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Hi, Regarding the "_HI", it's for high resolution--part of how radars collect data means that the spacing between data points can vary. The three dimensions are: scan, azimuth, and gate (or range). This reflects how radars collect data, at least usually operationally. What happens is the radar spins around, with the antenna pointing at a fixed angle above ground, called the elevation angle. While the radar is pointing in one particular angle, called the azimuth, the radar samples and collects data for multiple gates, with each gate representing a range from the radar. So technically, the native coordinates for the data are a spherical coordinate system. The notebook you linked to does a conversion from the spherical (or really simplified to polar in that case) to Cartesian (x, y) coordinates. If you want radar data that are already in x, y coordinates, you may want to look at MRMS, which is a gridded product combining all the US radars, though this won't have velocity data, it will have rotation tracks: http://thredds.ucar.edu/thredds/catalog/grib/NCEP/MRMS/BaseRef/catalog.html http://thredds.ucar.edu/thredds/catalog/grib/NCEP/MRMS/RotationTrack/catalog.html If you want to continue to use the raw NEXRAD Level 2 data, you may want to look at PyART: https://arm-doe.github.io/pyart/ This is a toolkit from ARM that makes it easier to work with weather radar data in Python. Ryan > Hi Ryan, > > I am a researcher at Wright State Research Institute near Dayton, OH. I am > interested in tornado formation and am looking at Level 2 NexRad data. I > read your Jupyter notebook at: > http://nbviewer.jupyter.org/gist/dopplershift/356f2e14832e9b676207 > and found it very informative (actually, it's awesome!). I started looking > at NexRad data and am baffled at two aspects of the data: > > 1. Every reading (RadialVelocity, Reflectivity etc...) has a > corresponding reading with a "_HI" extension (i.e. RadialVelocity_HI, > Reflectivity_HI). Could it be high resolution, high level in the > atmosphere or a Hail Index for each value? > 2. There are 3 dimensions in the downloaded data (e.g. [6, 360, 1192] > and [11, 360, 1192]). I assume the second and third dimensions are > longitude and latitude. I am puzzled by the first dimension. Could it be > different elevations or different moments in time? > > I havn't been able to find any documentation on these. Any knowledge you > can share is greatly appreciated. Thanks again for the work you did on the > Jupyter notebook and for making it available for others to learn. Ticket Details =================== Ticket ID: TFF-740074 Department: Support Python Priority: Low Status: Closed =================== NOTE: All email exchanges with Unidata User Support are recorded in the Unidata inquiry tracking system and then made publicly available through the web. If you do not want to have your interactions made available in this way, you must let us know in each email you send to us.