Source code for pyart.retrieve.qvp

"""
Retrieval of QVPs from a radar object

"""

import numpy as np

from ..core.transforms import antenna_to_cartesian


[docs]def quasi_vertical_profile(radar, desired_angle=None, fields=None, gatefilter=None): """ Quasi Vertical Profile. Creates a QVP object containing fields from a radar object that can be used to plot and produce the quasi vertical profile Parameters ---------- radar : Radar Radar object used. field : string Radar field to use for QVP calculation. desired_angle : float Radar tilt angle to use for indexing radar field data. None will result in wanted_angle = 20.0 Other Parameters ---------------- gatefilter : GateFilter A GateFilter indicating radar gates that should be excluded from the import qvp calculation Returns ------- qvp : Dictonary A quasai vertical profile object containing fields from a radar object References ---------- Troemel, S., M. Kumjian, A. Ryzhkov, and C. Simmer, 2013: Backscatter differential phase - estimation and variability. J Appl. Meteor. Clim.. 52, 2529 - 2548. Troemel, S., A. Ryzhkov, P. Zhang, and C. Simmer, 2014: Investigations of backscatter differential phase in the melting layer. J. Appl. Meteorol. Clim. 54, 2344 - 2359. Ryzhkov, A., P. Zhang, H. Reeves, M. Kumjian, T. Tschallener, S. Tromel, C. Simmer, 2015: Quasi-vertical profiles - a new way to look at polarimetric radar data. Submitted to J. Atmos. Oceanic Technol. """ # Creating an empty dictonary qvp = {} # Setting the desired radar angle and getting index value for desired radar angle if desired_angle is None: desired_angle = 20.0 index = abs(radar.fixed_angle['data'] - desired_angle).argmin() radar_slice = radar.get_slice(index) # Printing radar tilt angles and radar elevation print(radar.fixed_angle['data']) print(radar.elevation['data'][-1]) # Setting field parameters # If fields is None then all radar fields pulled else defined field is used if fields is None: fields = radar.fields for field in fields: # Filtering data based on defined gatefilter # If none is defined goes to else statement if gatefilter is not None: get_fields = radar.get_field(index, field) mask_fields = np.ma.masked_where(gatefilter.gate_excluded[radar_slice], get_fields) radar_fields = np.ma.mean(mask_fields, axis=0) else: radar_fields = radar.get_field(index, field).mean(axis=0) qvp.update({field:radar_fields}) else: # Filtereing data based on defined gatefilter # If none is defined goes to else statement if gatefilter is not None: get_field = radar.get_field(index, fields) mask_field = np.ma.masked_where(gatefilter.gate_excluded[radar_slice], get_field) radar_field = np.ma.mean(mask_field, axis=0) else: radar_field = radar.get_field(index, fields).mean(axis=0) qvp.update({fields:radar_field}) # Adding range, time, and height fields qvp.update({'range': radar.range['data'], 'time': radar.time}) _, _, z = antenna_to_cartesian(qvp['range']/1000.0, 0.0, radar.fixed_angle['data'][index]) qvp.update({'height': z}) return qvp