Version 1
: Received: 17 August 2024 / Approved: 20 August 2024 / Online: 20 August 2024 (16:24:56 CEST)
How to cite:
Shafei, A.; Cioffi, F. High-Resolution Early Warning Systems Using DL: Part I - Elevation-Integrated Temperature and Precipitation SRGAN Downscaling (E-TEPS). Preprints2024, 2024081420. https://doi.org/10.20944/preprints202408.1420.v1
Shafei, A.; Cioffi, F. High-Resolution Early Warning Systems Using DL: Part I - Elevation-Integrated Temperature and Precipitation SRGAN Downscaling (E-TEPS). Preprints 2024, 2024081420. https://doi.org/10.20944/preprints202408.1420.v1
Shafei, A.; Cioffi, F. High-Resolution Early Warning Systems Using DL: Part I - Elevation-Integrated Temperature and Precipitation SRGAN Downscaling (E-TEPS). Preprints2024, 2024081420. https://doi.org/10.20944/preprints202408.1420.v1
APA Style
Shafei, A., & Cioffi, F. (2024). High-Resolution Early Warning Systems Using DL: Part I - Elevation-Integrated Temperature and Precipitation SRGAN Downscaling (E-TEPS). Preprints. https://doi.org/10.20944/preprints202408.1420.v1
Chicago/Turabian Style
Shafei, A. and Francesco Cioffi. 2024 "High-Resolution Early Warning Systems Using DL: Part I - Elevation-Integrated Temperature and Precipitation SRGAN Downscaling (E-TEPS)" Preprints. https://doi.org/10.20944/preprints202408.1420.v1
Abstract
This study introduces an advanced method for climate downscaling using a Super-Resolution Generative Adversarial Network (SRGAN), referred to as Elevation-integrated TEmperature and Precipitation SRGAN downscaling (E-TEPS), to enhance the spatial resolution of climate data, specifically 2m-temperature above the surface and total precipitation, from the high-resolution Euro-Mediterranean Center on Climate Change (CMCC) dataset over Italy. Traditional Numerical weather prediction (NWP) as well as global climate models (GCMs) generally lack the resolution required for local applications, and this research addresses that gap by refining GCM outputs to provide detailed local climate insights. E-TEPS incorporates elevation maps as auxiliary inputs, significantly improving the accuracy of downscaled temperature and precipitation data. Utilizing Google Cloud's computational resources, the model processes large climate datasets and runs complex training operations efficiently. The system is exceptionally fast, delivering all results in under 10 seconds. The model demonstrates superior performance over two interpolation methods (bicubic and linear), used as traditional downscaling methods, achieving lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), higher Pearson Correlation for temperature, and higher agreement values for the precipitation. Visual comparisons confirm that E-TEPS generated high-resolution outputs exhibit fewer artifacts and better detail preservation, particularly in regions with complex terrain and variable climatic conditions. These findings underscore the potential of our model in delivering precise, high-resolution climate predictions, thereby enhancing the performance of early-warning systems and supporting more effective climate-related decision-making. This paper is the first part of a two-part series; the second part will demonstrate the integration of this downscaling method with the FourCastNet global forecasting model to enhance climate predictions at a regional level, particularly focusing on high-resolution forecasts for Italy.
Keywords
Downscaling Models; Deep Learning; Total Precipitation; Temperature; Elevation; SRGAN; Early Warning Systems
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
