Cosmo priority project tackle deficiencies in quantitative precipitation forecasts
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Cosmo priority project tackle deficiencies in quantitative precipitation forecasts

COSMO Priority Project ’Tackle deficiencies in quantitative precipitation forecasts’

S. Dierer1, M. Arpagaus1, U. Damrath2, A. Seifert2, J. Achimowicz8, E. Avgoustoglou7, M. Baldauf2, R. Dumitrache9, V. Fragkouli7, F. Grazzini3, P. Louka7, P. Mercogliano6, P. Mezzasalma3,M. Milelli4, D. Mironov2, A. Morgillo3, E. Oberto4, A. Parodi5, I.V. Pescaru9, U. Pflüger2, A. Sanna4, F. Schubiger1, K. Starosta8, M. S. Tesini3

1MeteoSwiss (CH), 2DWD (D), 3ARPA-ER (IT), 4ARPA-P (IT), 5Uni Genova (IT), 6CIRA-CMCC (IT), 7HNMS (GR), 8IMGW (PO), 9NMA (RO)

29th EWGLAM Meeting, 9 October 2007, Dubrovnic


Aim of pp qpf
Aim of quantitative precipitation forecasts’PP QPF

Good quantitative precipitation forecast is a challenging task – also for the COSMO model:

  • The aim of PP QPF is improved knowledge about

    • most suitable namelist settings or

    • parts of the model that need to be reformulated

  • to obtain a better QPF at 7 km horizontal grid size

The project has a focus on model deficiencies – not on errors from e.g. initial and large scale conditions


Overview of pp qpf
Overview of quantitative precipitation forecasts’PP QPF

  • Task 1: Selection of test cases representative for „typical“ QPF deficiencies of COSMO model

  • Task 2: Definition of sensitivity studies

  • Task 3: Run sensitivity studies and draw conclusions


List of test cases from all countries
List of test cases from all countries quantitative precipitation forecasts’


Forecast errors
Forecast errors quantitative precipitation forecasts’

  • 10 cases of stratiform overestimation (8 from D, CH and PO)

  • 4 cases of stratiform underestimation

  • 3 cases of convective overestimation

  • 7 cases of convective underestimation (6 from I and GR)


Sensitivity studies
Sensitivity studies quantitative precipitation forecasts’

  • 1. Changes of initial conditions

  • 2. Changes of numerical methods

  • 3.1 Changes of microphysics

  • 3.2 Changes of convection schemes

  • 3.3 Changes of PBL schemes


Sensitivity studies initial conditions
Sensitivity studies: initial conditions quantitative precipitation forecasts’

  • Soil moisture increased/decreased by 20%

  • Initial humidity increased/decreased by 10%


Sensitivity studies numerical methods
Sensitivity studies: numerical methods quantitative precipitation forecasts’

  • Halved time step

  • Leapfrog, tri-cubic semi-Lagrange advection of QR and QS

  • Runge-Kutta, tri-cubic semi-Lagrange advection of QV, QC, QI, QR and QS

  • Runge-Kutta, flux-form advection of QV, QC, QI, QR and QS

  • Runge-Kutta, flux form advection and T’-p’ dynamics

  • increased orography filtering


Sensitivity studies physics 1 microphysics
Sensitivity studies: physics 1 - microphysics quantitative precipitation forecasts’

  • New warm rain scheme (Seifert and Beheng; 2001)

  • Strong changes of ice microphysics and new warm rain scheme

  • Moderate changes of ice microphysics and new warm rain scheme


Sensitivity studies physics 2 convection
Sensitivity studies: physics 2 – quantitative precipitation forecasts’convection

  • Modified Tiedtke scheme

  • Kain-Fritsch/Bechtold scheme

  • No parameterization of deep convection


Sensitivity studies physics 3 pbl
Sensitivity studies: physics 3 – quantitative precipitation forecasts’PBL

  • Decreased/increased scaling factor of height of laminar boundary layer for heat

