fanghongbin Goto Github PK

access-om2-course

reveal JS based presentation outlining how to get started with the ACCESS-OM2 model

ams-2020-ml-python-course

Materials for the Machine Learning in Python for Environmental Science Problems AMS 2020 Short Course

ams-ml-python-course

Machine Learning in Python for Environmental Science Problems AMS Short Course Material

analytics_vidhya

Codes related to activities on AV including articles, hackathons and discussions.

auckland_python_workshop

Python for Climate and Meteorological Data Analysis and Visualisation

awesome-open-climate-science

Awesome Open Atmospheric, Ocean, and Climate Science

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。

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.

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.

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.

dataanalysis

气象数据分析代码和部分数据

dea-notebooks

Repository for Digital Earth Australia Jupyter Notebooks: tools and workflows for geospatial analysis with Open Data Cube and xarray

deeplearning-models

A collection of various deep learning architectures, models, and tips

dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

google_unet_nowcast

U-Net architecture inspired by "Machine Learning for Precipitation Nowcasting from Radar Images", implemented in Keras (Tensorflow).

graphgallery

GraphGallery is a gallery for benchmarking Graph Neural Networks (GNNs) with TensorFlow 2.x and PyTorch backend.

hagelslag

Hagelslag is an object-based severe storm hazard forecasting system.

introduction_to_xarray

lorenz_gan

Lorenz 96 model with GAN parameterization of unresolved scale (Y).

machine-learning-deep-learning-notes

机器学习、深度学习的学习路径及知识总结

meteoai

The papers or tutorials and relative source code of artificial intelligence for meteorology, ocean and environment science.

meteorological

气象数据处理及绘图

meteorological-books

气象相关书籍合集（持续更新）

meteorologicalpy

基于python\numpy\pandas\xarray\matplotlib\cartopy\metpy的气象数据处理、程序设计及绘图

metnet

METNet

metnet-1

PyTorch Implementation of Google Research's MetNet and MetNet-2

metnet_pytorch

A MetNet implementation in Pytorch and fastai

metpy_plots

metpy_plot

ml-design-patterns

Source code accompanying O'Reilly book: Machine Learning Design Patterns

ml_drought

Machine learning to better predict and understand drought. Moving github.com/ml-clim