Application of the WRF-LETKF Application of the WRF-LETKF System over Southern South System over Southern South America: Sensitivity to model America: Sensitivity to model

physics.physics.

María E. Dillon, Yanina Garcia Skabar, Juan Ruiz,

Eugenia Kalnay, Estela A. Collini,Pablo Echevarría, Marcos Saucedo,

Takemasa Miyoshi

Develop a state-of-the-art regional data assimilation system, that can be implemented operationally at the National Weather Service of Argentina and provide better forecasts.

GoalGoal

Why LETKF?Why LETKF?

Produces an ensemble of analysesIs independent from the modelComputationaly efficient Has tuneable parameters Tested over different regions and scales (Miyoshi et al., 2007;

Yang et al., 2009; Seko et al., 2011; Miyoshi y Kunii, 2012)

Why Multimodel?Why Multimodel?

Including model error in an ensemble can lead for a more realistic spread of the forecast solution (Houtekamer et al., 1996)

Different approaches for including model error in

ensemble forecasts

Consider different physical parameterization schemes (Stensrud et

al., 2000; Meng and Zhang, 2007)

WRF-LETKF System developed at the University of Maryland.

Test period: 01 Nov – 31 Dec 2012

40 Ensemble Members

Spatial Localization

I. C.: 01 Nov 00 UTC. The GFS Analysis was perturbed using differences between consecutive atmospheric states. To generate the 40 perturbations, analysis from October and November of 2010 were used.

B. C.: 3hs GFS 0.5° deterministic forecasts (not perturbed for each member)

MethodologyMethodology

Horizontal resolution: 40 km

Vertical resolution: 30 levels

H= 400 kmV~4 km

(Hunt et al. 2007)

Terrain heigth (m)

3 hours Analysis with a backward assimilation window of 3 hs

00 03 06 09 12

Observations from the NCEP prepbufr were assimilated

obsobs

obsobs

U

V

T

q

Tv

Ps

Lack of thermodynamic observations!

Example of data assimilated for 12 UTC:ADPSFC - SFCSHP ADPUPA - AIRCFT

SATWND (GOES)

A superobbing technique was applied to ASCAT winds

Physical parameterizatios used in the WRF model (v3.3.1)

Control

Cumulus: Kain-Fristch (Kain, 2004)Planetary Boundary Layer: YSU (Hong, Noh and Dudhia, 2006) Microphysics: WSM6 (Hong and Lim, 2006)SW radiation: Dudhia (Dudhia, 1989) LW radiation: RRTM (Mlawer, 1997)Soil model: Noah (Chen and Dudhia, 2001)

Multimodel Combination between different options of Cumulus and PBL parameterizations:

Kain-Fristch (Kain, 2004)

BMJ (Janjic, 1994, 2000)

Grell (Grell and Devenyi, 2002)

YSU (Hong, Noh and Dudhia, 2006) 5 members 5 members 5 members

MyJ (Janjic, 1994)5 members 4 members 4 members

Quasinormal (Sukoriansky, Galperin and Perov, 2005)

4 members 4 members 4 members

An adaptive inflation was used (Miyoshi, 2011)

(Period that we used to analyze the results)

Convergence of the inflation parameter

The multiplicative inflation parameters are

estimated adaptively

They are computed simultaneously with the

ensemble transform matrix at each grid

point

Spatially and temporally varying adaptive inlfation

Control

Multimodel

1st run of Nov-Dec 2012, starting with constant inflation 1.1

2nd run of Nov-Dec 2012, starting with the inflation obtained before

0 3 6 9 12 15 18 210

1

2

3

4

5

6 Control anaMultimodel anaGFS anaControl spreadMultimodel spread

DA cycles

Me

an

RM

SE

US

FC

(m

/s)

ResultsResults

Little enhancement of the spread with the multimodel exp

Mean of the RMSD for each DA cycle

0 3 6 9 12 15 18 210

1

2

3

4

5

6Control 3h fcstMultimodel 3h fcstGFS 3h fcst

DA cycles

Me

an

RM

SE

US

FC

(m

/s)

0 3 6 9 12 15 18 210

1

2

3

4

5

6Control 6h fcst

Multimodel 6h fcst

GFS 6h fcst

DA cycles

Me

an

RM

SE

US

FC

(m

/s)

AnalysesAnalyses

3hs fcst3hs fcst

6hs fcst6hs fcst

Bigger errors for 18 and 21 UTC

RMSD of Multimodel scheme is less than or

equal to RMSD of Control run.

We are happy that the RMSD in our first trial

are comparable to GFS based forecasts!

Case Study: 6-7 December 2012Case Study: 6-7 December 2012

A mesoscale convective system developed ahead of a cold front. Strong vertical shear, high values of CAPE.

