1. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany MOS philosophy and products Dr. Wilfried Jacobs Deutscher Wetterdienst Bildungs- und Tagungszentrum email: wilfried.jacobs@dwd.de

2. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany GMOS and TAF-Guidance (I) § Problems model is not able to consider all physical details model is not able to consider special locations § Principle of MOS: to find a relationship between model forecast (predictor) of different parameters (e.g. T850 hPa, vorticity 300 hPa) and observations at the special location (predictand) depending on the forecast parameter: observations (e.g. Markov-chains) § Remaining problems of MOS to smooth out extreme weather events in direction to the climate èMOS could be improved in the case of extreme storm and in the case of dense fog

3. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany MOS and TAF-Guidance (II) § MOS synoptic type world wide up to 174h (DWD) § TAF-Guidance aviation Germany long TAF, short TAF § Issue categorical probabilistic

4. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany model forecast (long time period) Observations (long time period) Linear Regression (minimum RMSE) Forecast MOS-coefficients develpoment Routine Principle of MOS, individual location (R. Thehos)

5. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Combination: EZMW-MOS & GME-MOS (R. Thehos) Synops GME -run EZMW -run EZMOS GMEMOS MOSMIX

6. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany German election 18 September 2005 "Forecast" 3 months before: Christina Democratic Party and Free Democratic Party will not win the election. MOS simple and very effective (R. Thehos)

7. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Regression equation (R. Thehos) http://www.sowi.uni-mannheim.de/lehrstuehle/lspol1/PVS-GschwendNorpoth05 STIM: Vote's percentage for governmental parties PAR: mean vote's percentage for governmental parties (3 years) KAN: Chancellor's support AMT: Time period after the last government's change

8. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Increasing climate's influence with increasing forecast length Example: Wind forecast over the North Sea (knots) ff (t) =Const + Koeff * FF_1000(t) t in h 12 4.60.46 48 5.00.44 120 8.20.30 100014.80.00

9. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Evaluation of MOS-probabilities 12h-averages Schwellenwert: Threshold Prozent: Percentage Best results for probabilities of 40% for categorical forecasts of "Gust 12 m/s: yes" Event"gust >12 m/s": Derivation of categorical "Yes / No"

10. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Example of MOS-interpretation t 12 13 14 15 16 17 18 UTC 10 18 30 40 60 30 10 shower / year p(E) / 1h 3% 5% 8% 11% 16% 8% 3% p(E) / 3h 27 % 38% p(E) / 6h 54% What are the probabilities of a shower during different day time intervalls of an entire year?

11. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Examples of predictors for MOS-equations § Grid point forecasts Wind, temperature, humidity § dedrived forecast values Thunderstorm-indices, vorticity § Recent observations Markov-chains, persistency elements (rather important for fog) § Forecasts of wind (speed and direction) e.g., in relation to fog § day of year e.g., for diurnal cycle

12. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Basics for interpretation § MOS exhibits the tendency to the climate if an event is seldom at a particular location! § Consequences Extreme weather (seldom) –If MOS "yes" believe it –If MOS "no" it is better to look to the model results, too –Exception: Inversion near a warm front and forecasts of high wind speed In seldom cases –Consider low probabilities as a hint that the event may happen. –Problem: Which probabilities should be used (experience!) § Consider areas instead of one location

13. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany When will MOS probably improve the model results (DWD)? § Frontal precipitation oWrong distribution due to orographic effects oOverforecasting of approaching fronts against a high pressure system over Eastern Europe (Winter) § Precipitation type oUnderforecasting of freezing rain oOverforecasting of snow  Summer convection oDiurnal cycle in direction "highest intensity during afternoon"  Wind and gusts coasts, oceans, mountains  Temperatures

14. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany How to work with MOS during seldom events (location!)? § Event forecast by MOS oBelieve it (careful in the case of an inversion near a warm front)  MOS does not forecast the event oAssumption that this event could happen although low probilities were forecast –Thunderstorms within 1 hour: about 20% (Germany) –Fog in flat areas: about 10% (near rivers about 20%, Germany) oBasic problem: Which thresholds at which location  question of climatology and/or your experiences.

15. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany MOS for medium range forecasts § Only useful during "normal events" § Not useful for extreme situations due to the increasing influence of climate  Better are ensemble products  disturbances at beginning of forecasts  model rerun  clusters (ECMWF-products)  clusters  input for local models (e.g., the DWD-COSMO-EU)  average of different model runs at one grid point (PEPS)  lagged average forecasts (succeeding model runs for the same forecast time)

16. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Categorical forecast "Sig.weather, MOSMIX

17. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany green>25kt yellow:>40kt red:>55kt Probability for gusts, however 12h, MOSMIX

18. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany categorical forecast ceiling (hft), MOSMIX

19. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Prob visibility <1km,MOSMIX) + ww

20. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Local forecast of probabilities (MAP, MOSMIX)

21. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany § Purpose: Automatic generation of a "Warn-Guidance" § Base Model data (GME) observations, relatively course, only every 7th county with a station remote sensing lightning § results probabilities for an area § high computer power required: Every day: about 500 probability values have to be processed final version: even about 2 Billion! WARNMOS

22. EUMETCAL NWP, 26 – 29 November 2007, DWD-BTZ Langen, Germany Example of WARNMOS