1. 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

2. Outline Development of the ensemble

3. Outline Step towards applications -weather warnings, flood warnings, airport management, renewable energy Development of the ensemble

4. Development of the ensemble: Setup and Motivation

5. model domain  COSMO-DE in operation since 2007  spatial grid length 2.8 km  no parametrization of deep convection (convection-permitting)  assimilation of radar data  lead time: 0-27 hours  8 starts per day (00, 03 UTC,...) Ensemble is based on model COSMO-DE ~ 1300 km  Baldauf et al. (2011)

6. Benefit of the fine grid (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. characteristic time scale characteristic length scale synoptic convective 10 km 1000 km 1 hour 1 week 100 m Challenge: Predictability atmospheric processes

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

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

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

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

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

14. The 20 COSMO-DE-EPS members entr_sc=0.002q_crit=4.0rlam_heat=0.1rlam_heat=10.tur_len=500. lhn_coef=0.5 IFS O  O GME  O  O GFS  O  GSM O  O 0.2 0.7 0.20.40.7 0.2 0.7 0.20.40.7 0.2 0.7 0.20.40.7 0.20.70.20.40.7 tkhmin und tkmmin = 0.2 / 0.4 / 0.7 soil moisture: no change ( O ) / anomaly  / anomaly (as of March 18th 2014) 1 6 11 16 2 7 12 17 3 8 13 18 4 9 14 19 5 10 15 20

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

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

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

18. The step towards applications

19.  probabilistic forecasts of high-impact weather  weather warnings  flood warnings  storm surge warnings  airport management  renewable energy  and more COSMO-DE-EPS is entering various applications

20. COSMO-DE-EPS for weather warnings 2010-2012: „evaluation“ phase since 2012: operational use of COSMO-DE-EPS

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

22.  precipitation & snow, 10m wind gusts, 2m temperature, simulated radar reflectivity, CAPE, low level cloud cover  tailored to DWD warning criteria  forecaster can see the forecast: 2 ¼ hours after start of simulation percentiles, exceeding probabilities, ensemble mean and spread, … DWD forecasters receive COSMO-DE-EPS

23.  precipitation & snow, 10m wind gusts, 2m temperature, simulated radar reflectivity, CAPE, low level cloud cover  tailored to DWD warning criteria  forecaster can see the forecast: 2 ¼ hours after start of simulation DWD forecasters receive COSMO-DE-EPS percentiles, exceeding probabilities, ensemble mean and spread, …

24.  precipitation & snow, 10m wind gusts, 2m temperature, simulated radar reflectivity, CAPE, low level cloud cover  tailored to DWD warning criteria  forecaster can see the forecast: 2 ¼ hours after start of simulation Favorites:  90%-percentiles  „upscaled“ probabilities DWD forecasters receive COSMO-DE-EPS

25. Why „upscaled“ probabilities? Feedback from the forecasters

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

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

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

29. Forecasters: „Probabilities are too low!“ probability of precipitation > 20 mm/6h Source of Figure: NinJo Visualization System at DWD  not confirmed by verification  forecasters did accept 90%-percentiles ???

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

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

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 Source of Figure: NinJo Visualization System at DWD 90 -100 % 80 - 89 % 70 - 79 %. 10 -19 % 1 - 9 % < 1 %

37. Ben Bouallègue, Z. and S.E. Theis (2013): Spatial techniques applied to precipitation ensemble forecasts: From verification results to probabilistic products. Meteorological Applications, DOI: 10.1002/met.1435. Upscaling:

38. Ben Bouallègue, Z. and S.E. Theis (2013): Spatial techniques applied to precipitation ensemble forecasts: From verification results to probabilistic products. Meteorological Applications, DOI: 10.1002/met.1435. Ben Bouallègue, Z. (2013): Calibrated short-range ensemble precipitation forecasts using extended logistic regression with interaction terms. Wea. Forecasting, 28, 515-524. Upscaling: Statistical Postprocessing:

39. Ben Bouallègue, Z. and S.E. Theis (2013): Spatial techniques applied to precipitation ensemble forecasts: From verification results to probabilistic products. Meteorological Applications, DOI: 10.1002/met.1435. Ben Bouallègue, Z. (2013): Calibrated short-range ensemble precipitation forecasts using extended logistic regression with interaction terms. Wea. Forecasting, 28, 515-524. Ben Bouallègue, Z., Theis, S.E. and C. Gebhardt (2013): Enhancing COSMO- DE ensemble forecasts by inexpensive techniques. Meteorologische Zeitschrift, 22 (1), 49-59. Upscaling: Statistical Postprocessing: Time-Lagging:

