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Optimization of Terminal Aerodrome Forecasts In a Cost/Loss Situation

Optimization of Terminal Aerodrome Forecasts In a Cost/Loss Situation Based on Probabilistic Guidance by Klaus Knüpffer METEO SERVICE weather research GmbH Teltower Damm 25, 14169 Berlin, Germany K.Knuepffer@mswr.de Workshop

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Optimization of Terminal Aerodrome Forecasts In a Cost/Loss Situation

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  1. Optimization of Terminal Aerodrome Forecasts In a Cost/Loss Situation Based on Probabilistic Guidance by Klaus Knüpffer METEO SERVICE weather research GmbH Teltower Damm 25, 14169 Berlin, Germany K.Knuepffer@mswr.de Workshop on Value Added Services for Air Navigation and User-oriented Aerodrome Forecasts Toulouse, from 9 to 10 September 2005

  2. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer • Contents • 1. Introduction: • 2. Optimum Decision Making Based on Probabilities • and Cost/Loss Information • 3. Implications for the Future of Terminal Aerodrome Forecasts • 4. Summary

  3. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer 1. Introduction: The theory of numerical weather prediction was well known for about 100 years before it could affect practice via numerical weather prediction The theory of optimum decision making is known for a long time. Now the time for this theory has come to affect applied meteorology. Why now - why not earlier, why not later?

  4. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer 2. Optimum Decision Making Based on Probabilities and Cost/Loss Ratio 2.1 Introductionary Example 2.2 Example for a Multiple Choice Decision Situation 2.3 Implications on Terminal Aerodrome Forecasts 2.3.1 Auto-TAF Guidance: The Probabilities are There 2.3.2 Weather Dependent Decisions in Aviation 2.4 Proposal to set up an Auto-Decision system

  5. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer • 2. Optimum Decision Making Based on Probabilities and Cost/Loss Ratio • 2.1 Introductionary Example • Problem: Late-spring frost may detoriate the blossom of trees • The weather forecast says: Minimum temperature 2 °C • --This information is not sufficient for optimum decision making – • We need probabilistic forecast: • The probability for Tmin< 0 °C is 13 % • “Probability is the language of the forecaster” C. Doswell

  6. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer Now we have sufficient meteorological (probabilistic) information for optimum decision making. Furthermore we need the cost/loss situation of the user. It can be described in a Cost-Loss Matrix:

  7. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer Optimum Decision:

  8. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer Decision Rule It can easily be shown that in such a situation the following decision rule applies: IF ( P(E)> Cost_of_Prevention(E)/Loss(E) ) THEN prevent ELSE do not prevent END IF

  9. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer 2.2 Example for a Multiple Choice Decision Situation -- from www.wetterturnier.de -- Problem: Which W1 Code (one out of 0,4,5,6,7,8,9) shall be predicted? Given: - MOS Probabilities for each event - Cost-Loss Matrix (Losses depending on the forecasts and observations) Solution: Minimize the expected losses

  10. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer Forecast W1 = 0 (no fog or precip) and Observation W1=6 (stratiform precip) --> 10 penalty points

  11. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer Complete information for optimum decision making is present: To each action(forecast) an expected number of penalty points can be assigned. Example: W1 Prob Points = Computation 0 51 3.63 = 0.51*0 + 0.27*7 + 0.22*8 8 27 3.72 = 0.51*6 + 0.27*0 + 0.22*3 9 22 3.60 = 0.51*6 + 0.27*2 + 0.22*0 Automatic Decision: W1=9 although P(W1=9)=22%

  12. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer Human players in the tournament have problems to take the right decisions on W1 – computers not. MOS produces any desired amount of probabilities in high quality. One limiting factor for automated decision making is not existent anymore! The other limiting factor is still present: Users are not able or willing to describe their Cost/Loss situation. The time has come for the users (at aerodromes) to do so. That’s why the time for automatic decision generating is now or in near future.

