the convection-permitting ensemble cosmo-de-eps from development to applications susanne theis,...

The convection-permitting ensemble

COSMO-DE-EPS

From development to applications

Susanne Theis, Christoph Gebhardt, Michael Buchhold

Deutscher Wetterdienst

Meteorological Modelling and Analysis

Predictability and Verification

Outline

• Development of the ensemble

Outline

•Step towards applications

- weather warnings, flood warnings,

airport management, renewable energy

• Development of the ensemble

Development of the ensemble:

Setup and Motivation

model domain

COSMO-DE in operation since 2007

spatial grid length 2.8 km

no parametrization of deep

convection

(convection-permitting)

assimilation of radar data

lead time: 0-27 hours

8 starts per day

(00, 03 UTC,...)

Ensemble is based on model COSMO-DE

~

1300

km

Baldauf et al. (2011)

Benefit of the fine grid (2.8 km)

improved forecasts of near-surface variables

precipitation, 2m-temperature, wind gusts

improved representation of atmospheric processes:

subsynoptic, mesoscale, convective

improved representation of severe weather

Challenge: Predictability

cha

ract

eri

stic

tim

e sc

ale

characteristic length scale

synoptic

convective

10 km 1000 km

1 hour

1 week

100 m


atmospheric processes

cha

ract

eri

stic

tim

e sc

ale


synoptic

convective

10 km 1000 km

1 hour

1 week

100 m



lead time of the forecast

predictability

cha

ract

eri

stic

tim

e sc

ale


synoptic

convective

10 km 1000 km

1 hour

1 week

100 m




predictability

Uncertainties in small scales grow faster (Lorenz 1969)

cha

ract

eri

stic

tim

e sc

ale


synoptic

convective

10 km 1000 km

1 hour

1 week

100 m




predictability

address the forecast in a probabilistic framework

ensemble members

The ensemble COSMO-DE-EPS

20 forecast scenarios

for the same time in the future

operational since 2010 / 2012

COSMO-DE-EPS 2.8 km

COSMO 7 km

including variations of• initial conditions• model physics• soil moisture

GME, IFS, GFS, GSM

Ensemble chain of COSMO-DE-EPS

Gebhardt et al (2011), Peralta et al (2012)

The 20 COSMO-DE-EPS members

entr_sc=0.002 q_crit=4.0 rlam_heat=0.1 rlam_heat=10. tur_len=500. lhn_coef=0.5

IFS O O

GME O O

GFS O

GSM O O

0.2 0.7 0.2 0.4 0.7

0.2 0.7 0.2 0.4 0.7

0.2 0.7 0.2 0.4 0.7

0.2 0.7 0.2 0.4 0.7

tkhmin und tkmmin = 0.2 / 0.4 / 0.7

soil moisture: no change (O) / anomaly / anomaly

(as of March 18th 2014)

11

66

1111

1616

22

77

1212

1717

33

88

1313

1818

44

99

1414

1919

55

1010

1515

2020

Forecast Lead Time

00 UTC 06 UTC 12 UTC 18 UTC

for a specific location:

10

0

Example of a Forecast Product

Source of Figure: NinJo Visualization System at DWD

Forecast Lead Time



10

0

90%-percentile= 10 mm rain



Forecast Lead Time



10

0





The step towards applications

probabilistic forecasts of high-impact weather

weather warnings

flood warnings

storm surge warnings

airport management

renewable energy

and more

COSMO-DE-EPS is entering various applications

COSMO-DE-EPS for weather warnings

2010-2012: „evaluation“ phase

since 2012: operational use of COSMO-DE-EPS

percentiles,exceeding probabilities,ensemble mean and spread, …

DWD forecasters receive COSMO-DE-EPS

precipitation & snow,

10m wind gusts,

2m temperature,

simulated radar reflectivity,

CAPE, low level cloud cover

tailored to DWD warning criteria

forecaster can see the forecast:

2 ¼ hours after start of simulation




10m wind gusts,

2m temperature,









10m wind gusts,

2m temperature,






Favorites:

90%-percentiles

„upscaled“ probabilities


Why „upscaled“ probabilities?

Feedback from the forecasters

probability of

precipitation > 20 mm/6h


probability of



90 -100 %80 - 89 %70 - 79 %.....10 - 19 % 1 - 9 % < 1 %

Forecasters:„Probabilities are too low!“

probability of



Forecasters:„Probabilities are too low!“

probability of



not confirmed by verification

forecasters did accept 90%-percentiles

???

Take a look at forecaster‘s desk

warning map

Source of Map: www.dwd.de

arbitrary example

Take a look at forecaster‘s desk

warning map

Source of Map: www.dwd.de

arbitrary example

click here

Source of Text: www.dwd.de

Warningfor County Ravensburg

„There will be heavy rain.“

Source of Text: www.dwd.de

probability of



probability of

precipitation > 20 mm/6h they needa different product


probability of


probability of


somewhere within a region


90 -100 %80 - 89 %70 - 79 %.....10 -19 % 1 - 9 % < 1 %

Ben Bouallègue, Z. and S.E. Theis (2013): Spatial techniques applied to precipitation ensemble forecasts: From verification results to probabilistic products. Meteorological Applications, DOI: 10.1002/met.1435.

Upscaling:


Ben Bouallègue, Z. (2013): Calibrated short-range ensemble precipitation forecasts using extended logistic regression with interaction terms. Wea. Forecasting, 28, 515-524.

