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Applications of Ensemble Prediction - a Historical Perspective Steve Tracton Office of Naval Research Arlington, VA (Formally of NWS/NCEP) KEY POINTS THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION SCREAMING MESSAGE:

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applications of ensemble prediction a historical perspective

Applications of Ensemble Prediction - a Historical Perspective

Steve Tracton

Office of Naval Research

Arlington, VA

(Formally of NWS/NCEP)

key points
KEY POINTS

THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION

SCREAMING MESSAGE:

THERE WILL ALWAYSBE VARYING DEGREES OF UNCERTAINTIES IN FORECASTS (“Chaos Theory”)

key points3
KEY POINTS

THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION

ENSEMBLE PREDICTION – FROM EARLY 90’S ON, REVOLUTIONARY CHANGE IN THE THRUST OF OPERATIONAL NWP (“WAVE OF THE FUTURE”),

First formal attention to the real possibility of OPERATIONAL

ensemble prediction at ECMWF Workshop on Predictability

In the Medium Range and Extended Range, 1986

Ultimately led to operational GLOBAL EPS at ECMWF and

NMC in Dec 1992

Followed from

Sufficient CPU resources becoming available

Scientific basis for generating “dynamically

constrained” initial state perturbations (SVs, Breeding)

Development of output products

slide4

Give Me Odds

ENSEMBLE FORECASTING

key points5
KEY POINTS

THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION

ENSEMBLE PREDICTION – FROM EARLY 90’S ON, REVOLUTIONARY CHANGE IN THE THRUST OF OPERATIONAL NWP (“WAVE OF THE FUTURE”), THE OBJECTIVES BEING TO:

PROVIDE RELIABLE INFORMATION ON FORECAST UNCERTAINTIES FROM THE SPREAD (DIVERSITY) AMONGST ENSEMBLE MEMBERS

USE TO:

  • Ascertain most likely deterministic prediction
  • Confidence in deterministic forecast
  • Same, plus identifying relative likelihood of alternative scenarios
  • Full probability distribution – maximum information
  • NET RESULT - ENHANCE UTILITY/VALUE OF NWP FOR VIRTUALLY ALL APPLICATIONS

NOT NECESSARILY SKILL

run to run model continuity

WHICH SOLUTION IS PREFERRED?

 DETERMINISTIC THINKING

RUN TO RUN MODEL CONTINUITY

TODAY’S DAY 3

YESTERDAY’S DAY 4

GOOD

NEW 84

HR MRF

OLD 108

HR MRF

BAD

NEW 96 HR

ECMWF

OLD 120

HR ECMWF

hpc s extended forecast discussion released mar 2 2001 at 3 38 p m

EPS NOW CONSIDERED INDISPENSIBLE TO HPC MEDIUM RANGE FORECASTS (Jim Hoke)

HPC’s Extended Forecast Discussion(released Mar 2, 2001, at 3:38 p.m.)

STEEP LEARNING CURVE

FOR NEW PARADIGM:NO A- PRIORI BEST SINGLE OUTCOME

“DETERMINISM IS DEAD” ??

NOT YET; BUT…

slide9

American Meteorological Society (AMS) Statement: Enhancing Weather Information with Probability Forecasts (3/02 BAMS!)

“The AMS endorses probability forecasts and recommends their use be substantially increased.”

“Would allow user (not forecaster!) to make decisions based on quantified uncertainties with resulting economic and social benefits” ( e.g., from taking umbrella along, through canceling a trip ,to evacuation from an impending threat)

slide10

Schematic of how a probabilistic model forecast can be used for risk-based decision-making.

P1

P2

P3

P4

.

.

PN

F(P)

Model

Pcr

Compare T

with Tcr

Take

appropriate

action

T

P

P1, P2 … PN = predicted variables of interest, for example, precipitation amount

Pcr = user-specified “critical value” of P which, if exceeded, requires an action or decision

T = chance of critical value being exceeded

Tcr = user-specified tolerance level (depending on societal, monetary, and/or environmental considerations).

slide11

American Meteorological Society (AMS) Statement: Enhancing Weather Information with Probability Forecasts (3/02 BAMS!)

“The AMS endorses probability forecasts and recommends their use be substantially increased.”

“Would allow user (not forecaster!) to make decisions based on quantified uncertainties with resulting economic and social benefits” ( e.g., from taking umbrella along, through canceling a trip, to evacuation from an impending threat)

“Probability Forecasts are particularly useful, even necessary, to reliably provide early warnings of extreme weather events”

TO AVOID, E.G.

slide12

MAJOR SNOWSTORM AMBUSHES WASHINGTON

Not Good- especially when effecting DC (just after announce-ment of new Super Computer by NWSHQ

slide14

American Meteorological Society (AMS) Statement: Enhancing Weather Information with Probability Forecasts (3/02 BAMS!)

“The AMS endorses probability forecasts and recommends their use be substantially increased.”

