Deep-learning based down-scaling of summer monsoon rainfall data over Indian region

TL;DR

This paper demonstrates that deep learning models, especially DeepSD, effectively downscale summer monsoon rainfall data over India, providing high-resolution data that improves spatial and temporal accuracy for climate monitoring.

Contribution

The study introduces the application of three deep learning algorithms, particularly DeepSD, for high-resolution downscaling of monsoon rainfall data, outperforming other methods in accuracy.

Findings

DeepSD yields the best spatial distribution and lowest error among tested models.

Downscaled ERA5 data shows improved spatial covariance and temporal variance.

Deep learning methods effectively address non-linear and chaotic rainfall variability.

Abstract

Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex Spatio-temporal process leading to non-linear or chaotic Spatio-temporal variations, no single downscaling method can be considered efficient enough. In data with complex topographies, quasi-periodicities, and non-linearities, deep Learning (DL) based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions. In this work, we employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods, to precipitation data, in particular, IMD and TRMM data to produce 4x-times high-resolution downscaled rainfall data during the summer monsoon season. Among the three algorithms, namely SRCNN, stacked SRCNN, and DeepSD, employed here, the best spatial distribution of rainfall amplitude and minimum root-mean-square error is produced by DeepSD based downscaling. Hence, the use of the DeepSD algorithm is advocated for future use. We found that spatial discontinuity in amplitude and intensity rainfall patterns is the main obstacle in the downscaling of precipitation. Furthermore, we applied these methods for model data postprocessing, in particular, ERA5 data. Downscaled ERA5 rainfall data show a much better distribution of spatial covariance and temporal variance when compared with observation.