1. Model Jumpiness and the Need for Ensembles Richard Grumm National Weather Service Office and Lance Bosart State Univesity of New York at Albany Richard Grumm National Weather Service Office and Lance Bosart State Univesity of New York at Albany

2. NWA- Probabilistic ForecastingOBJECTIVESOBJECTIVES Get a fix on some aspects of uncertainty and be able to recognize uncertainty in forecasts.Get a fix on some aspects of uncertainty and be able to recognize uncertainty in forecasts. Provide examples of uncertainty in the NCEP GFS and its impact on the MREF and SREF ensemble prediction systems via examples of model jumpiness.Provide examples of uncertainty in the NCEP GFS and its impact on the MREF and SREF ensemble prediction systems via examples of model jumpiness. Define model jumpiness as the changes or differences of forecasts of features and parameters from one run-to-run from a single numerical model. These inconsistencies may span intensity, gradient, and location of a feature or parameter.Define model jumpiness as the changes or differences of forecasts of features and parameters from one run-to-run from a single numerical model. These inconsistencies may span intensity, gradient, and location of a feature or parameter.

3. NWA- Probabilistic Forecasting Model Jumpiness through the eyes of a model or prediction system We all see uncertainty in deterministic models on a daily basis. Some common include:We all see uncertainty in deterministic models on a daily basis. Some common include: –Significant run-to-run differences –The NAM or GFS may change the track and intensity of a cyclone or frontal system. –The precipitation shield shifts to the east (north) or west (south). –The problem is typically –worse at longer forecast range though not always. –A function of scale and mesoscale details can be quite changeable –Like the rain snow line or area for heavy snow/rain Not to mention differences between different models!Not to mention differences between different models!

4. NWA- Probabilistic Forecasting 4 GFS Runs  Big cyclone disappears? All images valid the same time! Big cyclone most of PA might be rain. Weak storm…PA is now cold…snow in SE?

5. NWA- Probabilistic Forecasting Return of the cyclone? all images valid the same time!

6. NWA- Probabilistic Forecasting Things cannot get so bad so fast! Or can they as Robin might say “Holy short-wave Batman”

7. NWA- Probabilistic Forecasting Getting closer to event time …still lots of uncertainty

8. NWA- Probabilistic Forecasting Well…it passed to our West! Warm windy winter rain

9. NWA- Probabilistic Forecasting “Jump” right into some points The GFS showed run-to-run inconsistencies The GFS showed run-to-run inconsistencies  –These inconsistence  uncertainty. –Causes  same model each time suggests uncertainty in the initial conditions. The need for multiple sets of IC’s Significant impacts on sensible weather elements.Significant impacts on sensible weather elements. –Areas and amounts of rain or early on, snow –POPS and temperatures –Winds to include direction changes of over 180 degrees! We need to acknowledge, visualize, and be deal with uncertainty and quantify it.We need to acknowledge, visualize, and be deal with uncertainty and quantify it. Do you think this case is unique? It happened within 7 day of this event and it does all the time!Do you think this case is unique? It happened within 7 day of this event and it does all the time!

10. NWA- Probabilistic Forecasting Weather on 12 Feb 2006? In Washington, DC and NYC pick clouds, wind direction and PTYPE Light windsPossibly precip Rain? Rain/Snow wind?

11. NWA- Probabilistic Forecasting Pleasant NW winds or a NE gale? …and we want those winds in 3-hour increments…. Whale storm Major East Coast Storm Details of center location and pressure still varyl

12. NWA- Probabilistic Forecasting So there will be a storm but look at the variation of the depth and location Details still uncertain Over Cape Cod No Make that south of

13. NWA- Probabilistic Forecasting Large East Coast Storm solution But where will it snow big and not at all…winds for RI please At finer scales the devil is in the details.

14. NWA- Probabilistic Forecasting “Jump” right into more points Run-to-Run inconsistenciesRun-to-Run inconsistencies –even at 6-hr increments –Close in we got the Big Storm –But we had problems with the location and intensity Still hard to get the details nailed downStill hard to get the details nailed down –Winds direction and rain snow line looked elusive –Did not show QPF but it too must was hard to nail down. At the smaller scales, States and Counties the details due to jumpiness still remain elusive.At the smaller scales, States and Counties the details due to jumpiness still remain elusive.

15. NWA- Probabilistic Forecasting MREF forecasts-06UTC 8 Feb

16. NWA- Probabilistic Forecasting MREF forecasts-12UTC 8 Feb

17. NWA- Probabilistic Forecasting MREF forecasts-12UTC 9 Feb

18. NWA- Probabilistic Forecasting MREF Comparative QPF precipitation shield is moving east!

19. NWA- Probabilistic Forecasting SREF 09 and 21 UTC 9 Feb precipitation shield is moving east!

20. NWA- Probabilistic Forecasting Coastal storm or an offshore track even or EPS has issues

21. NWA- Probabilistic Forecasting A few more points The MREF & the GFS showed run-to-run inconsistencies.The MREF & the GFS showed run-to-run inconsistencies. –But had a cyclone in its solutions that could affect the coast before the single GFS –It slowly converged on a solution about T-4 days. The impacts on the forecast were significant even the SREF had trends and moved the threat area  EASTThe impacts on the forecast were significant even the SREF had trends and moved the threat area  EAST –Sunny NW winds or rain…or snow –It was not too clear where it would snow until about T-2days! The cases of 5 and 12 February are NOT uniqueThe cases of 5 and 12 February are NOT unique –They are ubiquitous

22. NWA- Probabilistic Forecasting 30 August GFS forecast heavy rains- Front and Ernesto

23. NWA- Probabilistic Forecasting 31 August GFS Heavy rain forecasts axis/location heavy rain and end time

24. NWA- Probabilistic ForecastingConclusionsConclusions There is considerable uncertainty in weather forecastingThere is considerable uncertainty in weather forecasting –Model jumpiness is a signal –Model differences are signals –Ensembles help us identify these signals Model uncertaintyModel uncertainty –Due to initial conditions and data are not unique. –We deal with them at various forecast lengths, and meteorological scales. –We see these problems on a daily basis.

25. NWA- Probabilistic Forecasting Hubris overbearing pride or presumption; arrogance : The 13 March 1993 storm was a relative successThe 13 March 1993 storm was a relative success It gave us confidence in modelsIt gave us confidence in models How big a success was it?How big a success was it? –Lucky at the scales presented. –We still have storms that are hard to predict –The mesoscale details are even harder to get right. High confidence and precise forecasts are quite likely  hubris.High confidence and precise forecasts are quite likely  hubris.

26. NWA- Probabilistic ForecastingQuestions?Questions?