A super lightweight Lagrangian model for calculating millions of trajectories using ERA5 data

Overview

Easy-ERA5-Trck

Easy-ERA5-Trck is a super lightweight Lagrangian model for calculating thousands (even millions) of trajectories simultaneously and efficiently using ERA5 data sets. It can implement super simplified equations of 3-D motion to accelerate integration, and use python multiprocessing to parallelize the integration tasks. Due to its simplification and parallelization, Easy-ERA5-Trck performs great speed in tracing massive air parcels, which makes areawide tracing possible.

Another version using WRF output to drive the model can be found here.

Caution: Trajectory calculation is based on the nearest-neighbor interpolation and first-guess velocity for super efficiency. Accurate calculation algorithm can be found on http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-14-00110.1, or use a professional and complicated model e.g. NOAA HYSPLIT instead.

Any question, please contact Zhenning LI ([email protected])

Galleries

Tibetan Plateau Air Source Tracers

tp_tracer

Tibetan Plateau Air Source Tracers (3D)

tp_tracer_3d

Install

If you wish to run easy-era5-trck using grib2 data, Please first install ecCodes.

Please install python3 using Anaconda3 distribution. Anaconda3 with python3.8 has been fully tested, lower version of python3 may also work (without testing).

Now, we recommend to create a new environment in Anaconda and install the requirements.txt:

conda create -n test_era5trck python=3.8
conda activate test_era5trck
pip install -r requirements.txt

If everything goes smoothly, first cd to the repo root path, and run config.py:

python3 config.py

This will convey fundamental configure parameters to ./conf/config_sys.ini.

Usage

test case

When you install the package ready. You may first want to try the test case. config.ini has been set for testcase, which is a very simple run:

[INPUT]
input_era5_case = ./testcase/
input_parcel_file=./input/input.csv

[CORE]
# timestep in min
time_step = 30
precession = 1-order
# 1 for forward, -1 for backward
forward_option = -1
# for forward, this is the initial time; otherwise, terminating time
start_ymdh = 2015080212
# integration length in hours
integration_length = 24
# how many processors are willing to work for you
ntasks = 4
# not used yet
boundary_check = False

[OUTPUT]
# output format, nc/csv, nc recommended for large-scale tracing
out_fmt = nc
out_prefix = testcase
# output frequency in min
out_frq = 60
# when out_fmt=csv, how many parcel tracks will be organized in a csv file.
sep_num = 5000

When you type python3 run.py, Easy-ERA5-Trck will uptake the above configurations, by which the ERA5 UVW data in ./testcase will be imported for driving the Lagrangian integration.

Now you will see your workers are dedicated to tracing the air parcels. After several seconds, if you see something like:

2021-05-31 17:32:14,015 - INFO : All subprocesses done.
2021-05-31 17:32:14,015 - INFO : Output...
2021-05-31 17:32:14,307 - INFO : Easy ERA5 Track Completed Successfully!

Congratulations! The testcase works smoothly on your machine!

Now you could check the output file in ./output, named as testcase.I20150802120000.E20150801120000.nc|csv, which indicates the initial time and endding time. For backward tracing, I > E, and vice versa.

You could choose output files as plain ascii csv format or netCDF format (Recommended). netCDF format output metadata looks like:

{
dimensions:
    time = 121 ;
    parcel_id = 413 ;
variables:
    double xlat(time, parcel_id) ;
        xlat:_FillValue = NaN ;
    double xlon(time, parcel_id) ;
        xlon:_FillValue = NaN ;
    double xh(time, parcel_id) ;
        xh:_FillValue = NaN ;
    int64 time(time) ;
        time:units = "hours since 1998-06-10 00:00:00" ;
        time:calendar = "proleptic_gregorian" ;
    int64 parcel_id(parcel_id) ;
}

setup your case

Congratulation! After successfully run the toy case, of course, now you are eager to setup your own case. First, build your own case directory, for example, in the repo root dir:

mkdir mycase

Now please make sure you have configured ECMWF CDS API correctly, both in your shell environment and python interface.

Next, set [DOWNLOAD] section in config.ini to fit your desired period, levels, and region for downloading.

[DOWNLOAD]
store_path=./mycase/
start_ymd = 20151220
end_ymd = 20160101
pres=[700, 750, 800, 850, 900, 925, 950, 975, 1000]

# eara: [North, West, South, East]
area=[-10, 0, -90, 360]
# data frame frequency: recommend 1, 2, 3, 6. 
# lower frequency will download faster but less accurate in tracing
freq_hr=3

Here we hope to download 1000-700 hPa data, from 20151220 to 20160101, 3-hr temporal frequency UVW data from ERA5 CDS.

