Experimental Ensembles with the LM/LMK Past and Future Work

1. Experimental Ensembles with the LM/LMK Past and Future Work Susanne Theis

2. Past Work: Stochastic Parametrization in the LM Susanne Theis (PhD Thesis) Supervisor: Prof. Andreas Hense, University of Bonn

3. Motivation of Stochastic Parametrization

4. Problem in Ensemble Forecasting Ensemble represents some sources of uncertainty, but not all Missing: uncertainty in parametrised processes (= stochastic effect of subgrid scale processes) uncertainty in initial conditions uncertainty in NWP model output uncertainty in lateral boundary conditions uncertainty in parametrised processes

5. Conventional Parametrizations …only simulate the mean effect of subgrid scale processes! Estimating the subgrid scale effect: experimental data subgrid scale effect mean effect resolved process

6. Conventional Parametrizations …only simulate the mean effect of subgrid scale processes! Estimating the subgrid scale effect: Subgrid scale effect for a fixed value of the resolved process: experimental data probability density function subgrid scale effect mean effect mean resolved process subgrid scale effect

7. Conventional Parametrizations …only simulate the mean effect of subgrid scale processes! Estimating the subgrid scale effect: Subgrid scale effect for a fixed value of the resolved process: experimental data variability neglected! probability density function subgrid scale effect mean effect mean resolved process subgrid scale effect

8. Aim of Stochastic Parametrization Problem: Neglect of subgrid scale variability potentially leads to insufficient ensemble spread Aim: • return some of this missing variability to the model • simulate the stochastic effect of subgrid scale processes on the resolved scales

9. Methodology of Stochastic Parametrization

10. Subscale Processes in the Model Model Simulation: Separation of the prognostic model equations: „Dynamics“ „Physics“ (parametrised processes)

11. noise Stochastic Parametrization Injection of „noise“ into the deterministic bulk formulae: „Physics“ (parametrised processes) „Dynamics“

12. noise Stochastic Parametrization Injection of „noise“ into the deterministic bulk formulae: „Physics“ (parametrised processes) „Dynamics“

13. noise Stochastic Parametrization Injection of „noise“ into the deterministic bulk formulae: „Physics“ (parametrised processes) „Dynamics“

14. noise Stochastic Parametrization Injection of „noise“ into the deterministic bulk formulae: „Physics“ (parametrised processes) „Dynamics“

15. Stochastic Parametrization in LM (1) Perturbation of the Net Effect of Diabatic Forcing

16. Stochastic Parametrization in LM (1) Perturbation of the Net Effect of Diabatic Forcing microphysics turbulence radiation convection

17. Stochastic Parametrization in LM (1) Perturbation of the Net Effect of Diabatic Forcing microphysics random number turbulence radiation • perturbation • in each time step • at each grid point convection

18. Perturbation Properties example: uniform distribution spatial correlation amplitude 10 x temporal correlation choice motivated by ECMWF ensemble setup further experiments: temporal correlation more smooth

19. Stochastic Parametrization in LM (2) Perturbation of the Roughness Length over Land • each member is assigned a specific (perturbed) field • the fields are constant with time • The roughness length is one of many parameters that need to be set experimentally. They are optimized with regard to their best performance and will not represent related uncertainty in a conventional setting.

20. Experiments with Stochastic Parametrization

21. Setup of Ensemble Experiments Long term goal: improvement of ensemble forecasts First step: look at effect of stochastic parametrization in isolation Focus: short-range precipitation forecasts

22. Setup of Ensemble Experiments • 16 ensemble forecasts are produced: • - Juli 09, 2002 00 UTC • - Juli 10, 2002 00 UTC • ... • - Juli 24, 2002 00 UTC • 10 ensemble members per forecast • = 9 perturbed members + 1 unperturbed • each forecast has a lead time of 48 hours

23. Setup of Ensemble Experiments perturbation of initial conditions perturbed ensemble member perturbation of lateral boundary conditions perturbation of parametrised processes net diabatic forcing roughness length

24. Example of Ensemble Experiment • 1h-precipitation • 10 July, 2002 • 17 – 18 UTC • lead time: • 18 hours original LM simulation (unperturbed) case study Berlin Ensemble [mm]

25. Example of Ensemble Experiment original LM simulation (unperturbed) ensemble spread [mm]

26. Results of Ensemble Experiments • The stochastic parametrization scheme… • has a considerable effect on precipitation amount • shows hardly any effect on precipitation occurence

27. Further Investigations • Sensitivity studies on the configuration of random numbers •  large sensitivity to amplitude and correlation • Relevance in comparison to initial condition perturbations •  low relevance of stochastic parametrisation • Verification of the experimental ensemble forecasts • (comparison to station data, 2 weeks) •  only marginal improvement of forecast quality and value, • when compared to the unperturbed forecast

28. Lessons Learned

29. Lessons Learned Implementation of a stochastic parametrization scheme is feasible • Need to clarify the following questions: • how to decide whether the stochastic representation is realistic • how to optimize the choice of input perturbations (amplitude etc) • without obtaining unphysical parameter values • how to obtain a larger spread from stochastic parametrization • technical issue: random number generator on parallel machine?

