CONSENS Priority Project Status report COSMO year 2008/2009

CONSENS Priority Project

Status report COSMO year 2008/2009

Involved scientists:Chiara Marsigli, Andrea Montani, Tiziana Paccagnella, Tommaso Diomede (ARPA-SIMC)

Flora Gofa, Petroula Louka (HNMS)Felix Fundel (MeteoSwiss)

Overview

Task 1: Running of the COSMO-SREPS suite suite maintenance implementation of the back-up suite

Task 2: Model perturbations perturbation of physics parameters perturbation of soil fields

Task 3: Ensemble mergingMulti-clustering

Task 4: Calibration

The COSMO-SREPS ensemble

COSMO-SREPS has been developed within the SREPS PP, aiming at the development of a Short-Range Ensemble Prediction System

3 days forecast range, 10 km of horizontal resolution

COSMO-SREPS provides boundary conditions for COSMO-DE-EPS, the 2.8 km ensemble system under development at DWD

Application: test of the use of COSMO-SREPS to estimate a flow-dependent B matrix in a 1D-Var DA of satellite data

COSMO-SREPS

COSMO at 25 km on IFSIFS – ECMWF global

by A

EMET

Sp

ain

COSMO at 25 km on GMEGME – DWD global

COSMO at 25 km on UMUM – UKMO global

COSMO at 25 km on GFSGFS – NCEP global

P1: control P2: physics pert p2P3: physics pert p3P4: physics pert p4…

•COSMO (v 4.7)

•00 UTC and12 UTC

•10 km

•40 levels

•16 members

•72 h

1. Running of the COSMO-SREPS suite

ARPA-SIMC Maintenance of the COSMO-SREPS suite at ECMWF

Adaptation of the data output for COSMO-DE-EPS

Implementation of a 12 UTC run (beside the 00 UTC one)

Implementation of the back-up suite: delayed (9 months)

The work involves also DWD, even if implicitly!

AEMET has provided the int2lm code adapted for the NCEP and UKMO models

An agreement with UKMO has been signed, in order to receive regularly the boundary conditions from the UM

Suite availability

COSMO-SREPS availability

0

10

20

30

40

50

60

70

80

90

100

giu-07

lug-07

ago-0

7se

t-07

ott-07

nov-0

7dic

-07

gen-0

8

feb-08

mar-08

apr-0

8

mag-08

giu-08

lug-08

ago-0

8se

t-08

ott-08

nov-0

8dic

-08

gen-0

9

feb-09

mar-09

apr-0

9

mag-09

giu-09

lug-09

ago-0

9

months

% o

f day

s

complete + incomplete

complete only

2.1 Model perturbations: parameters

CSPERT test suiteARPA-SIMC - HNMS In order to study new parameter perturbations, a test

suite (CSPERT) was already implemented at ECMWF, by ARPA-SIMC, during the SREPS PP. Results for SON 2007 can be found in the SREPS final report

According to the outcome of the SREPS PP, it was decided to analyse the impact of these perturbations on a dry season as well

New runs of the CSPERT suite were performed in autumn 2008, for the JJA 2008 period

Analysis of the results completed in May 2009

The CSPERT suite

16 LM runs at 10 km

P1: control (ope)P2: conv. scheme (KF)P3: parameter 1P4: parameter 2P5: …

IFS – ECMWF global

SON 07 + JJA 08

JJA 2008 – ITBIAS MAET2m

Td2m

JJA 2008 – GRBIAS RMSET2

m

Td2m

http://www.hnms.gr/hnms/greek/index_html

|T BIAS| T RMSE |Td BIAS| Td RMSE |U BIAS| U RMSEKF = = ~ = = ~ = ~ =

tur_len=150 = = = > = = > (day)

= > (day)

tur_len=1000 ~ = = < = = =

pat_len=10000 < =(day)

< = (day)

> < (day)

> = (day) ~ = ~ =

rat_sea=1 > > > = > ~ = ~ =

rat_sea=60 < < > = > ~ = ~ =

crsmin=50 > > > > = > (day)

= > (day)

crsmin=200 < < = < = = =

c_lnd=1 = < (day)

