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The Ensemble-MOS of Deutscher Wetterdienst 15th EMS/12th ECAM Session ECAM1 07 September 2015 Reinhold Hess, Jenny Glashoff, and Bernhard K. Reichert Deutscher Wetterdienst
The Ensemble-MOS of
Deutscher Wetterdienst
15th EMS/12th ECAM
Session ECAM1
07 September 2015
Reinhold Hess, Jenny Glashoff, and Bernhard K. Reichert
Deutscher Wetterdienst
The Ensemble-MOS of Deutscher Wetterdienst
Reinhold Hess, FEZE-B 2
Outline
Motivation: AutoWARN, operational MOS Systems at DWD
Ensemble MOS: basic idea and current setup
Application to COSMO-DE-EPS and ECMWF-EPS
ModelMIX: MOS of MOS
Ensemble-MOS and ModelMIX
Basis of the Decision Support System
AutoWARN for the
Weather Warning Service at DWD
Generation of Automated Warning
Proposals for Forecasters
Ensemble-MOS: MOS for ensembles:
designed for the calibration of
probabilistic forecasts
ModelMIX: Consistent combination of
individual MOS Systems for
ICON, IFS/ECMWF,
COSMO-DE-EPS, IFS-EPS
… ICON-EPS (DWD)
B.K. Reichert et al.
Reinhold Hess, FEZE-B 3
MOS Systems at DWD
Operational Systems at DWD
MOS-MIX: ICON-MOS, ECMWF-MOS
global, up to 240h, at synoptic stations, based on ICON and IFS/ECMWF
ICON-WarnMOS, ECMWF-WarnMOS, WarnMOS-MIX
provides 27 warning criteria on 1x1 km grid for Germany
AUTO-TAF spezialised forecasts for airports
CellMOS nowcasting thunderstorms on advecting cells (Lagrange)
Ensemble-MOS, ModelMIX (in development, based on WarnMOS)
calibration of ensemble forecasts (COSMO-DE-EPS, ECMWF-EPS, ICON-EPS)
Reinhold Hess, FEZE-B
MOS (Model Output Statistics): stepwise multiple regression of
predictors (known values as model forecasts and latest observations) to
predictands (unknown values as observations and derived elements at valid times)
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Ensemble-MOS
basic idea:
exploit probabilistic information of ensembles for event probabilities
statistical optimisation of ensemble forecasts using synoptical observations
optimisation and interpretation of deterministic variables (e.g. visibility)
calibration of event probabilities (e.g. prob(RR/1h>15mm) )
forecast of forecast errors (e.g. absolute error of 2m temperature)
apply for COSMO-DE-EPS and ECMWF-EPS (ICON-EPS, planned)
Reinhold Hess, FEZE-B 5
Ensemble-MOS
efforts:
ensemble products as model predictors: mean and stddev (quantiles, etc.)
surrounding of stations (mean and stddev of surrounding)
linear regression for deterministic forecast elements (e.g. 2m temperature)
logistic regression for probabilistic forecast elements (e.g. prob(RR/1h>15mm) )
forecast of forecast errors and forecast uncertainty
maximize number of extreme and rare events (wind gusts, heavy precipitation)
use of long time series, e.g. 4 years for COSMO-DE-EPS
multi station approach (9 climatological clusters in Germany)
multi time equations (up to +/- 9 hours depending on rareness)
gauge adjusted radar data additionally to precipitation observations
Reinhold Hess, FEZE-B 6
Gauge adjusted radar products as predictands (T. Hirsch)
Probabilities of Precipitation
Prob(RR>15 mm/ 1h)
Prob(RR>40 mm/12h)
standard: synoptic observations
idea: use radar-data (RW, 1x1 km)
surrounding of stations (r=8 km und 40 km)
relative frequencies of threshold exceedances in
surrounding
improved statistical sample
higher representativity
more extreme cases
Reinhold Hess, FEZE-B
1-hourly estimation of precipitation (gauge adjusted at stations)
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MOS: stepwise multiple linear and logistic regression
provide set of predictors, e.g. model forecasts, observations, derived predictors (e.g. sqrt RR, Rel_Div_10m, CAPE index, etc.)
select predictor with highest statistical correlation to predictand
select further predictors correlated to residuum, as long as statistically significant
example: 2m temperature
based on 3 UTC issue of COSMO-DE-EPS
_MS: medium scale: 28 km
_LS: large scale: 54 km
Co: coefficient of regression
Wgt: normalised weigth of predictor in equation
1 equation for each predictand (about 160), cluster (9), forecast time (21), season (4), issue of EPS(8)
Reinhold Hess, FEZE-B
forecast time: 1h
99903 Issue=02:00z +001:00
TTT Season: spr
Name Lin Reg 1 Co Wgt
-----------------------------------
T_G_MS 0.05 5
TD_2M_LS -0.02 2
TTT(-1)Obs 0.88 78
Td(+0)StF 0.08 7
…
-----------------------------------
Const. = -5.2 RMSE = 6.28
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Application to COSMO-DE-EPS
Calibration of wind gusts > 14m/s
Reinhold Hess, FEZE-B
reliability diagram for 3-hourly forecasts
COSMO-DE-EPS (not calibrated, grey)
shows low resolution and significant
overforecasting for high probabilities.
