An artificial neural network model to predict thunderstorms within 400 km² South Texas domains

Abstract

Artificial neural network (ANN) models were developed to predict thunderstorm occurrence within three separate 400 km 2 regions, 9, 12 and 15 h (±2 h) in advance. The predictors include output from deterministic Numerical Weather Prediction models and from sub-grid scale soil moisture magnitude and heterogeneity estimates. The feed-forward multi-layer perceptron ANN topology, with one hidden layer and one neuron in the output layer, was chosen. Two sets of nine ANN models each were developed; one set was developed after a filtering-based feature selection technique was used to determine the predictor subset from 43 potential predictors. The other models were developed based on all 43 predictors. For each of the 18 models, a wrapper technique was used to determine the optimal number of neurons in the hidden layer. Thunderstorm artificial neural network (TANN) model performance was compared to that of multi-linear regression (MLR) models, and to human forecasters (NDFD), based on a novel data set. Results reveal that for several of the nine box/prediction hour combinations with respect to at least one skill-based performance metric, the TANN model's performance exceeded that of the MLR models and NDFD. Yet, the performance of both the MLR models and NDFD were superior to that of the corresponding TANN models in several other cases. Results indicate that the TANN models can provide automated predictions with skills similar to that of operational forecasters. Comparisons of the two sets of TANN models reveal utility in the use of feature selection.

An investigation on the relationship between the Hurst exponent and the predictability of a rainfall time series

“…This algorithm updates the weights of the network by adjusting the error between observed and predicted, finally leading to a trained network after repeating this process a sufficient number of times (Haykin, 1999;ASCE Task Committee, 2000a;2000b;Nourani et al, 2011;Ajmera and Goyal, 2012;Sivapragasam et al, 2014). Many researchers have used an ANN as a "forecasting model" in the field of atmospheric sciences and meteorology (Gheiby et al, 2003;Tapiador et al, 2004;Chattopadhyay and Chattopadhyay, 2008;Dahamsheh and Aksoy, 2009;Babel et al, 2015;Collins and Tissot, 2015;Valipour, 2016;Modarres et al, 2018).…”

A Deep Learning Trained Clear-Sky Mask Algorithm for VIIRS Radiometric Bias Assessment

“…In this section, nine days (11)(12)(13)(14)(15)(16)(17)(18)(19) March 2019) of S-NPP CSMs were generated by the FCDN-CSM model to evaluate the model performance by checking the stability and accuracy of the O-M biases. Figure 6 shows the O-M error bars with STDs for the five TEB M-bands, using ACSPO CSM and FCDN-CSM to identify clear-sky pixels.…”

“…With cutting-edge artificial intelligence (AI) evolving rapidly, deep learning (DL), one of the most popular AI methods, has made a remarkable difference in many science and engineering fields. Its application in remote sensing [13,14] and numerical weather prediction [15][16][17][18] is also being explored. Deep learning is constructed using artificial neural networks (ANNs), including more than one hidden layer, with a so-called "deep" neural network distinguished from a "shallow" neural network.…”

Probabilistic thunderstorm forecasting by blending multiple ensembles

“…Several authors have proposed lightning risk diagnostics for numerical model output, but the published results have so far been restricted to relatively large spatial and temporal scales due to the high forecast uncertainty (e.g. Casati and Wilson, 2007;Schmeits et al, 2008;Yair et al, 2010;Collins and Tissot, 2015;Gijben et al, 2017;Simon et al, 2018).…”