> (day) > = = =

c_lnd=10 >< < > > (night) = < (day)

=

rlam_heat=0.1 > < (day)

= > (day)

> > = =

rlam_heat=10 < > (day)

= < (day)

> > > < ~ =

- -- +++=

Summary of the perturbation impact


Remarks from the CSPERT suite The effect of perturbing each physics parameter on

improving or worsening the statistical values of the results in comparison to the corresponding control was investigated

Based on these results, the next step was to explore the importance and the effect of selected physical perturbations further

It seems that the particular parameter perturbations do not influence greatly the mean horizontal wind apart from a few exceptions. Possibly looking at the vertical wind component would make the effects more apparent for some parameters


Remarks (cont)

Looking separately at each parameter perturbation compared to the control run:

scaling factors related to the laminar layer (rlam_heat, rat_sea), turbulent length scale (tur_len) and evapotranspiration (crsmin), all associated with the development of the turbulent surface layer, are the physical parameters on which the main focus is given


2.1 Model perturbations: parameters

the new COSMO-SREPS configurationARPA-SIMC - HNMS

On the basis of the analysis of these results, a new configuration of the COSMO-SREPS suite has been implemented in May 2009

An analysis of its performance over summer 2009 (JJA) has been carried out:in terms of 2m temperature only over the Alpine areaIn term of the continuous parameters (T, U and Td)

over GreecePrecipitation has not been considered up to now

mainly due to the summer season

member father itype_conv tur_len pat_len rlam_heat rat_sea crsmin1 ecmwf 0 150 500 1 20 1502 ecmwf 1 1000 500 1 20 1503 ecmwf 0 500 500 0.1 20 2004 ecmwf 1 500 10000 1 20 1505 gme 0 500 10000 1 20 1506 gme 1 500 500 0.1 20 1507 gme 0 500 500 1 1 2008 gme 1 500 500 1 1 1509 avn 0 1000 500 1 20 150

10 avn 1 150 500 1 20 15011 avn 0 500 500 10 20 15012 avn 1 500 500 10 20 15013 ukmo 0 500 500 1 60 15014 ukmo 1 500 500 1 60 15015 ukmo 0 500 500 1 20 5016 ukmo 1 500 500 1 20 50

COSMO-SREPS new configuration (from the 5th of May 2009)

convection scheme:0 Tiedtke 1 Kain-Fritsch

maximal turbulent length scale

length scale of thermal surface patterns

scaling factor of the laminar layer depth

ratio of laminar scaling factors for heat over sea

minimal stomata resistance

IFS

GME

NCEP

UM

Tiedtke

Tiedtke

Kain-Fritsch

Kain-Fritsch

tur_len <

tur_len >

rlam_heat <

crsmin >

pat_len >

pat_len >

rlam_heat < crsmin

>

rat_sea < rat_sea

<

tur_len >

tur_len <

rlam_heat >

rlam_heat >

rat_sea >

rat_sea >

crsmin <

crsmin <

Nearest grid point

Relationship between error and spread

JJA09Small sample, 30 days

only

SYNOP over the MAP D-PHASE domain

SYNOP over the whole domain

t2m

Relationship between error and spreadJJA09

SYNOP over the whole domain - Nearest grid point

00 UTC 12 UTCt2m

Nearest grid point

2m T – deterministic scoresglobal model

JJA09


ecmwfgmencepukmo

MAE

BIAS

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1.8

2.0

2.2

2.4

2.6

2.8

3.0

Nearest grid point

2m T – deterministic scoresconvection scheme

JJA09


TiedtkeKain-Fritsch

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1.8

2.0

2.2

2.4

2.6

2.8

3.0

MAE

BIAS

Nearest grid point

2m T – deterministic scorestur_len

JJA09


tur_len=150 – ecmwf Ttur_len=1000 – ecmwf KFtur_len=1000 – ncep Ttur_len=150 – ncep KF

