Comparing skill of historical rainfall data based monsoon rainfall
  prediction in India with NCEP-NWP forecasts
Apoorva Narula Aastha Jain Jatin Batra Sandeep Juneja
In this draft we consider the problem of forecasting rainfall across India
during the four monsoon months, one day as well as three days in advance. We
train neural networks using historical daily gridded precipitation data for
India obtained from IMD for the time period $1901- 2022$, at a spatial
resolution of $1^{\circ} \times 1^{\circ}$. This is compared with the numerical
weather prediction (NWP) forecasts obtained from NCEP (National Centre for
Environmental Prediction) available for the period 2011-2022. We conduct a
detailed country wide analysis and separately analyze some of the most
populated cities in India. Our conclusion is that forecasts obtained by
applying deep learning to historical rainfall data are more accurate compared
to NWP forecasts as well as predictions based on persistence. On average,
compared to our predictions, forecasts from NCEP-NWP model have about 34%
higher error for a single day prediction, and over 68% higher error for a three
day prediction. Similarly, persistence estimates report a 29% higher error in a
single day forecast, and over 54% error in a three day forecast. We further
observe that data up to 20 days in the past is useful in reducing errors of one
and three day forecasts, when a transformer based learning architecture, and to
a lesser extent when an LSTM is used. A key conclusion suggested by our
preliminary analysis is that NWP forecasts can be substantially improved upon
through more and diverse data relevant to monsoon prediction combined with
carefully selected neural network architecture.