1. A Review of the CSTAR Ensemble Tools Available for Operations
Brian A. Colle, Minghua Zheng, and Edmund K.M. Chang,
School of Marine and Atmospheric Sciences
Stony Brook University
Stony Brook, New York, USA
NROW 17
2-3 November 2016

2. Outline
Motivation
Brief Ensemble Tool Review
Hurricane Matthew Example
Verification Results Using Fuzzy Clustering Approach

3. Motivation
Ensembles are not skillful enough to fully automate the forecast process (e.g., populate grids), especially for significant weather events.
Need the the right “man-machine mix” in the forecast process.
Forecasters need tools to better dissect ensemble solutions in order to help with decision support.

4. CSTAR Tools
Ensemble Sensitivity: Determines upstream features leading to ensemble spread or dModel/dt
Fuzzy Clustering: Scenario determination and maps for 4-5 different clusters (EC+GEFS+CMC).
Ensemble Cyclone Tracks: GEFS+CMC+FNOC+SREF tracks, track probabilities, and GEFS bias correction using cyclone verification.
Ensemble Rossby Wave Packets: GEFS wave packet amplitude probabilities and spread .
Spread-Anomaly Tool: for SA Table (In development; see Taylor et al. NROW 2015)

5. Example: Hurricane Matthew: Why big change in track on Oct 4-5?
0000 UTC 5 Oct
1200 UTC 5 Oct

6. GEFS and EC Tracks for 00 UTC 10/4 and 00 UTC 10/5 (NOAA ESRL Page)

7. D(GEFS mean)/DT of SLP (hPa) for 0000 UTC 5 Oct minus 1200 UTC 4 Oct

8. Ensemble Sensitivity Evolution for D(GEFS)/DT region (black box area)

9. GEFS Mean Wave Packet Amplitude Probability for 1200 UTC 4 Oct 2016 Run

10. GEFS+EC+CMC Ensemble Spread for 1200 UTC 4 Oct 2016 Cycle and Model Tracks for Matthew
Courtesy: NOAA-NWS

11. Fuzzy Cluster Groups and Ens Mean
for 1200 UTC 4 Oct Cycle (1004 hPa SLP)

12. Fuzzy Cluster Groups and Ens Mean
for 1200 UTC 4 Oct Cycle (5640 m 500Z)

13. Ensemble Mean Cluster Members for day 4-5 (Members closest to full EM; 1004 hPa)

14. Cluster Groups 2 and 5 for day 5 (1004 hPa)

15. Ensembles Verification (Percentage of members within NCEP Analysis Cluster for ~160 Cyclone Cases )
GEFS is the best the first two days; EC ensemble is best days 3-6, but has no advantage after day 6.

16. Percentage of cases in which no members in each ensemble were part of the analysis cluster
Larger miss rates for the CMC and GEFS ensembles; but combining these two ensembles is much better and similar to EC ensemble.

17. Fraction of Cases with Analysis Outside of Full Ensemble Envelope (Given PC phase space
OOE cases are defined by the condition that the distance between the analysis and the closest member is significantly larger than the average distances between any two ensemble members.

18. The fraction of cases (red line with dot) in which the EM group is the same as the analysis group. EM most reliable only to day 2.
CMC (red), NCEP (green, and ECMWF
(blue), and EM (black for day 5 forecast of
day 5 for 26 Jan 2015 case

19. Ratio of Error to Spread for PC1 and PC2 (Ratio > 1 Underdispered; < 1 Overdispersed
PC1 (Mostly Intensity))
PC2 (Mostly Displacement))
The ratio of error to the spread of (a) PC1, and (b) PC2at each lead time for each EPS. The error is calculated based on the distance of analysis relative to each EPS or multi- model mean; while the spread is calculated using all the EPS ensemble members relative to the ensemble mean of each EPS or mulit-model.
Note: the error is calculated based on the distance of analysis relative to each EPS or multi- model mean; while the spread is calculated using all the EPS ensemble members relative to the ensemble mean of each EPS or mulit-model.

20. Minimum Pressure Error for PC1 Versus Lead Time
All ensembles have an underprediction bias in the medium range.

21. Mean Displacement Error for (Using PC2)
* EC ensemble has SW bias for days 2-3; NAEFS has bias to NE
* Multi-ensemble best on average – Emphasizes importance of looking at all ensemble solutions.
Days 5-6
Days 2-3

22. Summary
Hurricane Matthew is a good example of how various CSTAR tools can be utilized:
Emphasizes uncertainty in the central Pacific.
Rapid downstream spread of uncertainties within Rossby Wave Packet.
Cluster analysis helps separate into several scenarios days in advance.
Verification using Fuzzy Clustering has revealed the following:
Multi-model ensemble mean does not give one the best track and intensity forecast for coastal storms (other clusters/scenarios equally as likely after day 2).
Ensembles are underdispersed. Multi-model helps (but overdispersed for intensity before day 4), and 5-10% still outside envelope for medium range.
EC ensemble best for medium range (days 3-6), but not shorter and longer range (e.g., SW position bias in short term).
Ensembles are are too weak with storms in medium range.
Emphasizes uncertainty in the central Pacific responsible for large track
Rapid downstream spread of uncertainties within Rossby Wave Packet
Cluster analysis breaks down the several scenarios days in advance
Cluster Verification has Revealed the following: