Spatio-temporal downscaling emulator for regional climate models
Published in Environmetrics, 2023
This paper presents a spatio-temporal downscaling emulator for regional climate models (RCMs). Regional climate models describe mesoscale global atmospheric and oceanic dynamics and serve as dynamical downscaling models, using atmospheric and oceanic climate data.
The research focuses on developing computational methods to improve the spatial and temporal resolution of climate model outputs, which is essential for better understanding local climate patterns and making more accurate regional climate predictions. This type of statistical emulator can help bridge the gap between coarse global climate models and the fine-scale information needed for local climate impact studies.
The paper was a collaboration that started with one of my dissertation chapters, and was followed by colleagues from the Center for Research in Pure and Applied Math at the University of Costa Rica. It addresses an important challenge in climate science - how to efficiently translate broad-scale climate model outputs into locally relevant information.
Recommended citation: Barboza, L. A., Chou Chen, S. W., Alfaro Córdoba, M., Alfaro, E. J., & Hidalgo, H. G. (2023). "Spatio-temporal downscaling emulator for regional climate models." Environmetrics. DOI: 10.1002/env.2815
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