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Spatiotemporally Coherent Probabilistic Generation of Weather from Climate

Zapisane w:
Wydane w:arXiv.org (Dec 19, 2024), p. n/a
1. autor: Schmidt, Jonathan
Kolejni autorzy: Schmidt, Luca, Strnad, Felix, Ludwig, Nicole, Hennig, Philipp
Wydane:
Cornell University Library, arXiv.org
Hasła przedmiotowe:
Statistical methods
Physical properties
Weather
Probabilistic inference
Statistical analysis
Climate models
High resolution
Dostęp online:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
035 |a 3148681323 
045 0 |b d20241219 
100 1 |a Schmidt, Jonathan 
245 1 |a Spatiotemporally Coherent Probabilistic Generation of Weather from Climate 
260 |b Cornell University Library, arXiv.org  |c Dec 19, 2024 
513 |a Working Paper 
520 3 |a Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical downscaling methods infer these phenomena as temporally decoupled spatial patches. However, to preserve physical properties, estimating spatio-temporally coherent high-resolution weather dynamics for multiple variables across long time horizons is crucial. We present a novel generative approach that uses a score-based diffusion model trained on high-resolution reanalysis data to capture the statistical properties of local weather dynamics. After training, we condition on coarse climate model data to generate weather patterns consistent with the aggregate information. As this inference task is inherently uncertain, we leverage the probabilistic nature of diffusion models and sample multiple trajectories. We evaluate our approach with high-resolution reanalysis information before applying it to the climate model downscaling task. We then demonstrate that the model generates spatially and temporally coherent weather dynamics that align with global climate output. 
653 |a Statistical methods 
653 |a Physical properties 
653 |a Weather 
653 |a Probabilistic inference 
653 |a Statistical analysis 
653 |a Climate models 
653 |a High resolution 
700 1 |a Schmidt, Luca 
700 1 |a Strnad, Felix 
700 1 |a Ludwig, Nicole 
700 1 |a Hennig, Philipp 
773 0 |t arXiv.org  |g (Dec 19, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3148681323/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.15361