fe 1 koppert et al., dwd4cops cops – dwd contributions hans-joachim koppert, michael baldauf,...

FE 1 Koppert et al., DWD4COPS

COPS – DWD Contributions

Hans-Joachim Koppert, Michael Baldauf, Michael Denhard, Werner Wergen

Deutscher Wetterdienst


OverviewOverview COSMO-K

GoalsCase Studies & VerificationStatus

NinJo PEPS

BackgroundImplementationMicro PEPS

Data-Assimilation Issues


COSMO-K COSMO-K (formerly known as LMK (formerly known as LMK LLokal-okal-MModell-odell-KKürzestfrist)ürzestfrist)

Goals Development of a model-based NWP system for very short range (‘Kürzestfrist’) forecasts (18 h) of severe weather events on the meso- scale, especially those related to

deep moist convection (super- and multi-cell thunderstorms, squall-lines, MCCs, rainbands,...)

interactions with fine-scale topography(severe downslope winds, Föhn-storms, flash floodings, fog, ...)

GME (global)x = 40 km368642 * 40 GPt = 133 sec.T = 7 days

COSMO-E (Europe)x = 7 km665 * 657 * 40 GPt = 40 sec.T= 78 h

COSMO-K (regional)x = 2.8 km421 * 461 * 50 GPt = 25 sec.T = 18 h8 forecasts / day


Convectively enhanced frontal precipitation, 1.10.2006, 18 UTC

Obs.: up to 20 mm/12 h


LAF-ensemble, 1h-precip.-sum, target time: 1.10.2006, 18 UTC

0 + 18 h 3 + 15 h 6 + 12 h

9 + 9 h 12 + 6 h 15 + 3 h


Convectively enhanced frontal precipitation 1.10.2006, +06-30 h

(from 0 UTC & 12 UTC-run)


Problem: missed convection initiation in LMK 11.09.2006

other examples: July 2006


Sept. 2006

Oct. 2006

Synop-VerificationRMSE of wind speed |v|10m

LMK LME

0 UTC-runs 12 UTC-runs U. Damrath


Synop-Verification of pre-operational LMKGusts and Precipitation, 01.-31.Oct. 2006, 12 UTC-runs

LMK LME

ETS

TSS

Gusts

Precipitation

Precipitation: July ‘05: TSS generally higherSept. ‘06: LMK higher TSS due to LHN, Oct. + Nov. ‘06: LMK mostly higher TSS (FBI ~ equal)Dec. ‘06: LMK smaller TSS (no LHN?)

Gusts: ETS generally higher (but sometimes also higher FBI)


COSMO-K in pre-operational use since 14.08.2006 18 h- (21 h-) forecasts are simulated every 3 h (LAF-

ensemble) explicit simulation of deep moist convection with its life cycle

generates good predicitions of precipitation in the case of synoptic forced events(e.g. lines of thunderstorms)

dynamical effects better represented due to higher resolution strong downslope winds lee waves (e.g. improved glider forecasts)

radar observations of the DWD-radar network have an essential influence on the initial state(improved precipitation forecast for the first ~ 4..5 h)

Status and Summary COSMO-KStatus and Summary COSMO-K


NinJoNinJo NinJo is an international Workstation project

Partners are: MeteoSuisse, DMI, MSC

Focus on Supporting Process Weather Forecasting Base Functionality

Data decoding Data storage 2D-display of all data types needed for operations

Smooth 3D-extension currently worked on, prototype exists Interactive and batch ( this summer ) processing Chart-based display with zooming and panning Diagram-based display: time series, cross sections, tephigramms Data display in different layers Animation and automatic updates

Meteorological Functionality Interactive chart generation ( fronts etc. ) “On Screen” analyses Weather monitoring and warning generation

Old workstation system is currently phased out


NinJoNinJoSupported Data TypesSupported Data Types

Surface and upper air observations Synop, Ship, Metar, Temp ….

Grid GME, COSMO, ECMWF, HIRLAM,

GEM, GFS, Satellite

Geostationary satellite Polar orbiters

Radar SCIT Storm cell and identification

Lightning Different networks

Geo data Vector Raster


NinJoNinJoThe ApplicationThe Application

The main window Multiple scenes Layers Basic operation

bar Zoom, pan,

measure , reproject, print …

Layer bar Layer specific tool

bar Layer specific

menu bar Animation bar


NinJoNinJo - - DiagramsDiagrams

A COSMO-E (LME) sounding With several derived parameters

( CAPE .. ) Available interactively for every

point on the map and every model that’s in the database

A COSMO-K (LMK) Cross-Section Works with model and p-surfaces 2D-cross sections ( wind, temperature, clouds, …. ) 1D- cross section ( hourly rainrates, T2m… )


NinJo-StatusNinJo-StatusKarlsruhe and HohenheimKarlsruhe and Hohenheim

NinJo servers and clients availableSoftware installed by Consultant (paid by DWD)Single server installation Currently runs on data provided by DWD and routed

through FU BerlinSupply through DWDSAT also possible

2MBit satellite data streamObservations ( surface, Upper air, lightning, radar … ) and model

dataSubsampled GME and COSMO-E, no COSMO-K

Prepared for additional data e.g. COSMO-KAdditional data needed ( e.g. Konrad ) has to reported

– FTP based supply has to be set-up well in advance– Band-widths issues ?

Standard operational DWD-installation, based on NinJo 1.22

Still low-cost alternative JavaMAP


European regional multi-model ensemble SRNWP-PEPS

Combines the most

sophisticated operational

limited area models

in Europe

the ensemble size depends on location

ensemble size


SRNWP-PEPS …. used to generate warnings of extreme events

Output variables (surface fields only) Total precipitation Total snow Maximum 10 m wind speed Maximum 10 m wind gust

speed 2 m temperature relative humidity 2m global radiation at surface

Products:Ensemble meanProbabilities of

exceeding thresholds+18h and +30h

6 AM and 6 PM


Participating ModelsParticipating Models

FE 1 Koppert et al., DWD4COPS [%]

ensemble mean

observations

probabilities RR >50mm

[mm]

24h precipitationrun: 22.08.05 0 UTC, available: 22.08.05 6:05 UTC valid: 22. 8. - 23. 8., 6 UTC


Output variables: tigge+ list

Models:

model hor. res. institution

COSMO-CH2 2,2 MeteoSwiss

ITA-LM 2,8 CNMCA

COSMO-LAMI 2,8 ARPA-SIM

COSMO-K 2,8 DWD

MOLOCH 2,2 ISAC-CNR /

ARPAL-CFMI

AROME 2,5 Météo-France

GEM-LAM 2,5 Environment Canada

Time schedule: March dry-run : Thu 29.03 - Wed 04.04

April: set up of MICRO-PEPS

April dry-run : Tue 24.04 - Mon 30.04

4 5 6 7 8 9

ensemble size

MICRO-PEPS


ScenariosScenarios for Data Assimilation for Data Assimilation

Real time• Experiment data are available for

operational runs

• Impact studies in delayed mode by excluding data from experiment

• Pro: Potentially better operational forecasts, direct feedback from monitoring

• Con: More difficult to monitor and need for extra delayed mode assimilations because of incomplete coverage

Delayed mode

• Operational runs only use standard observations

• Impact studies in delayed mode by including data from experiment

• Pro: More controlled set-up and easier monitoring, complete data set

• Con: No impact on operational forecasts

Common requirements

Detailed list of experimental stations for blacklist and possibly whitelist.Distribution of data in agreed formats and in known ways

fe 1 koppert et al., dwd4cops cops – dwd contributions hans-joachim koppert, michael baldauf,...

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