Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing model
Artificial Intelligence for the Earth Systems(2023)
摘要
An ensemble post-processing method is developed for the probabilistic
prediction of severe weather (tornadoes, hail, and wind gusts) over the
conterminous United States (CONUS). The method combines conditional generative
adversarial networks (CGANs), a type of deep generative model, with a
convolutional neural network (CNN) to post-process convection-allowing model
(CAM) forecasts. The CGANs are designed to create synthetic ensemble members
from deterministic CAM forecasts, and their outputs are processed by the CNN to
estimate the probability of severe weather. The method is tested using
High-Resolution Rapid Refresh (HRRR) 1–24 hr forecasts as inputs and Storm
Prediction Center (SPC) severe weather reports as targets. The method produced
skillful predictions with up to 20
to other neural-network-based reference methods using a testing dataset of HRRR
forecasts in 2021. For the evaluation of uncertainty quantification, the method
is overconfident but produces meaningful ensemble spreads that can distinguish
good and bad forecasts. The quality of CGAN outputs is also evaluated. Results
show that the CGAN outputs behave similarly to a numerical ensemble; they
preserved the inter-variable correlations and the contribution of influential
predictors as in the original HRRR forecasts. This work provides a novel
approach to post-process CAM output using neural networks that can be applied
to severe weather prediction.
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