Intelligent modelling the impact of unpredictable adverse weather on ATM performance

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ComplexWorld PhD

Manuela Sauer, Leibniz Universität Hannover
Supervisor: Thomas Hauf


Adverse weather represents one of the major challenges for future aviation. Currently, it is responsible for more than 50% of all delays, no matter how, where and when that is accounted for, and is a contributing factor in at least 10% of all accidents and incidents. Adverse weather affects single flights (defined as microscale), air traffic in a certain area (mesoscale), and the global air traffic system on the whole (macroscale). Disturbances originating on the microscale may propagate through the system and disturb aviation not only on the mesoscale but also on the macroscale. Due to an inherent stochastic or unpredictable component in a weather forecast, due to the still limited availability of real-time weather information in the cockpit as well as at the controller’s work place, weather is one of the main sources for uncertainty in ATM. In addition we have insufficient resolution of observational tools or measurement errors, when e.g. retrieving satellite information. The human factor when considering the pilots reaction to adverse weather ahead and possibly an eventual initiation of a deviation is one other source of uncertainty in the air traffic system that has to be regarded.

Driven by the question “How to minimize the weather related delays and simultaneously increase safety?” the impact of adverse weather has been determined in numerous studies since the 1980s[1][2][3]. The center of research on this question was definitely located in the US.

As part of the US National Aviation Weather Program 1997 [4], technical solutions were sought which were based on the general assumption, that a better knowledge of actual weather and the related hazards are the best precondition for weather avoidance strategies. Thus observational tools were developed (NEXRAD, TDWR, LLWAS, .) and integrated into what is referred to as Integrated Terminal Weather Systems. The essential progress made is based on:

  1. development of diagnosis and forecast tools for the various weather hazards (Cb, in-flight icing[5][6], turbulence, etc.)
  2. the integration of that instant and that for future weather information in ATM.

Applications in the US airspace are mostly related to the capacity forecasts for certain air routes and the initiation of proactive measures. The former tools were mainly developed at the Research Application Facility at NCAR (Boulder, Co.), while many of the latter were proposed by MIT.

These approaches turned out to be very beneficial for aviation, but capacity tools for airspaces and/or air routes may be less applicable to the future European airspace which is governed by the SESAR 4D trajectory concept. Two arguments have to be considered here. First, following the free flight concept, a-priori non-limited routes rather than fixed air routes are assumed. The impact of adverse weather on such a given route has to be forecasted and safe and conflict-free diversion routes have to be determined. Resulting capacity limitations can then be calculated. Secondly, the concept of 4D trajectories in general is essentially a mechanistic one where all components follow deterministic behavior. Such a behavior is also implicitly assumed to hold for weather as well. Now, as mentioned above, it becomes more and more recognised in the ATM community that weather related uncertainty does not match the deterministic concept. One has to acknowledge that synoptic scale weather, beyond a forecast range of approximately seven days, on average, becomes chaotic and that this uncertainty is an inherent feature. It may also occur on a time scale less than seven days. Also, thunderstorm generation is still a non-deterministic problem, and thunderstorm propagation is at least partially non predictable [7]. Nowcasting systems as Rad-TRAM and Cb-TRAM [8] developed at the German Aerospace Center (DLR) provide an idea of the appearance and location of already existing thunderstorm cells up to 60 minutes ahead.

Research in this project

In the concept proposed in this PhD project we develop and apply an adverse weather avoidance model, referred to as DIVMET, and consider a time-dependent and simultaneously moving adverse weather field, for instance a field of moving thunderstorms. In a modeled CDM process DIVMET let the aircraft find its way for a flight from A to B. The mentioned nowcasting products of Rad-TRAM may be included in the simulations if real-time simulations are intended. In one research line we establish the integration of DIVMET in NAVSIM, a global air traffic simulation model [9]. Doing this, aircraft performance data from BADA will be considered. An assessment of complexity is established by using the principle of a receding horizon similar to [10] and varying the number of obstacles. To account for the stochastic nature of weather and especially thunderstorms we take simulated fields of randomly disturbed and stochastically behaving (based on previously derived observations) shower cells provided by a colleague’s PhD project work. The consideration of weather uncertainty could be implemented in a similar way as proposed by Nilim et al.[11]. The primary objective of this thesis is to contribute to the understanding of the interaction between the two complex systems air traffic and adverse weather. We choose the approach to model that interaction and confine ourselves – at present – to the case of thunderstorms. The PhD project outcome can be seen as a contribution to the complex weather-air traffic problem. Considering the, at least partially, stochastic nature of weather and the objective of safe and conflict-free flights through an adverse weather field, it becomes clear that any solution will be based on combined weather and traffic solutions. In this PhD thesis we explore the basic requirements for such simulations and demonstrate the applicability of the methodology.