./utlis/getERA5-UVW.py will help you to download the ERA5 reanalysis data for your case, in daily file with freq_hr temporal frequency.

cd utils
python3 getERA5-UVW.py

While the machine is downloading your data, you may want to determine the destinations or initial points of your targeted air parcels. ./input/input.csv: This file is the default file prescribing the air parcels for trajectory simulation. Alternatively, you can assign it by input_parcel_file in config.ini.

The format of this file:

airp_id, init_lat, init_lon, init_h0 (hPa)

For forward trajectory, the init_{lat|lon|h0} denote initial positions; while for backward trajectory, they indicate ending positions. You can write it by yourself. Otherwise, there is also a utility ./utils/take_box_grid.py, which will help you to take air parcels in a rectanguler domain.

plese also set other sections in config.ini accordingly, now these air parcels are waiting your command python3 run.py to travel the world!

Besides, ./utils/control_multi_run.py will help you to run multiple seriels of the simulation. There are some postprocessing scripts for visualization in post_process, you may need to modify them to fit your visualization usage.

Repository Structure

run.py

./run.py: Main script to run the Easy-ERA5-Trck.

conf

  • ./conf/config.ini: Configure file for the model. You may set ERA5 input file, input frequency, integration time steps, and other settings in this file.
  • ./conf/config_sys.ini: Configure file for the system, generate by run config.py.
  • ./conf/logging_config.ini: Configure file for logging module.

core

  • ./core/lagrange.py: Core module for calculating the air parcels Lagrangian trajectories.

lib

  • ./lib/cfgparser.py: Module file containing read/write method of the config.ini
  • ./lib/air_parcel.py: Module file containing definition of air parcel class and related methods such as march and output.
  • ./lib/preprocess_era5inp.py: Module file that defines the field_hdl class, which contains useful fields data (U, V, W...) and related method, including ERA5 grib file IO operations.
  • ./lib/utils.py: utility functions for the model.

post_process

Some visualization scripts.

utils

Utils for downloading, generating input.csv, etc.

Version iteration

Oct 28, 2020

  • Fundimental pipeline design, multiprocessing, and I/O.
  • MVP v0.01

May 31, 2021

  • Major Revision, logging module, and exception treatment
  • test case
  • Major documentation update
  • Utility for data downloading
  • Utility for taking grids in a box
  • Basic functions done, v0.10

Jun 09, 2021

  • The automatic detection of longitude range is added, allowing users to adopt two different ranges of longitude: [-180°, 180°] or [0°, 360°].
  • Currently, if you want to use the [-180°, 180°] data version, you can only set ntasks = 1 in the config.ini file.
You might also like...
A state of the art of new lightweight YOLO model implemented by TensorFlow 2.
A state of the art of new lightweight YOLO model implemented by TensorFlow 2.

CSL-YOLO: A New Lightweight Object Detection System for Edge Computing This project provides a SOTA level lightweight YOLO called "Cross-Stage Lightwe

A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.
A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.

AMAZ3DSim AMAZ3DSim is a lightweight python-based 3D network multi-agent simulator. It uses a cell-based congestion model. It calculates risk, battery

Ultra-lightweight human body posture key point CNN model. ModelSize:2.3MB  HUAWEI P40 NCNN benchmark: 6ms/img,
Ultra-lightweight human body posture key point CNN model. ModelSize:2.3MB HUAWEI P40 NCNN benchmark: 6ms/img,

Ultralight-SimplePose Support NCNN mobile terminal deployment Based on MXNET(=1.5.1) GLUON(=0.7.0) framework Top-down strategy: The input image is t

A simple and lightweight genetic algorithm for optimization of any machine learning model

geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins

MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet.
MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet.

Lightweight-Detection-and-KD MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet. This repo also includes detection knowledge di

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

🐤 Nix-TTS: An Incredibly Lightweight End-to-End Text-to-Speech Model via Non End-to-End Distillation

🐤 Nix-TTS An Incredibly Lightweight End-to-End Text-to-Speech Model via Non End-to-End Distillation Rendi Chevi, Radityo Eko Prasojo, Alham Fikri Aji

In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.
In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

Contrastive Learning of Object Representations Supervisor: Prof. Dr. Gemma Roig Institutions: Goethe University CVAI - Computational Vision & Artifici

Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.
Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

Releases(v0.10-beta)
  • v0.10-beta(Jun 2, 2021)

    This is a pre-release of Easy-ERA5-Trck. In this v0.10-beta pre-release, we establish the basic functions forward/backward tracing the air parcels in massive amount, exploiting the usage of multiprocessing in Python. You could use the tracing output for visualization, and analysis which does not require very high precession/accuracy. Boundary check has not been involved yet, and exception handlings are still under-developed, with no promise to cover your exceptional cases.

