## 4 Literature Review on Supervised Learning Based Weather Nowcasting  
The literature contains various machine learning-based approaches for weather nowcasting. Relevant results obtained recently in predicting short-term weather are summarized in the following and the limitations of the existing solutions are emphasized. [PERSON] et al. [31] use Support Vector Machines (SVM) are trained on box-based features in order to classify whether or not a radar echo \(>\)35 dBZ will appear on the radar within 30 min. The approach uses both temporal and spatial features, derived from vertical wind, and perturbation temperature. Greedy feature selection was used in order to finally select these features. The obtained results were around 0.61 Probability Of Detection (POD), 0.52 False Alarm Ratio (FAR), 0.36 Critical Success Index (CSI), which outperformed the Logistic Regression, J48, Adaboost and Maxent approaches compared against on the same data set. [PERSON] et al. [32] present the COALITION-3 (\"Context and Scale Oriented Thunderstorm Satellite Predictors Development\") algorithm developed by MeteoSwiss. Its goal is to identify, track and nowcast the position and development of storms in a robust manner. In order to do this, it employs optical flow methods in combination with winds predicted by COSMO. In order to separate the movement of the storm from its temporal evolution, Lagrangian translation is used. Following this, the algorithm estimates future intensification and quantifies the probability and severity of the different risks associated with thunderstorms. This information is extracted by applying machine learning techniques to a data archive containing observations from the MeteoSwiss dual polarized Doppler radar system and information from other available systems. [PERSON] et al. [33] introduce an extension of the Long-Short Term Memory Network (LSTM), named ConvLSTM. Through experiments done on Moving MNIST and Radar Echo Dataset, the authors proved their method suitable for spatiotemporal data, having all the perks which come with a simple LSTM, preserving spatiotemporal features due to the inherited convolutional structure. Besides the above mentioned aspect other advantages of ConvLSTM over a basic LSTM are: transitions input-to-state and state-to-state are made in a convolutional manner, deeper models can produce better results with a smaller number of parameters. Their proposed architecture contains 2 networks, an encoder composed of 2 ConvLSTM layers and a forecasting network containing the same number of ConvLSTM layers and an additional 1 \(\times\) 1 convolutional layer to generate final predictions. [PERSON] et al. [34] have also proposed a ConvLSTM-based model for precipitation prediction, but they used three-dimensional and four-channel data unlike [PERSON] et al. [33], who used three-dimensional and only one-channel data. The four channels correspond to four altitudes. The proposed model, called DeepRain, predicts, based on radar reflectivity data, the amount of rainfall on a large scale. The experimental evaluation has proved a decrease of root mean square error (RMSE) with 23% when compared to linear regression and also a superior performance as compared to a fully connected LSTM. For rainfall prediction a RMSE value of 11.31 has been obtained on the test set. [PERSON] et al. [35] present a practical solution for leveraging the precipitation nowcasting problem starting from how the data is stored and preprocessed, the deep learning model used and up to the necessary frameworks and hardware. They used the ConvLSTM with peepholes cells described by [PERSON] et al. [33] in an architecture inspired from those used for machine translation. The model is composed of an encoder and a decoder each made out of four ConvLSTM layers. They experimented with both taking the last step of the encoder and using attention for transferring information to the decoder, with the former producing better results in terms of Probability of Detection and Critical Success Rate and worse for the False Alarm Rate.