Pyrad: A Real-Time Weather Radar Data Processing Framework Based on Py-ART

Abstract

Pyrad is a real-time data processing framework developed by MeteoSwiss. The framework is aimed at reading, processing and visualizing polar data from individual weather radars as well as composite Cartesian products both off-line and in real time. The processing flow is controlled by three simple configuration files. This allows the construction of reproducible data processing chains. In the off-line mode, data from multiple radars can be ingested. It is written in the Python programming language. Most of the signal processing and part of the data visualization is performed by a MeteoSwiss-developed version of the Py-ART radar toolkit, which contains enhanced features. Thanks to the broad types of input files accepted and its flexibility it can be easily adapted and used by any member of the weather radar community. The source code is available on GitHub. Compiled versions are also available on PyPI and conda-forge. They are distributed under a BSD license.

Lagrangian matches between observations from aircraft, lidar and radar in a warm conveyor belt crossing orography

“…Browning, 1971) and then brought up again by Neiman et al (1993). Recently, Flaounas et al (2016) and Oertel et al (2019) confirmed the presence of convection embedded in WCBs with radar observations. Oertel et al (2020) and Oertel et al (2021) showed that WCB-embedded convection can result in locally enhanced precipitation and that diabatic heating by convection can influence the upper-level jet by the formation of PV dipole bands.…”

Insights into wind turbine reflectivity and RCS and their variability using X-band weather radar observations

“…The core data processing for both campaigns was done by the MeteoSwiss in-house-developed open-source real-time weather radar data processing framework Pyrad (Figueras i Ventura et al, 2020a), which is based on the Py-ART radar toolkit (Helmus and Collis, 2016).…”

“…In the following part of the paper, all the horizontal reflectivity Z H measurements and retrieved RCS data obtained from PPI and RHI scans in 2019 are statistically analyzed in order to characterize the returns from the three wind turbines by using the radar data processing framework Pyrad (Figueras i Ventura et al, 2020a). Global statistics considering the median and maximum Z H and RCS are presented.…”

Calibration of X-Band Radar for Extreme Events in a Spatially Complex Precipitation Region in North Peru: Machine Learning vs. Empirical Approach

“…Therefore, in this study, we focused on the optimization of these three hyperparameters by using a grid search approach. Thus, a vector of possible values for each of these hyperparameters was defined and set for the grid search implementation (n_estimators = (100, 150, 200, 300, 400, 500, 600), max_features = (total number of features [13], square root of total number of features [4]), max_depth = (3,5,8,10,13,15,20)). Here, exhaustive combinations of all hyperparameters were used to build independent models (i.e., one set of hyperparameters defines one model) and to perform their evaluation (OOB score) by using a K-fold crossvalidation.…”

“…Kumar et al [12] have pointed out the geometric issues of beam broadening and weakening of the signal with distance. More recently, many of these correction procedures have been integrated into comprehensive software products, such as the processing software supplied by radar manufacturers [13], national weather services [14], or community efforts such as PyRad [15] or ωradlib [16]. Nevertheless, the situation in Peru (and Ecuador) still requires customized solutions: first, because the radar systems used are quite basic instruments (single elevation, no polarization, no Doppler information) and secondly due to the specific circumstances of the region.…”