1. CSTAR Update: New Tools for More Efficient Use of Ensembles in 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 15 12-13 November 2014

2. Outline Motivation Motivation Ensemble Sensitivity Update: Brief review and QPF example Ensemble Sensitivity Update: Brief review and QPF example Fuzzy clustering approach for multi-model ensembles Fuzzy clustering approach for multi-model ensembles Summary and Ongoing Research Summary and Ongoing Research

3. Motivation 1.Forecasters need ensemble tools to extract useful information from ensembles other than mean, spread, and anomalies. 2.More evaluation of ensemble forecasts of high impact weather over East US is necessary. 3.Forecasters need more guidance about potential multi- model biases and outliers.

4. Composite Rossby Wave Packet anomalies for 75 large error cases for 300Z in the GFS day 7 (2007-2012) for 75 large error cases for 300Z in the GFS day 7 (2007-2012) Initial Positive RWPA Anomaly Develop and Propagate into VR Unit: m/s purple The purple contour corresponds to 95% significance level

5. http://dendrite.somas.stonybrook.edu/CSTAR/Ensemble_Sen sitivity/EnSense_Main.html ). Figure 8 shows the cover of the page, in which users can Ensemble Sensitivity Web Page

6. One Approach for Forecast Metric (J) Using Spread: 1. Determination of Empirical Orthogonal Functions (EOFs): rank them as % of variance explained… 2. Project each ensemble member one that EOF pattern to get the Principal Components (PCs). L L 65% of variance explained 16% of variance explained

7. Projection of a pattern in domain of interest: Pattern: p i Ensemble member anomaly (member – ensemble mean): x i Projection of Pattern onto Ensemble member (Principal Component):  p i x i Basically value of projection is large when the anomaly of the ensemble member resembles the EOF pattern + PC (large + J) EOF Pattern - PC (large - J)~0 PC (small J) Member 1: neg SLP anomaly Member 2: pos SLP anomaly Member 3: small SLP anomaly

8. Calculate the sensitivity at some earlier time by correlating that forecast metric J with the anomaly of a state (Xi) variable (500Z anomaly): Or, “Sensitivity” = Cor(J,Xi) + Xi (large + J) At day -4: 500 Z ensemble mean over C. Pacific - Xi (large - J)~0 Xi (small J) L Member 1: weaker upstream trough Member 2: stronger upstream trough Member 3: similar to mean L L L

9. - J to + J Metric At each point in the plot (day-5) calculate the correlation between J and Xi to derive the “sensitivity.” Plot that correlation (sensitivity) at each point on the plot. Only shade those regions that are significant at 95% level. - Xi to + Xi Mem1 Mem3 Mem2 Cor(J,Xi) is large; sensi ~0.9 Positive correlation (+ sensitivity) Therefore, if the heights rise over this location at day -5, this will result in the negative (blue dashed) EOF pattern:  lower SLP (deeper storm) Note: A negative sensi (correlation) will mean the opposite.

10. Case 17 (VT: Dec 23 2013) RWPS

12. ECMWF mean (50-member) 7-day 24-h Precipitation Initialized 1200 UTC 16 December 2013

13. EOF1 Pattern for the day 7 Precipitation and Analyzed Precipitation (in mm)

14. Ensemble Sensitivity Based on EOF1 Pattern

15. Development of Clustering Tool Clustering tool can quickly separate different scenarios in multi-model ensemble. Comparing the analysis to the clusters from different ensemble systems for several high impact weather events can provide information about which ensemble is doing better. It also provides guidance about the time evolution of different scenarios. Different scenarios determined by clustering method can be related with ensemble sensitivity.

16. The process of fuzzy clustering in diagnosing ensemble dataset STEP 1: given a set of ensemble forecasts (M) + analysis (1) for a state variable X STEP2: perform EOF analysis of X on M+1 members of forecasts at valid time (VT) STEP3: group ensemble members into N clusters based on each pair of (PC1, PC2) using a fuzzy clustering method (Harr et al. MWR 2008) STEP4: pick up a contour line, and plot spaghetti plot for each group as well as the analysis Ensemble forecast data: NCEP(20 mem) + CMC(20 mem) + ECMWF(50 mem) Analysis: NCEP 6-hr analysis

17. Case study 1 (VT: 2010122700Z): Eastern US winter storm Deeper Weaker SWestNEast Analysis

19. Case study 2 (VT: 2012103000Z): Hurricane Sandy Deeper Weaker SEastNWest Analysis

21. Percentage of ensemble members in same cluster as analysis for 27 High Impact Weather Cases (Day 6 Forecast) Day 6 forecasts Y axis: #% members are in the same group as analysis in Mean: 19% 20% 32% 26%

22. For real-time forecast, replace analysis with ensemble mean: What members are closest to mean?

24. Summary Ensemble sensitivity approach has been expanded to include precipitation. Large error cases and sensitivity patterns are associated with the presence of enhanced Rossby Wave Packet Activity upstream. Clustering tool can quickly separate different scenarios in a multi-model ensemble. Comparing the analysis to the clusters from different ensemble systems for several high impact weather events can provide information about which ensemble is doing better. One can cluster around the ensemble mean to find those members.

25. Ongoing Research Implement the cluster approach in real-time Validation of East coast and western Atlantic cyclones in GEFS, EC, and CMC ensembles. Goal: Calibrate these ensembles for cyclone events. Participate in the Winter Weather Experiment at WPC. Students assist in making blogs for the various tools to allow more interaction with the various WFOs.