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1.8

2.0

2.2

2.4

2.6

2.8

3.0

MAE

BIAS

tur_len: maximal turbulent length scale U10m: BIAS

-1.5

-0.5

0.5

1.5

6 12 18 24 30 36 42 48 54 60 66 72

m1 m2 m9 m10

U10m: RMSE

1.5

2

2.5

3

6 12 18 24 30 36 42 48 54 60 66 72

m1 m2 m9 m10


Nearest grid point

2m T – deterministic scorespat_len

JJA09


pat_len=10000 – ecmwf KF

pat_len=10000 – gme T

MAE

BIAS

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1.8

2.0

2.2

2.4

2.6

2.8

3.0

Nearest grid point

2m T – deterministic scoresrlam_heat

JJA09


rlam_heat=0.1 crsmin=200 – ecmwf Trlam_heat=0.1 – gme KFrlam_heat=10 – ncep Trlam_heat=10 – ncep KF

MAE

BIAS

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1.8

2.0

2.2

2.4

2.6

2.8

3.0

T2m: RMSE

1

2

3

4

5

6

7

8

9

6 12 18 24 30 36 42 48 54 60 66 72

m3 m6 m11 m12

T2m: BIAS

-2

-1

0

1

2

6 12 18 24 30 36 42 48 54 60 66 72

m3 m6 m11 m12

rlam_heat: scaling factor of the laminar layer depth


Td2m: BIAS

-7

-6

-5

-4

-3

-2

-1

0

1

6 12 18 24 30 36 42 48 54 60 66 72

m7 m8 m13 m14

Td2m: RMSE

1

2

3

4

5

6

7

8

9

0 6 12 18 24 30 36 42 48 54 60 66 72

m7 m8 m13 m14

rat_sea: ratio of laminar scaling factor for heat over sea


Nearest grid point

2m T – deterministic scorescrsmin

JJA09


rlam_heat=1 crsmin=200 – ecmwf Trat_sea=1 crsmin=200 – gme Tcrsmin=50 – ukmo Tcrsmin=50 – ukmo KF

MAE

BIAS

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1.8

2.0

2.2

2.4

2.6

2.8

3.0

crsmin: minimal stomata resistance Td2m: BIAS

-3

-2

-1

0

1

6 12 18 24 30 36 42 48 54 60 66 72

m3 m7 m15 m16

Td2m: RMSE

1

2

3

4

5

6

6 12 18 24 30 36 42 48 54 60 66 72

m3 m7 m15 m16


Remarks Some of the perturbations produce common effects on

both regions (e.g. rlam_heat, crsmin, tur_len) However, the impact of some of the physical

perturbations (e.g. rat_sea) depends on the geographical characteristics of the region

Large values of rlam_heat produce an increase in the error, implying that, theoretically, a deeper laminar layer suppresses the vertical fluxes

The value of pat_len will be decreased in the new implementation to be more consistent

A paper about the SREPS outcomes is in preparation!

Test of new parameter perturbations

(new CSPERT suite)member conv pat_len rlam_heat rat_sea crsmin cloud mu_rain gscp1 T 500 0.1 20 200 5.00e+08 0.5 42 KF 500 0.1 20 200 5.00e+08 0.5 43 T 500 1 1 200 5.00e+08 0.5 44 KF 500 1 1 200 5.00e+08 0.5 45 T 500 1 20 150 5.00e+07 0.5 46 KF 500 1 20 150 5.00e+07 0.5 47 T 500 1 20 150 5.00e+08 0 48 KF 500 1 20 150 5.00e+08 0 49 T 500 1 20 150 5.00e+08 0.5 3 (no gra)10 KF 500 1 20 150 5.00e+08 0.5 3 (no gra)11 T 10000 1 20 150 5.00e+07 0.5 412 KF 10000 1 20 150 5.00e+07 0.5 413 T 500 1 20 150 5.00e+07 0 414 KF 500 1 20 150 5.00e+07 0 415 T 500 1 20 150 5.00e+08 0.5 416 KF 500 1 20 150 5.00e+08 0.5 4

