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Model Jumpiness and the Need for Ensembles

Model Jumpiness and the Need for Ensembles. Richard Grumm National Weather Service Office and Lance Bosart State Univesity of New York at Albany. OBJECTIVES. Get a fix on some aspects of uncertainty and be able to recognize uncertainty in forecasts.

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Model Jumpiness and the Need for Ensembles

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

  2. OBJECTIVES • 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. • Define model jumpinessas 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. 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: • 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!

  4. 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. Return of the cyclone?all images valid the same time!

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

  7. Getting closer to event time…still lots of uncertainty

  8. Well…it passed to our West!Warm windy winter rain

  9. “Jump” right into some points • The GFS showed run-to-run inconsistencies • These inconsistence a 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. • 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. • Do you think this case is unique?It happened within 7 day of this event and it does all the time!

  10. Weather on 12 Feb 2006?In Washington, DC and NYC pick clouds, wind direction and PTYPE Light winds Possibly precip Rain? Rain/Snow wind?

  11. 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. So there will be a stormbut look at the variation of the depth and location Over Cape Cod No Make that south of Details still uncertain

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

  14. “Jump” right into more points • Run-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 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.

  15. MREF forecasts-06UTC 8 Feb

  16. MREF forecasts-12UTC 8 Feb

  17. MREF forecasts-12UTC 9 Feb

  18. MREF Comparative QPFprecipitation shield is moving east!

  19. SREF 09 and 21 UTC 9 Febprecipitation shield is moving east!

  20. Coastal stormor an offshore track even or EPS has issues

  21. A few more points • 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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 unique • They are ubiquitous

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

  23. 31 August GFS Heavy rain forecastsaxis/location heavy rain and end time

  24. Conclusions • There is considerable uncertainty in weather forecasting • Model jumpiness is a signal • Model differences are signals • Ensembles help us identify these signals • Model 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. Hubrisoverbearing pride or presumption; arrogance: • The 13 March 1993 storm was a relative success • It gave us confidence in models • 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 a hubris.

  26. Questions?

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