observational historical climate data
• in response to the first validation result, select a second global climate simulation dataset from the plurality of candidate simulation datasets;
• train the NN-based climate forecasting model on the selected second global climate simulation dataset, wherein each input and corresponding desired output used in the training are elements of the selected second global climate simulation dataset;
• validate the NN-based climate forecasting model on the second set of observational historical climate data to generate a second validation result;
• in response to the second validation result, forecast a primary target climate variable at a target lead time using the NN-based climate forecasting model;
• forecast a secondary climate variable at a plurality of geographical locations using a second predictive neural network, wherein an input of the second predictive neural network is connected to the output of the NN-based climate forecasting model to intake the primary target climate variable;
• and
• generate for display, a map showing the secondary climate variable at the plurality of geographical locations.