Warm and moisture advection at 850 hPa

07 Dec 00 UTC

Accum precip 18 UTC 06dec – 06 UTC 07dec

Deterministic WRF I.C. GFS Ens mean I.C. Control Ens mean I.C. Multimodel

3B42-V7 estimation

One of the advantages of having an ensemble: to have an estimation of the uncertainty

Spread: shadedMean: contoured

Estimation 3B42-V7 Deterministic WRF

Domain A Domain B

The ensemble schemes represent better the structure of the precipitation evolution,

although the intensity is underestimated and there is a time lag.

The Multimodel scheme shows higher spread over both domains, and some members are closer to

the estimation than the ensemble mean

Conclusions and Future workConclusions and Future work

These are the first experiments of DA of real observations in Argentina

Promising results for an operational application!!!

Method performance OK3hs regional analysesEnsemble forecasts

Multimodel scheme better than the Control

Wider spreadLess or equal RMSDBetter performance in a case study of intense precipitation

Problems? Lack of thermodynamic observationLittle amount of observations for verificationMore processing and storage capacity is needed

Future work...

More exhaustive verification

Assimilation of thermodynamical observations such as vertical profiles from AIRS or ATOVS AMSU-A radiances

Implementation of no cost smoother for a “cheap” optimization of the analyses (Kalnay and Yang, 2010)

Study of more intense precipitation cases

Thank you for listening!Thank you for listening!

We are grateful to Celeste Saulo for her sugestions. NCEP has very generously made available the GFS analyses and forecasts, as well as the prepbufr observations. Without this essential information, this work would not have been possible.

We also acknowledge the World Meteorological Organization (WMO), for the financial support for the assistance to this Conference.

The equipment used for this research is supported by PIDDEF 41/2010, PIDDEF 47/2010We are grateful to the NWS of Argentina, the University of Buenos Aires and the CIMA, which are supporting

this project.

AcknowledgementsAcknowledgements

ReferencesReferencesHoutekamer P.L., and L. Lefaivre, 1997. Using ensemble forecasts for model validation. MWR, 125, 2416-2426.Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007. Efficient data assimilation for spatiotemporal chaos: a local ensemble

transform Kalman filter. Physica D, 77, 437–471. Kalnay, E. and Yang, S., 2010. Accelerating the spin-up of Ensemble Kalman Filtering. QJRMS Meng Z. and Zhang F., 2007. Tests of an ensemble kalman filter for mesoscale and regional-scale data assimilation. Part

II: imperfect model experiments. MWR, 135, 1403-1423. DOI: 10.1175/MWR3352.1 Miyoshi, T., 2011. The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble

transform Kalman filter. Mon. Wea. Rev., 139, 1519–1535.Miyoshi T., S. Yamane, T. Enomoto, 2007. Localizaing the error covariance by physical distances within a local ensemble

transform kalman filter (LETKF). SOLA, Vol 3, 089-092, doi:10.2151/sola.2007-023Miyoshi T. and Kunii M., 2011. The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting

Model: Experiments with Real Observations. Pure Appl. Geophys. 169, 321-333. DOI 10.1007/s00024-011-0373-4Miyoshi T. and Kunii M., 2012. Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather

prediction. Tellus, 64A, 18408. DOI 10.3402/tellusa.v64i0.18408Seko H., T. Miyoshi, Y. Shoji, K. Saito, 2011. Data assimilation experiments of precipitable water vapour using the LETKF

system:intense rainfall event over Japan 28 July 2008. Tellus, 63A, 402-414 Stensrud D.J., J-W Bao, and T.T. Warner, 2000. Using initial condition and model physics perturbations in short-range

ensemble simulations of mesoscale convective systems. MWR, 128, 2077-2107.Yang S-C, E. Kalnay, B. Hunt, N. Bowler, 2009. Weight interpolation for efficient data assimilation with the local ensemble

transform kalman filter. QJRMS, 135, 251-262, doi:10.1002/qj.353

More Details ...More Details ...

Initial Conditions:

01 Nov 00 UTC. The GFS Analysis was perturbed using differences between consecutive atmospheric states.

To generate the 40 perturbations, analysis from October and November of 2010 were used.

Example I. C. Member 1:

X 1 novM 1 =X 1nov +0 . 2∗( X 16 oct− X 15 oct )

Vertical levels: not a regular distribution, with top at 50 hPa

GFS Analysis from 01 Nov 2012 00 UTC

GFS Analysis from 16 Oct 2010 00 UTC minus GFS Analysis from 15 Oct 2010 00 UTC

*

3B42-V7 estimation

Case study: some particular members presented a better estimation of the precipitation than the mean of the ensemble, for both experiments.

Ens mean I.C. Multimodel

Accum precip 18 UTC 06dec – 06 UTC 07dec

Ens mean I.C. Control

MEM 34 Multimodel MEM 31 Multimodel MEM 20 Control

Both with BMJ and YSU