MOS with linear regression
(blue, green) shows underforecasting
for high probabilites.
Ensemble MOS with logistic regression
(red) is correcting, however not yet
perfectly. Still overforecasting for small
probabilities (problem found).
Impact of logistic regression
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Application to COSMO-DE-EPS
C. Primo
EnsembleMOS for ECMWF-EPS (J. Glashoff)
Verification of 2013 – 2m temperature errors
Reinhold Hess, FEZE-B
Frankfurt
: MOS Forecasts of MOS Errors
: MOS Errors
TIGGE/THORPEX data
50 ensembles, 1 high resolution run
2m temperature, mean wind, cloud
coverage, 24h precipitation
observations as predictands
ensemble products, mean, stddev
as predictors
training sample 2002-2012
free forecasts for 2013
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Application to ECMWF-EPS
C. Primo
compute regression of any forecast element
use absolute error of residuum as predictand
compute regression of this predictand
example: absolute error of 2m temperature
based on 12 UTC issue of ECMWF-EPS (TIGGE/THORPEX data, 4 variables)
temp: ensemble mean of temperature
temp_dev: standard deviation of ensemble
99906 Issue=12:00z +024:00
TTT Season: win
Name Lin Reg 1 Co Wgt
-----------------------------------
temp 1.04 88
Cos_Dag 0.07 3
temp_dev 0.04 3
Sin_3*Dag 0.01 3
wind -0.16 3
-----------------------------------
Const. = -2.5 RMSE = 10.90
99906 Issue=12:00z +024:00
E_TTT Season: win
Name Lin Reg 1 Co Wgt
-----------------------------------
temp_dev 0.05 37
wind -0.19 29
temp -0.02 18
Sin_3*Dag -0.01 16
-----------------------------------
Const. = 9.3 RMSE = 7.10
T2m
abs. err. of
T2m
Forecast of Forecast Errors (of MOS forecast)
stddev is increased and calibrated
(stddev = absolute error/0.8 for Gaussian distribution)
Reinhold Hess, FEZE-B 11
Application to ECMWF-EPS
EnsembleMOS for ECMWF-EPS (J. Glashoff)
Verification of 2013 – 24h precipitation errors
Reinhold Hess, FEZE-B
Frankfurt Dublin
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Application to ECMWF-EPS
C. Primo C. Primo
ModelMix – MOS of MOS
combination of MOS systems
ICON-WarnMOS
ECMWF-WarnMOS
COSMO-DE-EPS-EnsembleMOS
ECMWF-EPS-EnsembleMOS
…ICON-EPS-EnsembleMOS
statistically optimal combinations
consistent probabilitstic products for warning criteria
at locations of stations and on 1km-grid
basis for the automated warning proposals for AutoWARN
Reinhold Hess, FEZE-B 13
ModelMIX: Thunderstorm
Probability for thunderstorm +16h
Thunder
oper. WarnMOS
(GME + ECMWF)
COSMO-DE-EPS-MOS ModelMIX
signal for thunderstorm is enhanced Combination of MOS forecasts
Reinhold Hess, FEZE-B 14
T. Hirsch
normalised weights for
COSMO-DE-EPS-EmsebleMOS, GME-WarnMOS und ECMWF-WarnMOS (all issues, seasons, stations)
ModelMIX
consistent transition from COSMO-DE-EPS to GME/ICON and ECMWF
Reinhold Hess, FEZE-B 15
EnsembleMOS for ECMWF-EPS
Verification of 2013 – 24h precipitation errors
Reinhold Hess, FEZE-B
Frankfurt Dublin
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Application to ECMWF-EPS
C. Primo C. Primo
Thank you for attention
Reinhold Hess, FEZE-B
References: • Knüpffer, K., 1996. Methododical and predictability aspects of MOS systems, in: 13th Conf. on
Probability and Statistics in Atmosph. Sciences, Amer. Meteorol. Soc., San Francisco, CA. pp. 190–197.
• Kriesche, B., R. Hess, B. K. Reichert, and V. Schmidt (2015). A probabilistic approach to the prediction
of area weather events, applied to precipitation. Spat. Stat. 12, 15–30.
• Peralta, C., Ben Bouallègue, Z., Theis, S.E., Gebhardt, C. and M. Buchhold (2012): Accounting for initial
condition uncertainties in COSMO-DE-EPS. J. Geophys. Res., 117 (D7)
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