Based on the code of Daliakopoulos I.N., Katsanevakis, S., and Moustakas, A.
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Updated
Jun 3, 2018
Based on the code of Daliakopoulos I.N., Katsanevakis, S., and Moustakas, A.
A collection of notebooks and tools for analyzing the LOCA dataset
Generate stocastic Gaussian realization constrained to a coarse scale image.
Given a global mean temperature pathway, generate random global climate fields consistent with it and with spatial and temporal correlation derived from an ESM
This repository contains three packages that assemble codes and scripts to downscale coarse-resolution reanalysis fields to finer resolutions, accounting for subgrid-scale variability and/or topographic effects.
Created algorithm in C to detect and highlight best image match with template (2 px accuracy) using pixelwise brute force. The algorithm is optimized by 16x to take less than 5 seconds per image-template pair on i7 processor by down-sampling.
Multicore! Faster!
Using Stereo SGM to calculate the disparity map of two images :Stereo Processing by Semiglobal Matching and Mutual Information .
Probabilistic Downscaling of Climate Variables Using Denoising Diffusion Probabilistic Models
Statistical dowscaling of climate data at daily scale using quantile mapping (QPM) technique.
Bayesian Network-Infored Conditional Random Forests
Deep Learning for empirical DownScaling. Python package with state-of-the-art and novel deep learning algorithms for empirical/statistical downscaling of gridded data
Python tool for downsizing Microsoft PowerPoint presentations (pptx) files.
Analyzer that shows why your Kubenetes cluster does not scale down like you expect
Ease the use of the climate4r package to downscale TraCE21ka and CMIP5 climate data in combination with UERRA reanalysis data.
Weather Generators with Bayesian Networks
A project on how to incorporate physics constraints into deep learning architectures for downscaling or other super--resolution tasks.
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