*3.2. Parameter Setting*

Our model was trained to minimize the errors between the ground truth radar data values and the predicted outputs using the root mean square error loss. The Adam optimizer was also used with an initial learning rate of 0.0005 for the MET experiments and an initial learning rate of 0.001 for the NMA experiments. A learning rate scheduler was applied to reduce the learning rate by a factor of two after every five epochs with no improvements in the validation loss. For the MET case study, the 32 grouped convolutions were used in the ResNeXt blocks while for the NMA case study, 64 groups were used.

As described in Section 2.3.4, for performance evaluation purposes, a threshold *τ* was used to transform the continuous output of the *NeXtNow* model into a discrete output. The values that we considered for the threshold *τ* were 5, 10, 15, 20 and 30, which corresponded to light to moderate rainfall.
