2. Motivation Investigation of predictability using ensemble forecasts
Courtesy: D.Anwender
ET of Tropical Cyclones
Reduction in forecast skill
ET of Maemi
Investigation of predictability using ensemble forecasts
Ensemble Spread ≈ Measure of predictability
Different possible development scenarios
Examine physical properties, reducing forecast skill
Recent studies with ECMWF & NCEP EPS
(Anwender et al. 2008, Harr et al., 2008)

3. New studies using TIGGE Up to ten EPS instead of one
Compare predictability of different EPS
Are there other development scenarios?
Statistically reliable in applied analysis method due to more ensemble members
First approach:
Can we learn more about ET-related processes by using TIGGE instead of the ECMWF-EPS?

4. Basics of the applied technique
Calculating Empirical Orthogonal Functions (EOFs)
Regions of largest variability
Determining Principal Components (PC)
Contribution of each ensemble member
Fuzzy Clustering of members with related contributions
Overview of possible development
scenarios an their probability

5. Case Study: Tropical Storm Bavi (2008)
TS: 06 UTC, 19 October – 09 UTC, 20 October
ET: 12 UTC, 20 October – 00 UTC, 25 October
ECMWF Analysis: gph of 200 hPa (shading) and mslp (contour)
Strong intensification as ET-System
Rather zonal flow before interaction strong T-R-pattern thereafter

6. Case Study: Tropical Storm Bavi (2008)
Rather
zonal
before interaction
Strong
T-R pattern
thereafter

7. Uncertainties in the different EPS
stdev of gph at 200 hPa, averaged over 40-60°N
Forecast
initiated
18 Oct 2008
12 UTC
=Surface
Position
of Bavi
(out of cf)

8. Uncertainty in TIGGE-EPS
stdev of gph at 200 hPa, averaged over 40-60°N
Forecast
initiated
18 Oct 2008
12 UTC
TIGGE
=Surface
Position
of Bavi
(EC cf)

9. Differences in Standard Deviation
Regions of increased uncertainty differ between EPS, e.g.:
MSC:Vicinity of Bavi
ECMWF:Upstream of Bavi
Overall decreased stdev
Pattern related to ECMWF

10. Scope of the investigations Comparison of TIGGE and ECMWF forecasts
fcst 1 initialized: 17 Oct 2008, 12 UTC
fcst 2 initialized: 18 Oct 2008, 12 UTC
clustering time: 21 Oct 2008, 12 UTC
Variable for clustering: gph at 200 hPa
Based on TIGGE without Korea and France EPS
France: Only 2.5 day forecast available
Korea: Outlier in PC phase space
Lower gph at 200 hPa after +48h
south of ~30° N
different development
Korea

11. Distribution of uncertainty
EOF 1 & 2 and 200 hPa gph ensemble mean
27%
27%
26%
18%
19%
22%
ECMWF fcst 2
TIGGE fcst 1(2 similar)
ECMWF fcst 1
ECMWF fcst 2

12. Different development scenarios
Case 1: Mainly TIGGE fcst 1
Weak Bavi, less amplitude in ridge,moderate downstream dev
12 UTC, 21 Oct
12 UTC, 23 Oct
geop. height
at 200 hPa
mslp
pot. temp &
rel. vort.
at 850 hPa .
Bavi
Clustering time
48 h later

13. Different development scenarios
Case 2: Only TIGGE fcst 1
Strong Bavi, high amplitude in ridge, moderate downstream dev
12 UTC, 21 Oct
12 UTC, 23 Oct
geop. height
at 200 hPa
mslp
pot. temp &
rel. vort.
at 850 hPa
Bavi
Clustering time
48 h later

14. Different development scenarios
Case 3: TIGGE and ECMWF fcst 1&2
Moderate Bavi and amplitude in ridge, weaker downstream dev
12 UTC, 21 Oct
12 UTC, 23 Oct
geop. height
at 200 hPa
mslp
pot. temp &
rel. vort.
at 850 hPa
Bavi
Clustering time
48 h later

15. Result of qualitative comparison between TIGGE and ECMWF
TIGGE fcsts show weak, strong and moderate pattern
ECMWF fcsts have less differences, rather strong pattern
Capture none of the both extremes found in TIGGE
A pattern resembling analysis can be found in each fcst
Conclusion
Using TIGGE instead of ECMWF EPS
new development scenarios offer possibility to study processes during ET

16. Outlook Other case studies (i.e. Hurricanes 2008)
Selection of interesting development scenarios
Investigation of ET related processes, using
Eddy-Kinetic-Energy-Analysis
(Orlanski&Sheldon, 1995, presented by B. Sanabia on Monday)
PV-Inversion
(Davis, 1992)
New insights in processes during ET
leading to reduction of predictability