# Comparing skill of historical rainfall data based monsoon rainfall prediction in India with NWP forecasts

arXiv: https://arxiv.org/abs/2402.07851 · HTML: https://mlsanity.com/paper/2402.07851/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model](https://mlsanity.com/paper/1706.03458/) (2017)
- [ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast](https://mlsanity.com/paper/2402.01295/) (2024)

## Abstract (arXiv)

The Indian summer monsoon is a highly complex and critical weather system that directly affects the livelihoods of over a billion people across the Indian subcontinent. Accurate short-term forecasting remains a major scientific challenge due to the monsoon's intrinsic nonlinearity and its sensitivity to multi-scale drivers, including local land-atmosphere interactions and large-scale ocean-atmosphere phenomena. In this study, we address the problem of forecasting daily rainfall across India during the summer months, focusing on both one-day and three-day lead times. We use Autoformers - deep learning transformer-based architectures designed for time series forecasting. These are trained on historical gridded precipitation data from the Indian Meteorological Department (1901--2023) at spatial resolutions of 0.25^circ × 0.25^circ, as well as 1^circ × 1^circ. The models also incorporate auxiliary meteorological variables from ECMWFs reanalysis datasets, namely, cloud cover, humidity, temperature, soil moisture, vorticity, and wind speed. Forecasts at 0.25^circ × 0.25^circ are benchmarked against ECMWFs High-Resolution Ensemble System (HRES), widely regarded as the most accurate numerical weather predictor, and at 1^circ × 1^circ  with those from National Centre for Environmental Prediction (NCEP). We conduct both nationwide evaluations and localized analyses for major Indian cities. Our results indicate that transformer-based deep learning models consistently outperform both HRES and NCEP, as well as other climatological baselines. Specifically, compared to our model, forecasts from HRES and NCEP model have about 22% and 43% higher error, respectively, for a single day prediction, and over 27% and 66% higher error respectively, for a three day prediction.