Yuhang Zhang's starred repositories
cs-self-learning
计算机自学指南
CCAM-China-Catchment-Attributes-and-Meteorology-dataset
Accompanying code for CCAM dataset
DDPM_ver1.0
Distributed dynamic process model (DDPM) is a bidirectional coupling eco-hydrological model for (but not limited to) steppe inland river basins in arid and semi-arid regions, which is driven by meteorological data and developed by Dr. Mingyang Li and Prof. Tingxi Liu. And we also call it "MY eco-hydrology model". MY means “my”, which will be released as open source and gradually optimized and updated to get more support from researchers and better improve the model. The MYEH model mainly includes evapotranspiration, runoff, confluence, grazing disturbance, carbon and nitrogen cycle, etc. It absorbs the advantages of various existing ecological models, hydrological models, as well as the framework and algorithm of eco-hydrological models.
PCR-GLOBWB_model
PCR-GLOBWB (PCRaster Global Water Balance) is a large-scale hydrological model intended for global to regional studies and developed at the Department of Physical Geography, Utrecht University (Netherlands). Contact: Edwin Sutanudjaja (E.H.Sutanudjaja@uu.nl).
surgeon-pytorch
A library to inspect and extract intermediate layers of PyTorch models.
the-gan-zoo
A list of all named GANs!
convCNPClimate
Implementation of convolutional conditional neural processes for statistical downscaling
WAF_ML_Tutorial_Part1
Python code to assist in familiarizing meteorologists with machine learning
precip_change
Coelho, G. de A. et al. (2022) Potential Impacts of Future Extreme Precipitation Changes on Flood Engineering Design across the Contiguous United States. Published in Water Resources Research. https://doi.org/10.1029/2021WR031432
scientific-visualization-book
An open access book on scientific visualization using python and matplotlib
challenges_2022
Discover the ECMWF Summer of Weather Code 2022 challenges.
Autoformer
About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008
paper-reading
深度学习经典、新论文逐段精读