  13. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer • 2.3 Implications on Terminal Aerodrome Forecasts • 2.3.1 Auto-TAF Guidance: The Probabilities are There • New situation for algorithmic decision making: • Probabilistic forecasts for any airport can be generated by • MOS (Model Output Statistics). Characteristics of them: • bias-free • reliable • sharp • - quality: Similar to or better than human forecasts

  14. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer TAF-Guidance We 07.09.2005 10z 07630 PREDICTAND 09z 00 03 06 09 12 Thursday

  15. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer • 2.3.2 Weather Dependent Decisions in Aviation • Capacity Planning: How much fuel is needed? • Frequency of starts and landings • Much money involved: AVIATION COMPANIES - fuel costs • Additional costs for staff and technology • PASSENGERS • - safety • time lost due to delay

  16. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer • As there is so much money involved and all modules are there or • known: Why is automatic decision generating not widely applied yet? • The probabilities were/are not present • - now they can be available to anyone • 2. Missing quantitative description of the Cost/Loss situation • - Such descriptions are helpful for subjective and • objective decision making • They should be elaborated by the air companies 3. “Psychological” reasons - human beings fear competition from the machine.

  17. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer • 2.4 Proposal to set up an Auto-Decision system • What do we need? • Interested air company • TAF Guidance probabilities • Description of a suitable Cost/Loss-Situation • Comparative verification of human made decisions. • * Reference: Automatic decision guidance.

  18. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer 3. Implications for the Future of Terminal Aerodrome Forecasts 3.1 Example of an automatically generated TAF 3.2 Disadvantage of current TAF Code 3.3 Conclusions for State-of-the-art Terminal Aerodrome Forecasts

  19. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer • 3.1 Example of an automatically generated TAF • LFBO 071812 13007KT 9999 BKN025 TEMPO 0407 RA • BKN009 BECMG 0608 BKN014 BECMG 0911 BKN035 • automatically converted from the Auto-TAF guidance • ICAO rules applied • How is the quality of Auto-TAF? • - Some results from official DWD verification of encoded TAFs follow • (KNMI has similar results for their TAF guidance, not shown)

  20. Combined Evaluation of Visibility and Ceiling Forecasts Comparative Verification of the most important aviation forecast elements for 17 German airports and lead times from 1 to 10 hours ahead. Winter half year 2000/01 PER – persistency AUTO – Auto-TAF forecast TAF – Official final TAF forecast (human) Categories A,B,C: A: Extreme rare events B: Rare events C: Not rare events HSS – Heidke Skill Score Source: K.Balzer (DWD): Zum Mensch-Maschine-Konflikt in der Wettervorhersage, DACH-MT 2001, Wien, Österreich

  21. The same as before but for different lead times (instead of categories)

  22. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer • 3.2 Disadvantage of current TAF Code • Huge loss of relevant TAF-Guidance information in the process of encoding is the main disadvantage: • - ICAO rules allow only a few probabilities to be expressed: 30%, 40% and >50% (body). However: Each user has his individual Cost/Loss-dependent probability thresholds. They are • often much smaller than the lowest expressable 30 %. • - Only the most important weather events or changes can be mentioned. • The user with his individual needs is the looser.

  23. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer • 3.3 Conclusions for State-of-the-art Terminal • Aerodrome Forecasts • Current TAF Code should be replaced by something better: Bias-free high quality probabilistic forecasts for each element of interest and each hour. • 2. Automatically generated Auto-TAF guidance may play an important role in this process. • 3. The results of (2) can be displayed in a user-oriented • way (e.g. flashing when certain, very user-dependent probabilities are exceeded).

  24. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer 4. Summary The introduction of automatic guidance forecasts has improved the quality of weather forecast (see next page). The introduction of automatic decision making guidance has the potential to improve the decisions in a similar way. Terminal Aerodrome Forecast have a high potential to profit from this development They need to be re-designed for this purpose Users need to declare their Cost/Loss situation

  25. Reduction of Variance relative to EM+Kal 07/98 – 06/99, Forecasts for next day 00, 06, 12 & 18 UTC, 14 German stations

  26. OPTIMIZATION OF TERMINAL AERODROME FORECASTS... Klaus Knüpffer Thank you

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