Experimental Ensembles with the LM/LMK Past and Future Work Susanne Theis.
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Transcript of Experimental Ensembles with the LM/LMK Past and Future Work Susanne Theis.
Experimental Ensembles with Experimental Ensembles with the LM/LMKthe LM/LMK
Past and Future WorkPast and Future Work
Susanne Theis
Past Work:Past Work:
Stochastic ParametrizationStochastic Parametrization
in the LMin the LM
Susanne Theis (PhD Thesis)
Supervisor: Prof. Andreas Hense, University of Bonn
Motivation ofMotivation of
Stochastic ParametrizationStochastic Parametrization
Problem in Ensemble ForecastingProblem in Ensemble Forecasting
uncertainty ininitial conditions
uncertainty inparametrised processes
uncertainty inNWP model output
uncertainty inlateral boundary
conditions
Ensemble represents some sources of uncertainty, but not all
Missing: uncertainty in parametrised processes(= stochastic effect of subgrid scale processes)
Conventional ParametrizationsConventional Parametrizations
resolved process
sub
grid
sca
le e
ffect
experimentaldata
mean effect
Estimating the subgrid scale effect:
…only simulate the mean effect of subgrid scale processes!
Conventional ParametrizationsConventional Parametrizations
resolved process
sub
grid
sca
le e
ffect
pro
ba
bili
ty d
en
sity
fu
nct
ion
subgrid scale effect
experimentaldata
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:
Conventional ParametrizationsConventional Parametrizations
resolved process
sub
grid
sca
le e
ffect
pro
ba
bili
ty d
en
sity
fu
nct
ion
subgrid scale effect
variability neglected!
experimentaldata
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:
Aim of Stochastic ParametrizationAim 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
Methodology ofMethodology of
Stochastic ParametrizationStochastic Parametrization
Subscale Processes in the ModelSubscale Processes in the Model
);;(P);(A tetet
e
„Physics“(parametrised processes)
„Dynamics“
dtt
ete
t
t
1
0 0
1)(
Model Simulation:
Separation of the prognostic model equations:
Stochastic ParametrizationStochastic Parametrization
);;(P);(A tetet
e
„Physics“(parametrised processes)
„Dynamics“
Injection of „noise“ into the deterministic bulk formulae:
noise
Stochastic ParametrizationStochastic Parametrization
);;(P);(A tetet
e
„Physics“(parametrised processes)
„Dynamics“
Injection of „noise“ into the deterministic bulk formulae:
noise
Stochastic ParametrizationStochastic Parametrization
);;(P);(A tetet
e
„Physics“(parametrised processes)
„Dynamics“
Injection of „noise“ into the deterministic bulk formulae:
noise
Stochastic ParametrizationStochastic Parametrization
);;(P);(A tetet
e
„Physics“(parametrised processes)
„Dynamics“
Injection of „noise“ into the deterministic bulk formulae:
noise
Stochastic Parametrization in LMStochastic Parametrization in LM
(1) Perturbation of the Net Effect of Diabatic Forcing
);;P();;(P' , texte tr
Stochastic Parametrization in LMStochastic Parametrization in LM
turbulence
radiation
microphysics
convection
(1) Perturbation of the Net Effect of Diabatic Forcing
);;P();;(P' , texte tr
Stochastic Parametrization in LMStochastic Parametrization in LM
turbulence
radiation
microphysics
convection
randomnumber
(1) Perturbation of the Net Effect of Diabatic Forcing
perturbation• in each time step• at each grid point
);;P();;(P' , texte tr
Perturbation PropertiesPerturbation Properties
10 x
example:
ampl
itude
temporal correlation
spatialcorrelation
uniform distribution
choice motivated by ECMWF ensemble setupfurther experiments: temporal correlation more smooth
(2) Perturbation of the Roughness Length over Land
Stochastic Parametrization in LMStochastic 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.
);;(P te
Experiments withExperiments with
Stochastic ParametrizationStochastic Parametrization
Setup of Ensemble ExperimentsSetup 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
Setup of Ensemble ExperimentsSetup 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
Setup of Ensemble ExperimentsSetup of Ensemble Experiments
);;P( te );;P( te
perturbation ofinitial conditions
perturbation ofparametrised processes
perturbation oflateral boundary
conditions
net diabatic forcing
roughness length
perturbedensemble member
Example of Ensemble ExperimentExample of Ensemble Experiment
• 1h-precipitation
• 10 July, 2002 17 – 18 UTC
• lead time: 18 hours
[mm]
original LM simulation (unperturbed)
case study Berlin
Ensemble
Example of Ensemble ExperimentExample of Ensemble Experiment
[mm]
ensemble spreadoriginal LM simulation
(unperturbed)
Results of Ensemble ExperimentsResults of Ensemble Experiments
The stochastic parametrization scheme…
• has a considerable effect on precipitation amount
• shows hardly any effect on precipitation occurence
Further InvestigationsFurther 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
Lessons LearnedLessons Learned
Lessons LearnedLessons Learned
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
Future Work:Future Work:
Experimental EnsemblesExperimental Ensembles
with the LMK (EELMK)with the LMK (EELMK)
Volker Renner, Peter Krahe, Susanne Theis
Aim of EELMKAim 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.
