# DeepDownscale: a Deep Learning Strategy for High-Resolution Weather Forecast

arXiv: https://arxiv.org/abs/1808.05264 · HTML: https://mlsanity.com/paper/1808.05264/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 28 of 28)

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- [MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling](https://mlsanity.com/paper/2408.10854/) (2024)
- [Efficient High-Resolution Deep Learning: A Survey](https://mlsanity.com/paper/2207.13050/) (2022)
- [Super-resolution data assimilation](https://mlsanity.com/paper/2109.08017/) (2021)
- [Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability](https://mlsanity.com/paper/2201.08671/) (2022)
- [Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction](https://mlsanity.com/paper/2206.15241/) (2022)
- [RainShift: A Benchmark for Precipitation Downscaling Across Geographies](https://mlsanity.com/paper/2507.04930/) (2025)
- [Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion](https://mlsanity.com/paper/2410.00381/) (2024)
- [Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms](https://mlsanity.com/paper/2102.00696/) (2021)
- [Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks](https://mlsanity.com/paper/2202.11099/) (2022)
- [Decadal Forecasts with ResDMD: a Residual DMD Neural Network](https://mlsanity.com/paper/2106.11111/) (2021)
- [Solving Image PDEs with a Shallow Network](https://mlsanity.com/paper/2110.08327/) (2021)
- [VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting](https://mlsanity.com/paper/2405.04252/) (2024)
- [IndiaWeatherBench: A Dataset and Benchmark for Data-Driven Regional Weather Forecasting over India](https://mlsanity.com/paper/2509.00653/) (2025)
- [A PDE-Informed Latent Diffusion Model for 2-m Temperature Downscaling](https://mlsanity.com/paper/2510.23866/) (2025)
- [On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling](https://mlsanity.com/paper/2512.01400/) (2025)
- [Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review](https://mlsanity.com/paper/2101.01866/) (2021)
- [Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models](https://mlsanity.com/paper/2306.11144/) (2023)
- [DUNE: A Machine Learning Deep UNet++ based Ensemble Approach to Monthly, Seasonal and Annual Climate Forecasting](https://mlsanity.com/paper/2408.06262/) (2024)
- [Improving sub-seasonal wind-speed forecasts in Europe with a non-linear model](https://mlsanity.com/paper/2411.19077/) (2024)
- [Period-LLM: Extending the Periodic Capability of Multimodal Large Language Model](https://mlsanity.com/paper/2505.24476/) (2025)
- [Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift](https://mlsanity.com/paper/2607.05645/) (2026)
- [Evaluating Cross-region Generalization for Wavelet-Diffusion Precipitation Downscaling](https://mlsanity.com/paper/2609.28749/) (2026)

## Abstract (arXiv)

Running high-resolution physical models is computationally expensive and essential for many disciplines. Agriculture, transportation, and energy are sectors that depend on high-resolution weather models, which typically consume many hours of large High Performance Computing (HPC) systems to deliver timely results. Many users cannot afford to run the desired resolution and are forced to use low resolution output. One simple solution is to interpolate results for visualization. It is also possible to combine an ensemble of low resolution models to obtain a better prediction. However, these approaches fail to capture the redundant information and patterns in the low-resolution input that could help improve the quality of prediction. In this paper, we propose and evaluate a strategy based on a deep neural network to learn a high-resolution representation from low-resolution predictions using weather forecast as a practical use case. We take a supervised learning approach, since obtaining labeled data can be done automatically. Our results show significant improvement when compared with standard practices and the strategy is still lightweight enough to run on modest computer systems.