fanghongbin's repositories
rain-nowcasting-with-fusion-of-rainfall-and-wind-data-article
Repository for article "Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting"
dea-notebooks
Repository for Digital Earth Australia Jupyter Notebooks: tools and workflows for geospatial analysis with Open Data Cube and xarray
xarray-tutorial
Xarray Tutorials
seaborn-data
Data repository for seaborn examples
metnet-1
PyTorch Implementation of Google Research's MetNet and MetNet-2
d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.
MeteorologicalPy
基于python\numpy\pandas\xarray\matplotlib\cartopy\metpy的气象数据处理、程序设计及绘图
chinese-poetry
The most comprehensive database of Chinese poetry 🧶最全中华古诗词数据库, 唐宋两朝近一万四千古诗人, 接近5.5万首唐诗加26万宋诗. 两宋时期1564位词人,21050首词。
vpncn.github.io
2021**翻墙软件VPN推荐以及科学上网避坑,稳定好用。对比SSR机场、蓝灯、V2ray、老王VPN、VPS搭建梯子等科学上网与翻墙软件,**最新科学上网翻墙梯子VPN下载推荐。
data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
ml_drought
Machine learning to better predict and understand drought. Moving github.com/ml-clim
neuralhydrology
Python library to train neural networks with a strong focus on hydrological applications.
hagelslag
Hagelslag is an object-based severe storm hazard forecasting system.
pycwr
The China Weather Radar Toolkit, support most of China's radar formats(WSR98D, CINRAD/SA/SB/CB, CINRAD/CC/CCJ, CINRAD/SC/CD)
awesome-open-climate-science
Awesome Open Atmospheric, Ocean, and Climate Science
metnet_pytorch
A MetNet implementation in Pytorch and fastai
Daily-Rainfall-Prediction-Using-Radar
Reliable daily rainfall predictions can play an important role in a) watershed management, b) disaster management, and c) helping people to plan their day. Numerous factors can have an effect on the patterns of rainfall and therefore it can be difficult to predict. Recent papers studied deep learning for rainfall prediction using various prediction models with an emphasis on short-term predictions (hourly), however, few have looked at daily rainfall prediction using deep learning. This paper discusses a deep learning model, ConvLSTM, for daily rainfall prediction using various sequence lengths of radar images and predicting 1, 2, 4, 7, and 12 days ahead. The aim of this paper is to investigate how well the ConvLSTM model fairs against a Multivariate Regressor and also to look at whether increasing the length of the sequences of images a model can learn from decreases the prediction error of the model. To establish the effectiveness of the ConvLSTM model, we compare it against the Multivariate Regressor and a Last Frame Regressor model. The results of our work show that the ConvLSTM model outperformed the Multivariate Regressor and Last Frame Regressor models when predicting 1 day ahead, however, when predicting 2, 4, 7, and 12 days ahead, the results of the ConvLSTM and Multivariate Regressor models are quite similar. When comparing sequence lengths, our results show that an increase in sequence length does not necessarily decrease the prediction error of a model.
METNet
METNet
access-om2-course
reveal JS based presentation outlining how to get started with the ACCESS-OM2 model
deeplearning-models
A collection of various deep learning architectures, models, and tips
ml-design-patterns
Source code accompanying O'Reilly book: Machine Learning Design Patterns
Meteorological-Books
气象相关书籍合集(持续更新)
Metpy_Plots
metpy_plot
Weather-data-analysis
气象数据分析
google_unet_nowcast
U-Net architecture inspired by "Machine Learning for Precipitation Nowcasting from Radar Images", implemented in Keras (Tensorflow).
PFST-LSTM
This is a Pytorch implementation of PFST-LSTM, a recurrent model for precipitation nowcasting (radar echo extrapolation) as described in the following paper: PFST-LSTM: a SpatioTemporal LSTM Model with Pseudo-flow Prediction for Precipitation Nowcasting