the convection-permitting ensemble cosmo-de-eps from development to applications susanne theis,...
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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
Challenge: Predictability
atmospheric processes
cha
ract
eri
stic
tim
e sc
ale
characteristic length scale
synoptic
convective
10 km 1000 km
1 hour
1 week
100 m
Challenge: Predictability
atmospheric processes
lead time of the forecast
predictability
cha
ract
eri
stic
tim
e sc
ale
characteristic length scale
synoptic
convective
10 km 1000 km
1 hour
1 week
100 m
Challenge: Predictability
atmospheric processes
lead time of the forecast
predictability
Uncertainties in small scales grow faster (Lorenz 1969)
cha
ract
eri
stic
tim
e sc
ale
characteristic length scale
synoptic
convective
10 km 1000 km
1 hour
1 week
100 m
Challenge: Predictability
atmospheric processes
lead time of the forecast
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
00 UTC 06 UTC 12 UTC 18 UTC
for a specific location:
10
0
90%-percentile= 10 mm rain
Source of Figure: NinJo Visualization System at DWD
Example of a Forecast Product
Forecast Lead Time
00 UTC 06 UTC 12 UTC 18 UTC
for a specific location:
10
0
75%-percentile= 7 mm rain
90%-percentile= 10 mm rain
Source of Figure: NinJo Visualization System at DWD
Example of a Forecast Product
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
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
DWD forecasters receive COSMO-DE-EPS
percentiles,exceeding probabilities,ensemble mean and spread, …
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
Favorites:
90%-percentiles
„upscaled“ probabilities
DWD forecasters receive COSMO-DE-EPS
Why „upscaled“ probabilities?
Feedback from the forecasters
probability of
precipitation > 20 mm/6h
Source of Figure: NinJo Visualization System at DWD
probability of
precipitation > 20 mm/6h
Source of Figure: NinJo Visualization System at DWD
90 -100 %80 - 89 %70 - 79 %.....10 - 19 % 1 - 9 % < 1 %
Forecasters:„Probabilities are too low!“
probability of
precipitation > 20 mm/6h
Source of Figure: NinJo Visualization System at DWD
Forecasters:„Probabilities are too low!“
probability of
precipitation > 20 mm/6h
Source of Figure: NinJo Visualization System at DWD
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
precipitation > 20 mm/6h
Source of Figure: NinJo Visualization System at DWD
probability of
precipitation > 20 mm/6h they needa different product
Source of Figure: NinJo Visualization System at DWD
probability of
precipitation > 20 mm/6h
probability of
precipitation > 20 mm/6h
somewhere within a region
Source of Figure: NinJo Visualization System at DWD
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. 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.
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. 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.
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
„High-impact“ weather
severe precipitation eventsomewhere within a
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
COSMO-DE-EPS for flood warnings
take members of COSMO-DE-EPS
several simulations with
hydrological model
ensemble for runoff
COSMO-DE-EPS for flood warnings
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
COSMO-DE-EPS for flood warnings
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)
„High-impact“ weather
high water levels of a river
(predicted by hydrological models which use ensemble weather
forecasts in their inputs)
severe precipitation eventsomewhere within a
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)
COSMO-DE-EPS for airport management (Frankfurt)
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
COSMO-DE-EPS for airport management (Frankfurt)
LuFo iPort WiWi project (I.Alberts, N.Schuhen, M.Buchhold)
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 airport management (Frankfurt)
COSMO-DE-EPS for renewable energy
EWeLiNE project (K. Lundgren et al.)
„High-impact“ weather
high water levels of a river
(predicted by hydrological models which use ensemble weather
forecasts in their inputs)
severe precipitation eventsomewhere within a
certain region
exceeding a certain threshold of the tailwind or crosswind component
relative to the airport runwayalong the glide path
„High-impact“ weather
high water levels of a river
(predicted by hydrological models which use ensemble weather
forecasts in their inputs)
severe precipitation eventsomewhere within a
certain region
exceeding a certain threshold of the tailwind or crosswind component
relative to the airport runwayalong the glide path
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
„High-impact“ weather
high water levels of a river
(predicted by hydrological models which use ensemble weather
forecasts in their inputs)
severe precipitation eventsomewhere within a
certain region
exceeding a certain threshold of the tailwind or crosswind component
relative to the airport runwayalong the glide path
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
PBPV – 03/2013 60
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