Objectives directly related to the development of the DIVMET model are:

  1. The selection of an adverse weather diagnosis and forecast model, respectively for thunderstorms, and if necessary its adjustment to the given problem. An extension to account for the stochastic nature of storms, especially of decay and generation of cells and their propagation might be necessary.
  2. The development of the DIVMET model, which realistically simulates the circumnavigation of a field of thunderstorms. The detailed objectives are:
    • The calculated solution should belong to the set of observable solutions. Calculated delays and diversions should be similar to observed ones.
    • It should model the effect of hazard recognition realistically.
    • It should take into account all relevant safety regulations.
    • It should model the decision making process: recognition, “wait and see”, decide upon available information, prioritizes proposed routes in case ::of aircraft-aircraft conflicts, to return to the original flight route.
    • It should reflect the human incalculability and risk acceptance.
    • It should provide the extra diversions, delays, additional costs and CO2 emissions.
    • It should provide an adverse weather safety measure and determine the risks.
    • It should provide an optimum solution with respect to one or many given measures/key performance indicators (CO2, costs, risk etc.).
  3. The integration of DIVMET into the global air traffic model NAVSIM should allow for:
    • studies on strategic route finding under slightly unpredictable or unknown weather conditions,
    • the development of forecast guidance for controllers and pilots under adverse weather conditions,
    • diagnostic studies on weather related delays, costs, CO2 emissions and risks together with optimization strategies.

Applications of the DIVMET model

Objectives and research questions related to the application of the DIVMET model are:

  1. What is the effect of increased adverse weather knowledge? Especially, what would be the gain in safety and efficiency, if e.g. satellite weather products are available in the cockpit and to ATC? (These questions contribute to the suggested case studies on “Information sharing protocols to reduce uncertainty” in ATM given in the ComplexWorld Position Paper (Uncertainty in ATM, Section 3.1).)
  2. What are worst case weather scenarios? Where and when are the vulnerable points of the system, where a storm or a set of storms has or have the strongest impact on ATM performance? (These questions account for the resilience in air transport).
  3. What are the best ATM strategies to account for the stochastic nature of the problem?
  4. Provide guidance for controllers and pilots to find a safe and efficient route through a field of thunderstorms ahead. Thus, we contribute to the case study on “Stochastic decision support tools” given in the ComplexWorld Position Paper (Uncertainty in ATM, Section 3.2)


  • Sauer, M., T. Hauf, C. Forster (2014): Uncertainty Analysis of Thunderstorm Nowcasts for Utilization in Aircraft Routing, Fourth SESAR Innovation Days, 25 - 27 November 2014, Madrid, Spain
  • Sauer, M., T. Gerz (2014): Wettereinfluss auf Sicherheit und Effizienz im Luftverkehr - Analysen und Minimierungskonzepte, promet Meteorologische Fortbildung, Jahrg. 38, Nr. 3/4, pp. 157-165
  • Hauf T., L. Sakiew, M. Sauer (2013): Adverse weather diversion model DIVMET, Journal of Aerospace Operations, Vol. 2, No. 3-4: 115-133, DOI: 10.3233/AOP-130037
  • Sauer, M., L. Sakiew, T. Hauf, P. Hupe (2013): Some applications of the adverse weather diversion model DIVMET, Paper 8.6, 93th AMS Annual Meeting, 16th Conference on Aviation, Range, and Aerospace Meteorology, 6 - 10 Januar 2013, Austin, TX

