Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning
AbstractDeep Learning (DL) post-processing methods are examined to obtain reliable and accurate probabilistic forecasts from single-member numerical weather predictions of integrated vapor transport (IVT). Using a 34-year reforecast, based on the Center for Western Weather and Water Extremes West-WRF mesoscale model of North American West Coast IVT, the dynamically/statistically derived 0-120 hour probabilistic forecasts for IVT under atmospheric river (AR) conditions are tested. These predictions are compared to the Global Ensemble Forecast System (GEFS) dynamic model and the GEFS calibrated with a neural network. Additionally, the DL methods are tested against an established, but more rigid, statistical-dynamical ensemble method (the Analog Ensemble). The findings show, using continuous ranked probability skill score and Brier skill score as verification metrics, that the DL methods compete with or outperform the calibrated GEFS system at lead times from 0-48 hours and again from 72-120 hours for AR vapor transport events. Additionally, the DL methods generate reliable and skillful probabilistic forecasts. The implications of varying the length of the training dataset are examined and the results show that the DL methods learn relatively quickly and ~10 years of hindcast data are required to compete with the GEFS ensemble.