    Source code(tar.gz)
    Source code(zip)
Owner
Zhenning Li
Wind extinguishes a candle but energizes fire.
Zhenning Li
VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection (ICCV 2021)

Preparation Please see dataset/README.md to get more details about our datasets-VIL100 Please see INSTALL.md to install environment and evaluation too

82 Dec 15, 2022
Code for "Typilus: Neural Type Hints" PLDI 2020

Typilus A deep learning algorithm for predicting types in Python. Please find a preprint here. This repository contains its implementation (src/) and

47 Nov 08, 2022
SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers

SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers This repo contains our codes for the paper "No Parameters Left Behind: Sensitivity Gu

Chen Liang 23 Nov 07, 2022
This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset.

FACT This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset. To cite, please use:

105 Dec 17, 2022
A web application that provides real time temperature and humidity readings of a house.

About A web application which provides real time temperature and humidity readings of a house. If you're interested in the data collected so far click

Ben Thompson 3 Jan 28, 2022
PyTorch implementation of "PatchGame: Learning to Signal Mid-level Patches in Referential Games" to appear in NeurIPS 2021

PatchGame: Learning to Signal Mid-level Patches in Referential Games This repository is the official implementation of the paper - "PatchGame: Learnin

Kamal Gupta 22 Mar 16, 2022
Assessing syntactic abilities of BERT

BERT-Syntax Assesing the syntactic abilities of BERT. What Evaluate Google's BERT-Base and BERT-Large models on the syntactic agreement datasets from

Yoav Goldberg 147 Aug 02, 2022
Download from Onlyfans.com.

OnlySave: Onlyfans downloader Getting Started: Download the setup executable from the latest release. Install and run. Only works on Windows currently

4 May 30, 2022
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English ⚖️ 🏆 🧑‍🎓 👩‍⚖️ Dataset Summary Inspired by the recent widespread use of th

95 Dec 08, 2022
An open source Jetson Nano baseboard and tools to design your own.

My Jetson Nano Baseboard This basic baseboard gives the user the foundation and the flexibility to design their own baseboard for the Jetson Nano. It

NVIDIA AI IOT 57 Dec 29, 2022
Official PyTorch implementation of SyntaSpeech (IJCAI 2022)

SyntaSpeech: Syntax-Aware Generative Adversarial Text-to-Speech | | | | 中文文档 This repository is the official PyTorch implementation of our IJCAI-2022

Zhenhui YE 116 Nov 24, 2022
Users can free try their models on SIDD dataset based on this code

SIDD benchmark 1 Train python train.py If you want to train your network, just modify the yaml in the options folder. 2 Validation python validation.p

Yuzhi ZHAO 2 May 20, 2022
Restricted Boltzmann Machines in Python.

How to Use First, initialize an RBM with the desired number of visible and hidden units. rbm = RBM(num_visible = 6, num_hidden = 2) Next, train the m

Edwin Chen 928 Dec 30, 2022
Official implementation for "Low-light Image Enhancement via Breaking Down the Darkness"

Low-light Image Enhancement via Breaking Down the Darkness by Qiming Hu, Xiaojie Guo. 1. Dependencies Python3 PyTorch=1.0 OpenCV-Python, TensorboardX

Qiming Hu 30 Jan 01, 2023
A Partition Filter Network for Joint Entity and Relation Extraction EMNLP 2021

EMNLP 2021 - A Partition Filter Network for Joint Entity and Relation Extraction

zhy 127 Jan 04, 2023
[CVPR 2022] TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing

TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing (CVPR 2022) This repository provides the official PyTorch impleme

Billy XU 128 Jan 03, 2023
Underwater industrial application yolov5m6

This project wins the intelligent algorithm contest finalist award and stands out from over 2000teams in China Underwater Robot Professional Contest, entering the final of China Underwater Robot Prof

8 Nov 09, 2022
Implementation of the Transformer variant proposed in "Transformer Quality in Linear Time"

FLASH - Pytorch Implementation of the Transformer variant proposed in the paper Transformer Quality in Linear Time Install $ pip install FLASH-pytorch

Phil Wang 209 Dec 28, 2022
QHack—the quantum machine learning hackathon

Official repo for QHack—the quantum machine learning hackathon

Xanadu 72 Dec 21, 2022
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

News December 27: v1.1.0 New loss functions: CentroidTripletLoss and VICRegLoss Mean reciprocal rank + per-class accuracies See the release notes Than

Kevin Musgrave 5k Jan 05, 2023