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The convection-permitting ensemble COSMO-DE-EPS From development to applications

The convection-permitting ensemble COSMO-DE-EPS From development to applications. Susanne Theis, Christoph Gebhardt, Michael Buchhold Deutscher Wetterdienst Meteorological Modelling and Analysis Predictability and Verification. Outline. Development of the ensemble. Outline.

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The convection-permitting ensemble COSMO-DE-EPS From development to applications

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  1. The convection-permittingensemble COSMO-DE-EPS Fromdevelopmenttoapplications Susanne Theis, Christoph Gebhardt, Michael Buchhold Deutscher Wetterdienst Meteorological Modelling and Analysis Predictability and Verification

  2. Outline • Development oftheensemble

  3. Outline • Steptowardsapplications • weatherwarnings, floodwarnings, • airportmanagement, renewableenergy • Development oftheensemble

  4. Development oftheensemble: Setup and Motivation

  5. Ensemble is based on model COSMO-DE • COSMO-DE in operationsince 2007 • spatialgridlength 2.8 km • noparametrizationofdeepconvection • (convection-permitting) • assimilationofradardata • lead time: 0-27 hours • 8 starts per day (00, 03 UTC,...) ~ 1300 km modeldomain  Baldauf et al. (2011)

  6. Benefitofthefinegrid (2.8 km) • improved forecasts of near-surface variables precipitation, 2m-temperature, wind gusts • improved representation of atmospheric processes: subsynoptic, mesoscale, convective • improved representation of severe weather

  7. Challenge: Predictability

  8. Challenge: Predictability atmospheric processes synoptic 1 week convective characteristic time scale 1 hour 100 m 10 km 1000 km characteristic length scale

  9. Challenge: Predictability atmospheric processes predictability synoptic 1 week convective characteristic time scale 1 hour 100 m 10 km 1000 km lead time of the forecast characteristic length scale

  10. Challenge: Predictability atmospheric processes predictability synoptic 1 week convective characteristic time scale 1 hour Uncertainties in small scales grow faster (Lorenz 1969) 100 m 10 km 1000 km lead time of the forecast characteristic length scale

  11. Challenge: Predictability atmospheric processes predictability synoptic 1 week convective characteristic time scale 1 hour  address the forecast in a probabilistic framework 100 m 10 km 1000 km lead time of the forecast characteristic length scale

  12. The ensemble COSMO-DE-EPS ensemble members 20 forecast scenarios for the same time in the future operational since 2010 / 2012

  13. including variations of • initial conditions • model physics • soil moisture Ensemble chainof COSMO-DE-EPS COSMO-DE-EPS 2.8 km COSMO 7 km GME, IFS, GFS, GSM  Gebhardt et al (2011), Peralta et al (2012)

  14. The 20 COSMO-DE-EPS members 0.2 0.2 0.4 0.7 1 2 3 5 4 0.7 8 6 0.2 7 0.2 9 0.4 10 0.7 0.7 11 12 13 15 14 0.2 0.2 0.4 0.7 0.7 0.2 0.7 0.2 0.4 0.7 16 17 18 20 19 tkhmin und tkmmin = 0.2 / 0.4 / 0.7 soilmoisture: nochange (O) / anomaly / anomaly (asof March 18th 2014)

  15. Exampleof a Forecast Product for a specific location: 10 0 00 UTC 06 UTC 12 UTC 18 UTC Forecast Lead Time Source of Figure: NinJo Visualization System at DWD

  16. Exampleof a Forecast Product for a specific location: 90%-percentile = 10 mm rain 10 0 00 UTC 06 UTC 12 UTC 18 UTC Forecast Lead Time Source of Figure: NinJo Visualization System at DWD

  17. Exampleof a Forecast Product for a specific location: 90%-percentile = 10 mm rain 10 0 75%-percentile = 7 mm rain 00 UTC 06 UTC 12 UTC 18 UTC Forecast Lead Time Source of Figure: NinJo Visualization System at DWD

  18. The steptowardsapplications

  19. COSMO-DE-EPS isenteringvariousapplications • probabilisticforecastsof high-impact weather • weatherwarnings • floodwarnings • stormsurgewarnings • airportmanagement • renewableenergy • andmore

  20. COSMO-DE-EPS forweatherwarnings 2010-2012: „evaluation“ phase since 2012: operational useof COSMO-DE-EPS

  21. DWD forecasters receive COSMO-DE-EPS percentiles, exceeding probabilities, ensemble mean and spread, …

  22. DWD forecastersreceive COSMO-DE-EPS • precipitation & snow, 10m wind gusts, 2m temperature, simulatedradarreflectivity, CAPE, lowlevelcloudcover • tailoredto DWD warningcriteria • forecastercanseetheforecast: 2 ¼ hours after startofsimulation percentiles, exceeding probabilities, ensemble mean and spread, …

  23. DWD forecastersreceive COSMO-DE-EPS • precipitation & snow, 10m wind gusts, 2m temperature, simulatedradarreflectivity, CAPE, lowlevelcloudcover • tailoredto DWD warningcriteria • forecastercanseetheforecast: 2 ¼ hours after startofsimulation percentiles, exceeding probabilities, ensemble mean and spread, …

  24. DWD forecastersreceive COSMO-DE-EPS • precipitation & snow, 10m wind gusts, 2m temperature, simulatedradarreflectivity, CAPE, lowlevelcloudcover • tailoredto DWD warningcriteria • forecastercanseetheforecast: 2 ¼ hours after startofsimulation Favorites: • 90%-percentiles • „upscaled“ probabilities