15: ctrl T16: ctrl KF

Nov 08 - MAMJ 09

2.2 Model perturbations: Developing perturbations for the lower

boundaryHNMS

AimImplement a technique for perturbing soil moisture

conditions and explore its impacts

ReasoningThe lack of spread is typically worse near the surface

rather than higher in the troposphere. Also, soil moisture is of primary importance in determining the partition of energy between surface heat fluxes, thus

affecting surface temperature forecasts


Soil Perturbation methodBased on the method proposed by Sutton and Hamill (2004)• Select a period that provides variability in soil moisture

e.g. spring • Use of data from a land–surface model analysis for the

defined period for a few years in order to create some “climatology” (DWD SMA)

• Implement the EOF (Empirical Orthogonal Function – Principal Component Analysis) to the data in order to generate random perturbations while retaining the spatial structure of the field

• Define the number of perturbations that will be initially used

• Test the impact of the perturbation within the COSMO-SREPS suite


3. Ensemble merging: development of the COSMO-LEPS

clustering(A. Montani, A. Corigliano)

A dynamical downscaling where driving members for COSMO are taken from different global ensembles is under testing

The cluster analysis is applied on a large set of members coming from different global ensembles

Up to now, ECMWF EPS and UKMO MOGREPS have been considered

initial conditions by EPS

initial conditions by MOGREPS

Issues

Consider both ECMWF EPS and UKMO MOGREPS and study the properties of the cluster analysis on multi-ensemble:

How many times do the 2 ensembles mix?Where do the RMs come from? How to they score depending on their “origin”?Is there added value with respect to single-model ensemble:

BEFORE dowscalingAFTER downscaling

Forecast and analysis datasets data from TIGGE-PORTAL (everything in GRIB2) 90 days (MAM09) of ECMWF-EPS and UKMO-MOGREPS run at 00 and

12 UTC use Z500 at fc+96h as clustering variable; for verifying analysis (at 00 and 12 UTC), consider Z500:

“consensus analysis” (average of UKMO and ECMWF high-res analyses), independent analysis (e.g. from NCEP);

generate the following global ensembles: EPS (50+1): 51 members MOGREPS (23+1): 24 members MINI-MIX (EPS24 + MOGREPS24): 48 members MEGA-MIX (EPS51 +MOGREPS24): 75 members

Strategy perform cluster analysis with 16 clusters and select RMs

(like in operations); generate 16-member global ensembles (EPS_REDU,

MOGREPS_REDU, MINI_REDU, MEGA_REDU). How do “REDUs” ensembles rank with respect to EPS,

MOGREPS, MINI-MIX, MEGA-MIX? Where do the best (and the worst) elements of REDU

ensembles come from? How do they score depending on their “origin”? BEFORE DOWNSCALING: is there added value with

respect to single-model ensemble?

Future plans

finish by March 2010!

Future future plans Implement dynamical downscaling: nest COSMO model in the selected RMs and generate “hybrid” COSMO-LEPS using boundaries from members of different global ensembles. For a number of case, compare operational COSMO-LEPS and “hybrid” COSMO-LEPS.

Summary results The availability of the COSMO-SREPS suite has been

around 90% during this year, but the system is complete only about 50-60% of the times -> back-up suite!

The analysis of the parameter perturbations introduced in the SREPS PP has been completed in Spring, and new selected perturbations have been introduced in the COSMO-SREPS suite in May

There is a good impact of the new perturbations on the spread of the system

A new set of perturbations, also for the microphysics scheme, is currently under testing

A methodology for soil moisture perturbation has been selected and is being implemented at HNMS

The work on multi-clustering has started, using the GRIB2 fields of the TIGGE-PORTAL

4. CalibrationARPA-SIMC - MeteoSwiss

At MeteoSwiss (F. Fundel, Sep 08-Feb 09):Sensitivity testsDocumentation/paper

At ARPA-SIMC (T. Diomede):Data collection:

• observations• MeteoSwiss reforecast• COSMO-LEPS forecasts

Choice of the methodsCode implementationEvaluation

Preparatory step: visit of Tom Hamill and Felix Fundel at ARPA-SIMC, June 2008

Calibration Method

x

Ret

urn

Perio

d

x

Reforecasts

Observations Reforecasts

Ret

urn

Perio

d

30 years COSMO-LEPS reforecasts (1971-2000) Observations (stations, gridded fields)

CDF (for one grid point)

