Xingang Fan

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METR 430 - Meteorological Computing

 

Python-based meteorological data processing and visualization

Anaconda python environment (https://www.continuum.io/downloads)

Resources:

Python – online courses: https://training.enthought.com/ (apply for free academic license)
Python – a tutorial: https://docs.python.org/2/
Numpy – n-D array for large datasets: http://www.numpy.org/
Matplotlib – all about plotting: http://matplotlib.org/
Basemap – geographical maps: http://matplotlib.org/basemap/
PyNIO/PyNGL – specialized atmospheric data visualization: http://www.pyngl.ucar.edu/

Example Meteorological Data:

Formats: ASCII text, CSV, GRIB, NetCDF, HDF, binary, etc.

GFS global 0.5 degree analysis (GRIB2) http://thredds.ucar.edu/thredds/catalog.html
NARR data (GRIB) https://nomads.ncdc.noaa.gov/data/narr/
Storm Events Data (CSV) https://www.ncdc.noaa.gov/stormevents/
Sounding Data IGRA (TEXT): https://www.ncdc.noaa.gov/data-access/weather-balloon/integrated-global-radiosonde-archive
Surface METAR (NetCDF): http://thredds.ucar.edu/thredds/catalog/nws/metar/ncdecoded/files/catalog.html
WRF model output (NetCDF):
Radar data (GRIB2): http://thredds.ucar.edu/thredds/idd/radars.html

 
Copyrightę2008-2015       Updated 2017-01-24