  • Decreased/increased stomatal resistance

  • Decreased/increased laminar scaling factor for heat over sea


Relative change of 24h area average precipitation first forecast day 06 30
Relative change of 24h area average precipitation, first forecast day (06 – 30)

Convection scheme

Snow microphysics

Runge-Kutta

Δrel = (rrexp–rrref)/rrref

Initial humidity

Cases

Vertical heat/moisture exchange

Romanian {

Polish{

Greek {

Ital.(EuroLM){

Ital. (LAMI){

Swiss{

Δrr > +30%

+10% < Δrr <+30%

0% < Δrr < +10%

Δrr = 0%

0% > Δrr > -10%

-10% > Δrr > -30%

Δrr < -30%

German{

rlam01

sto50

sea01

ws80

dt20

RKsl

RKtp

micro1

micro2

qv90

conmod

ws120

qv110

RKbott

micro3

conoff

rlam50

sto250

sea40

LFsl

oro

kfb


Relative change of 24h area average precipitation
Relative change of 24h area average precipitation forecast day (06 – 30)

RK

RLAM

QV0

MP

CON

Δrel = (rrexp–rrref)/rrref

 Δrel

 | Δrel |


Change of bias between simulated and measured area average precipitation
Change of bias between simulated and measured area average precipitation

overestimation

underestimation

stratiform

bias > 200%

100% < bias <200%

bias = 100%

100% > bias > 50%

bias < 50%

convective

convection

Initial humidity

Runge-Kutta

snow microphysics


Conclusions until now
Conclusions until now … precipitation

  • Strongest effect (5-40%) on area average precipitation by:

    • Initial humidity

    • Runge-Kutta

    • microphysics

    • convection scheme

  • Strong effect for Roman and Greek cases

    • Vertical heat/moisture exchange (extreme change of RLAM)

  • Runge-Kutta

    • reduces mean precipitation in most of the cases

    • and has an overall positive effect on the results

  • None of the studies completely solves a QPF problem, but some give a significant improvement for single cases like

    • changes of snow microphysics for a case with overestimation of stratiform precipitation

    • Kain-Fritsch/Bechtold for underestimated convective precipitation


Cross experiments
Cross experiments precipitation


Relative change of area average precipitation in cross experiments compared to control simulation
Relative change of area average precipitation in cross experiments compared to control simulation

Δrr > +30%

+10% < Δrr <+30%

0% < Δrr < +10%

Δrr = 0%

0% > Δrr > -10%

-10% > Δrr > -30%

Δrr < -30%


Bias of reference run and cross experiments
Bias of reference run and cross experiments experiments compared to control simulation

M-Swiss

Italy

HNMS

IMGW

NMA

DWD


Relative bias of cross experiments
Relative bias of cross experiments experiments compared to control simulation

M-Swiss

Italy

HNMS

IMGW

NMA

DWD


Improvement of area average precipitation in numbers
Improvement of area average precipitation in numbers… experiments compared to control simulation

  • 17 cases improved by one of the studies: COSMO4.0+

    • KFB: 5 cases (3C- / 2S+)

    • QV90+RK+Tiedtkemod : 3 cases

    • QV90+RK+KFB : 3 cases

    • -/Tiedtkemod/RK+QV90: 2 cases

  • 7 cases hardly affected or worse (5C-)

}

8S+/3S-


Conclusions
Conclusions experiments compared to control simulation

  • COSMO4.0+

    • reduced initial humidity

    • modified convection

    • Runge-Kutta

  • has a positive impact on stratiform overestimation

  • little or negative impact on convective underestimation

  • Few cases are “solved”

  • COSMO Version 4.0 is a step forward!

  • Further improvements expected from Runge-Kutta.

  • We should have a closer look at the (initial) humidity fields. – Any improvements in data assimilation expected?

  • Convection schemes are the next thing to look at.

  • Draft of a final report has been written and will be revised based on the discussion of PP QPF sessions in Athens and will be available in the next weeks

  • publication of results planned until end of the year


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