Verification Results I

raw forecasts are overconfidentcalibrated forecasts nearly perfect reliable

strong improvements during winter

summer forecast already are reliable, onlylittle improvement possible

Sensitivity Study (precip)

current setup (18% improvement)best, cheap setup (15% improvement)

Cost for 1 member is ~equal to 2 reforecasts

15% improvement (over 16 member CLEPS DMO) using 11-12 members and calibrate with 8-10 years reforecasts

Depending on season:- more improvement during winter- less improvement during summer

rel. improvement in RPS over 16 Member CLEPS DMO

Observations Emilia-Romagna Region 24-h precipitation (08-08 UTC), 1970-2007

COSMO-LEPS reforecasts (done by MeteoSwiss) 30 years: 1971-2000 1 member, nested on ERA40, COSMO v4.0 1 run every third day (+90h)

COSMO-LEPS QPFs operational 5 years: 2003-2007

[m]

Emilia-RomagnaRegion

(22000 km2)

281 COSMO-LEPS grid points158 raingauges

Calibration – data collection

Calibration – choice of the methods choice of methodologies which enable a calibration of the

quantitative precipitation forecasts, not only of the probabilities of exceeding a threshold

aim:

improve COSMO-LEPS output (QPF)

hydrological applications

chosen methods up to now:

Cumulative Distribution Function (CDF) based

Linear regression

Analogues, based on the similarity of forecast fields:• precipitation

• geopotential height

CDF-based corrections

Ref: Zhu and Toth, 2005 AMS Annual Conf., and many others

For each model grid point:

• blue line CDF of COSMO-LEPS reforecasts

• red line CDF of historical observations

• “raw forecast” each member of the operational COSMO-LEPS

Calibration methodologies

Linear Regression

Ref: any applied statistics textbook

For each model grid point:

x-axis: COSMO-LEPS reforecasts

y-axis: historical observations

Calibration methodologies

yi 0 1xi i

1 analog date for the whole Emilia-Romagna Region

and for each 24-h forecast period

For each ensemble member’s forecast and 24-h forecast period (+ 20-44h , 44-68h , 68-92h , 92-116h):

- the analog search is performed in terms of 24-h rainfall pattern over the Emilia-Romagna Region

- the root-mean-square (rms) difference between the current forecast and each reforecast is computed, over all the grid points of the Emilia-Romagna Region

- the historical date with the smallest rms difference is chosen as the date of the analog, then the past raingauge recordings are used as the calibrated forecast

Calibration methodologiesAnalogues

Calibration – analogues

# #

# #

# #

# #

domain used for the analogue search

example on the methodology used for the analogue search in terms of geopotential at 700 hPa

Method comparisonautumn

threshold: 5 mm/24 h threshold: 20 mm/24 hAutumn 2003-2007 threshold: 5 mm/day fc: +20-44 h