40. Look into other applications What is their „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 for flood warnings

44. take members of COSMO-DE-EPS  several simulations with hydrological model  ensemble for runoff COSMO-DE-EPS for flood warnings

45. take members of COSMO-DE-EPS  several simulations with hydrological model  ensemble for runoff COSMO-DE-EPS for flood warnings Source: Christoffer Biedebach (2013) „Einsatzmöglichkeiten des wahrscheinlichkeitsbasierten Vorhersagesystems COSMO-DE-EPS im Hochwasser-Informationssystem von Emschergenossenschaft und Lippeverband“, Hochschule für angewandte Wissenschaften Würzburg-Schweinfurt. 80 60 40 20 0 runoff (m 3 /s) runoff at specific water gauge (river „Emscher“ at Königstraße) time

46. COSMO-DE-EPS for flood warnings  current work - at various hydrological centers:  set up technical environment  find useful visualization  evaluation for many cases  open: statistical postprocessing

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

48. „High-impact“ weather high water levels of a river (predicted by hydrological models which use ensemble weather forecasts in their inputs) severe precipitation event somewhere within a certain region 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 for airport management (Frankfurt)  already acheived:  product design  useful visualization  statistical postprocessing  quasi-operational environment LuFo iPort WiWi project (I.Alberts, N.Schuhen, M.Buchhold)

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

51. COSMO-DE-EPS for airport management (Frankfurt) LuFo iPort WiWi project (I.Alberts, N.Schuhen, M.Buchhold) crosswind (kt) 0 20 40 60 80 100 80 60 40 20 0 -20 0 +20 probability of wind > (+ 20kt) probability of wind < (- 20kt) probability (%) Time (UTC) Crosswind

52.  statistical postprocessing method: EMOS, bivariate Gaussian distribution Schuhen et al. (2012): Ensemble Model Output Statistics for Wind Vectors. Mon. Wea. Rev., 140, 3204–3219. COSMO-DE-EPS for airport management (Frankfurt)

53. COSMO-DE-EPS for renewable energy EWeLiNE project (K. Lundgren et al.)

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

55. „High-impact“ weather high water levels of a river (predicted by hydrological models which use ensemble weather forecasts in their inputs) severe precipitation event somewhere within a certain region exceeding a certain threshold of the tailwind or crosswind component relative to the airport runway along the glide path very quick change in wind speed at hub height taking place over a large area (?) EWeLiNE project (K. Lundgren et al.)

56.  UAS-PS 304.03  SCI-POT 1138  SCI-POT 1028  UAS-POM 3014 „High-impact“ weather high water levels of a river (predicted by hydrological models which use ensemble weather forecasts in their inputs) severe precipitation event somewhere within a certain region exceeding a certain threshold of the tailwind or crosswind component relative to the airport runway along the glide path very quick change in wind speed at hub height taking place over a large area (?)

57. Summary Convection-permitting ensemble COSMO-DE-EPS - in operation since 2010 / 2012 Discovered by increasing number of applications - weather & flood warnings, airport, renewable energy, and more Communication with users -product design, visualization, postprocessing method -can be essential for acceptance

58. Ben Bouallègue, Z. (2013): Calibrated short-range ensemble precipitation forecasts using extended logistic regression with interaction terms. Wea. Forecasting, 28, 515-524. Ben Bouallègue, Z., Theis, S.E. and C. Gebhardt (2013): Enhancing COSMO-DE ensemble forecasts by inexpensive techniques. Meteorologische Zeitschrift, 22 (1), 49-59. Ben Bouallègue, Z. and S.E. Theis (2013): Spatial techniques applied to precipitation ensemble forecasts: From verification results to probabilistic products. Meteorological Applications, DOI: 10.1002/met.1435. Gebhardt, C., Theis, S.E., Paulat, M. and Z. Ben Bouallègue (2011): Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Atmospheric Research, 100, 168-177. Peralta, C., Z. Ben Bouallègue, S.E. Theis, C. Gebhardt, and M. Buchhold (2012): Accounting for initial condition uncertainties in COSMO-DE-EPS. J. Geophys. Res., 117 (D7), doi:10.1029/2011JD016581 References around COSMO-DE-EPS

59. -data access for research (in addition to the producing center itself) -forecasts of several high-resolution ensemble systems (incl COSMO-DE-EPS) -selected set of variables (there is more at the producing center itself) -technical description of the systems  information: https://software.ecmwf.int/wiki/display/TIGGE/TIGGE-LAM https://software.ecmwf.int/wiki/display/TIGGE/TIGGE-LAM  data access: http://apps.ecmwf.int/datasets/data/tigge_lam/ http://apps.ecmwf.int/datasets/data/tigge_lam/ Data Access for Research: TIGGE-LAM archive

60. PBPV – 03/201360