View Service (WMS) for Synoptic observations

View Service (WMS) for Automatic weather station (AWS) observations

Download Service (WFS) for Synoptic observations

RADQPE provides high resolution radar-based quantitative precipitation estimation in realtime for Belgium and its surroundings. This product is not publically available yet.

View Service (WMS) for Networks of automatic lidar-ceilometer systems The data are updated each 10 minutes. Only the archives of the last 30 days are available.

Download Service (WFS) for Networks of automatic lidar-ceilometer systems The data are updated each 10 minutes. Only the archives of the last 30 days are available.

Download Service (WFS) for Automatic weather station (AWS) observations

Bird density profiles are derived from weather radar volume data in real time, by the vol2bird algorithm as described in Dokter et al. (2011, 2019). The vol2bird algorithm exploits the radar reflectivity characteristics of different scatterers in the atmosphere, in order to distinguish biological from non-biological radar echoes. Once biological scatterers are isolated in the volume files, the reflectivity of these scatterers is converted in an estimate of the bird density per vertical layer of 200m, using a mean cross section of 11 cm2. The vbird profiles are provided for the following radars, with the radar owner in parentheses: Jabbeke (RMI), Wideumont (RMI), Helchteren (VMM), Zaventem (Skeyes), Herwijnen (KNMI), Den Helder (KNMI), Neuheilenbach (DWD), Essen (DWD), Abbeville (Météo-France) and Avesnois (Météo-France). References: - Dokter A.M., Liechti F., Stark H., Delobbe L., Tabary P., Holleman I., Bird migration flight altitudes studied by a network of operational weather radars, J. R. Soc. Interface, 8, 30–43, 2011, DOI 10.1098/rsif.2010.0116 - Dokter A.M., Desmet P., Spaaks J.H., van Hoey S., Veen L., Verlinden L., Nilsson C., Haase G., Leijnse H., Farnsworth A., Bouten W., Shamoun-Baranes J., bioRad: biological analysis and visualization of weather radar data, Ecography, 42, 852-860, 2019, DOI 10.1111/ecog.04028