pyart.retrieve.compute_evp#

pyart.retrieve.compute_evp(radar, field_names, lon, lat, ref_time=None, latlon_tol=0.0005, delta_rng=15000.0, delta_azi=10, hmax=10000.0, hres=250.0, avg_type='mean', nvalid_min=1, interp_kind='none', qvp=None)[source]#

Computes enhanced vertical profiles.

Parameters:

  • radar (Radar) – Radar object used.

  • field_names (list of str) – list of field names to add to the QVP

  • lat, lon (float) – latitude and longitude of the point of interest [deg]

  • ref_time (datetime object) – reference time for current radar volume

  • latlon_tol (float) – tolerance in latitude and longitude in deg.

  • delta_rng, delta_azi (float) – maximum range distance [m] and azimuth distance [degree] from the central point of the evp containing data to average.

  • hmax (float) – The maximum height to plot [m].

  • hres (float) – The height resolution [m].

  • avg_type (str) – The type of averaging to perform. Can be either “mean” or “median”

  • nvalid_min (int) – Minimum number of valid points to accept average.

  • interp_kind (str) – type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. ‘none’ will select from all data points within the regular grid height bin the closest to the center of the bin. ‘nearest’ will select the closest data point to the center of the height bin regardless if it is within the height bin or not. Data points can be masked values If another type of interpolation is selected masked values will be eliminated from the data points before the interpolation

  • qvp (QVP object or None) – If it is not None this is the QVP object where to store the data from the current time step. Otherwise a new QVP object will be created

Returns:

  • qvp (qvp object) – The computed enhanced vertical profile

  • Reference

  • ———

  • Kaltenboeck R., Ryzhkov A. 2016 (A freezing rain storm explored with a)

  • C-band polarimetric weather radar using the QVP methodology.

  • Meteorologische Zeitschrift vol. 26 pp 207-222