This repository contains Python code to Download the official Australia Bureau of Meteorology RMM Index data, convert it from text to NetCDF, and plot phase diagrams for a specified time period.
For more information on the RMM index, consult the following journal article:
Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 1917–1932, doi:10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.
See also the following websites:
- http://www.bom.gov.au/climate/mjo/
- https://www.cpc.ncep.noaa.gov/products/precip/CWlink/MJO/mjo.shtml
The Python dependencies are: Numpy, netCDF4, matplotlib, and colorcet. It should not be very picky about versions.
The file requirements.txt can be used to build a PIP virtual environment like this:
python3 -m venv ./venv
source ./venv/bin/activate
pip3 install --upgrade pip
pip3 install -r requirements.txt
There are three Python scripts with functions that can be adopted in other scripts.
This script downloads the latest data from the BOM. It also runs the script rmm2nc.py to convert the data to NetCDF format.
It will save existing data in the directory to backup files.
This Python script converts the text file rmm.74toRealtime.txt to the NetCDF file rmm.nc.
This Python function and script plots a RMM Index phase diagram for the date range specified.
Specify a beginning and ending date in YYYYMMDD format, for example:
python plot_rmm_phase_diagram 20230419 20230530
The plot naming convention is rmm_20230419_to_20230530.png.
If no date range is specified, it will print out: Usage: plot_rmm_phase_diagram.py YYYYMMDD_begin YYYYMMDD_end.
Here are three plots of the RMM Index for January - May 2023. This period of time is significant because a strong El Nino was spinning up, and MJO events are often connected to warming sea surface temperatures during the onset of El Nino:
Jauregui, Y. R., and S. S. Chen, 2023a: MJO-induced Warm Pool Eastward Extension Prior to the Onset of El Niño: Observations from 1998-2019, J. Clim, accepted.
The plots were generated using these commands:
do.update_rmm.sh
python plot_rmm_phase_diagram.py 20230118 20230223
python plot_rmm_phase_diagram.py 20230226 20230405
python plot_rmm_phase_diagram.py 20230419 20230530
