post-processing methods for probabilistic convection ... · conversion into cosmo-de grid...

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


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


COSMO-DE COSMO-DE-EPS

„variations“

within the system

ensemble

members




COSMO-DE-EPS

1 2 3 4 5

GME

IFS

GSM

GFS

20 members




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

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


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


Sept) 2012


Bayes theorem

variable: TWATER (total

water content)



Sept) 2012


Bayes theorem

variable: DBZ_CMAX

(radar reflectivity column

maximum)



Sept) 2012


Bayes theorem

variable: OMEGA (vertical

velocity)



Sept) 2012


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 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


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

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


product design


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)


Example: combined mean radar refelectivity


Thanks for your

attention!

Comments?

Questions?

post-processing methods for probabilistic convection ... · conversion into cosmo-de grid...

Documents