2017/10/23: Version 0.1 released: Original scripts from Wenshan
2018/01/22: Version 0.2 released: Conversion of GCNet, PROMICE and AAWS networks complete
JAWS is a scientific software workflow to ingest Level 2 (L2) data in the multiple formats now distributed, harmonize it into a common format, and deliver value-added Level 3 (L3) output suitable for distribution by the network operator, analysis by the researcher, and curation by the data center. NASA has funded JAWS (project summary) from 20171001–20190930.
Automated Weather Station (AWS) and AWS-like networks are the primary source of surface-level meteorological data in remote polar regions. These networks have developed organically and independently, and deliver data to researchers in idiosyncratic ASCII formats that hinder automated processing and intercomparison among networks. Moreover, station tilt causes significant biases in polar AWS measurements of radiation and wind direction. Researchers, network operators, and data centers would benefit from AWS-like data in a common format, amenable to automated analysis, and adjusted for known biases.
The immediate target recipient elements are polar AWS network managers, users, and data distributors. L2 borehole data suffers from similar interoperability issues, as does non-polar AWS data. Hence our L3 format will be extensible to global AWS and permafrost networks. JAWS will increase in situ data accessibility and utility, and enable new derived products.
Convert L2 data (usually ASCII tables) into a netCDF-based L3 format compliant with metadata conventions (Climate-Forecast and ACDD) that promote automated discovery and analysis.
Include value-added L3 features like the Retrospective, Iterative, Geometry-Based (RIGB) tilt angle and direction corrections, solar zenith angle, standardized quality flags, GPS-derived ice velocity, and turbulent fluxes.
Provide a scriptable API to extend the initial L2-to-L3 conversion to newer AWS-like networks and instruments.
First-time users can execute this to copy JAWS to their local machines:
$ git clone https://github.com/jaws/jaws.gitThose with GitHub accounts who wish to contribute to JAWS should clone via SSH instead:
$ git clone [email protected]:/jaws/jaws.gitUsers should periodically update their local repositories to the current version:
$ git pullJAWS works with any Python distribution, and especially well with Anaconda and Miniconda
- matplotlib: Install with
conda install matplotlibor as instructed here - netCDF4: Install with
conda install netcdf4or as instructed here - pandas: Install with
conda install pandasor as instructed here - xarray: Install with
conda install xarrayor as instructed here
The current version can translate L2 ASCII data from the GCNet, PROMICE and AAWS networks to netCDF format. The user provides the input file path. By default, the output file will be stored within the current directory with the network name (e.g. promice.nc). The user can optionally give their own output path/name. Execute this from the JAWS source directory:
$ python jaws.py ../sample_data/PROMICE_EGP_20160501.txtor from anywhere, by specifying longer paths, and with options:
$ python ~/jaws/source/jaws.py -4 -o ~/PROMICE_EGP_20160501.nc ~/jaws/sample_data/PROMICE_EGP_20160501.txt
$ python ~/jaws/source/jaws.py -4 -o ~/GCNET_Summit_20140601.nc ~/jaws/sample_data/GCNET_Summit_20140601.txt
$ python ~/jaws/source/jaws.py -4 -o ~/AAWS_AGO-4_20161130.nc ~/jaws/sample_data/AAWS_AGO-4_20161130.txtwhere the argument to the optional -o is the user-defined output filename
Currently, JAWS can be used to analyse the data for GCNet in multiple ways such as:
i. Plotting monthly diurnal cycle to see hourly changes for any variable throughout the month.
The user provides input file path, variable name (on which analysis needs to be done) and plot type (i.e. diurnal, monthly, annual or seasonal). Year and month are optional arguments. If the input file contains data for only single year, then the user doesn't need to provide the '-y' argument. Similar is the case for '-m' argument.
$ python analysis.py file.nc temperature_tc_1 diurnal -y 2002 -m 5
ii. Avg, max and min values for each day of a month for any variable
$ python analysis.py file.nc temperature_cs500_1 monthly -y 2013 -m 2
iii. Annual cycle with daily mean, max and min
Since it is annual plot, user shouldn't provide the '-m' argument
$ python analysis.py file.nc temperature_tc_1 annual -y 2016
iv. Climatological seasonal cycle showing variation for each month through multiple years
Since it is seasonal plot, user shouldn't provide both '-y', '-m' argument.
$ python analysis.py file.nc temperature_tc_1 seasonal
This software is being developed by the University of California Irvine under NASA Advanced Information Systems Technology (AIST) Proposal and Project 80NSSC17K0540.
See the Wiki for full documentation, examples, operational details and other information.
For bugs, questions and discussions please use the GitHub Issues.
Copyright (C) 2017--2018 Regents of the University of California. You may redistribute and/or modify JAWS under the terms of the Apache License, Version 2.0.