post-processing methods for probabilistic convection forecasts based on the limited-area ensemble...

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

set-up COSMO-DE-EPS

EPS convection project

methodologyobservation

forecast – probabilistic products (case study)

Bayes theorem

LASSOdata

result

further researches

product design

SESAR (Single European Sky)


model chain at DWD

COSMO-DE: 2.8 km

convection-permitting forecast model50 vertical levels

modelrun every 3 hours: + 27 h

GME: 20 km

COSMO-EU: 7 km

set-up COSMO-DE-EPSfurther details: see talk SCI-PS166.01

Susanne Theis: Tue 13:30, room 524A


COSMO-DE-EPS

11 22 33 44 55

GMEGME

IFSIFS

GSMGSM

GFSGFS

20 members



further details: see talk SCI-PS166.01



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


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)


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

lightning from NCM network very accurate observations – only 0,02% of all lightnings have errors >2,8km

adaptions:

every grid point within 3km gets a distance weighted amount of a lightning measure


case study – thunderstorms 28th July 2013


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)


Bayes theorem

variable: TWATER (total water content)

forecast: 00UTC + 0h



Bayes theorem

variable: TWATER (total water content)




Bayes theorem

variable: DBZ_CMAX (radar reflectivity column maximum)




Bayes theorem

variable: OMEGA (vertical velocity)




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


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 sumradar: 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


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)


further research

LASSOlonger 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


ObjectivesTo 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)


Example: combined mean radar refelectivity


Thanks for your attention!

Comments?

Questions?


Supplementary slides


product design


Direct Model Output (DMO)

Zusammenhang Wahrscheinlichkeit(DMO>Schwellenwert) Ereignis

Wahrscheinlichkeiten dieser DMO-Variablen nicht zwingend gut kalibriert

Ansatz: Vorhersage “ja” für Wahrscheinlichkeiten(DMO>Schwelle) > A%

Bestimme hit rate/ false alarms für verschiedene A

Optimales A ist nutzerabhängig! (hit rate/ false alarms)

Datenlage der Beobachtungen von DMO oft flächig nicht beobachtbar (CAPE, TWATER, …)

Ereignis: Gewitter aus Radar + Blitz


Weitere Arbeiten in EPS Konvektion

WX getestet: Vorteil: größeres Gebiet: keine Radarmessungen werden verworfen

Nachteil: nicht sicher ab wann operationel, wird aller Vorraussicht nicht nachberechnet

Programme für abgeleitete Variablen aus DMO Variablen KO-index

Convective time scale – Klassifizierung in synoptische/Luftmassengewitter Situationen

Erstellung des technischen und fachlichen Rahmens des Fachkonzeptes

Studien zu statistischen Eigenschaften der gewählten ‘high-priority’ Variablen CAPE, CIN, dBz_cmax, TWATER, OMEGA@700hPA


Beobachtung Gewitter

Schwellenwerte für Gewitterklassifikation

class radar reflectivity [dbz]

lightning strikes [no./15 minutes]

associated weather

moderate >37 1 moderate rainwind gust up to 7 Bft

strong >46 tbd heavy rain (10-25 l/m² in 1h, 20-35 l/m² in 6h)wind gusts 8-10 Bfthail possible (Ø <1,5cm)

severe >53 tbd very heavy rain (>25 l/m² in 1h, >35 l/m² in 6h)wind gusts >11 Bftlarge hail possible (Ø >1,5cm)


COSMO-DE


Beispiel – Gewitterlage 28. Juli 2013


Super-Ensemble Mesoscale Forecast of Convection(SESAR-JU WP11.2.2, lead Meteo France)

Ziel: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

Kombination dreier konvektionserlaubender Ensemblesysteme.

AROME-EPS (MF)

COSMO-DE-EPS (DWD)

UKV-EPS(UKMO)

Erstellung von konsistenten (räumlich verschnittenen) Produkten für die Flugsicherung (allerdings nur post-processing)


Modellgebiete


SESAR domain

Gemeinsames SESAR ‘Modellgebiet’

Dünkirchen (2.38E, 51N) als gemeinsamer Gitterpunkt Dünkirchen kein GP in originalem COSMO-DE-Gitter

Auflösung 0.027°/0.022° (lon/lat – reguläres Gitter)

Anpassungen:

Interpolation von rotiertem Gitter (0.025° lon/lat) auf SESAR-Gitter

Variablenanpassung: z.B. Windstärke auf SESAR-Gitter aus staggered grid

Korrekte Einstellungen der grib2 header


Zwei Phasen der Datenarchivierung

1. Phase (Sommer 2012): 22. Juli – 30. August 2012

93 Variablen

20 Member

21h Vorhersage (stündlich)

03UTC Vorhersage

2. Phase (Frühling 2014): 1. April bis 10. Juni 2014 (71 Tage – 40 ausgewählte)

Selbe Spezifikationen wie in Phase 1

Vorhersage bis 27h neu


Observation thunderstorm


COSMO-DE COSMO-DE-EPS

„variations“within the system

ensemble members



post-processing methods for probabilistic convection forecasts based on the limited-area ensemble...

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