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RAVRE: Easy processing of weather radar data to vertical profiles of biological scatterers. |
Introduction: The airspace is becoming increasingly crowded. High-rises, wind farms and airports all contribute to conflict with aerial organisms. Information about the movements of organisms in the air is required to identify stop-over sites, migratory routes, and patterns. This can inform mitigation of conflicts by, for example, wind-turbine curtailments or early warning systems for aviation. Weather radars, that continuously monitor the sky across continents, can be used to study movements of birds, bats, and insects. However, for continental scale analysis, large volumes of data are required to be processed and analyzed, which often rely on institute-specific tools and computational resources. This severely hampers collaborative efforts because of the initial investment of time and resources to gain access to existing computing infrastructure. Here we show a Radar Aeroecology Virtual Research Environment (RAVRE) which uses the Lifewatch ERIC infrastructure to facilitate collaboration and re-use of infrastructure and tools. By providing RAVRE, we aim to facilitate collaboration between ornithological institutes.
Readiness level: L1 - co-development
This virtual lab (VL) allows users to easily obtain biological scatters from radar data. The lab reduces the initial investment of acquiring access and expertise to computational resources and provide immediate access to tools built by domain experts. These tools are then run in the cloud leveraging the performance and flexibility of cloud computing. The VL is shipped with the data management plan used by the University of Amsterdam's Animal Movement Ecology group (UvA IBED-TCE AME) to provide an out of the box solution for managing large datasets. RAVRE is currently capable of accessing, processing, managing and visualizing data from the The Royal Netherlands Meteorological Institute's (RNMI) open Radar Data repository. The VL has multi-language support, and has well known libraries such as bioRad in R and xradar in Python installed. Furthermore, it uses vol2bird for processing biological echoes found in Polar Volume files to Vertical Profiles.
- Aerial animal movement
- Biological scatter
- Bird migration
- Radar ontology
- Weather radar
- Altitude profile
- Vol2bird algorithm
- Mapping to Vol2bird input format
- Easy KNMI data retrieval
License: Apache-2.0
Related virtual labs: None
Dokter AM, Liechti F, Stark H, Delobbe L, Tabary P, Holleman I, J. R. Soc.
Bird migration flight altitudes studied by a network of operational weather radars
Interface, 8, 30–43, 2011, DOI 10.1098/rsif.2010.0116
This virtual lab uses the vol2bird algorithm, which can be found on github.
- Select a date range to create a vertical profiles of biological scatters.
- Write your own pre-processor to map weather radar data from sources other than KNMI to a format readable by vol2bird.
Picking custom dates to study biological scatters using KNMI weather radar data is possible by only setting parameters.
Preprocessing other weather radar data to the vol2bird input format requires some experience in handling APIs and data in Python.
See the documentation page. Additionally, a user guide and tutorial is available in the virtual lab.
Berend-Christiaan Wijers
IBED - University of Amsterdam
Email: [email protected]
ToDo: description of the standards used for data exchange with application programming interfaces and databases.