Upscaling:

Statistical Postprocessing:


Ben Bouallègue, Z. (2013): Calibrated short-range ensemble precipitation forecasts using extended logistic regression with interaction terms. Wea. Forecasting, 28, 515-524.

Ben Bouallègue, Z., Theis, S.E. and C. Gebhardt (2013): Enhancing COSMO-DE ensemble forecasts by inexpensive techniques. Meteorologische Zeitschrift, 22 (1), 49-59.

Upscaling:

Statistical Postprocessing:

Time-Lagging:

Look into other applications

What is their „high-impact“ weather?

„High-impact“ weather

severe precipitation eventsomewhere within a

certain region



certain region

high water levels of a river

(predicted by hydrological models which use ensemble weather

forecasts in their inputs)

COSMO-DE-EPS for flood warnings

take members of COSMO-DE-EPS

several simulations with

hydrological model

ensemble for runoff


take members of COSMO-DE-EPS

several simulations with

hydrological model

ensemble for runoff


Source: Christoffer Biedebach (2013)„Einsatzmöglichkeiten des wahrscheinlichkeitsbasierten Vorhersagesystems COSMO-DE-EPSim Hochwasser-Informationssystem von Emschergenossenschaft und Lippeverband“,Hochschule für angewandte Wissenschaften Würzburg-Schweinfurt.

80

60

40

20

0ru

noff

(m

3 /s)

runoff at specific water gauge(river „Emscher“ at Königstraße)

time


current work - at various hydrological centers:

set up technical environment

find useful visualization

evaluation for many cases

open: statistical postprocessing

COSMO-DE-EPS for airport management

LuFo iPort WiWi project

(I.Alberts, N.Schuhen, M.Buchhold)






certain region

exceeding a certain threshold of the tailwind or crosswind component

relative to the airport runwayalong the glide path

source: LuFo iPort WiWi project

COSMO-DE-EPS for airport management (Frankfurt)

already acheived:

product design

useful visualization

statistical postprocessing

quasi-operational environment

LuFo iPort WiWi project (I.Alberts, N.Schuhen, M.Buchhold)



win

d p

ara

llel t

o r

un

wa

y (

kt)

-2

0

0

+20

probability of wind > (+ 5kt)

probability of wind < (- 5kt)

pro

ba

bili

ty (

%)

0 20406080100806040200

Time (UTC)

Tailwind



cro

ssw

ind

(kt

)

0 20406080100806040200

-2

0

0

+20

probability of wind > (+ 20kt)

probability of wind < (- 20kt)

pro

ba

bili

ty (

%)

Time (UTC)

Crosswind

statistical postprocessing method:

EMOS, bivariate Gaussian distribution

Schuhen et al. (2012):

Ensemble Model Output Statistics for Wind Vectors.

Mon. Wea. Rev., 140, 3204–3219.


COSMO-DE-EPS for renewable energy

EWeLiNE project (K. Lundgren et al.)






certain region








certain region



very quick changein wind speed at hub height

taking place over a large area(?)

EWeLiNE project (K. Lundgren et al.)

UAS-PS 304.03SCI-POT 1138SCI-POT 1028UAS-POM 3014






certain region



very quick changein wind speed at hub height

taking place over a large area(?)

Summary

• Convection-permitting ensemble COSMO-DE-EPS

- in operation since 2010 / 2012

• Discovered by increasing number of applications

- weather & flood warnings, airport, renewable energy, and more

• Communication with users

-product design, visualization, postprocessing method

-can be essential for acceptance

• Ben Bouallègue, Z. (2013): Calibrated short-range ensemble precipitation forecasts using extended logistic regression with interaction terms. Wea. Forecasting, 28, 515-524.

• Ben Bouallègue, Z., Theis, S.E. and C. Gebhardt (2013): Enhancing COSMO-DE ensemble

forecasts by inexpensive techniques. Meteorologische Zeitschrift, 22 (1), 49-59.

• Ben Bouallègue, Z. and S.E. Theis (2013): Spatial techniques applied to precipitation ensemble forecasts: From verification results to probabilistic products. Meteorological Applications, DOI: 10.1002/met.1435.

• Gebhardt, C., Theis, S.E., Paulat, M. and Z. Ben Bouallègue (2011): Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Atmospheric Research, 100, 168-177.

• Peralta, C., Z. Ben Bouallègue, S.E. Theis, C. Gebhardt, and M. Buchhold (2012): Accounting for initial condition uncertainties in COSMO-DE-EPS. J. Geophys. Res., 117 (D7), doi:10.1029/2011JD016581

References around COSMO-DE-EPS

-data access for research (in addition to the producing center itself)

-forecasts of several high-resolution ensemble systems (incl COSMO-DE-EPS)

-selected set of variables (there is more at the producing center itself)

-technical description of the systems

information: https://software.ecmwf.int/wiki/display/TIGGE/TIGGE-LAM

data access: http://apps.ecmwf.int/datasets/data/tigge_lam/

Data Access for Research:

TIGGE-LAM archive

https://software.ecmwf.int/wiki/display/TIGGE/TIGGE-LAM

http://apps.ecmwf.int/datasets/data/tigge_lam/

PBPV – 03/2013 60

the convection-permitting ensemble cosmo-de-eps from development to applications susanne theis,...

Documents

ensemble slide

forecast predictability

dwd slide

forecast product slide

forecast lead time

verification slide

motivation slide

ifs o o gme o o gfs