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Airport, Airside and Runway Throughput

The goal of the project is to enhance meteorological information, both measured and forecast, for use in novel air traffic management tools supporting enhanced time based and weather dependent separation modes for airport arrival and departure runway operations. This can in turn reduce wake turbulence separation, i.e., the planes arrival and departure schedule may be stacked more closely.

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The problem at hand will require advanced wind information products derived from meteorological infrastructure elements, such as real time aircraft measured wind data in the vicinity of the airport, long range 3D scanning Doppler wind lidar, small-scale numerical weather prediction models as well as standard automated weather observing system.

Due to limitations in the lidar based forecast with regard to temporal range it is paramount to have complementary numerical weather prediction long term forecast for strategical planning purposes. It is also possible to use the model data to substitute non existing observational data which might happen in certain weather conditions when the maximum operational range of the lidar is reduced.

SINTEF will contribute with expertise in computational fluid dynamics and provide a multiscale numerical weather prediction system to complement the observational data. In particular, the Weather Research and Forecasting (WRF) model will be used in nested domains to achieve high resolution around the airport. Coupling to programs like SIMRA and OpenFoam can take higher resolution from topography and buildings into account to achieve even higher accuracy. Although the numerical model mainly complements the temporal limitations of the lidar based forecast it can also be used in combination to achieve more accurate results. SINTEF will investigate the possibility for hybrid methods where the classical physics-based methods is used in combination with data driven method (with observation data from, i.e., the lidar) through a machine learning framework. This we refer to as Hybrid Analysis and Modelling (HAM). Combining this with Reduced Order Models (ROM) for the physics-based methods then yields an accurate, robust and efficient framework for computational fluid dynamics.

Key Factors

Project duration

2019 - 2023



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This project has received funding from the SESAR Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under grant agreement No 874477. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union.