KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 – 11 Sept....

KENDA (Km-Scale Ensemble-based Data Assimilation)

COSMO General Meeting, Offenbach, 7 – 11 Sept. 2009KENDA [email protected]

Contributions / input by:

Hendrik Reich, Andreas Rhodin, Klaus Stephan, Werner Wergen (DWD)

Daniel Leuenberger, Tanja Weusthoff (MeteoSwiss)

Marek Lazanowicz (IMGW)

Mikhail Tsyrulnikov (HMC)

PP Kenda : Status Report [email protected]

Deutscher Wetterdienst, D-63067 Offenbach, Germany

• status & outlook

• general issues in the convective scale experiments for assessing importance of km-scale details in IC

• deterministic analysis



Task 1: General issues in the convective scale and evaluation of COSMO-DE-EPS

Purpose: Guides decision how resources will be spent on/ split betw. LETKF and SIR

(COSMO-NWS and universities); part of the learning process

main disadvantage of LETKF: assumes Gaussian error distributions

Task 1.1.A: investigate non-Gaussianity by means of O – B statistics (convective / larger scales, different forecast lead times): provides an upper limit estimate of the non-Gaussianity to deal with

talk by Daniel Leuenberger:Statistical characteristics of high-resolution COSMO Ensemble forecastsin view of Data Assimilation

Task 1.2: investigate non-Gaussianity by examining perturbations of very-short range(2009) forecasts from COSMO-DE-EPS



RWWRP / THORPEX Workshop on 4D-Var and EnKF intercomparison• we should continue with KENDA (LETKF) as planned

(4DVAR / EnKF ok for HR DA: everybody pushes the way further on the current road; ensemble size stable (30 – 40); synergistic approaches)

• model errors a (the) key issue for advanced DA important for DA to work on model improvement

• worthwhile to obtain options for EnKF implementations which also make use of 3DVar(by implementing tasks 2.1 , 2.5)

talk by Breogan Gomez:Single-column experiments on the vertical localisation in the LETKF

difficulty of assimilating non-local satellite data and achieving good resolution in local analysis

Task 1.4: M. Tsyrulnikov: Review on Hunt et al. implementation of LETKF



Task 1.1 D: assess importance of km-scale details versus larger-scale conditions in the IC

(do we have to analyse the small scales, or is it sufficient to analyse the large scales, as e.g. incremental 4DVAR (ECMWF) would do ?)

Comparison: ‘IEU’: IC from interpolated COSMO-EU analysis of ass. cycle (no LHN)

‘IDE’: IC from COSMO-DE analysis of ass. cycle (no LHN)

‘LHN’: IC from COSMO-DE analysis of ass. cycle (IDE + LHN for use of radar-

derived precipitation)

Note: – IC from assimilation cycle → late cut-off, very similar set of observations

– identical correlation functions (scales) used in nudging for IEU and IDE

– identical soil moisture, taken from IEU (with variational soil moisture initialisation)

– model version as operational in summer 2009

Period: 31 May – 13 June 2007: air mass convection



00 UTC runs 06 UTC runs

ETS

FBI

LHNIDE (no-LHN)IEU (coarse IC)

31.05. – 13.06.07: air-mass convection

# radar ‘obs’ with rain


time of day time of day

0.1 mm




ETS

FBI






1 mm




ETS

FBILHNIDE (no-LHN)IEU (coarse IC)


0.1 mm




12 18




ETS

FBI



1 mm






12-UTC runs,6 – 18 h fcsts.

(nighttime precipitation)

radar (12-h sum) LHN (fine-scale IC)IEU (coarse IC)

10 June 18 UTC

– 11 June 06 UTC

2007

4 June 18 UTC

– 5 June 06 UTC

2007

Examples



Task 1.1.D: assess importance of km-scale details

Results of comparison to coarse-scale ‘IEU’ :

─ ‘IDE’ better than ‘IEU’ for 12- and 18-UTC runs up to +15h (similar FBI, higher ETS) similar for 0- and 6-UTC runs

─ improvement by LHN during first 6 hours

─ ‘LHN’ has better precipitation patterns (6 – 18 h forecast) than ‘IEU’ in some cases

RadarWith LHN Without LHN (dashed: determininistic)

Past experiments (Leuenberger):environment affects impact of fine-scaledetails in analysis from LHN



Task 2: Technical implementation of an ensemble data assimilation framework / LETKF

analysis step (LETKF) outside COSMO code ensemble of independent COSMO runs up to next analysis time separate analysis step code, LETKF included in 3DVAR code of DWD



perturbed forecasts

– ensemble mean forecast

(local, inflated) transform matrix Wa analysis mean perturbed

analyses

LOCAL Ensemble Transform Kalman Filter (LETKF)(chart based on a slide of Neil Bowler, UK MetOffice)

= background perturbations Xb

→ flow-dep. backgr. error covar. analysis error covariance

in the (5-dimensional) sub-space S spanned by background perturbations :(linearise obs operator around the mean of ensemble of simulations of an obs)

explicit solution for minimisation of cost function (Hunt et al., 2007)

( - ) + =( - ) + =

( - ) + =( - ) + =( - ) + =

0.9 Pert 1-0.1 Pert 2-0.1 Pert 3-0.1 Pert 4-0.1 Pert 5

aa xw

)( )()( ibibb H xyy

→ analysis ensemble member : )()()( iaabbiaaia WwXxXxx 1/2PW aw

a k )1(

and in model space : Tbaw

baabba XPXP,wXxx

-111 )1( bTbaw

bTbaw

a k YRY I P,yyRYPw O → analysis (mean) and analysis error covariance : bibb yyY )(:columns of



Analysis for a deterministic forecast run : use Kalman Gain K of analysis mean

11

1)1()(

RYYRY I XwXK,xyKxx O TbbTbbabBBA kH

K is already computed in the (L)ETKF ;

if the resolution of the deterministic run is higher than of ensemble, the analysis increments have to be interpolated to the fine grid

determ.

