The Operational Program on the Exchange of Weather Radar Information (OPERA) has co-ordinated radar co-operation among national weather services in Europe for more than 20 years. It has introduced its own, manufacturer-independent data model, runs its own data center, and produces Pan-European radar composites. The applications using this data vary from data assimilation to flood warnings and the monitoring of animal migration. It has used several approaches to provide a homogeneous combination of disparate raw data and to indicate the reliability of its products. In particular, if a pixel shows no precipitation, it is important to know if that pixel is dry or if the measurement was missing.

Weather surveillance radars are increasingly used for monitoring the movements and abundances of animals in the airspace. However, analysis of weather radar data remains a specialised task that can be technically challenging. Major hurdles are the difficulty of accessing and visualising radar data on a software platform familiar to ecologists and biologists, processing the low‐level data into products that are biologically meaningful, and summarizing these results in standardized measures. To overcome these hurdles, we developed the open source R package bioRad, which provides a toolbox for accessing, visualizing and analyzing weather radar data for biological studies. It provides functionality to access low‐level radar data, process these data into meaningful biological information on animal speeds and directions at different altitudes in the atmosphere, visualize these biological extractions, and calculate further summary statistics. The package aims to standardize methods for extracting and reporting biological signals from weather radars. Here we describe a roadmap for analyzing weather radar data using bioRad. We also define weather radar equivalents for familiar measures used in the field of migration ecology, such as migration traffic rates, and recommend several good practices for reporting these measures. The bioRad package integrates with low‐level data from both the European radar network (OPERA) and the radar network of the United States (NEXRAD). bioRad aims to make weather radar studies in ecology easier and more reproducible, allowing for better inter‐comparability of studies.

Nocturnal avian migration flyways remain an elusive concept, as we have largely lacked methods to map their full extent. We used the network of European weather radars to investigate nocturnal bird movements at the scale of the European flyway. We mapped the main migration directions and showed the intensity of movement across part of Europe by extracting biological information from 70 weather radar stations from northern Scandinavia to Portugal, during the autumn migration season of 2016. On average, over the 20 nights and all sites, 389 birds passed per 1 km transect per hour. The night with highest migration intensity showed an average of 1621 birds km ^–1 h ^–1 passing the radar stations, but there was considerable geographical and temporal variation in migration intensity. The highest intensity of migration was seen in central France. The overall migration directions showed strong southwest components. Migration dynamics were strongly related to synoptic wind conditions. A wind‐related mass migration event occurred immediately after a change in wind conditions, but quickly diminished even when supporting winds continued to prevail. This first continental‐scale study using the European network of weather radars demonstrates the wealth of information available and its potential for investigating large‐scale bird movements, with consequences for ecosystem function, nutrient transfer, human and livestock health, and civil and military aviation.

A novel dealiasing algorithm for Doppler radar velocity data has been developed at the Swedish Meteorological and Hydrological Institute (SMHI). Unlike most other methods, it does not need independent wind information from other instruments (e.g., nearby radiosonde or wind profiler) or numerical weather prediction (NWP) models. The innovation of the new technique is that it maps the measurements onto the surface of a torus. Dealiased volume radar data can be used in variational assimilation schemes for NWP models through the generation of so-called superobservations. Their use is expected to improve with the introduction of the proposed dealiasing method. Abstract A novel dealiasing algorithm for Doppler radar velocity data has been developed at the Swedish Meteorological and Hydrological Institute (SMHI). Unlike most other methods, it does not need independent wind information from other instruments (e.g., nearby radiosonde or wind profiler) or numerical weather prediction (NWP) models. The innovation of the new technique is that it maps the measurements onto the surface of a torus. Dealiased volume radar data can be used in variational assimilation schemes for NWP models through the generation of so-called superobservations. Their use is expected to improve with the introduction of the proposed dealiasing method.