CSTAR Update: New Tools for More CSTAR Update: New Tools for More Efficient Use of Ensembles in Operations Efficient Use of Ensembles in Operations

Brian A. Colle, Minghua Zheng, and Edmund K.M. Chang, Brian A. Colle, Minghua Zheng, and Edmund K.M. Chang, School of Marine and Atmospheric SciencesSchool of Marine and Atmospheric Sciences

Stony Brook UniversityStony Brook UniversityStony Brook, New York, USAStony Brook, New York, USA

NROW 1512-13 November 2014

OutlineOutline

MotivationMotivation

Ensemble Sensitivity Update: Brief Ensemble Sensitivity Update: Brief review and QPF examplereview and QPF example

Fuzzy clustering approach for multi-Fuzzy clustering approach for multi-model ensemblesmodel ensembles

Summary and Ongoing ResearchSummary and Ongoing Research

MotivationMotivation

1. Forecasters need ensemble tools to extract useful information

from ensembles other than mean, spread, and anomalies.

2. More evaluation of ensembleforecasts of high impact weather

over East US is necessary.

3. Forecasters need more guidance about potential multi-model biases and outliers.

Composite Rossby Wave Packet anomaliesComposite 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

The purplepurple contour corresponds to 95% significance level

http://dendrite.somas.stonybrook.edu/CSTAR/Ensemble_Sensitivity/EnSense_Main.html). Figure 8 shows the cover of the page, in which users can

Ensemble Sensitivity Web Page

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

Projection of a pattern in domain of interest:

Pattern: pi Ensemble member anomaly (member – ensemble mean): xi

Projection of Pattern onto Ensemble member (Principal Component):

pi xi

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

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)

( , )" " ;

( ) ( )

CovSensitivity

Var Var i

i

J X

X JL

Member 1: weaker upstream trough

Member 2: stronger upstream trough

Member 3: similar to mean

LL

L

- 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.

- X

i

to

+ X

i

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.

Case 17 (VT: Dec 23 2013) Case 17 (VT: Dec 23 2013) RWPSRWPS

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

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

Ensemble Sensitivity Based on EOF1 Pattern

Development of Clustering ToolDevelopment of Clustering Tool

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

The process of fuzzy clustering in The process of fuzzy clustering in diagnosing ensemble datasetdiagnosing ensemble dataset

STEP 1: given a set of ensemble forecasts STEP 1: given a set of ensemble forecasts ((MM) + analysis (1) for a state variable X ) + analysis (1) for a state variable X STEP2: perform EOF analysis of X on STEP2: perform EOF analysis of X on MM+1 +1 members of forecasts at valid time (VT)members of forecasts at valid time (VT)STEP3: group ensemble members into STEP3: group ensemble members into NN clusters based on each pair of (PC1, PC2) clusters based on each pair of (PC1, PC2) using a fuzzy clustering method (Harr et al. using a fuzzy clustering method (Harr et al. MWR 2008)MWR 2008)STEP4: pick up a contour line, and plot STEP4: pick up a contour line, and plot spaghetti plot for each group as well as the spaghetti plot for each group as well as the analysisanalysis

Ensemble forecast data: NCEP(20 mem) + CMC(20 mem) + ECMWF(50 mem) Analysis: NCEP 6-hr analysis

Case study 1 (VT: 2010122700Z): Eastern US winter storm Case study 1 (VT: 2010122700Z): Eastern US winter storm

Deeper

Weaker

SWest NEast

Analysis

Case study 2 (VT: 2012103000Z): Hurricane SandyCase study 2 (VT: 2012103000Z): Hurricane Sandy

Deeper

Weaker

SEastNWest

Analysis

Percentage of ensemble members in same cluster as analysis Percentage of ensemble members in same cluster as analysis for 27 High Impact Weather Cases (Day 6 Forecast)for 27 High Impact Weather Cases (Day 6 Forecast)

Day 6 forecastsY axis: #% members are in the same group as analysis in

Mean: 19% 20% 32% 26%

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

to mean?to mean?

SummarySummaryEnsemble sensitivity approach has been expanded to include Ensemble sensitivity approach has been expanded to include precipitation.precipitation.

Large error cases and sensitivity patterns are associated with Large error cases and sensitivity patterns are associated with the presence of enhanced Rossby Wave Packet Activity the presence of enhanced Rossby Wave Packet Activity upstream.upstream.

Clustering tool can quickly separate different scenarios in a Clustering tool can quickly separate different scenarios in a multi-model ensemble.multi-model ensemble.

Comparing the analysis to the clusters from different Comparing the analysis to the clusters from different ensemble systems for several high impact weather events can ensemble systems for several high impact weather events can provide information about which ensemble is doing better.provide information about which ensemble is doing better.

One can cluster around the ensemble mean to find those One can cluster around the ensemble mean to find those members.members.

Ongoing ResearchOngoing ResearchImplement the cluster approach in real-timeImplement the cluster approach in real-time

Validation of East coast and western Atlantic Validation of East coast and western Atlantic cyclones in GEFS, EC, and CMC ensembles. cyclones in GEFS, EC, and CMC ensembles. Goal: Calibrate these ensembles for cyclone Goal: Calibrate these ensembles for cyclone events.events.

Participate in the Winter Weather Experiment Participate in the Winter Weather Experiment at WPC. Students assist in making blogs for at WPC. Students assist in making blogs for the various tools to allow more interaction the various tools to allow more interaction with the various WFOs.with the various WFOs.