post-processing methods for probabilistic convection ... · conversion into cosmo-de grid...
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Post-processing methods for probabilistic
convection forecasts based on the limited-area
ensemble COSMO-DE-EPS of DWD
Lars Wiegand, Christoph Gebhardt
German Meteorological Service (DWD), Germany
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
Content
EPS convection project
set-up COSMO-DE-EPS
methodology
observation
forecast – probabilistic products (case study)
Bayes theorem
LASSO
data
result
further researches
product design
SESAR (Single European Sky)
conclusion
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
model chain at DWD
COSMO-DE: 2.8 km
convection-permitting forecast model
50 vertical levels
modelrun every 3 hours: + 27 h
GME: 20 km
COSMO-EU: 7 km
set-up COSMO-DE-EPS further details: see talk SCI-PS166.01
Susanne Theis: Tue 13:30, room 524A
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
COSMO-DE COSMO-DE-EPS
„variations“
within the system
ensemble
members
set-up COSMO-DE-EPS further details: see talk SCI-PS166.01
Susanne Theis: Tue 13:30, room 524A
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
COSMO-DE-EPS
1 2 3 4 5
GME
IFS
GSM
GFS
20 members
set-up COSMO-DE-EPS further details: see talk SCI-PS166.01
Susanne Theis: Tue 13:30, room 524A
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
project „EPS Convection“
predictability of small scale processes with non-linear and stochastic
processes (e.g. convection) is strongly limited
i.e. leads to strong uncertainty already at short lead times
severe events and high impact weather are highly important for warnings in
general or in aviation in particular
“charakteristics of HIW” and “limited predictability” leads to use of probabilistic
estimation of high-resulotion forecasts of deep convection based on
COSMO-DE-EPS
aims at supporting aviation weather forecasts and general weather warning
process at DWD
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
methodology
probabilistic products for convective parameters from COSMO-DE-EPS
DMO (direct model output) variables as well as direct calculatable variables
(e.g. KO-index) from DMO
IMO (indirect model output), e.g. thunderstorms produced with regression
methods
requirements for IMO (thunderstorm) forecasts:
observation of IMO (thunderstorm) as predicand radar + lightning
EPS DMO forecasts as predictor(s)
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
observation thunderstorm
combination of radar reflectivity and lightning
RX product
advantages: warning criterias are known within DWD (28, 37, 46, … dBz), high spatial/temporal
resolution
adaptions:
conversion into COSMO-DE grid
adjustment possible for used lead times
use of quality product QY
lightning from NCM network
very accurate observations – only 0,02% of all lightnings have errors >2,8km
detection of 90% of any light lightning and nearly 100% of strong lightnings
adaptions:
every grid point within 3km gets a distance weighted amount of a lightning measure
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
case study – thunderstorms 28th July 2013
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
case study – thunderstorms 28th July 2013
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
case study – thunderstorms 28th July 2013
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
case study – thunderstorms 28th July 2013
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
case study – thunderstorms 28th July 2013
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
case study – thunderstorms 28th July 2013
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
Bayes theorem
variables: CAPE, CIN,
TWATER, OMEGA,
DBZ_CMAX, TOT_PREC
period: summer (Apr-
Sept) 2012
forecast: 00UTC +
0/6/12/18h
based on grid points
1/0 event occurs/does not occur
X = variable from COSMO-DE(-EPS)
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
Bayes theorem
variable: TWATER (total
water content)
forecast: 00UTC + 0h
period: summer (Apr-
Sept) 2012
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
Bayes theorem
variable: TWATER (total
water content)
forecast: 00UTC + 18h
period: summer (Apr-
Sept) 2012
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
Bayes theorem
variable: DBZ_CMAX
(radar reflectivity column
maximum)
forecast: 00UTC + 12h
period: summer (Apr-
Sept) 2012
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
Bayes theorem
variable: OMEGA (vertical
velocity)
forecast: 00UTC + 0h
period: summer (Apr-
Sept) 2012
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
LASSO –
least absolute shrinkage and selection operator
(Tibshirani 1996)
search for suitable predictors
comparison of predictors (variables (DMO) and their probabilistic products)
choose of predictors, which depict the observation best
tool: R statistic software (package glmnet + dependences)
logistic regression:
optimal for extreme values
Input/output can be probabilities
error measure: RMSE
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
COSMO-DE-EPS
forecast: daily 03UTC + 21h
40 days in summer 2012 (22th July – 30th August)
93 variables (CAPE, CIN, T2m, TWATER, TQ, TI, …)
EPS products: mean, minimum, maximum
5 days, i.e. 8 calculations
observation: 1h radar maximum and lightning sum
radar: 16:30 – 17:25 UTC (available every 5 minutes)
lightning: 16:30 – 17:29 UTC (exact to the second)
LASSO summer 2012 (40 days)
data basis
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
mean TWATER
maximum TQ (Graupel)
maximum radar reflectivity (maximum RR in atmospheric column)
all 3 variables are amongst the first 5 predictors for the 8 calculations
maximum TWATER in 5 out of 8 calculations within the first 5 predictors
to check: stability of predictors for longer time periods
result just shows the predictors for 8 x 5 days in summer 2012
result - predictors
LASSO summer 2012 (40 days)
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
further research
LASSO
longer time periods statistical robustness
quantiles and probabilities as predictors
time offset
neighborhood method
different synoptical regimes (convective time scale)
generation of thunderstorm forecast product from 3 or more predictors
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
product design
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
Objectives
To develop ensemble post-processing techniques in order to provide consistent
short-range probabilistic NWP products of convective risks across Europe,
at the highest possible NWP resolution
combination of three convection-permitting ensembles systems.
AROME-EPS (MF), COSMO-DE-EPS (DWD) and the UKV-EPS(UKMO)
Super-Ensemble Mesoscale Forecast of Convection (SESAR-JU WP11.2.1, lead Meteo France)
generation of consistent, blended probability
products for ATC
2 data phases
Summer 2012 (mid July – end August)
Spring 2014 (mid April – end June)
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
Example: combined mean radar refelectivity
WWOSC 2014 Montréal, Canada Lars Wiegand, DWD 18th August 2014
Thanks for your
attention!
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