Gousios, G.; Mamouka, T.; Vourlioti, P.; Kotsopoulos, S. Downscaling Seasonal Weather Forecasting with Generative Adversarial Networks. Preprints2023, 2023061492. https://doi.org/10.20944/preprints202306.1492.v1
APA Style
Gousios, G., Mamouka, T., Vourlioti, P., & Kotsopoulos, S. (2023). Downscaling Seasonal Weather Forecasting with Generative Adversarial Networks. Preprints. https://doi.org/10.20944/preprints202306.1492.v1
Chicago/Turabian Style
Gousios, G., Paraskevi Vourlioti and Stylianos Kotsopoulos. 2023 "Downscaling Seasonal Weather Forecasting with Generative Adversarial Networks" Preprints. https://doi.org/10.20944/preprints202306.1492.v1
Abstract
Accurate seasonal weather forecasts are vital across a broad spectrum of applications, bearing significant environmental and socioeconomic implications. This importance renders the subject a matter of primary interest to a wide range of stakeholders, including the general public, agricultural sector, emergency responders, financial institutions, and policy strategists. The need for precision in long-term predictive models necessitates the development of innovative methodologies. These methodologies should be capable of deciphering atmospheric patterns and mechanisms with detail, especially at a local level, without the resource constraints that dynamical downscaling imposes. In response to this expanding demand, this study presents a novel solution, combining a stochastic deep learning methodology, specifically Generative Adversarial Networks (GANs), with a Digital Elevation Model (DEM). The cornerstone of the proposed model is the enhancement of gridded temperature fields from seasonal forecasts. The area of focus was the Hellenic region, wherein the spatial resolution is amplified from a coarse 1° x 1° grid to an impressively detailed 0.1° x 0.1° grid. This offers a transformative perspective for interpreting and employing this crucial meteorological data. The results suggest that the downscaled fields adequately approximate the actual spatial distribution, although the predicted values tend to slightly overestimate and in some cases underestimate the original ones. This study underlines the potential of this approach to significantly enhance the resolution and utility of weather forecasts, thereby contributing to a variety of sectors dependent on reliable meteorological data.
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.