  25. Why „upscaled“ probabilities? Feedback fromtheforecasters

  26. probability of precipitation > 20 mm/6h Source of Figure: NinJo Visualization System at DWD

  27. probability of precipitation > 20 mm/6h 90 -100 % 80 - 89 % 70 - 79 % . . . . . 10 - 19 % 1 - 9 % < 1 % Source of Figure: NinJo Visualization System at DWD

  28. probability of precipitation > 20 mm/6h Forecasters: „Probabilities are too low!“ Source of Figure: NinJo Visualization System at DWD

  29. probability of precipitation > 20 mm/6h Forecasters: „Probabilities are too low!“ • notconfirmedbyverification • forecastersdidaccept 90%-percentiles ??? Source of Figure: NinJo Visualization System at DWD

  30. Take a look at forecaster‘s desk warning map arbitrary example Source of Map: www.dwd.de

  31. Take a look at forecaster‘s desk click here warning map arbitrary example Source of Map: www.dwd.de

  32. Source of Text: www.dwd.de

  33. Warning for County Ravensburg „There will be heavy rain.“ Source of Text: www.dwd.de

  34. probability of precipitation > 20 mm/6h Source of Figure: NinJo Visualization System at DWD

  35. probability of precipitation > 20 mm/6h they need a different product Source of Figure: NinJo Visualization System at DWD

  36. probability of precipitation > 20 mm/6h probability of precipitation > 20 mm/6h somewhere within a region 90 -100 % 80 - 89 % 70 - 79 % . . . . . 10 -19 % 1 - 9 % < 1 % Source of Figure: NinJo Visualization System at DWD

  37. Upscaling: Ben Bouallègue, Z. and S.E. Theis (2013): Spatialtechniquesappliedtoprecipitationensembleforecasts: Fromverificationresultstoprobabilisticproducts. Meteorological Applications, DOI: 10.1002/met.1435.

  38. Upscaling: Statistical Postprocessing: Ben Bouallègue, Z. and S.E. Theis (2013): Spatialtechniquesappliedtoprecipitationensembleforecasts: Fromverificationresultstoprobabilisticproducts. Meteorological Applications, DOI: 10.1002/met.1435. Ben Bouallègue, Z. (2013): Calibratedshort-range ensembleprecipitationforecastsusingextendedlogisticregressionwithinteractionterms. Wea. Forecasting, 28, 515-524.

  39. Upscaling: Statistical Postprocessing: Time-Lagging: Ben Bouallègue, Z. and S.E. Theis (2013): Spatialtechniquesappliedtoprecipitationensembleforecasts: Fromverificationresultstoprobabilisticproducts. Meteorological Applications, DOI: 10.1002/met.1435. Ben Bouallègue, Z. (2013): Calibratedshort-range ensembleprecipitationforecastsusingextendedlogisticregressionwithinteractionterms. Wea. Forecasting, 28, 515-524. Ben Bouallègue, Z., Theis, S.E. and C. Gebhardt (2013): Enhancing COSMO-DE ensembleforecastsbyinexpensivetechniques. Meteorologische Zeitschrift, 22 (1), 49-59.

  40. Look intootherapplications Whatistheir „high-impact“ weather?

  41. „High-impact“ weather severe precipitation event somewhere within a certain region

  42. „High-impact“ weather severe precipitation event somewhere within a certain region high water levels of a river (predicted by hydrological models which use ensemble weather forecasts in their inputs)

  43. COSMO-DE-EPS forfloodwarnings

  44. COSMO-DE-EPS forfloodwarnings • takemembersof COSMO-DE-EPS • severalsimulationswith • hydrologicalmodel • ensembleforrunoff

  45. COSMO-DE-EPS forfloodwarnings runoff at specific water gauge (river „Emscher“ at Königstraße) • takemembersof COSMO-DE-EPS • severalsimulationswith • hydrologicalmodel • ensembleforrunoff 80 60 40 20 0 runoff (m3/s) time Source: ChristofferBiedebach (2013) „Einsatzmöglichkeiten des wahrscheinlichkeitsbasierten Vorhersagesystems COSMO-DE-EPS im Hochwasser-Informationssystem von Emschergenossenschaft und Lippeverband“, Hochschule für angewandte Wissenschaften Würzburg-Schweinfurt.

  46. COSMO-DE-EPS forfloodwarnings • currentwork- at varioushydrologicalcenters: • setuptechnicalenvironment • find usefulvisualization • evaluationformanycases • open: statisticalpostprocessing

  47. COSMO-DE-EPS forairportmanagement LuFo iPort WiWi project (I.Alberts, N.Schuhen, M.Buchhold)

  48. „High-impact“ weather severe precipitation event somewhere within a certain region high water levels of a river (predicted by hydrological models which use ensemble weather forecasts in their inputs) exceeding a certain threshold of the tailwind or crosswind component relative to the airport runway along the glide path source: LuFo iPort WiWi project

  49. COSMO-DE-EPS forairportmanagement (Frankfurt) • alreadyacheived: • product design • usefulvisualization • statisticalpostprocessing • quasi-operational environment LuFo iPort WiWi project (I.Alberts, N.Schuhen, M.Buchhold)

  50. COSMO-DE-EPS forairportmanagement (Frankfurt) probabilityof wind > (+ 5kt) 0 20 40 60 80 100 80 60 40 20 0 Tailwind probability (%) -20 0 +20 wind parallel to runway (kt) probabilityof wind < (- 5kt) Time (UTC) LuFo iPort WiWi project (I.Alberts, N.Schuhen, M.Buchhold)

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