STARCH compuets regional extreme storm physical characteristics and moisture balance based on spatiotemporal precipitation data from reanalysis or climate model data.

Related tags

Deep Learningstarch
Overview

STARCH (Storm Tracking And Regional CHaracterization)

STARCH computes regional extreme storm physical and moisture balance characteristics based on spatiotemporal precipitation data from reanlaysis or climate model data. The algorithm is proposed and implemented in (paper) to identify extreme storms and analyze their moisture balance in the Mississippi Basin based on ERA5 reanlaysis data. Detailed description and implementation of the algorithm can be found in the Jupyter Notebook "Example.ipynb".

Installation

git clone https://github.com/lorenliu13/starch.git

Dependencies

Name Version
geographiclib 1.52
matplotlib 3.4.2
mpu 0.23.1
numpy 1.20.3
pandas 1.3.3
Pillow 9.0.0
scikit_image 0.18.1
scikit_learn 1.0.2
scipy 1.6.3
skimage 0.0
tqdm 4.62.0

cdsapi and urllib3 are necesary when using the code "ERA5_single_levels_download.py" to group download ERA5 data from the ECMWF data center. Basemap Matplotlib Toolkit is necessary when using the plotting function "sequence_strom_plot_basemap()" in the Example.ipynb to visualize the storm tracking results.

Usage

Introduction and implementation of the codes can be found in "Example.ipynb".

Contributing

Feel free to open an issue for bugs and feature requests.

License

STARCH is released under the MIT License.

Authors

Acknowledgements

  • Guo Yu - Feature improvement
You might also like...
Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.
Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.

PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning The predictive learning of spatiotemporal sequences aims to generate future

THUML: Machine Learning Group @ THSS 243 Dec 26, 2022
PyTorch Code of "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics"

Memory In Memory Networks It is based on the paper Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spati

Yang Li 12 May 30, 2022
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

PhyCRNet Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs Paper link: [ArXiv] By: Pu Ren, Chengping Rao, Yang

Pu Ren 11 Aug 23, 2022
Multivariate Time Series Forecasting with efficient Transformers. Code for the paper
Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

Spacetimeformer Multivariate Forecasting This repository contains the code for the paper, "Long-Range Transformers for Dynamic Spatiotemporal Forecast

QData 440 Jan 2, 2023
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination

Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination (ICCV 2021) Dataset License This work is l

DongYoung Kim 33 Jan 4, 2023
Diabet Feature Engineering - Predict whether people have diabetes when their characteristics are specified

Diabet Feature Engineering - Predict whether people have diabetes when their characteristics are specified

Şebnem 6 Jan 18, 2022
A new data augmentation method for extreme lighting conditions.
A new data augmentation method for extreme lighting conditions.

Random Shadows and Highlights This repo has the source code for the paper: Random Shadows and Highlights: A new data augmentation method for extreme l

Osama Mazhar 35 Nov 26, 2022
Code repo for
Code repo for "RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network" (Machine Learning and the Physical Sciences workshop in NeurIPS 2021).

RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network An official PyTorch implementation of the RBSRICNN network as desc

Rao Muhammad Umer 6 Nov 14, 2022
Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.
Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)1 to train Physical Neural Networks (PNNs) - neural networks whose building blocks are physical systems.

McMahon Lab 230 Jan 5, 2023
Comments
  • Consider combination, division, and deformation of the storm objects in tracking

    Consider combination, division, and deformation of the storm objects in tracking

    The track function may be improved following the figure in the study of Zhou et al. (2018) to deal with storm split and merge: image

    Zhou, Yang, Hyemi Kim, and Bin Guan. “Life Cycle of Atmospheric Rivers: Identification and Climatological Characteristics.” Journal of Geophysical Research: Atmospheres 123, no. 22 (2018): 12,715-12,725. https://doi.org/10.1029/2018JD029180.

