A deep learning model to predict thunderstorms within 400km2 south Texas domains

Abstract

High resolution predictions, both temporally and spatially, remain a challenge for the prediction of thunderstorms and related impacts such as lightning strikes. The goal of this work is to improve and extend a machine learning method to predict thunderstorms at a 3km resolution with lead times of up to 15 hours. A deep learning neural networks (DLNN) was developed to post process deterministic High-Resolution Rapid Refresh (HRRR) numerical weather prediction (NWP) model output to develop DLNN thunderstorm prediction models (categorical and/or probabilistic output) with performance exceeding that of the HRRR and other models currently available to National Weather Service (NWS) operational forecasters. Notwithstanding the discovery that shallow neural network models can approximate any continuous function (provided the number of hidden layer neurons is sufficient), studies have demonstrated that DLNN models based on representation learning can perform superior to shallow models with respect to weather and air quality predictions. In particular, we use the method known as stacked autoencoder representation learning, yet more specifically, greedy layer-wise unsupervised pretraining. The training domain is slightly large specific area in Corpus Christi, Texas (CRP). The domain is separated into a grid of 13×22 equidistant points with a grid spacing of 20 km. These points serve as boundaries/centres for 286 20 × 20 km (400 km2 ) square regions. The strategy is that DLNN model train on whole boxes and test on three most important boxes to evaluate the model. The target refers to the existence, or non-existence, of thunderstorms (categorical). Cloud to Ground (CG) lightning was chosen as the proxy for thunderstorm occurrence. Logistic regression was then applied to SDAE output to train the predictive model. An iterative technique was used to determine the optimal SDAE architecture. The performance of the optimized DLNN classifiers exceeded that of the corresponding shallow neural network models developed by Collins and Tissot [12], a classifier via a combination of principal component analysis and logistic regression, and operational weather forecasters, based on the same dataset.