Download Report - Cosmo

Priority project
Advanced interpretation
Pierre Eckert
COSMO General Meeting, 18. September 2006
Recognition of high impact weather
boosting method for thunderstorm prediction
Initialisation of forecast matrix
use either MOS on global models or DMO from LM
Gridpoint statistics
neighbourhood method
Hydrological Applications
applications with COSMO LEPS (MAP D-PHASE)
Automatic Weather Interpretation
using Boosting
Donat Perler (ETH Zürich)
Oliver Marchand (MeteoSwiss)
Monday, September 18, 2006
COSMO General Meeting 2006, WG4
Supervised Learning
New
Data
Historic Data
(a) Input Data
(Model Output)
Learner
Classifier
(b) Label Data
(SYNOP &
lightning data)
yes/no
4
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
5
Operational Implementation of
Boosting
Example: 11 August 2006
6
Lightning data indicate thunderstorm
in northeastern Switzerland
7
3h aLMo sums of precipitation for the
same period show no signal!
8
What is LMK?
LMK
Lokal-Modell Kürzestfrist
• Kürzestfrist =
very short range (< 18 h)
LME
GME
• gridbox size: 2,8 km
• developed at DWD
(Baldauf, Seifert, Förstner,
Reinhardt, Lenz, Prohl,
Stephan, Klink, Schraff)
• pre-operational since
late summer 2006
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)
New Focus: Warning Events
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
Example for Thunderstorm Prediction
25 June 2006
00 UTC + 18 h
LMK test suite 3.3d
probability of thunderstorm occurence
from the neighbourhood method
%
Shape of the neighborhood
(P. Kaufmann)
• cylindrical rather than
ellipsoidal
• independent spatial and
temporal uncertainty
• true for no or weak
t
advection, wrong for
strong advection
y
x
13
Linearly fading weights
1
0.5
0
-20
-15
-10
-5
0
5
10
15
weight
large, small neighborhood
20
spatial radius
•
•
•
•
Circles around singular high model values too well visible
Idea: smoother edges
Introduce linear fading of weights (relaxation)
Adds sponge layer around cylindrical neighborhood
14
2006-08-16 18:00 UTC
moderate prob. – event occurred
50 mm / 24 h
15
2006-08-16 18:00 UTC
raw model output
50 mm / 24 h
16
Neighborhood method
• Combination of Ensemble and
Neighborhood method would combine
both synoptic-scale and small-scale
uncertainties
17
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
18
The problem we face
Six hour accumulations 10 to 16 UTC 13th May 2003
Radar
12 km forecast
1 km forecast
0
0.125 0.5
1
2
From N. Roberts, UKMO
4
8
16
100 km
32 mm
19