Studies of the Process Chain and the Predictability of Precipitation With the D-PHASE Ensemble and COPS Observations Volker Wulfmeyer Institute of Physics

Studies of the Process Chain and the Predictability of Precipitation

With the D-PHASE Ensemble and COPS Observations

Volker WulfmeyerInstitute of Physics and Meteorology

University of Hohenheim

April 10-11, 2008 DFG PP 1167 Kick-off Meeting, University of Bonn, Germany

Claudia Wunram and Michael LautenschlagerWorld Data Center for Climate

Max Planck Institute for Meteorology

Reinhold Steinacker and Manfred DorningerInstitute of Meteorology and Geophysics

University of Vienna

Mathias RotachSwiss Federal Office for Meteorology and Climatology

Meteo Swiss

Christoph KottmeierInstitute of Meteorology and Climate Research

University of Karlsruhe

Moisture Net (Hauck, IMK)

SurfAce fLuxes and Valley convEction

(U Bayreuth, U Mainz, Kalthoff, IMK)

COPS-CI Remote Sensing(Behrendt, IPM)

Life Cycle of Deep Convection during COPS (Hagen, DLR)

Lower and Upper Tropospheric Forcing

of Convection (Barthlott, IMK)

D-PHASE/COPS Process Chain (IPM; Lautenschlager, WDCC;

Steinacker, UniVie; Rotach, MeteoSwiss)

Parameterization of Cumulus Convection (Küll, U Bonn)

CI

ACMPPL

Coordination

& DAP COPS-GRID(Bauer, IPM; Dick, GFZ; Wergen, DWD)

COPS-LIDAR-IFT(Ansmann, IfT)

COPS-UK

COPS-Italy

COPS-France

COPS-Austria

D-PHASE

ETReC07

TRACKS

ARM

WWRP MWF

WWRP FDP D-PHASE


• 6 ensemble prediction systems• 7 convection permitting models• 9 models with convection parameterization

Central HypothesisThe next generation of convection permitting

models provides a breakthrough in modeling of the initiation and organization of convective

systems. This results in an improvement of the skill of probabilistic precipitation forecasting.


Research Strategy

optimal combination

Proposals of suitable combinations for process

studies in order to:

Investigate the initiation and

organization of convection

Studies on predictability by algorithms based on Bayesian statistics

COPS 4D data set

D-PHASE forecasts

Analyze specific failures of

parameterizations and evaluate

model physics

Evaluate the impact of

different data assimilation systems and observations

An

alyze an

d im

pro

ve m

od

el e

ns

em

ble

sk

ills

Extension of the results from the COPS to the D-PHASE domain

Case studies of process chain

Forecasts with different- multi-model ensemble members- parameterizations- data assim. and obs. systems- boundary conditions

Evaluation

COPS website

COPS, GOP D-PHASE

data archive

• Combination of process studies

and skill analyses in the COPS

domain.

• Links to PQP projects:

-Process studies

-Parameterizations

-Ensemble design

-Verification

COPS/D-PHASE will become international convection and data assimilation testbed of


COPS Highlight 1:Airmass convection, July 15, 2007, IOP8b

MSG Rapid Scan Service (RSS)


The role of humidity

Wind and moisture fields

DWD Radar radial velocity GPS IWV (Galina Dick, GFZ Potsdam)


Airmass convection, July 15, 2007, IOP8b: Model results

MESO-NH and AROME were the only models, which predicted CI. Proposed as international reference case (GCSS)!

Windfield with convergence(Evelyne Richard, Lab. d‘Aerology, Toulouse).

CI in nature and model very similar.


5-min MSG RSS initiated by COPS team

COPS Highlight 2:Pre-frontal convergence line, July 20, 2007

Cyclogenesis over France, development of a mesoscale convective system. Flooding events in UK and eastern Germany.


DWD radar network, July 20, 2007

Extreme precipitation was caused by a squall line, which developed ahead of the mesoscale convective system.


Highlight 3: Reduction of windward/lee effect

D-PHASE model evaluation using COSMO-EU with convection parameterization and convection permitting COSMO-DE.


State-of-the-art of convection permitting modeling in COPS domain

Meteo Swiss

First evaluation of D-PHASE models in COPS domain.

HH H

H

H

L

L

Meteo France

DWD IPM

H

mm


ii

i

qiMdl2

1exp

detdet

det

)2(

1)|(

0

1

KK

G

10

1 KKG ii

A variable d is predictable in dependence of forecast range if (sign. nonzero).

cir BdBdP )(:)(

)|(

)|(

r

iir

Mdl

MdlB

Bayesian factor B:

l: likelihood, d: data vector, Mi: ith model, Mr: reference model.

Predictability in collaboration with NUMEX

Design of an optimized multi-model, convection permitting ensemble prediction system. Proposal of future optimizations.