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1forecast probability

obse

rved

rela

tive

frequ

ency

rawCDFANLLRanl Z

no resolution

no skill

Autumn 2003-2007 threshold: 20 mm/day fc: +20-44 h

0

0.2

0.4

0.6

0.8

1


obse

rved

rela

tive

frequ

ency

rawCDFANLLRanl Z

no resolution

no skill

+20-44h

Method comparison

threshold: 5 mm/24 h threshold: 20 mm/24 h

+68-92h


0

0.2

0.4

0.6

0.8

1


obse

rved

rela

tive

frequ

ency

rawCDFANLLRanl Z

no resolution

no skill


0

0.2

0.4

0.6

0.8

1


obse

rved

rela

tive

frequ

ency

rawCDFANLLRanl Z

no resolution

no skill

autumn

Method comparison

threshold: 5 mm/24 h threshold: 20 mm/24 h

+20-44h

Spring 2003-2007 threshold: 5 mm/day fc: +20-44 h

0

0.2

0.4

0.6

0.8

1


obse

rved

rela

tive

frequ

ency

rawCDFANLLRanl Z

no resolution

no skill

Spring 2003-2007 threshold: 20 mm/day fc: +20-44 h

0

0.2

0.4

0.6

0.8

1


obse

rved

rela

tive

frequ

ency

rawCDFANLLRanl Z

no resolution

no skill

spring

Autumn 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h

-1

-0.75

-0.5

-0.25

0

0.25

0.5

a c e g b d f hsub-areas

BSS

rawCDFANL LRanl Z

Different sub-areas

mountain plain

+20-44h20 mm/24 h

Spring 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h

-1

-0.75

-0.5

-0.25

0

0.25

0.5


BSS

rawCDFANL LRanl Z

Winter 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h

-1

-0.75

-0.5

-0.25

0

0.25

0.5


BSS

rawCDFANL LRanl Z

Summer 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h

-1

-0.75

-0.5

-0.25

0

0.25

0.5


BSS

rawCDFANL LRanl Z

raw

calibrated (CDF)

Spatial variability of the Mean ErrorMean Error of the ensemble mean

autumn

mm/24h

underestimation

overestimation

+20-44h

Spatial variability of the Mean Error

raw

calibrated CDF

mm/24h

underestimation

overestimation

Mean Error of the ensemble mean

spring

+20-44h

Spatial variability of the Mean Error

raw

calibrated CDF

Mean Error of the ensemble mean

summer

+20-44h

mm/24h

underestimation

overestimation

raw

calibrated CDF

Spatial variability of the Mean ErrorMean Error of the ensemble mean

winter

+20-44h

mm/24h

underestimation

overestimation

Autumn 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h

-1

-0.75

-0.5

-0.25

0

0.25

0.5


BSS

rawCDF no windCDF wind LR no windLR wind

Flow direction

mountain plain

+20-44h20 mm/24 h

Spring 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h

-1

-0.75

-0.5

-0.25

0

0.25

0.5


BSS


Summer 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h

-1

-0.75

-0.5

-0.25

0

0.25

0.5


BSS


Winter 2003-2007 Brier Skill Score threshold: 20 mm/24h fc +20-44 h

-1

-0.75

-0.5

-0.25

0

0.25

0.5


BSS


0

5000

10000

15000

20000

25000

0 5000 10000 15000 20000 25000

forecast (mm*100 / 24h)

obse

rved

(mm

*100

/ 24

h)

upper mountainous macro-areas

overestimation underestimation

Wind S-SW-W

Linear Regression

autumn

[m2/s2][m2/s2]

Remarks

The lack of improvement can be ascribed to the lack of a strong relationship between forecast and observed data.

It is necessary to generate correction functions which are weather-type specific. The training sample should be divided into sub-samples which have similar characteristic with respect to the meteorological situation. Hence, a model error which is systematic with respect to the meteorological situation could be identified and reduced by a specific correction function.

Next developments improve the analogue search, by using upper air fields

(geopotential and specific humidity) at different levels and daytimes and testing the size of the domain used for the analogue search

apply LR and CDF on a limited sample of analogues verify results by the coupling with hydrologic models extend the calibration over other areas, if observed data will

be made available (data over Switzerland are available) reduce the size of the reforecast dataset (in order to use

more recent hourly data to calibrate precipitation forecasts accumulated over 12 or 6 h and enable more detailed hydrological applications)

Final remarks

Problems encountered

The implementation of the back-up suite has just started (with delay, not critical for the moment)

Difficulty in objectively evaluating COSMO-SREPS since the ensemble is often incomplete; problems in verifying precipitation in the summer season

Calibration: The performance of the calibration methodology is

dependent on the precipitation threshold and on the considered area

Difficulty in “catching the bias” of precipitation over Emilia-Romagna, dependent on weather type

Are good data over other areas available?

Decisions needed

In order to calibrate the ensemble over the whole domain, very good (dense and covering a log period, i.e. years) observations should be made available by other regions/countries within the Consortium. And in principle also outside!

Lessons learned

The development of a methodology always introduces new questions, not foreseen, which need to be answered within the project => increase of the amount of work needed (e.g. assess the effect of parameter perturbations in a robust manner, calibration)

Some re-shuffling of the timing of the tasks has been applied, but without influencing the project development, since the tasks are independent

Documents

CONSENS Priority Project Status report COSMO year 2008/2009