davitpapikyan/Probabilistic-Downscaling-of-Climate-Variables
Probabilistic Downscaling of Climate Variables Using Denoising Diffusion Probabilistic Models
This tool helps climate scientists and meteorologists transform broad, regional climate model outputs into highly detailed, localized predictions. By taking coarse-resolution climate data (like temperature at 2m height) as input, it generates finer-scale, probabilistic climate variable maps. This allows for more precise understanding of local weather patterns and impacts.
No commits in the last 6 months.
Use this if you need to derive detailed, local climate information from lower-resolution climate model data to support regional impact assessments or planning.
Not ideal if you need deterministic, single-point forecasts rather than a range of probable outcomes for localized climate variables.
Stars
28
Forks
7
Language
Python
License
MIT
Category
Last pushed
Apr 18, 2022
Commits (30d)
0
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