COPS GOP D-PHASE WORLDScientific community

DFG rules for good scientific practice

Maintenance of long term archive

COPS/GOP/D-PHASE Common Data Policy

WDCC

Meta data description

Alerts

Pictures

Additional info files

PQP2


Hydro model data

Atmosperic model data

Observation data

Data inputInterface design

Common data formatsData base design

Upload survey

User supportUser administration

Data extractionUser requirementsInterface design User support

User administration


Assimilation products

Validation products

New data sets

Detailed observation data

Gridded data products

Higher value products

Data source synergyIOP access

Data Provider COPS GOP D-PHASEPQP2/

3PQP3

Highlight 3

January 10-11, 2007 DFG PP 1167 Review Panel Meeting, Bad Honnef, Germany

MESO-NH, MM5 simulation (with cloud liquid water) and VERA analysis valid for 14 UTC. Pronounced moisture flux con-vergence is observed over the Black Forest.

Work Packages and Time Table


WP IPM1 COPS Coordination and management by COPS Project Office

IPM

Short description: At IPM, the collection and quality control of the data has been organized and shall be continued. The COPS coordinator organizes the COPS Project Office.

WP WDCC

Joint COPS-GOP-D-PHASE Data Archive WDCC

Short description: At WDCC the long-term data archiving, well founded user support and reliable maintenance of the common COPS/GOP, D-PHASE data archive shall be continued. Establishing a well-informed contact person at the data archive as direct support is a crucial advantage when handling this data collection in a joint project context.

A prerequisite of the success of COPS was the funding of dedicated staff for COPS coordination and the COPS data

archive, and the development of the COPS web site. WP IMK1 Maintenance and extension of COPS Website IMKShort description: The COPS website www.cops2007.de has been proven as a unique source of information about COPS IOPs. It provides easy access to key information such as forecasts and quicklooks of first results.



WP IPM 2: D-PHASE Process Chain

WP IPM2_1

Long-term evaluation of the model performance during COPS

IPM

Short description: In this coordinated action, the models of the D-PHASE ensemble are investigated concerning their long-term behavior during the COPS period. Different products from the project partners are collected and analyzed.

WP IPM2_2

Model performance during IOPs IPM

Short description: Detailed investigation of the model performance for selected IOPs by comparing with the COPS data set

WP IPM2_3

Detailed process studies with model subset IPM

Short description: In this WP the set of models selected in WP IPM2_2, forced with initial conditions as close to reality as possible, are used to evaluate the impact of the different assimilation and observing systems in the respective models. The emphasis is to reveal errors in the ABL, convection, and cloud physics parameterizations.



WP UV1 D-PHASE quality controlled and gridded data set UniVieShort description: The collected non-GTS data (only surface data are regarded) over the D-PHASE domain have to be quality controlled. This is an important preceding step for all further use of the data for any verification study. In a second step the data are gridded by using the VERA approach.

WP UniVie 2 (UV2): Verification of D-PHASE model ensemble

WP UV2_1

Definition of parameters and verification measures

UniVie

Short description: In cooperation with the project partners parameters to be verified and the verification measures are selected.

WP UV2_2

Set up of verification tools and test runs UniVie

Short description: The verification will be conducted on a grid point basis. VERA analyzed grid-point fields will be compared to the selected NWP model fields. Verification measures (defined in WP UV2_1) are calculated and displayed

WP UV2_3

Determination of verification measures for NWP model chain

UniVie

Short description: The verification tool developed in WP UV2_2 is applied to the selected NWP-models of comparable coarse- (10 to 20 km) and high-resolution models (< 10 km).



WP MS1 D-PHASE coordination and model evaluation MeteoSwissShort description: In this WP the coordination of this project with D-PHASE Project Office and model comparison studies are refined.

WP MS2 D-PHASE model verification refined MeteoSwissShort description: In this WP, new tools for model verification will be developed and tested.

Winter 06/07

120 days

New warning index:Probability to exceed a Return Period, PRP

Approach:- Use the model climatology to find a return level for a certain return period (for each grid point)- Find number of forecasts exceeding the return level- Give a probability to exceed the return level/period (PRP)(- Use Extreme Value Aanalysis for very rare events (e.g. fit of a GPD))

Syntax:PRPx = Probab. to exceed an event occurring with a return period according to the x-quantile

Example:PRP0.8 = event occurs every 5th day



WP IPM 3: Predictability

WP IPM3_1

Predictability studies using the selected ensemble of models. Collaboration with NUMEX

IPM

Short description: The subset of models selected in WP IPM2 shall be used for predictability studies

ii

i

qiMdl2

1exp

detdet

det

)2(

1)|(

0

1

KK

G

10

1 KKG ii

)()( 1110 iii

Tii fdfd

KGK

A variable d is predictable in dependence of forecast range if (sign. nonzero).

cir BdBdP )(:)(

)|(

)|(

r

iir

Mdl

MdlB

Bayesian factor B:

l: likelihood, d: data vector, Mi: ith model, Mr: reference model.



4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3

WP IPM1

WP IPM2

WP IPM2_1

WP IPM2_2

WP IPM2_3

WP IPM3

WP IPM3_1

WP IPM3_2

WP IMK

WP UniVie

WP UV1

WP UV2_1

WP UV2_2

WP UV2_3

WP MS

WP MS1

WP MS2

WP WDCC

2008 2009 2010

Time schedule for performing the work packages by IPM, IMK, UniVie, MeteoSwiss (MS), and WDCC. All these work packages cover the two years, for which funding is requested.

Documents

Studies of the Process Chain and the Predictability of Precipitation With the D-PHASE Ensemble and COPS Observations Volker Wulfmeyer Institute of Physics