Radar software may fix weather forecast issues caused by wind farms

The movement of wind turbine propellers can mimic weather when viewed by the Doppler radar used by Environment Canada to predict storms. (Robert F. Bukaty/Associated Press)

Environment Canada is preparing to roll out new radar technology in order to combat wind farm clutter, which clouds weather forecasts, misleads meteorologists and can even block radar signals.

Jim Young, who works at the agency's national radar program, said new software will be incorporated into Canada's radar system this fall in an effort to address the "contamination" caused by wind turbines.

The Python ART Radar Toolkit

The Python ARM Radar Toolkit, Py-ART, is a Python module containing a collection of weather radar algorithms and utilities. Py-ART is used by the Atmospheric Radiation Measurement (ARM) Climate Research Facility for working with data from a number of its precipitation and cloud radars, but has been designed so that it can be used by others in the radar and atmospheric communities to examine, processes, and analyze data from many types of weather radars.

What can Py-ART do?

Py-ART has the ability to ingest (read) from a number of common weather radar formats including Sigmet/IRIS, MDV, CF/Radial, UF, and NEXRAD Level II archive files. Radar data can be written to NetCDF files which conform to the CF/Radial convension.

Py-ART also contains routines which can produce common radar plots including PPIs and RHIs.

Algorithms in the module are able to performs a number of corrections on the radar moment data in antenna coordinate including attenuation correction of the reflectivity, velocity dealiasing, and correction of the specific (Kdp) and differential (PhiDP) phases.

A sophisticated mapping routines is able to efficiently create uniform Cartesian grids of radar fields from one or more radars. Routines exist in Py-ART for plotting these grids as well as saving them to NetCDF files.

GitHub repository

Software Defined Radar Simulator

Github software project “The Software Defined Radar Simulator” is designed to provide in SystemC an accurate simulation of commonly-used SDR blocks. These models provide both run-time and compile-time adjustable parameters. Currently these blocks include:

1. Phase Accumulator
2. CORDIC down-converter
3. CIC Filter (includes optional bit-pruning)
4. FIR Filter
5. Stimulus class to assist in test bench development
6. Signal Generator classes to assist in testing.
7. Recorder class to collect test output data.

Dependencies:

1. SystemC version 2.3 software.
2. Latest version of GCC.
3. Latest version of Boost libraries ( www.boost.org ).
4. Waf build tool.
5. Yaml-cpp version 0.5.1 software.

A Multipurpose Radar Simulation Package: QuickBeam

Haynes, J. M., Marchand, R. T., Luo, Z., Bodas-Salcedo, A. and Stephens, G. L., 2007: A Multipurpose Radar Simulation Package: QuickBeam. Bulletin of the American Meteorological Society, 88(11), pp. 1723–1727.

Abstract. The launch of the CloudSat cloud radar has provided some of the first near-global views of the three-dimensional structure of clouds from space. To evaluate clouds in numerical models and compare them to the observations made by CloudSat, it is useful to have a tool that converts modeled clouds to radar returns that might be viewed by a radar system on a satellite passing over the model domain. QuickBeam is a user-friendly radar simulation package that performs this function and is freely available to the meteorological community. The workings of the simulator are briefly described and several applications of the simulator to numerical models are demonstrated.

Abstract | PDF (2.55M)

The source code is written in Fortran 90 and is thus highly portable to a wide variety of platforms. The package and a more technically oriented guide to the simulator can be downloaded from http://cloudsat.atmos.colostate.edu/radarsim