KXw1

ba,bX

Kalman gain / analysis increments not optimal, if deterministic run (strongly) deviates from analysis mean of ensemble



LETKF

COSMO

read obs (NetCDF) + Grib analysis of ensemble member

compute obs–fg (obs. increm.) + QC (contains obs operator)

write NetCDF feedback files (obs + obs–fg + QC flags) + Grib files (model)

read ensemble of NetCDF feedback files + ensemble of COSMO S-R forecast Grib files

perform LETKF (based on obs–fg values around each grid pt.,calc. transformation matrices and analysis (mean & pert.))(adapt: C-grid, specific variables (w), efficiency)

write ensemble of COSMO S-R analysis Grib files + NetCDF feedback files with additional QC flags ( verif.)

exp.system

Task 2.3: finished

Task 2.2: almost finishedTask 2.4: not yet done

However: scripts written to do a few stand-alone cycles with LETKF → preliminary tests can start soon



y: observationsx: model stated = y – H(x): innovationH: observation operatorqf: quality flagQC: quality control

Verification / diagnostics: new ‘stat’ tool : compute model (forecast) – obs , as input for verification

GRIBnew tool (included in 3DVar code)

NetCDFobservation y

qf

read obswrite

feed-

backxH(x)

QCH

read

feedback

y

qf

H(x)read grib

NetCDFfeedbacky, qf, d

want have capability of computing distance of model (ensemble member) to observationsthat have not been used previously in a COSMO run ( SIRF)

need to include

NetCDFobservation

QCH& in ‘stat’ module

common codes in COSMO & ‘stat’ tool



Task 2.1: extract from COSMO:

- ‘library 2’ : in progress

- ‘library 1’ : finished

Task 2.5: - in progress: include above libraries in 3DVar environment, (translate COSMO data structure into 3DVar data structure and vice versa)

- not yet started: extend flow control (e.g. reading several Grib files and temporal interpolat.)

Task 2: Technical implementation of an ensemble data assimilation framework / LETKF

for verification: ‘stat’-module: compute model (forecast) – obs :adapt verification mode of 3DVar/LETKF package

Advantages:

– COSMO obs operators available in 3DVAR/LETKF environment 3DVar/ EnKF approaches requiring 3DVar in principle applicable to COSMO LETKF for ICON will require COSMO obs operators in the future

– 1 common code for GME/ICON and COSMO to produce input for diagnostics / verif..

Disadvantages: – more complex code for this diagnostic task

– possibly additional transformation from COSMO data structure into 3DVAR data structure and vice versa required for new COSMO obs operators.

observation operators H with QC

modules for reading obs from NetCDF

Task 2.6: Adapt ensemble-related diagnostic tools : not yet started



Task 3: Evaluate and tune LETKF

address primary scientific issues related to the LETKF on the convective scale(using only in-situ data at first)

• investigate / control noise, adapt in view of non-hydrostatic aspectsif mixing of hydrostatically balanced model states (ensemble forecasts) varies with height (vertical localisation), the resulting model analysis will usually not be hydrostatically balanced

→ get version that can be used to do first tests with radar radial winds

• primary scientific issues:– model model (physical) perturbations– multiplicative covariance inflation– localisation (multi-scale DA)– additive covariance inflation with red noise, backscatter, statistical forecast error

covariances (‘3DVAR-B’) ‘– bias removal– noise control (update frequency, digital filter initialisation, lateral boundary cond.)– (convection initiation (warm bubbles, LHN)



Task 4: Inclusion of additional observations

Research funded by DWD to develop suitably sophisticated + efficient observation operators forradar data (radial winds + (3-dim.) reflectivity) (Blahak + 2 PhD’s)

Task 4.1: Radar radial winds:• implement simple obs operator, then test and tune• particular issues: obs error variances and correlations, superobbing, thinning, localisation

Task 4.2: Radar reflectivity (after 2010):

Task 4.3: Ground-based GPS slant path delay (after 2010):• particular issue: localisation for non-local obs

Task 4.4: Cloud info based on satellite and conventional data(DWD: applied for Eumetsat fellowship, start end of 2010)

• derive incomplete cloud analysis, use obs increments of cloud or humidity• use SEVIRI brightness temperature directly in LETKF in cloudy (+ cloud-free) conditions

Task 5: Sequential importance resampling (SIR) filter

research projects continue within DAQUA (MeteoSwiss investigating on norms)



thank you for your attention



set up (G-) SIRF test with COPS period

time

Weighting +

Resam

pling

Guiding steps

Weighting +

Resam

pling

final weightingafter free forecast is computed

SREPS (ass.) SREPS (pred.)

radar / satellite / in-situ obs.

free forecast

… …

HRES

select bestmembers( ≤10 )

Set up standard and G-SIRF with and without standard data assimilation (MIUB, DWD)

• assess impact of conventional DA (LHN, PIB) on ensemble development (spread generation, keeping ensemble on track)

• implement optimal stepping to a new driving mesoscale ensemble

Evaluate classical and spatial (object oriented, fuzzy) metricsfor weighting mesoscale (SREPS) and km-scaleensemble members (DLR, MCH)• assess correlation of metrics betw. models of different res.• assess persistence of skill in different metrics

KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 – 11 Sept....

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