Talks and Poster Presentations

  • Sauer, M., C. Forster, T. Hauf (2015): The Uncertainty of Thunderstorm Nowcasting and its Use in Weather Avoidance Modeling, Talk/Paper 11.1, 95th AMS Annual Meeting, 17th Conference on Aviation, Range, and Aerospace Meteorology, 4 - 8 Januar 2015, Phoenix, AZ
  • Sauer, M., T. Hauf, C. Forster (2014): Uncertainty Analysis of Thunderstorm Nowcasts for Utilization in Aircraft Routing, Talk, Fourth SESAR Innovation Days, 25 - 27 November 2014, Madrid, Spain
  • Hupe, P., T. Hauf, M. Sauer, C.-H. Rokitansky, J. Lang (2014): Real-time Flugverkehrssimulation der Gewitterumfliegung basierend auf dem Wetterausweichmodell DIVMET und dem Luftverkehrsmodell NAVSIM, Talk, Sitzung 2 / 2014 der DGON – Luftfahrtkommission, Thema "Flugmeteorologie", 24 Oktober 2014, Offenbach, Germany
  • Sauer, M., L. Sakiew, A. Fiehn, T. Hauf, C.-H. Rokitansky, M. Kerschbaum (2013): Simulation des Ausweichverhaltens von Flugzeugen in Schlechtwettersituationen mit DIVMET – Anwendung auf ein Squall Line Ereignis über Österreich, Talk, DACH Meteorologentagung, 2 - 6 September 2013, Innsbruck, Austria
  • Sauer, M., L. Sakiew, T. Hauf, P. Hupe (2013): The Adverse Weather Diversion Model DIVMET – Concept and Applications, Talk, 93th AMS Annual Meeting, 16th Conference on Aviation, Range, and Aerospace Meteorology, 6 - 10 Januar 2013, Austin, TX
  • Sauer, M., P. Hupe, L. Sakiew, T. Hauf, C.-H. Rokitansky, M. Kerschbaum (2013): Sector Occupancy Analysis with the Adverse Weather Diversion Model DIVMET, Poster, 93th AMS Annual Meeting, 16th Conference on Aviation, Range, and Aerospace Meteorology, 6 - 10 Januar 2013, Austin, TX
  • Sauer, M., Sakiew, L., Hupe, P. (2013): Studie mit DIVMET zur Sektorbelastung bei Gewittern, Talk, DFS – 10. Nutzerkonferenz Wetterdaten, 31 Januar 2013, Langen, Germany
  • Sauer, M., T. Hauf (2013): Intelligent Modeling the Impact of Unpredictable Adverse Weather on ATM Performance, Talk, ComplexWorld Workshop 1 „Uncertainty in ATM“, 27 Mai 2013, Naples, Italy
  • Sauer, M., L. Sakiew, P. Hupe (2012): A theoretical sector capacity analysis with DIVMET, Talk, 2nd SESAR Innovation Days, 27 - 29 November 2012, Braunschweig, Germany
  • Sauer, M., L. Sakiew, T. Hauf, P. Hupe, J. Siedler (2012): Intelligent Modeling the Impact of Unpredictable Adverse Weather on ATM Performance, Poster, 2nd SESAR Innovation Days, 27 - 29 November 2012, Braunschweig, Germany
  • Sauer, M., L. Sakiew, T. Hauf (2011): Intelligent Modeling the Impact of Unpredictable Adverse Weather on ATM Performance, Poster, 1st SESAR Innovation Days, 29 November - 1 Dezember 2011, Toulouse, France
  • Sauer, M., T. Hauf (2011): Intelligent Modeling the Impact of Unpredictable Adverse Weather on ATM Performance, Talk, 1st ComplexWorld Annual Conference, 6 – 7 Juli 2011, Sevilla, Spain


  1. Robinson, P. J., 1989. The Influence of Weather on Flight Operations at the Atlanta Hartsfield International Airport, Weather and Forecasting, 4, 461-468.
  2. DeLaura, R. and Evans, J., 1994. The Integrated Terminal Weather System (ITWS), The Lincoln Laboratory Journal, 18 (7).
  3. Allan, S. S. and Gaddy, S. G., 2001. Delay Causality and Reduction at New York Airports Using Terminal Weather Information Systems, Project Report ATC-291, MIT Lincoln Laboratory.
  4. NAWPC, National Aviation Weather Program Strategic Plan. Prepared by the Joint Action Group for Aviation Weather, for the National Aviation Weather Program Council. OFCM Document FCM-P32-1997.
  5. Bernstein, B. C., Integrated Icing Diagnostic Algorithm (WEB address:
  6. Tafferner, A. and Hauf, T., 2003. ADWICE – Advanced Diagnoses and Warning System for Aircraft Icing Environments, Weather and Forecasting, 19, 184-203.
  7. Wilson, J. W. and Ebert, E., 2004. Sydney 2000 Forecast Demonstration Project: Convective Storm Nowcasting, Weather and Forecasting, 19, 131-150.
  8. Forster, C. and Tafferner, A., 2012. Nowcasting Thunderstorms for Munich Airport, Gerz, T. and Schwarz, C. (eds.), The DLR Project Wetter & Fliegen, Forschungsbericht 2012-02, pp. 32-45.
  9. Rokitansky, C., 2009. VDL Mode 2 Capacity Analysis trough Simulations: WP3.B – NAVSIM Overview and Validation Results. Eurocontrol
  10. Bellingham, J., Richards, A. and How, J. P., 2012. Receding Horizon Control of Autonomous Aerial Vehicles, Proceedings of the American Control Conference, pp. 3741-3746.
  11. Nilim, A., El Ghaoui, L., Hansen, M. and Duong, V., 2003. Trajectory-Based Air Traffic Management (TB-ATM) under Weather Uncertainty, Proceedings of the 5th USA-Europe ATM Seminar ATM2003.

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 783287.