Post-processing methods for probabilistic convection forecasts based on the limited-area ensemble COSMO-DE-EPS of DWD Lars Wiegand, Christoph Gebhardt.

Präsentation zum Thema: "Post-processing methods for probabilistic convection forecasts based on the limited-area ensemble COSMO-DE-EPS of DWD Lars Wiegand, Christoph Gebhardt."—  Präsentation transkript:

1 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

2 Content set-up COSMO-DE-EPS EPS convection project methodology
observation forecast – probabilistic products (case study) Bayes theorem LASSO data result further researches product design SESAR (Single European Sky) 2

3 set-up COSMO-DE-EPS model chain at DWD COSMO-DE: 2.8 km
further details: see talk SCI-PS166.01 Susanne Theis: Tue 13:30, room 524A set-up COSMO-DE-EPS model chain at DWD COSMO-DE: 2.8 km convection-permitting forecast model 50 vertical levels modelrun every 3 hours: + 27 h COSMO-EU: 7 km GME: 20 km

4 further details: see talk SCI-PS166.01
Susanne Theis: Tue 13:30, room 524A set-up COSMO-DE-EPS COSMO-DE-EPS 20 members further details: see talk SCI-PS166.01 Susanne Theis: Tue 13:30, room 524A 1 2 3 4 5 IFS GME GFS GSM

5 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 5

6 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) 6

7 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 every grid point within 3km gets a distance weighted amount of a lightning measure 7

8 case study – thunderstorms 28th July 2013

9 case study – thunderstorms 28th July 2013
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10 case study – thunderstorms 28th July 2013
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11 case study – thunderstorms 28th July 2013
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12 case study – thunderstorms 28th July 2013
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13 case study – thunderstorms 28th July 2013

14 Bayes theorem 1/0 event occurs/does not occur
Bayes theorem 1/0 event occurs/does not occur X = variable from COSMO-DE(-EPS) variables: CAPE, CIN, TWATER, OMEGA, DBZ_CMAX, TOT_PREC period: summer (Apr-Sept) 2012 forecast: 00UTC + 0/6/12/18h based on grid points 14

15 Bayes theorem variable: TWATER (total water content)
forecast: 00UTC + 0h period: summer (Apr-Sept) 2012 15

16 Bayes theorem variable: TWATER (total water content)
forecast: 00UTC + 18h period: summer (Apr-Sept) 2012 16

17 Bayes theorem variable: DBZ_CMAX (radar reflectivity column maximum)
forecast: 00UTC + 12h period: summer (Apr-Sept) 2012 17

18 Bayes theorem variable: OMEGA (vertical velocity) forecast: 00UTC + 0h
period: summer (Apr-Sept) 2012 18

19 LASSO –. least absolute shrinkage and selection operator
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 19

20 LASSO summer 2012 (40 days) data basis 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) 20

21 LASSO summer 2012 (40 days) result - predictors 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 21

22 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 22

23 generation of consistent, blended probability products for ATC
Super-Ensemble Mesoscale Forecast of Convection (SESAR-JU WP11.2.1, lead Meteo France) 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) generation of consistent, blended probability products for ATC 2 data phases Summer 2012 (mid July – end August) Spring 2014 (mid April – end June) 23

24 Example: combined mean radar refelectivity

25 Thanks for your attention!
Comments? Questions?

26 Supplementary slides Verification summarizes all sorts of situations - also those which are less interesting for the forecaster. Verification does not necessarily tell whether guidance helps the forecaster. 26

27 product design 27

28 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 28

29 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, 29

30 Beobachtung Gewitter Schwellenwerte für Gewitterklassifikation 30
class radar reflectivity [dbz] lightning strikes [no./15 minutes] associated weather moderate >37 1 moderate rain wind 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 Bft hail possible (Ø <1,5cm) severe >53 very heavy rain (>25 l/m² in 1h, >35 l/m² in 6h) wind gusts >11 Bft large hail possible (Ø >1,5cm) 30

31 COSMO-DE 31

32 Beispiel – Gewitterlage 28. Juli 2013
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33 Super-Ensemble Mesoscale Forecast of Convection (SESAR-JU WP11. 2
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)

34 Modellgebiete 34

35 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 35

36 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

37 Observation thunderstorm
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38 COSMO-DE COSMO-DE-EPS
further details: see talk SCI-PS166.01 Susanne Theis: Tue 13:30, room 524A set-up COSMO-DE-EPS COSMO-DE COSMO-DE-EPS „variations“ within the system ensemble members