## 4 Literature Review on Supervised Learning Based Weather Nowcasting
The decoder uses both the encoded actual reflectivity and the predicted reflectivity. For accelerating learning, during training the ground truths are fed back into the decoder, while for evaluation the prior predictions are used. Additionally they noticed that using as start symbol a tensor of ones which represents high reflectivity when data is scaled to [0, 1] improves Probability of Detection and Critical Success Rate. [PERSON] et al. [36] proposed a benchmark for the nowcasting prediction problem and a new model. The benchmark includes a new data set, 2 testing protocols and 2 measurements for training, Balanced Mean Squared Error and Balanced Mean Absolute Error. Those measurements were necessary due to an imbalanced between the small amount of times where potentially dangerous events occured and normal amount of rainfall. The proposed model, Trajectory Gated Recurrent Unit (TrajGRU), deals better than Convolutional GRU, with the representation of location variant relationships. TrajGRU allows aggregation of the state along a learned trajectories. The architecture consist of an encoder and a forecaster part, inserting between RNN layers downsampling or upsampling operations depending upon region of the architecture. Their proposal show to be flexible and superior than other methods. The experiments done on Moving MNIST and precipitation nowcasting HKO-7 dataset emphasize the ability of the model to capture the spatiotemporal correlation. [PERSON] and [PERSON] [37] have proposed a hybrid model combining Deep Belief Networks (DBN) with Restricted Boltzmann Machine (RBM) to predict different parameters of weather--air temperature, pressure and relative humidity--at a local level (i.e., restricted to a particular geographical area). Initially, the RBMs are trained unsupervisedly. Subsequently, the already trained RBMs are stacked to create a DBN. The DBN is supervisedly trained so as to predict the weather parameters. [PERSON] et al. [38] approached foehn prediction using AdaBoost machine learning algorithm. The authors motivate the choice of the model through the reasonableness of the balance between the predictive power, the computational speed and the interpretability of the results. Being trained with three years of hourly simulations data and using a modified decision stumps as weaker learners, the model achieved, on a validation data set, 0.88 sensitivity (or probability of detection) and 0.29 probability of false detection. [PERSON] [39] approached the problem of short-term precipitation prediction from radar observations, using _artificial neural networks_. The nowcasting of the rain intensity was carried out on radar raw data and rain gauge data collected from China from 2010 to 2012. The reflectivity values were extracted from the raw data and were interpolated a 3D rectangular lattice grid of 1 km \(\times\) 1 km in horizontal direction at the height of 1.5 and 3 km [39]. The collected data set was afterwards used for training the predictive model. A _root mean squared error_ less than 5 (a minimum of 0.97 and a maximum of 4.7) was obtained. The experiments have shown a correlation coefficient \(R\) in the radar-rainfall estimation more than 0.6 and indicate that the accuracy rate of 36 mins forecast is higher than 50% [39]. In a recent approach, [PERSON] and [PERSON] [40] proposed a new loss function for the convolutional neural network (CNN) based models used for weather nowcasting. Using a computer vision perspective, the authors used Image Quality Assessment Metrics as loss functions in training, finding that the Structural Similarity function performed better than both MSE and MAE, especially by improving the quality of the images (i.e., the output images were much less blurry). However, the best performance was achieved by combining the Structural Similarity with the MAE and MSE loss functions. [PERSON] et al. [41] proposed a CNN model for convective storms nowcasting based on radar data.