30. Future Work: Experimental Ensembles with the LMK (EELMK) Volker Renner, Peter Krahe, Susanne Theis

31. Aim of EELMK • produceexperimental ensembles with the model LMK • LMK: very short-range forecasting with explicit convection • (see presentation of M.Baldauf) • explore its benefit… • …for high-resolution weather prediction • …forhydrological applications • (application of hydrological models for ensemble verification) • The project is considered to be part of the development of a planned operational ensemble prediction system based on the LMK.

32. Methodology Envisaged perturbation of initial conditions perturbed ensemble member some simple approach? perturbation of lateral boundary conditions perturbation of parametrised processes • INM-Ensemble? • COSMO-SREPS? • LAF-Ensemble? all sorts of tunable parameters

33. Thank you for your Attention!

34. Backup Slides

35. Problems in Ensemble Forecasting • Ensemble prediction sometimes fails in capturing • the pdf of the atmospheric state • the risk of extreme events • variations in forecast uncertainty observation

36. Simulation of the Stochastic Effect Approximation by noise: ≈ time subgrid scale processes in model grid box noise

37. Standardabw. / Mittel Vorhersagezeit [Stunden] Fehlerwachstum mit der Zeit • nur Fälle mit • Mittel > 0.01 mm • Flächenmittel • über das Gebiet • gemittelt über • 10. – 24.Juli 2002

38. Skalenbetrachtung Originalvorhersage (gestört – original) [mm] [mm]

39. Skalenbetrachtung Originalvorhersage (gestört – original) [mm] [mm] die Differenzen scheinen räumlich autokorreliert

40. Skalenbetrachtung Autokorrelation der Differenzen zwischen gestörter und ungestörter Simulation • 1h-Niederschlag • nur Fälle mit • Mittel > 0.01 mm • Vorhersagezeit: • 25 – 48 Stunden • komplettes Gebiet • 10. – 24.Juli 2002 • 9 gestörte Simulat. Autokorrelation räumlicher Abstand [km] zeitl. Abstand [h]

41. Relevanz des Stochast. Effektes ...im Vergleich zu Störungen der Anfangsbedingung Originalvorhersage • 1h-Niederschlag • Juli 10, 2002 • 17 – 18 UTC • Vorhersagezeit: • 18 Stunden [mm]

42. 3 Simulationen 3 Simulationen 3 Simulationen Analyse von 00 UTC Analyse von 23 UTC (vorher. Tag) Analyse von 01 UTC Relevanz des Stochast. Effektes Zusätzlich zur stochastischen Parametrisierung: Simple Störung der Anfangsbedingung

43. Relevanz des Stochast. Effektes Ensemble Standardabweichung Originalvorhersage [mm] [mm] Versatz der Maxima

44. Vorgehensweise im LM • Rauigkeitslänge • Zufällig gestörte Felder der Rauigkeitslänge: Jedes Ensemblemitglied erhält ein eigenes, zeitlich konstantes Feld

45. Vorgehensweise im LM • Rauigkeitslänge • Zufällig gestörte Felder der Rauigkeitslänge: Jedes Ensemblemitglied erhält ein eigenes, zeitlich konstantes Feld (2) Netto-Effekt der Parametrisierungen Zufällige Störung des diabatischen Gesamt-Antriebs in jedem Integrations-Zeitschritt

46. Störung der Rauigkeitslänge LM Rauigkeitslänge Annahme über die zufällige Variabilität der Rauigkeitslänge? [m]

47. Störung der Rauigkeitslänge gestörte Rauigkeitslänge LM Rauigkeitslänge [m] Unsere Störungen lassen großskalige Strukturen unangetastet...

48. Störung der Rauigkeitslänge [Stand.Abw. zwischen Ensemble-Läufen] x 10 LM Rauigkeitslänge [m] ... und die Störungs-Amplitude hängt von der lokalen räumlichen Variabilität ab

49. Verteilung der Zufallszahlen • Gleichverteilung • zeitliche Autokorrelation: • nimmt mit t exponentiell • ab:r (t = 5min) = 1/e • keine räumliche • Autokorrelation über eine • Modellgitterbox hinaus Beispiel: Vorhersagezeit t [Zeitschritt]

50. 10 x 5 x Sensitivität des Stochast. Effektes ...auf Eigenschaften des Rauschens Konfiguration „schwach“ Konfiguration „stark“