“Would allow user (not forecaster!) to make decisions based on quantified uncertainties with resulting economic and social benefits” ( e.g., from taking umbrella along through canceling a trip to evacuation from an impending threat)

Requires:

Conveying rationale and nature of uncertainties

Products/graphics/presentations that are readily comprehensible and relevant

key points15
KEY POINTS

THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION

ENSEMBLE PREDICTION - REVOLUTIONARY CHANGE IN THE THRUST OF OPERATIONAL NWP (“WAVE OF THE FUTURE”) - CONSISTS OF MULTIPLE PREDICTIONS FROM SLIGHTLY DIFFERENT INITIAL CONDITIONS AND/OR WITH VARIOUS VERSIONS OF MODELS, THE OBJECTIVES BEING TO:

REALIZING THE PRACTICAL UTILITY/VALUE OF ENSEMBLES ACCOMPLISHED VIA A VARIETY OF PRODUCTS DESIGNED TO CONDENSE AND MAXIMIZE INFORMATION CONTENT FOR USERS

Products/graphics/presentations must be readily comprehensible

and relevant

(MUST BE USER SPECIFIC AND USER FRIENDLY)

USER FEEDBACK ESSENTIAL

slide17
TYPES OF PRODUCTS

Roots of EPS products largely provided by Ed Epstein with graphical depictions to illustrate how uncertainty information could enhance forecast value (1971);

But, no further formalconsideration of post processing and presenting EPS output until 1992 ECMWF Workshop on New Developments in Predictability (List of recommended generic products largely reflected “concept demonstration” mode of EPS experiments at NMC/CAC, Tracton and Kalnay, 1993)

 SPAGHETTI CHARTS: now one of the most recognizable and symbolic products of EPS

slide21
TYPES OF PRODUCTS

SPAGHETTI CHARTS

MEAN/SPREAD

PROBABILITIES

STORM TRACKS

CLUSTERS

VERTICAL PROFILES

METEOGRAMS

ENSEMBLE DERIVED MOS

slide25
TYPES OF PRODUCTS

SPAGHETTI CHARTS

MEAN/SPREAD

PROBABILITIES

STORM TRACKS

CLUSTERS

VERTICAL PROFILES

METEOGRAMS

ENSEMBLE DERIVED MOS

  • CAN BE APPLIED TO VIRTUALLY ALL MODEL AND MODEL DERIVED PARAMETERS AND MODEL OUTPUT DRIVERS OF SECONDARY SYSTEMS, E.G., WAVE, HYDROLOGICAL,POLUTION DISPERSION MODELS

ULTIMATELY CAN (MUST) BE PROPOGATED TO USER SPECIFCIC

QUANTITIES, E.G., UMBRELLA SALES, ENERGY USEAGE, TYPE

OF “SMART” MUNITIONS, ETC., ETC., ETC., ETC., ETC., ETC ….

slide26

MANY NEW AND VARIED PRODUCTS POSSIBLE;

BUT, MOST SIGNIFICANT ADVANCE OVER PAST YEARS HAS BEEN EXPONENTIAL GROWTH IN GRAPHICS CAPABILITIES - ANIMATION, ZOOMING, 3-D, ETC. - AND PROLIFERATION OF PCs AND HIGH SPEED INTERNET

slide27

MANY NEW AND VARIED PRODUCTS POSSIBLE;

BUT, MOST SIGNIFICANT ADVANCE OVER PAST YEARS HAS BEEN EXPONENTIAL GROWTH IN GRAPHICS CAPABILITIES - ANIMATION, ZOOMING, 3-D, ETC. - AND PROLIFERATION OF PCs AND HIGH SPEED INTERNET

SO, REAL CHALLENGE IS MORE FULLY EXPLOITING POTENTIAL VALUE OF EPS IN BOTH PUBLIC AND PRIVATE SECTORS

slide29

MON TUES WED THURS FRI

40%

36 28 23 21 31

40

30

20

THIS

10

slide31

Visualizing UncertaintyinMesoscale Meteorology

APL Verification Methodology

21 May 2002

Scott Sandgathe

slide40

High Resolution Mesoscale models

    • allow us to see features not in coarser models
    • But: even small timing and placement errors can be significant in attempt to accurately forecast details (see Mass, et al., 3/02 BAMS!!!).
    • ButBut: Forcaster judgement could mitigate
  • One model (even with forecaster input)
  • is an all or nothing proposition =>

“One detailed mesoscale model based

forecast could allow the user to

make highly specific and detailed

inaccurate forecasts.” (after Grumm)

slide41

Why we need ensembles

    • Deal with uncertainties in analyses and model formulation
    • But: Requires tradeoffs when computer resources limited (e.g., model resolution)
    • ButBut: Mesoscale predictability often substantially controlled by synoptic predictability (and uncertainties therein)
    • => Subjective or statistically based downscaling possible to get uncertainties in mesoscale weather
    • Ideal: Ensembles with highest resolution justifiable
    • Compromise: Combination of single (or few) high resolution and coarser resolved ensemble
slide42

ADDITIONAL WEB SITES WITH ENSEMBLE PRODUCTS AND INFORMATION

Univ of Utah:

http://www.met.utah.edu/jhorel/html/models/

model_ens.html

FNMOC:

http://152.80.49.204/PUBLIC/

Canada:

http://www.cmc.ec.gc.ca/rpn/ensemble_products/

index.html

CDC:

http://www.cdc.noaa.gov/~map/maproom/ENS/

ens.html

State College:

http://bookend.met.psu.edu/ensembles/Ensemble.html

slide43

Give Me Odds

ENSEMBLE FORECASTING

slide47

YOUR FORECAST HAS A 30%

CHANCE OF BEING 70% CORRECT

slide48

3-day forecast from 00 UTC 11/2/01, spaghetti diagram for ensemble

global

Uncertain location of

incoming western trough

Uncertain amplitude

of eastern trough

From CDC web site:

http://www.cdc.noaa.gov/map/images/ens/ens.html

slide49

“SPAGHETTI” DIAGRAM

564 DM CONTOUR

NCEP ENSEMBLE

SOME NCEP ENSEMBLE MEMBERS OFFER A MORE “DIGGY” CENTRAL U.S. TROF THAN THE OPERATIONAL MRF..BUT NOT AS STRONG AS THE ECMWF

DAY3