The existence of significant uncertainties in the models and systems required for trajectory prediction represent a major challenge for Trajectory-Based Operations concept. Weather can be considered as one of the most relevant sources of uncertainty. Understanding and managing the impact of these uncertainties is necessary in order to increase the predictability of the ATM system. We present preliminary results on robust trajectory planning in which weather is assumed to be the unique source of uncertainty. State-of-the-art forecasts from Ensemble Prediction Systems are used as input data for the wind field and to calculate convective risk. The term convective area is defined here as an area within which individual convective storms may develop, i.e., a necessary (though not sufficient) condition. An ad-hoc robust optimal control methodology is presented. A set of Pareto-optimal trajectories is obtained for different preferences between predictability, convective risk and average efficiency. 2020 research and innovation programme. Consortium members are UNI-VERSITY OF SEVILLE (Coordinator),

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There is a considerable discrepancy between the temporal and spatial resolution required by climate impact researchers, policy makers, and adaptation planners on the one hand and climate data providers on the other hand. While the spatial and temporal aggregation of climate data is necessary to increase the reliability and robustness of climate information, this often counteracts or even prohibits their use in adaptation planning. The problem is twofold (i.e., space and time) and needs to be approached accordingly. Climate impact research and adaptation planning are the domain of impact experts, politicians, and planners, rather than climate experts. Thus, besides the spatial and temporal resolution, information also needs to be provided on platforms and in data formats that are easily accessible, easy to handle, and easy to understand. We discuss possible steps toward bridging the gap using an example from the federal state Hesse (Germany) as illustration. We aggregate the climate data at a level of “natural units” and provide them as monthly data. We discuss the pros and cons of this kind of processed data for impact research and decision making. The spatial aggregation to “natural units” delivers suitable spatial aggregation, while maintaining physical geographic structures and their climatic characteristics. Within these “natural units,” single grid cell values are usable for climate impact analyses or decision making. The temporal resolution is monthly values, i.e., deviations of single month values for the scenario period from climatological monthly values in the (simulated) reference period. This resolution allows analyzing compound events or consecutive events on a monthly scale within a climatological (30-year) period.