Priority projectAdvanced interpretation

COSMO General Meeting, 18. September 2006

Pierre Eckert

Recognition of high impact weather boosting method for thunderstorm prediction

Initialisation of forecast matrix use either MOS on global models or DMO from LMGridpoint statistics neighbourhood method

Hydrological Applications applications with COSMO LEPS (MAP D-PHASE)

Automatic Weather Interpretationusing Boosting

Monday, September 18, 2006

COSMO General Meeting 2006, WG4

Donat Perler (ETH Zürich)

Oliver Marchand (MeteoSwiss)

4

Supervised Learning

Historic Data

(a) Input Data(Model Output)

(b) Label Data(SYNOP &

lightning data)

Learner Classifier

New Data

yes/no

5

Average final scores for 5-fold cross validation for the whole year 2005

Classifier POD FAR FBI CSI HSS

DWD

(optimized for DE)18% 94% 3.12 0.05 0.08

DWD

(optimized for CH)45% 68% 1.42 0.23 0.34

AdaBoost.M1

(DWD features)

57% 59% 1.44 0.32 0.46

AdaBoost.M1

(51 features)

72% 34% 1.10 0.52 0.67

Linear Discriminant

(51 features)

57% 58% 1.43 0.32 0.46

6

Operational Implementation of BoostingExample: 11 August 2006

7

Lightning data indicate thunderstorm in northeastern Switzerland

8

3h aLMo sums of precipitation for the same period show no signal!

Lokal-Modell Kürzestfrist

• Kürzestfrist = very short range (< 18 h)

• gridbox size: 2,8 km

• developed at DWD(Baldauf, Seifert, Förstner, Reinhardt, Lenz, Prohl, Stephan, Klink, Schraff)

• pre-operational since late summer 2006

LME

GME

LMK

What is LMK?What is LMK?

What is „Neighbourhood Method“?What is „Neighbourhood Method“?

Aims:• account for general predictability limits in LMK output• interpret small scales of LMK output statistically• derive probabilistic forecasts from a single simulation

Method:• statistical post-processing• spatio-temporal neighbourhood around each grid point• derive pseudo-ensemble

Application:• surface fields of LMK output (Hoffmann, COSMO Newsletter No.6)

13 elements have been covered so far:

• 2m-temperature below freezing point

• wind gusts exceeding certain thresholds (14 m/s, 18 m/s, 25 m/s, 29 m/s, 39 m/s)

• rain amount exceeding certain thresholds (10 mm/h, 25 mm/h)

• thunderstorm (3 categories of severity)

• black ice

New Focus: Warning EventsNew Focus: Warning Events

%

probability of thunderstorm occurencefrom the neighbourhood method

Example for Thunderstorm PredictionExample for Thunderstorm Prediction

25 June 200600 UTC + 18 hLMK test suite 3.3d

13

Shape of the neighborhood(P. Kaufmann)

• cylindrical rather than ellipsoidal

• independent spatial and temporal uncertainty

• true for no or weak advection, wrong for strong advection

x

y

t

14

0

0.5

1

-20 -15 -10 -5 0 5 10 15 20

spatial radius

wei

gh

t

Linearly fading weights

• Circles around singular high model values too well visible• Idea: smoother edges• Introduce linear fading of weights (relaxation)• Adds sponge layer around cylindrical neighborhood

large, small neighborhood

15

2006-08-16 18:00 UTCmoderate prob. – event occurred

50 mm / 24 h

16

2006-08-16 18:00 UTCraw model output

50 mm / 24 h

17

Neighborhood method

• Combination of Ensemble and Neighborhood method would combine both synoptic-scale and small-scale uncertainties

18

Plans for next year

• The weight of the project will be displaced on the verification of very high resolution models, mainly precipitation

• Proposed verification methods always use some aggregation on gridpoints

• The optimisation of the aggregation is using the verification

• WG4-WG5 project

19

Radar 12 km forecast 1 km forecast

0.125 0.5 1 2 4 8 16 32 mm

The problem we face

0 100 km

Six hour accumulations 10 to 16 UTC 13th May 2003

From N. Roberts, UKMO