Methodology EnvisagedMethodology Envisaged
);;P( te
perturbation ofinitial conditions
perturbation ofparametrised processes
perturbation oflateral boundary
conditions
all sorts of tunable parameters
perturbedensemble member
• INM-Ensemble?
• COSMO-SREPS?
• LAF-Ensemble?
some simple approach?
Thank youThank you
for your Attention!for your Attention!
Backup SlidesBackup Slides
Problems in Ensemble ForecastingProblems 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
Simulation of the Stochastic EffectSimulation of the Stochastic Effect
Approximation by noise:
time
≈
subgrid scale processesin model grid box noise
Vorhersagezeit [Stunden]
Sta
ndar
dabw
. /
Mitt
elFehlerwachstum mit der ZeitFehlerwachstum mit der Zeit
• nur Fälle mit Mittel > 0.01 mm
• Flächenmittel über das Gebiet
• gemittelt über 10. – 24.Juli 2002
SkalenbetrachtungSkalenbetrachtung
Originalvorhersage (gestört – original)
[mm] [mm]
SkalenbetrachtungSkalenbetrachtung
Originalvorhersage (gestört – original)
[mm] [mm]
die Differenzen scheinen räumlich autokorreliert
Au
toko
rre
latio
n
räumlicher Abstand [km] zeitl. Abstand [h]
• 1h-Niederschlag
• nur Fälle mit Mittel > 0.01 mm
• Vorhersagezeit: 25 – 48 Stunden
• komplettes Gebiet
• 10. – 24.Juli 2002
• 9 gestörte Simulat.
SkalenbetrachtungSkalenbetrachtung
Autokorrelation der Differenzen zwischen gestörter und ungestörter Simulation
Relevanz des Stochast. EffektesRelevanz des Stochast. Effektes
• 1h-Niederschlag
• Juli 10, 2002 17 – 18 UTC
• Vorhersagezeit: 18 Stunden
Originalvorhersage
[mm]
...im Vergleich zu Störungen der Anfangsbedingung
Relevanz des Stochast. EffektesRelevanz des Stochast. Effektes
Analysevon 01 UTC
Analysevon 00 UTC
3 Simulationen 3 Simulationen 3 Simulationen
Analysevon 23 UTC(vorher. Tag)
Zusätzlich zur stochastischen Parametrisierung:Simple Störung der Anfangsbedingung
Relevanz des Stochast. EffektesRelevanz des Stochast. Effektes
[mm][mm]
Originalvorhersage Ensemble Standardabweichung
Versatz der Maxima
(1) Rauigkeitslänge
Zufällig gestörte Felder der Rauigkeitslänge:
Vorgehensweise im LMVorgehensweise im LM
Jedes Ensemblemitglied erhält ein eigenes, zeitlich konstantes Feld
(1) Rauigkeitslänge
Zufällig gestörte Felder der Rauigkeitslänge:
Vorgehensweise im LMVorgehensweise im LM
(2) Netto-Effekt der Parametrisierungen
Zufällige Störung des diabatischen Gesamt-Antriebs injedem Integrations-Zeitschritt
Jedes Ensemblemitglied erhält ein eigenes, zeitlich konstantes Feld
Störung der RauigkeitslängeStörung der Rauigkeitslänge
[m]
LM Rauigkeitslänge
Annahme über die zufällige Variabilität der Rauigkeitslänge?
Störung der RauigkeitslängeStörung der Rauigkeitslänge
[m]
Unsere Störungen lassen großskalige Strukturen unangetastet...
LM Rauigkeitslängegestörte
Rauigkeitslänge
Störung der RauigkeitslängeStörung der Rauigkeitslänge
[m]
LM Rauigkeitslänge[Stand.Abw. zwischen Ensemble-Läufen] x 10
... und die Störungs-Amplitude hängt von der lokalen räumlichen Variabilität ab
Verteilung der ZufallszahlenVerteilung der Zufallszahlen
Vorhersagezeit t [Zeitschritt]
• Gleichverteilung
• zeitliche Autokorrelation:
nimmt mit exponentiell
ab: r (= 5min) = 1/e
• keine räumliche Autokorrelation über eine Modellgitterbox hinaus
Beispiel:
Sensitivität des Stochast. EffektesSensitivität des Stochast. Effektes
...auf Eigenschaften des Rauschens
5 x10 x
Konfiguration „schwach“ Konfiguration „stark“
Sensitivität des Stochast. EffektesSensitivität des Stochast. Effektes
[mm] [mm]
Konfiguration „schwach“ Konfiguration „stark“
Ensemble Standardabw.Ensemble Standardabw.
Fallstudie Fallstudie BerlinBerlin
• 1h-Niederschlag
• Juli 10, 2002 18 – 19 UTC
• Vorhersagezeit: 43 Stunden
[mm]
Originalvorhersage
Ensemble StandardabweichungEnsemble Standardabweichung
Ensemblemittel Ensemble Standardabw.
[mm]
• Standardabweichung nur hoch in Gegenden mit RR > 0• kein Versatz von Niederschlagsgebieten
Methodology EnvisagedMethodology 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)
);;P( te