This work was carried out in the context of the Virtual Laboratory for e-Science project. This project is supported by a BSIK grant from the Dutch Ministry of Education, Culture and Science (OC&W) and is part of the ICT innovation program of the Ministry of Economic Affairs (EZ). J.Shamoun-Baranes et al. CBPG, IBED, University of Amsterdam, Nieuwe Achtergracht 166, 1018WV Amsterdam, The Netherlands URL: Bird avoidance modeling for flight safety J. Shamoun-Baranes, W. Bouten, L. Buurma, A. Dekker, K. Grimmerink, H. Sierdsema, F. Sluiter, J. van Belle, H. van Gasteren and E. van Loon Computational Biogeography and Physical Geography, IBED, University of Amsterdam, Nieuwe Achtergracht 166, 1018WV Amsterdam, The Netherlands The Netherlands BAM ( is a decision support tool used by the RNLAF. Migration predictions are used to provide real- time and advance warnings to pilots and squadron leaders during the migration season. High intensity of migration in some cases results in cancellation or rescheduling of flight training. In the context of vl-e we are currently working towards a generic Problem Solving Environment for automated calibration of models that require intensive computation. This PSE facilitates data fusion, the assimilation of on-line observations into models to improve forecasts some days ahead. The Bird Avoidance System will serve as a PSE test case. Conclusions During autumn and spring millions of birds migrate across the Netherlands over a broad front from Scandinavia towards their wintering quarters in Africa and from NE Asia westwards towards Great Britain and back. The vl-e infrastructure is being used to explore and model the relations between radar measurements of migration activity and meteorological conditions, which are then built into predictive models. Migration predictions Collisions between birds and aircraft result in aircraft damage or destruction, the loss of flight hours, injury and even the loss of lives in civil and military aviation worldwide. However, there are ways to improve flight safety and reduce the risk of serious accidents, such as aeronautical engineering solutions, habitat management, population control, proper flight scheduling and provide advance warnings to aircrew. Together, the University of Amsterdam, the Royal Netherlands Air Force (RNLAF) and Sovon, the Dutch Centre for field ornithology are developing an operational Bird Avoidance Model (BAM) for Northwest Europe as a decision support tool to improve military flight safety. By combining radar observations, bird counts and modeling results from distributed systems, the spatial (3D) and temporal densities of birds are predicted under changing environmental conditions. Introduction Three military long-range radars (20-40 kW, 3 GHz), located in the Netherlands in Wier-Friesland, Milligen-Veluwe and in Belgium in Glons-Ardennes, are used to detect birds within a 150 km radius and a 30 m range discrimination. The radars have parabolic antennas with 12 stacked beams and 1.2o opening angles. Ten beam rotations (100s) are connected in sequential frames, generating about 10 Mb data per minute per radar system, or 1.6 TB per year in total. The ROBIN IV tracking system, developed by D&V-TNO, is used for on-line filtering of ground and rain clutter and to calculate bird tracks, speed, direction and density. These data are compressed, encrypted and transmitted over a DSL data line. Radar measurements Dynamic bird behaviour MODELS Bird distribution Ensembles Calibration and Data assimilation Predictions and on-line warnings