# Presentation: 745386

## Introduction to Ensemble Products

- Session 2, 8 October 2007
- Bill Bua, UCAR/COMET
- and
- Rich Grumm, NOAA/NWS

## OVERVIEW

- Introduction to ensemble terminology
- The need to post-process the ensemble data
- Basic ensemble charts
- Identification, interpretation
- Advanced applications
- Confidence images
- Measures of predictability
- Summary

## Terminology for Ensembles	

- Ensemble Prediction Systems = EPS for short
- Familiar EPSs
- National Centers for Environmental Prediction (NCEP, U.S.) :
-  Global Ensemble forecast system (GEFS)
- Canadian Meteorological Center (CMC)
- Canadian ensemble forecast system (CEFS)
- North American Ensemble Forecast System (NAEFS)  GEFS + CEFS

## Terminology for Ensembles	

- Ensemble member
- One from among a full set of ensemble forecasts
- Ensemble control
- The ensemble member run from the control initial conditions
- Ensemble perturbation
- Initial condition differing from the control initial condition
- Post-processing
- Development of meaningful EPS products from the raw ensemble output
- Uses statistical methods (we’ll be covering those more in depth in a later lecture)

## The Need to Post-Process the Data

- Raw data per forecast cycle = 2Gb of information!
- 500-hPa height plot at right is *all* ensemble members and ensemble mean
- 10-member ensemble contoured at 10-dm interval
- Need to extract *meaningful *information through post-processing

## Basic Ensemble Charts

- Plan view maps
- Mean and spread
- Spaghetti plots
- Probability of exceedance
- Dominant forecast (e.g. precipitation type)
- Grid box/grid point diagrams
- Box and whisker
- Plume

## Plan View Maps:  Ensemble Mean and Spread

- Ensemble mean = arithmetic average of all ensemble members
- Typically contoured in plan view maps
- Also called “consensus” forecast
- May be useful to choose critical values to contour
- Spread = *sample* standard deviation *s* of ensemble members
- Typically shaded in plan view maps
- Can be applied to any prognostic (e.g. temperature) or diagnostic (e.g. CAPE) variable
- Advantage:
- Unpredictable features smoothed out by averaging process
- Disadvantage
- Significant forecast outcomes may become hidden

**00z21Jun2007 GEFS run **
**Valid time 06z27Jun2007**
**850-hPa temperature (****°C)**

**Question:  What do you notice about the ensemble mean contours in areas of high spread?**

**NWS**

## Plan View Maps:  Ensemble Mean and Spread

- High spread on one side of a predictable feature  some ensemble members are clustered in the area of high spread
- Question:  How would you interpret the large spread in terms of the location of resolvable features?

**00z21Jun2007 GEFS run **
**Valid time 06z27Jun2007**
**PMSL (hPa)**

**Predictable features**

**High**

**High**

**Trough/**
**cold front**

**NWS**

## Plan View Maps:  Spaghetti Diagram

- Graphic depicts same forecast data as previous mean and spread diagram
- Plots a little information for *all *ensemble members
- Contoured for one or a few levels ... here for 850-hPa 8°C and 16°C temperature contours
- In this diagram, colored contours are members and black contours are ensemble mean
- Spread is also plotted here in grey scale
- Can be applied to any prognostic (e.g. temperature) or diagnostic (e.g. CAPE) variable

**Question:  How do the contours behave in areas of high spread?**

**NWS**

## Plan View Maps:  Ensemble Mean and Spread and Spaghetti Diagrams Used Together

- Example:  850-hPa heights during TC Gamede
- Maximum spread *tending* to be aligned with predictable feature
- Bottom panel: Mean/spread implies most uncertainty as to strength of feature
- Top panel:  Spaghetti diagram shows concentric contours at different locations

**NWS**

## Plan View Maps:  Ensemble Mean and Spread and Spaghetti Diagrams Used Together

- Bottom panel: repeat of previous slide
- Cluster of solutions to the west of the ensemble mean position of trough/cold front over western S Africa
- Cluster of 1020 hPa contours west of ensemble mean contour in spaghetti diagram
- One or two *closed *1020 hPa contours on west S African coast

**NWS**

## Plan View Maps: Probability of Exceedance

- Always uses a critical threshold value
- Gives percentage of ensemble members that exceed (or are below) the critical threshold
- “Exceedance” can be greater (as here) *or* less than (e.g. 2-m temperature < 0°C
- Probabilities (in %) are usually shaded

**Question:  What are some critical thresholds you use for significant weather warnings to the public?**

**NWS**

## Plan View Maps: Dominant forecast

- Shows the MODE of the forecast
- Doesn’t *have* to be 50% or more
- Useful for categorical forecasts
- In North America, used in short-range ensemble forecast for precipitation type
- Example for Alaska shown at right
- Can also be applied to convection type, using stability and other related indices

## Basic Ensemble Charts: What we’ve covered so far ...

- Plan view maps
- Mean and spread
- Spaghetti plots
- Probability of exceedance
- Dominant forecast (e.g. precipitation type)

- Any QUESTIONS?

