Applications of Ensemble Prediction - a Historical Perspective Steve Tracton Office of Naval Research Arlington, VA (Formally of NWS/NCEP)

Slides:



Advertisements
Similar presentations
Aims and Requirements for Ensemble Forecasting By T.N.Palmer ECMWF.
Advertisements

Chapter 13 – Weather Analysis and Forecasting
National Weather Service The Short-Range Ensemble Forecast: Applying Uncertainty and Probabilistic Forecasts of Winter Storms Matt Steinbugl, NOAA/NWS.
Report of the Q2 Short Range QPF Discussion Group Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux.
INTRODUCTION TO MODELING
PRESENTS: FORECASTING FOR OPERATIONS AND DESIGN February 16 th 2011 – Aberdeen.
UCL global drought monitor Benjamin Lloyd-Hughes.
Page 1© Crown copyright 2006ESWWIII, Royal Library of Belgium, Brussels, Nov 15 th 2006 Forecasting uncertainty: the ensemble solution Mike Keil, Ken Mylne,
SIPR Dundee. © Crown copyright Scottish Flood Forecasting Service Pete Buchanan – Met Office Richard Maxey – SEPA SIPR, Dundee, 21 June 2011.
Statistical post-processing using reforecasts to improve medium- range renewable energy forecasts Tom Hamill and Jeff Whitaker NOAA Earth System Research.
The Storm Surge Toolkit Jamie Rhome Storm Surge Specialist/Team Lead National Hurricane Center Jamie Rhome Storm Surge Specialist/Team Lead National Hurricane.
Engineering Design Rubric Dimensions 1, 2 and 7.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Improving COSMO-LEPS forecasts of extreme events with.
Part 5. Human Activities Chapter 13 Weather Forecasting and Analysis.
Predictability and Chaos EPS and Probability Forecasting.
Frank Yu Australian Bureau of Statistics Unstructured Data 1.
Improving Excessive Rainfall Forecasts at HPC by using the “Neighborhood - Spatial Density“ Approach to High Res Models Michael Eckert, David Novak, and.
2012: Hurricane Sandy 125 dead, 60+ billion dollars damage.
14th ALADIN Workshop, Innsbruck 1-4 June 2004 First LAMEPS experiments at the Hungarian Meteorological Service Edit Hágel and Gabriella Szépszó Hungarian.
Description and Preliminary Evaluation of the Expanded UW S hort R ange E nsemble F orecast System Maj. Tony Eckel, USAF University of Washington Atmospheric.
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Chapter Chapter 4: Modeling Decision Processes Decision Support Systems in the.
Update on the Regional Modeling System NASA Roses Meeting April 13, 2007.
Hydrometeorological Prediction Center HPC Medium Range Grid Improvements Mike Schichtel, Chris Bailey, Keith Brill, and David Novak.
The Collaborative Effort Between Stony Brook University and the National Weather Service, Part 1 - Previous Results, Current Status and Future Plans 
National Weather Service The Short-Range Ensemble Forecast: SREF Applying Uncertainty and Probabilistic Forecasts of Winter Storms Matt Steinbugl, NOAA/NWS.
Introduction to Numerical Weather Prediction and Ensemble Weather Forecasting Tom Hamill NOAA-CIRES Climate Diagnostics Center Boulder, Colorado USA.
Exploitation of Ensemble Output (and other operationally cool stuff) at NCEP HPC Peter C. Manousos NCEP HPC Science & Operations Officer
Water Management Presentations Summary Determine climate and weather extremes that are crucial in resource management and policy making Precipitation extremes.
Chapter 13 – Weather Analysis and Forecasting. The National Weather Service The National Weather Service (NWS) is responsible for forecasts several times.
Evaluation of Potential Performance Measures for the Advanced Hydrologic Prediction Service Gary A. Wick NOAA Environmental Technology Laboratory On Rotational.
MDSS Challenges, Research, and Managing User Expectations - Weather Issues - Bill Mahoney & Kevin Petty National Center for Atmospheric Research (NCAR)
Medium Range Forecast - Global System Out To 14 Days Yuejian Zhu Ensemble Team Leader EMC/NCEP/NWS/NOAA Presents for NWP Forecast Training Class March.
On-going WMO Demonstration Projects related to EXPO2010 Multi Hazard Early Warning System Multi Hazard Early Warning System Leading by SMB/CMALeading by.
Chapter 9: Weather Forecasting
The Climate Prediction Project Global Climate Information for Regional Adaptation and Decision-Making in the 21 st Century.
ADVANCED KNOWLEDGE IS POWER Protect Life and Property Promote Economic Vitality Environmental Stewardship Promote Fundamental Understanding.
How can LAMEPS * help you to make a better forecast for extreme weather Henrik Feddersen, DMI * LAMEPS =Limited-Area Model Ensemble Prediction.
