mesoscale data assimilation for the cosmo model: status and perspectives at the ims

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MESOSCALE DATA ASSIMILATION FOR THE

COSMO MODEL: STATUS AND PERSPECTIVES AT THE IMS

Massimo Bonavita, Lucio Torrisi, Antonio Vocino and Francesca Marcucci

CNMCA

Italian Meteorological Service

Pratica di Mare, Rome, Italy

29° EWGLAM, Dubrovnik, 8-11 October 2007

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Summary

Current DAS configuration at the IMS

Recent changes in the DAS

Observation usage

Initialization for the COSMO Model: 3D-VAR vs Nudging

The quest for a ”flow-dependent” analysis


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NWP-DAS at the IMS

Domain size 769x513

Grid spacing 0.125° (14 Km)

Number of layers 40

Time step 150 sec

Forecast range 72 hrs

Initial time of model run 00/12 UTC

L.B.C. IFS

L.B.C. update frequency 3 hrs

Initial state CNMCA 3D-VAR

Initialization Digital Filter

External analysis None

Status Operational

Hardware IBM P690 (ECMWF)

N° of processors used 32 (Model), 90 (Analysis)


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NWP-DAS at the IMS

• Data assimilation cycle:–3D-VAR FGAT analysis step;–3h refresh cycle;–Prognostic model: HRM

hydrostatic model at 14 Km grid (0.125°)


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3h vs 6h DAS cycle

NWP-DAS at the IMS

6E-SAT/ST Meeting, Reading 14-15/05/2007

FGAT vs OPE DAS cycle

NWP-DAS at the IMS

7E-SAT/ST Meeting, Reading 14-15/05/2007

0.125° vs 0.25° DAS cycle

NWP-DAS at the IMS

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Daily observation usage stats. Synoptic Asynoptic• RAOB ~19000 - AIREP ~5500

• PILOT ~250 - AMDAR ~38000

• SYNOP ~5500 - ACAR ~8500

• SHIP,BUOY ~1200 - WIND PROF ~1200

- QSCAT/ERS2 ~1800

- ASCAT ~4000

- AMV (MET9/MET7/MODIS)~14000

- AMSU-A Rad. (NOAA1X) ~14000

Synoptic Obs ~26000 Asynoptic Obs ~87000

Total ~ 113000 obs/day

Observation usage


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1. Use of METOP data: a) AMSU-A rad.: extension of current NOAA1x treatment, currently in passive monitoring configuration b) ASCAT winds: in place, impact and obs error characteristics under investigationc) GRAS Temperature/Spec. Hum. Profiles

2. Use of hyper-spectral sounders data:a) IASI L2 NRT products when available b) AIRS L2 products give positive NWP impact (Riishojgaard et al., 2007) but NRT availability is unclear

Observation usage

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Initialization of COSMO Model

• Initialization of COSMO Model at 7 Km resolution


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Domain size 641 x 401

Grid spacing 0.0625 (7 km)

Number of layers 40

Time step 40 s

Forecast range 72 hrs

Initial time of model run 00 UTC

Lateral bound. condit. IFS

L.B.C. update freq. 3 hrs

Initial state Interpolated 3D-VAR

Initialization D.F.I.

External analysis T,u,v, q, SP

Special features Filtered topography

Status Operational

Hardware IBM P690 (ECMWF)

N° of processors 120

Initial Conditions: Interpolated 14 Km 3D-VAR analysis

Initialization of COSMO Model

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Initial Conditions: Nudging Data Assimilat. cycle

2 run per day starting at 00 and 12 UTC

Forecast length + 72 hours

Horizontal resolution about 7 km

40 vertical levels

3-hourly boundary conditions from IFS/ECMWF forecast

Initial Conditions through continuous assimilation cycle based on nudging

COSMO I7

• Initialization of COSMO Model

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COSMO-ME/COSMO-I7(LAMI)(7km)

COSMO-ME vs COSMO-I7 Temp T+12 00runMAM

COSMO-ME vs COSMO-I7 Temp T+24 00run

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Mean Error (dot) and Mean Absolute Error (cont.)

