Detection of High Convective Precipitation Events Using Machine Learning Methods
Authors: Abed Waed , Coppola Erika 
Published: March 2024

Leveraging Machine Learning (ML) models, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) like Long-Short Term Memory (LSTM), and Artificial Neural Networks (ANN), has become pivotal in addressing the escalating frequency and severity of extreme events such as heatwaves, hurricanes, floods, and droughts. In climate modeling, ML proves invaluable for analyzing diverse datasets, including climate data and satellite imagery, outperforming traditional methods by adeptly handling vast information and identifying intricate patterns. Focusing on the study's emphasis on extreme precipitation events, the urgency arises from climate change, demanding more accurate and timely methods to predict and manage the impacts of these events.In this study, we completed two main experiments to understand if ML algorithms can detect the extreme events. In both experiment the predictors that have been used are eastern and northern wind (u,v), geopotential height (z), specific humidity (q) and temperature (t) at four pressure levels, which are 1000hpa, 850hpa, 700hpa, and 500hpa. The frequency for the predictors is 3 hours, while the predictand being the precipitation accumulated over 3 hours. The data used in this study are the Re-Analysis -5th generation- (ERA5) produced by European Center for Medium-Range Weather Forecast (ECMWF), which provides global hourly estimates of large number of atmospheric, land and oceanic climate variables with a resolution of 25 km at different pressure levels and for the surface (precipitation in our case).In this study, two main architectures have been applied. The first emulator, ERA-Emulator, contains 14 layers, divided in 4 blocks (input, convolutional, dense, output). In the convolutional block we have 6 convolutional layers, one layer&#160;of type ConvLSTM2D, that combines a 2D Convolutional layer and an LSTM layer, and five simple 2D convolutional layers, with two of them&#160;followed by a MaxPooling layer. In the Dense block there are&#160;three fully connected Dense layers followed by one Flatten layer and one Dropout layer. Then, we have the output layer, also a Dense layer. We used the same architecture for the second emulator, GRIPHO-Emulator, with one extra MaxPooling in the convolutional block, for a total of 15 layers. The first emulator uses variables from ERA5 both as input and output at 25 km resolution, while the second one uses variables from ERA5 as input, and the Gridded Italian Precipitation Hourly Observations dataset (GRIPHO) as output at 3 km resolution.The ERA-Emulator is designed to approximate the downscaling function by utilizing low-resolution simulations to generate equivalent low resolution precipitation fields. ERA-Emulator resulted in a viable approach to address this challenge. The emulator demonstrates the capability to derive precipitation fields that align with ERA5 low-resolution simulations.&#160; GRIPHO-emulator aims to downscale high resolution precipitation from low-resolution large-scale predictors. The emulator aims to estimate the downscaling function. GRIPHO-Emulator is able to create realistic high-resolution precipitation fields that well represent the observed precipitation distribution from the high resolution GRIPHO dataset.