Machine Learning Forecast Products
The products are provided “as is” and are intended for research purposes only (disclaimer).
Integrated Water Vapor Transport (IVT) with a Convolutional Neural Network (ARcnn)
The ARcnn……(description of ARcnn). For more information contact:……
Plot Description: Vertically Integrated Water Vapor Transport (IVT) with magnitude shaded in units of kg m-1 s-1 from the Global Forecast System (GFS; second row), Global Ensemble Forecast System mean (GEFS; third row), and the ARcnn (bottom row). For more information contact:……
Deterministic Precipitation Forecast with a U-Net Convolutional Neural Network
A U-Net convolutional neural network model has been developed based on a combination of classification and dual regression approach (Badrinath et al. 2023) to generate daily accumulated precipitation forecasts over the western U.S (0-4 day lead times). The model is trained using CW3E’s 34-year high-resolution deterministic West-WRF precipitation reforecasts and tested on four different water years conditioned on the state of the El Niño-Southern Oscillation (ENSO). Please refer to Badrinath et al. (2023) for the skill assessment (including skill for prediction of extreme events) associated with this forecast product.
Deep Learning-based Probabilistic Quantitative Precipitation Forecasts (PQPF)
A Deep Learning model, UNET, has been trained to postprocess CW3E’s 34-year deterministic West-WRF reforecast and generate probabilistic forecasts for 0-to-4-day daily accumulated precipitation (Hu et al. 2023, Mon. Wea. Rev.).
Plot Description: Top row: Ensemble mean 24-hour precipitation from the UNET colored in millimeters. Second row: Ensemble standard deviation of 24-hour precipitation shaded in mm from the UNET. Bottom five rows: Probability of 24-hour precipitation amount over various thresholds shaded in percentage. Probability is calculated based on the number of ensemble members predicting precipitation over the selected threshold.