    Another idea of dealing with spliting and merging of storm objects can be found in Guan et al. (2019), which gives more detailed cases: image

    Guan, Bin, and Duane E. Waliser. “Tracking Atmospheric Rivers Globally: Spatial Distributions and Temporal Evolution of Life Cycle Characteristics.” Journal of Geophysical Research: Atmospheres 124, no. 23 (2019): 12523–52. https://doi.org/10.1029/2019JD031205.

    opened by lorenliu13 0
Releases(v1.0.1)
Owner
Onosama
Onosama
GitHub Repository
DeepAL: Deep Active Learning in Python

DeepAL: Deep Active Learning in Python Python implementations of the following active learning algorithms: Random Sampling Least Confidence [1] Margin

Kuan-Hao Huang 583 Jan 03, 2023
ArcaneGAN by Alex Spirin

ArcaneGAN by Alex Spirin

Alex 617 Dec 28, 2022
A no-BS, dead-simple training visualizer for tf-keras

A no-BS, dead-simple training visualizer for tf-keras TrainingDashboard Plot inter-epoch and intra-epoch loss and metrics within a jupyter notebook wi

Vibhu Agrawal 3 May 28, 2021
nn_builder lets you build neural networks with less boilerplate code

nn_builder lets you build neural networks with less boilerplate code. You specify the type of network you want and it builds it. Install pip install n

Petros Christodoulou 157 Nov 20, 2022
Implementation of "Semi-supervised Domain Adaptive Structure Learning"

Semi-supervised Domain Adaptive Structure Learning - ASDA This repo contains the source code and dataset for our ASDA paper. Illustration of the propo

3 Dec 13, 2021
Image to Image translation, image generataton, few shot learning

Semi-supervised Learning for Few-shot Image-to-Image Translation [paper] Abstract: In the last few years, unpaired image-to-image translation has witn

yaxingwang 49 Nov 18, 2022
Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

258 Dec 29, 2022
Compares various time-series feature sets on computational performance, within-set structure, and between-set relationships.

feature-set-comp Compares various time-series feature sets on computational performance, within-set structure, and between-set relationships. Reposito

Trent Henderson 7 May 25, 2022
PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

About PyTorch 1.2.0 Now the master branch supports PyTorch 1.2.0 by default. Due to the serious version problem (especially torch.utils.data.dataloade

Sanghyun Son 2.1k Jan 01, 2023
Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning Source Code

Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning Trevor Ablett*, Bryan Chan*,

STARS Laboratory 8 Sep 14, 2022
The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Introduction This repository includes the source code for "Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks", which is pu

machen 11 Nov 27, 2022
中文语音识别系列，读者可以借助它快速训练属于自己的中文语音识别模型，或直接使用预训练模型测试效果。

MASR中文语音识别(pytorch版) 开箱即用 自行训练 使用与训练分离(增量训练) 识别率高 说明：因为每个人电脑机器不同，而且有些安装包安装起来比较麻烦，强烈建议直接用我编译好的docker环境跑 目前docker基础环境为ubuntu-cuda10.1-cudnn7-pytorch1.6.

发送小信号 180 Dec 17, 2022
CLIP (Contrastive Language–Image Pre-training) trained on Indonesian data

CLIP-Indonesian CLIP (Radford et al., 2021) is a multimodal model that can connect images and text by training a vision encoder and a text encoder joi

Galuh 17 Mar 10, 2022
Image-to-image translation with conditional adversarial nets

pix2pix Project | Arxiv | PyTorch Torch implementation for learning a mapping from input images to output images, for example: Image-to-Image Translat

Phillip Isola 9.3k Jan 08, 2023
[ICCV'2021] "SSH: A Self-Supervised Framework for Image Harmonization", Yifan Jiang, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, Kalyan Sunkavalli, Simon Chen, Sohrab Amirghodsi, Sarah Kong, Zhangyang Wang

SSH: A Self-Supervised Framework for Image Harmonization (ICCV 2021) code for SSH Representative Examples Main Pipeline RealHM DataSet Google Drive Pr

VITA 86 Dec 02, 2022
App for identification of various objects. Based on YOLO v4 tiny architecture

Object_detection Repository containing trained model yolo v4 tiny, which is capable of identification 80 different classes Default feed is set to be a

Mateusz Kurdziel 0 Jun 22, 2022
Neural network pruning for finding a sparse computational model for controlling a biological motor task.

MothPruning Scientific Overview Originally inspired by biological nervous systems, deep neural networks (DNNs) are powerful computational tools for mo

Olivia Thomas 0 Dec 14, 2022
Mercury: easily convert Python notebook to web app and share with others

Mercury Share your Python notebooks with others Easily convert your Python notebooks into interactive web apps by adding parameters in YAML. Simply ad

MLJAR 2.2k Dec 27, 2022
Focal Loss for Dense Rotation Object Detection

Convert ResNets weights from GluonCV to Tensorflow Abstract GluonCV released some new resnet pre-training weights and designed some new resnets (such

17 Nov 24, 2021
A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset.

A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset. This repo contains scripts to train RL agents to navigate the closed world and collect vi

MUGEN 11 Oct 22, 2022