  Accurate short range weather forecasting has significant implications for
various sectors. Machine learning based approaches, e.g., deep learning, have
gained popularity in this domain where the existing numerical weather
prediction (NWP) models still have modest skill after a few days. Here we use a
ConvLSTM network to develop a deep learning model for precipitation
forecasting. The crux of the idea is to develop a forecasting model which
involves convolution based feature selection and uses long term memory in the
meteorological fields in conjunction with gradient based learning algorithm.
Prior to using the input data, we explore various techniques to overcome
dataset difficulties. We follow a strategic approach to deal with missing
values and discuss the models fidelity to capture realistic precipitation. The
model resolution used is (25 km). A comparison between 5 years of predicted
data and corresponding observational records for 2 days lead time forecast show
correlation coefficients of 0.67 and 0.42 for lead day 1 and 2 respectively.
The patterns indicate higher correlation over the Western Ghats and Monsoon
trough region (0.8 and 0.6 for lead day 1 and 2 respectively). Further, the
model performance is evaluated based on skill scores, Mean Square Error,
correlation coefficient and ROC curves. This study demonstrates that the
adopted deep learning approach based only on a single precipitation variable,
has a reasonable skill in the short range. Incorporating multivariable based
deep learning has the potential to match or even better the short range
precipitation forecasts based on the state of the art NWP models.
