Abstract: The present solution is directed to systems and methods for weather forecasting systems to generate reliable long-term weather predictions over large geographical areas while maintaining high resolution and accuracy and conserving computational resources. The solution can identify a dataset comprising variables indicative of characteristics of weather during a first time interval and determine, based on the dataset, parameters of marginal distributions of the variables using a first function for maximum likelihood estimation and a second function for probability weighted moment matching of the parameters. The solution can construct, based on the variables, a correlation matrix configured for parallel processing and generate, via parallel processing by the one or more processors and based on the correlation matrix, predicted sample of variables for a weather forecast indicating the characteristics of weather during a second time interval.

Abstract: Systems and methods for long-term weather forecasting framework using statistical and machine learning modeling are provided. The solution can include a system having one or more processors that are coupled with memory. The memory can include instructions and data to configured the one or more processors to determine, based on principle component analysis (PCA), latent variables corresponding to characteristics of weather over the time interval in the geographical area. The one or more processors can be configured to construct, with a non-parametric joint distribution model (NJDM), a two-dimensional (2D) array of parameters that represents weather over a time interval in a geographical area. The one or more processors can be configured to generate, using a diffusion model, based on the latent variables and the 2D array, one or more predictions of 2D characteristics of weather over a second time interval for the geographical area.

Abstract: Multi-model blending of probabilistic weather forecasts is described. A system segments a first training data set into a plurality of second training data sets each including corresponding subsets of a first output of a first probabilistic model and a second output of a second probabilistic model. The system modifies, for each of the subsets, weights of a machine learning model with. The system generates a control parameter indicative of alignment of the machine learning model with one or more of the plurality of second training data sets, and provides, responsive to the control parameter satisfying a threshold indicative of a level of alignment with the plurality of second training data sets, the machine learning model trained to generate, according to the one or more weights, a weighted output of the first probabilistic model and the second probabilistic model at the first point and the second point.

Abstract: Multi-model blending via a neural network for probabilistic weather forecasts is described. A system segments a first training data set into a plurality of second training data sets each including subsets of a first output of a first probabilistic model and a second output of a second probabilistic model. The system modifies, for each of the subsets, weights of a neural network model according to first points of each of the plurality of second training data sets, second points of each of the plurality of second training data sets, and a tuning parameter of the neural network corresponding to the weather condition. The system generates a control parameter indicative of alignment of the neural network model with one or more of the plurality of second training data sets. The system provides the neural network model to generate a weighted output of the first probabilistic model and the second probabilistic model.