[python #TFF-740074]: NexRad data
- Subject: [python #TFF-740074]: NexRad data
- Date: Fri, 17 May 2019 15:02:29 -0600
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:
If you want to continue to use the raw NEXRAD Level 2 data, you may want to
look at PyART:
This is a toolkit from ARM that makes it easier to work with weather radar data
> 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:
> 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 ID: TFF-740074
Department: Support Python
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