Correction of dual-PRF velocity dealiasing errors

This repository includes a function for correcting the dealiasing errors that arise in weather radar Doppler velocity data that have been collected using the dual-PRF technique.

The function integrates four image processing methods developed in the literature, all of which require spatial continuity by comparison of the gate velocity with a reference estimated from the velocities in the surrounding gates.

The function allows the user to tailor the dual-PRF error correction by specifying the neighbour kernel and selecting the statistic used for the estimation of the reference velocity. The correction procedures proposed in the literature may be reproduced through the particular selection of these statistics.

TRY! Usage example (Takes a good while to load the environment, excuses for the inconvenience)

Detection/correction methods

• 'mean' : local mean velocity, Joe and May, 2003
• 'median' : local median velocity, Holleman and Beekhuis, 2003
• 'cmean_sc' : local circular mean velocity, (with PRF-based scaling) Altube et al., 2017
• 'cmean' : local circular mean velocity, Hegstebeck et al., 2018

Common framework

The function is developed in Python language and builds upon the Py-ART module^[1], as it takes a Py-ART radar object as input. The radar object provides a common framework for the application of the function to the wide range of data formats used by the growing community of Py-ART users.

^[1]_{Helmus, J.J. & Collis, S.M., (2016). The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. Journal of Open Research Software. 4(1), p.e25. DOI: http://doi.org/10.5334/jors.119}

Positional function parameters

correct_dualprf (radar, method_det, ...)

• radar: Py-ART radar instance

  Contains RAW file data and metadata. Must contain a dual-PRF radial velocity field.

• method_det: str

  Method used for detection of outliers.

Optional parameters/settings

Parameter	Class	Default value	Description	Stage
vel_field	str	'velocity'	Input velocity field name (dual-PRF)	NA
kernel_det	array	None (7x7 array)	Neighbour kernel for local statistics, 1/0 values	detection
min_valid_det	int	1	Minimum number of valid neighbours	detection
max_dev	float	1	Maximum deviation fraction from 2xNyquist velocity	detection
two_step	bool	1.0	Separate detection and correction stages? (detected outliers are removed before correction)	NA
method_cor	str	same as method_det	Method used for correction of outliers. Only applied if two_step=True	correction
kernel_cor	array	same as kernel_det	Neighbour kernel for local statistics, 1/0 values	correction
min_valid_cor	int	1	Minimum number of valid neighbours	correction
new_field	str	'velocity_cor'	Corrected velocity field name in the output radar object	NA
new_field_lname	str	'Outlier corrected dual-PRF velocity''	Corrected velocity field LONG name in the output radar object	NA

Further work

• Test on different input data formats

• Implement a subroutine that identifies the PRF at which each radial has been scanned (see e.g. Holleman and Beekhuis, 2003)

• Speed median filtering with masked arrays?

ALL SUGGESTIONS ARE WELCOME!!!