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

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Präsentation zum Thema: "Experimental Ensembles with the LM/LMK Past and Future Work Susanne Theis."—  Präsentation transkript:

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

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

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

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

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 „Physics“ (parametrised processes) „Dynamics“ Model Simulation: Separation of the prognostic model equations:

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

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

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

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

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

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

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

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

19 (2) Perturbation of the Roughness Length over Land Stochastic Parametrization in LM 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, UTC - Juli 10, UTC... - Juli 24, 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 perturbation of parametrised processes perturbation of lateral boundary conditions net diabatic forcing roughness length perturbed ensemble member

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

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

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 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? Implementation of a stochastic parametrization scheme is feasible

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

31 Aim of EELMK produce experimental 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 …for hydrological 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 perturbation of parametrised processes perturbation of lateral boundary conditions all sorts of tunable parameters perturbed ensemble member INM-Ensemble? COSMO-SREPS? LAF-Ensemble? some simple approach?

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 Vorhersagezeit [Stunden] Standardabw. / Mittel 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]

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

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

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

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

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

44 (1) Rauigkeitslänge Zufällig gestörte Felder der Rauigkeitslänge: Vorgehensweise im LM Jedes Ensemblemitglied erhält ein eigenes, zeitlich konstantes Feld

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

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

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

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

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

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

51 Sensitivität des Stochast. Effektes [mm] Konfiguration „schwach“ Konfiguration „stark“ Ensemble Standardabw.

52 Fallstudie Berlin 1h-Niederschlag Juli 10, – 19 UTC Vorhersagezeit: 43 Stunden [mm] Originalvorhersage

53 Ensemble Standardabweichung EnsemblemittelEnsemble Standardabw. [mm] Standardabweichung nur hoch in Gegenden mit RR > 0 kein Versatz von Niederschlagsgebieten

54 Methodology Envisaged physics perturbation: a set of tunable parameters will be perturbed (e.g. plant cover, leaf area index, maximal turbulent length scale, roughness length, etc)  physical reasoning possible lateral boundary conditions: an available coarse-resolution ensemble will be applied, e.g. the INM-Ensemble, COSMO-SREPS, or a LAF-Ensemble initial conditions: perhaps only a simple approach (low priority in this project)


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