Four 1-day training courses will be offered ahead of the PrePEP-conference.
- Where: Dept. of Meteorology, University of Bonn, Auf dem Hügel 20, 53121 Bonn 🖈
- When: 16 March 2025, 9 am – 5 pm
- Format: in-person only
- Costs: 30 € (coffee breaks and lunch included)
- Registration: Please register until 16 February 2025 for the short courses here
Please click on the course titles for more specific information.
Weather radar system design including antenna front-end aspects 🗎
By means of a hands-on exercise, the workshop is designed to let participants experiment with several crucial aspects to be considered when designing a pulsed radar with both mechanically or electronically steered array antenna. On the basis of genuine specifications for commercial weather radars, topics like system architecture, power budget, background noise, waveform design, antenna arrays and feeding networks will be addressed. Dependencies in between different design areas will be highlighted to learn which parameters play a major role in setting up the overall performance. Tutoring will be provided to reach the workshop goals and achieve a system level overview of the many aspects involved in a radar design.
Microphysical fingerprinting in multi-peaked radar Doppler spectra 🗎
Cloud and precipitation processes are still a main source for uncertainties in weather prediction and climate change projections. Doppler spectra of polarimetric radars provide a wealth of information on microphysical processes, but multi-peaked situations are still challenging to analyze, especially on a large scale. This short course gives insights in fingerprints of microphysical processes inherent in polarimetric radar observations and how this information can be extracted with the peako and peakTree toolkits, including a hands-on part. Using example datasets from recent campaigns, the attendees will learn how to find the optimum parameters for the peak finding algorithm, how these parameters are then used to transfer the Doppler spectrum into a binary tree of radar moments, and how this data structure can be used to gain insights into microphysical processes.
Open Radar – Open Source software tools for radar data processing 🗎
The course will discuss the principles of open science and provide an overview of the most mature and exciting software packages available for radar data processing (eg. wradlib, PyART, tbc) and how they connect with the scientific software stack. The course will be built with Jupyter Notebooks as hands-on approach for interactive user experience. The main course programming language is Python. Amongst others, special emphasis will be paid to the “xradar” package, implementing the newly adopted FM301/CfRadial2 WMO standard. These tools will be used to showcase how to harness the power of xarray and dask for efficient, distributed radar data processing. Finally, participants will be enabled to implement their own algorithms. Therefore, guidance will be given to create workflows for different aspects of weather radar data processing using open datasets of interest for participants (e.g. the Ahrtal flooding in 2021). Participants are invited to make their suggestions.
Processing of opportunistic rainfall sensors data from CML, PWS and SML networks 🗎
Opportunistic rainfall sensors offer an attractive solution to increase the spatial and temporal coverage of rainfall observations. As the name implies, opportunistic sensors were originally not meant to provide high-quality rainfall information. Because of that, data processing and quality control is crucial when using these sensors. In this workshop we will introduce the basics of rainfall estimations with data from commercial microwave links (CMLs), which form large parts of the backbone of the cellular network, and from satellite microwave links (SMLs), which are an emerging solution to provide cheap internet connectivity via two-way communication to geostationary satellites. In addition, we will introduce quality control and bias correction methods for rainfall data from personal weather stations (PWS), low-cost meteorological sensors that people install in their own garden. We will briefly introduce each sensor and discuss their pros and cons. Based on open datasets, e.g. the OpenMRG dataset, and open-source tools, e.g. pycomlink, we will then dive into the processing of the datasets. During these hands-on sessions you will learn about different processing methods, the effect of different parameters and the interplay of processing methods. We will conclude with an example application, showing how to use CML and PWS data for radar adjustment.
