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
Collective Multi-type Entity Alignment Between Knowledge Graphs (WWW'20)

CG-MuAlign A reference implementation for "Collective Multi-type Entity Alignment Between Knowledge Graphs", published in WWW 2020. If you find our pa

Bran Zhu 28 Dec 11, 2022
Code for MSc Quantitative Finance Dissertation

MSc Dissertation Code ReadMe Sector Volatility Prediction Performance Using GARCH Models and Artificial Neural Networks Curtis Nybo MSc Quantitative F

2 Dec 01, 2022
DeRF: Decomposed Radiance Fields

DeRF: Decomposed Radiance Fields Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi Links Paper Project Page Abstract

UBC Computer Vision Group 24 Dec 02, 2022
Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL)

Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL) This repository contains all source code used to generate the results in the article "

Charlotte Loh 3 Jul 23, 2022
Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

🍐 quince Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding 🍐 Installation $ git clone

Andrew Jesson 19 Jun 23, 2022
Byzantine-robust decentralized learning via self-centered clipping

Byzantine-robust decentralized learning via self-centered clipping In this paper, we study the challenging task of Byzantine-robust decentralized trai

EPFL Machine Learning and Optimization Laboratory 4 Aug 27, 2022
This repository contains the code and models for the following paper.

DC-ShadowNet Introduction This is an implementation of the following paper DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised

AuAgCu 65 Dec 27, 2022
Simulation environments for the CrazyFlie quadrotor: Used for Reinforcement Learning and Sim-to-Real Transfer

Phoenix-Drone-Simulation An OpenAI Gym environment based on PyBullet for learning to control the CrazyFlie quadrotor: Can be used for Reinforcement Le

Sven Gronauer 8 Dec 07, 2022
MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

Octave Convolution MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution Imag

Meta Research 549 Dec 28, 2022
AugLiChem - The augmentation library for chemical systems.

AugLiChem Welcome to AugLiChem! The augmentation library for chemical systems. This package supports augmentation for both crystaline and molecular sy

BaratiLab 17 Jan 08, 2023
A tool to analyze leveraged liquidity mining and find optimal option combination for hedging.

LP-Option-Hedging Description A Python program to analyze leveraged liquidity farming/mining and find the optimal option combination for hedging imper

Aureliano 18 Dec 19, 2022
XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks ACL 2020 Microsoft Research [Paper] [Video] Releasing [XtremeDistilTransf

Microsoft 125 Jan 04, 2023
Making a music video with Wav2CLIP and VQGAN-CLIP

music2video Overview A repo for making a music video with Wav2CLIP and VQGAN-CLIP. The base code was derived from VQGAN-CLIP The CLIP embedding for au

Joel Jang | 장요엘 163 Dec 26, 2022
potpourri3d - An invigorating blend of 3D geometry tools in Python.

A Python library of various algorithms and utilities for 3D triangle meshes and point clouds. Managed by Nicholas Sharp, with new tools added lazily as needed. Currently, mainly bindings to C++ tools

Nicholas Sharp 295 Jan 05, 2023
Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

Transfer Learning for Text Classification with Tensorflow Tensorflow implementation of Semi-supervised Sequence Learning(https://arxiv.org/abs/1511.01

DONGJUN LEE 82 Oct 22, 2022
Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems"

Code Artifacts Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driv

Andrea Stocco 2 Aug 24, 2022
Code for 2021 NeurIPS --- Towards Multi-Grained Explainability for Graph Neural Networks

ReFine: Multi-Grained Explainability for GNNs This is the official code for Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 20

Shirley (Ying-Xin) Wu 47 Dec 16, 2022
This is the code for our paper "Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text"

Iconary This is the code for our paper "Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text". It includes the

AI2 6 May 24, 2022
Walk with fastai

Shield: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Walk with fastai What is this p

Walk with fastai 124 Dec 10, 2022
QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing

QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing Environment Tested on Ubuntu 14.04 64bit and 16.04 64bit Installation # disabl

gts3.org (<a href=[email protected])"> 581 Dec 30, 2022