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COSMO-ME vs COSMO-I7 Wmod T+12 00runMAM

COSMO-ME vs COSMO-I7 Wmod T+24 00runMAM

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COSMO-ME/COSMO-I7(LAMI)(7km)COSMO-ME vs COSMO-I7 Wmod T+36 00run

MAM COSMO-ME vs COSMO-I7 Wmod T+48 00run

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• Interpolating a 3D-VAR analysis at 14 Km works well for the 7 Km COSMO Model

• Significant improvements from 28 to 14 Km analysis: will investigate further resolution increase in 3DVAR DAS (hydrostatic limit)

• Clear improvement over parallel COSMO implementation initialized with nudging. Not clear cut comparison, lack of explicit balance constraints in nudging could be an issue at 7 Km scale



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• Initialization of COSMO Model at 2.8 Km resolution



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•For the 2.8Km version (COSMO-IT) an experiment was run comparing two identical model configurations: one initialized from interpolated 14 Km 3D-VAR, the other with Nudging data assimil. cycle


20Convegno FAI, Ischia 14/06/2007


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• Interpolating a 3D-VAR analysis at 14 Km does not provide balanced I.C. for 2.8 Km LM

• Observation nudging is able to reduce/suppress precipitation spin-up present in the 3DVAR initialized version

• After the first 6-9h skill scores of 3DVAR vs Nudging COSMO-IT are very similar: at 2-3 Km scale, nudging intrinsic balance constraints seems effective and the method looks competitive with 3D-Var

• Currently, nudging initialization is employed in operational 2.8Km COSMO-IT



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• Extensive discussion inside COSMO community

• Lack of resources/expertise to develop 4DVAR

• EnKF approach is simpler and seems to be ripe for trial in operational environment (shown to outperform 3DVAR in perfect model simulations and, more recently in real world experiments)

The quest for a flow dependent analysis


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• Possible EnKF advantages for high resolution DAS:

1. Complex observation operators (i.e. precipitation) coped with automatically

2. Covariances are evolved indefinitely

3. Can be extended to assimilate asynchronous observations (4DEnKF)

4. Gives “optimal” initial perturbations for ensemble forecasting


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• Which EnKF version to use? Agreement on LETKF (Hunt et al. 2005) because:

1. Version of Ensemble Square Root Filter (EnSRF), avoids additional sampling error of perturbed observations

2. Avoids inefficient sequential analysis of observations of other EnSRF

3. Computationally efficient (computations performed in ensemble subspace)

4. Very efficient parallel implementation29° EWGLAM, Dubrovnik, 8-11 October

2007

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On the other hand:1. Rank deficiency of sampled B matrix

can be detrimental for affordable ensemble size. Observation localization can help, possible need of hybrid analysis step with 3DVAR

2. Effective treatment of model error still an issue


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At which resolution should the filter be

used? Different ideas...1. DWD has proposed the KEnDA project

(COSMO project): Kilometer scale EnDA

2. Trying to tackle convection as an initial value problem too.

3. Running an ensemble DA and Forecast system at the Kilometer scale is very computationally expensive..


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At which resolution should the filter be

used? Different ideas...1. At IMS we will not have the computing

power of DWD for the foreseeable future!

2. Our forecasting target is the very short to extended short range, i.e. +3h->+72h

3. For non organized convection +3h is very long range forecasting

4. Convective systems whose life cycle and predictability extends beyond 3h can usually be modelled at the mesoscale (7-10 Km)


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MESO-DAPS project:

Data Assimilation and Prediction System at

the Mesoscale

Main advantage of unified approach:

“DA step samples initial uncertainties at

correct spatial scales for subsequent

ensemble forecast”

29th EWGLAM, Dubrovnik, 8-11 October 2007

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MESO-DAPS project:

1. Based on LETKF (or LETKF-3DVAR hybrid approach)

2. 10-14 Km resolution of ensemble members

3. Will provide lateral and initial conditions for nested Km scale COSMO model (+3-24h)

4. Currently national project. Will be proposed to COSMO community if proven

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Thank you!


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3rd SRNWP Workshop on short-range EPS

www.meteoam.it

[email protected]

Rome, 10-12 December 2007Università Roma I “Sapienza”

mesoscale data assimilation for the cosmo model: status and perspectives at the ims

Documents

runmam cosmo

vs cosmoi7 temp t

vs cosmoi7 wmod t

parallel cosmo implementation

fgat vs

absolute error

h das cyclenwpdas

dvar vs nudgingthe quest