## Grid Point Plots:  Plume Diagram

- Time series of prognostic or diagnostic variable for a grid point (usually one nearest a station of interest)
- Plume on right is of (diagnostic) 2-m temperature at 6-hour intervals for 14 ensemble perturbation forecasts, one control forecast (grey), and the ensemble mean (heavy black with closed circles)
- Note how temps are distributed at each valid time
- Ensemble “cluster”:  a subset of ensemble member forecasts grouped together
- “Outliers”:  Ensemble member forecast very different from the others
- Analogous to “spaghetti diagram” in plan view

- Question:  Where do you see ensemble clusters in this diagram, if any?  Where are the outliers, if any?

**COMET/NCEP data**

## Grid Point Plots: Box (and Whisker) Diagrams

- Box (and whisker) diagram
- Rank the data, and cut up into quartiles (0-25%, 25-50%, 50-75%, 75-100%; more on quartiles later)
- Same data as for plume shown previously
- Box represents middle two quartiles
- Top of upper whisker is maximum value
- Bottom of lower whisker is minimum value
- Point within box is the median (50% lower, 50% higher)
- Quasi-equivalent to ensemble mean and spread diagram

**COMET/NCEP data**

## Basic Ensemble Charts: What We Covered

- Plan view maps
- Mean and spread
- Spaghetti plots
- Probability of exceedance
- Most frequently forecast (e.g. precipitation type)
**Grid box/grid point diagrams**
**Box and whisker**
**Plume**

- Any QUESTIONS?

## More Examples

- From 12 UTC 11 Sep 2007 ensemble run
- Valid time 12 UTC 17 Sep 2007
- What features seem to be most predictable?
- Where do you see the largest uncertainty?

## More Examples

- From *experimental* NCEP website for Africa (for same valid time as mean/spread diagram previous)
- Where is the largest uncertainty in the 500-hPa flow at 540 and 582 dm?
- Features at 582-dm over S. Africa associated with 25-mm prec in 24 hours

**20070917/1200v144 GEFS 540 and 582 dm 500 hPa height contours**
**09/11/2007 12UTC 144Hr Fcst Valid Mon 9/17/2007 12UTC NCEP/NWS/NOAA**

**NCEP/**
**NWS/NOAA**

## Advanced applications

- Ensembles  *flow-dependent* probability distributions for forecast outcomes
- Perceived low spread  high *subjective* confidence in forecast
- Perceived high spread  low *subjective* confidence in forecast
- How can we *objectively *measure confidence in current forecast (i.e. quantify what “high” and “low” spread **is**)
- Requires a reference or “baseline” spread for comparison
- Climatological spread from long term analyses or observations (e.g. ERA-40, NCEP reanalysis)
- Blends different flow regimes, though keeps seasonality of spread
- Recent past ensemble spread “climatology”
- Takes into account dependence of spread on prevailing flow regime

## Advanced Applications

- Confidence measures
- Plan view maps:
- Normalized spread
- Relative measure of “predictability”
- Grid point/grid box diagrams:
- Confidence plumes

## Normalized Spread

- Top panel:
- 96 hour forecast of GENS ensemble spread shaded with high resolution GFS 500-hPa height forecastcontoured
- Bottom panel:
- Same forecast but for GENS ensemble mean 500-hPa height and ***normalized*** spread
- Normalizing based on past 30 days of mean ensemble spread
- High uncertainty relative to past 30 days where normalized spread  > 1
- Low uncertainty relative to past 30 days where normalized spread < 1

## Relative Measure of Predictability (RMOP)

- Based on recent *and* long-term ensemble performance
- Both predictability of the flow (recent performance) and probability of verification (long-term performance) are considered
- Contour = ensemble mean
- Shading is RMOP and probability of verification
- Numbers on color bar:
- Right in black:  relative predictability compared to recent verfication
- Left in blue: prob of verification compared to long-term ensemble performance (> 5 years)

**11**
**17**
**20**
**25**
**27**
**31**
**38**
**45**
**58**
**83**

## Confidence plumes

- Black:  Time series of current ensemble forecast spread
- Blue:  Average spread for variable for 45 days centered on forecast valid time
- Yellow: Climatological standard deviation from NCEP reanalysis
- Green shading = above average confidence
- Red shading = below average confidence (no significant areas in this graphic)

## Summary

- EPS terminology
- Why we need ensemble products:
- Distillation of volumes of data to useable form
- Probabilistic forecasts require statistical analysis
- Plan view basic charts
- Mean and spread
- Spaghetti
- Probability of exceedance
- Dominant forecast

## Summary

- Grid point basic charts
- Box and whisker diagrams
- Plume diagrams
- Advanced applications (measures of “predictability” and/or “confidence”)
- Normalized spread
- RMOP
- Confidence plumes

## Important Note

- This is only an *introduction* to the ensemble products
- A “taste” of what the products are and what they look like
- More details will be presented in Session 3
- Statistical theory behind the products will be covered in more detail in Session 3 lectures
- Verification of EPS forecasts will be covered in Session 4

## If you have questions later...

- Ensemble modules at COMET (you should already be familiar with these)
- E-mails:
- _[Bill.Bua@noaa.gov](mailto:Bill.Bua@noaa.gov)_
- Richard.Grumm@noaa.gov