Where the Research Meets the Road: Climate Science, Uncertainties, and Knowledge Gaps First National Expert and Stakeholder Workshop on Water Infrastructure.
Data assimilation and observing systems strategies Pierre Gauthier Data Assimilation and Satellite Meteorology Division Meteorological Service of Canada.
WORKSHOP ON SHORT-RANGE ENSEMBLE PREDICTION USING LIMITED-AREA MODELS Instituto National de Meteorologia, Madrid, 3-4 October 2002 Limited-Area Ensemble.
The Australian Community Climate Earth-System Simulator The Australian Community Climate and Earth System Simulator Kamal Puri (ACCESS Group Leader)
Communicating Climate and Weather Information Chris Elfring, Director Board on Atmospheric Sciences and Climate The National Academies 500 Fifth Street.
Overview of the Hydrometeorological Prediction Center Precipitation/Flash Flood Products/Services Michael Eckert
Storm Surge Modeling and Forecasting LTJG Jeffrey Pereira, NOAA Storm Surge Unit National Hurricane Center NOAA Storm Surge Workshop May 2011 LTJG Jeffrey.
Celeste Saulo and Juan Ruiz CIMA (CONICET/UBA) – DCAO (FCEN –UBA)
Ensemble Forecasting and You The very basics Richard H. Grumm National Weather Service State College PA
© Crown copyright Met Office Probabilistic turbulence forecasts from ensemble models and verification Philip Gill and Piers Buchanan NCAR Aviation Turbulence.
PARTNERING WITH THE NATIONAL SCIENCE FOUNDATION Michael C. Morgan Director, Division of Atmospheric and Geospace Sciences National Science Foundation.
Using Ensemble Models to Develop a Long-Range Forecast and Decision Making Tool Brandon Hertell, CCM Con Edison of New York Brian A. Colle, Mike Erickson,
Plans for Short-Range Ensemble Forecast at INM José A. García-Moya SMNT – INM Workshop on Short Range Ensemble Forecast Madrid, October,
3 rd Annual WRF Users Workshop Promote closer ties between research and operations Develop an advanced mesoscale forecast and assimilation system   Design.
The Challenge of Mesoscale Probabilistic Weather Prediction and Communication Cliff Mass University of Washington.
SOFTWARE PROJECT MANAGEMENT
GIVE ME ODDS FORECASTING ENSEMBLE. KEY POINTS THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION.
NCEP Models and Ensembles By Richard H. Grumm National Weather Service State College PA and Robert Hart The Pennsylvania State University.
17 TH BMRC MODELLING WORKSHOP – OCTOBER Harvey Stern 17 th BMRC Modelling Workshop, Bureau of Meteorology, Melbourne, 6 October, Generating.
Model Jumpiness and the Need for Ensembles Richard Grumm National Weather Service Office and Lance Bosart State Univesity of New York at Albany Richard.
Ensembling Medium Range Forecast MOS GUIANCE By Richard H. Grumm National Weather Service State College PA and Robert Hart The Florida State University.
Ensembles and Probabilistic Prediction. Uncertainty in Forecasting All of the model forecasts I have talked about reflect a deterministic approach. This.
Fly - Fight - Win 2 d Weather Group Mr. Evan Kuchera HQ AFWA 2 WXG/WEA Template: 28 Feb 06 Approved for Public Release - Distribution Unlimited AFWA Ensemble.
1 Symposium on the 50 th Anniversary of Operational Numerical Weather Prediction Dr. Jack Hayes Director, Office of Science and Technology NOAA National.
Technology Forecasting. Forecasting predict or estimate a future event or trend. "rain is forecast for Lahore“ The use of historic data to determine the.
An Ensemble Primer NCEP Ensemble Products By Richard H. Grumm National Weather Service State College PA and Paul Knight The Pennsylvania State University.
NCEP CMC ECMWF MEAN ANA BRIAN A COLLE MINGHUA ZHENG │ EDMUND K. CHANG Applying Fuzzy Clustering Analysis to Assess Uncertainty and Ensemble System Performance.
Judith Curry James Belanger Mark Jelinek Violeta Toma Peter Webster 1
A Guide to Tropical Cyclone Guidance
Naval Research Laboratory
Storm Surge Modeling and Forecasting
Update of NMC/CMA Global Ensemble Prediction System
The Impact of Moist Singular Vectors and Horizontal Resolution on Short-Range Limited-Area Ensemble Forecasts for Extreme Weather Events A. Walser1) M.
Presentation transcript:

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: THERE WILL ALWAYS BE VARYING DEGREES OF UNCERTAINTIES IN FORECASTS (“Chaos Theory”)

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”), ENSEMBLE PREDICTION – FROM EARLY 90’S ON, REVOLUTIONARY CHANGE IN THE THRUST OF OPERATIONAL NWP (“WAVE OF THE FUTURE”), OPERATIONAL First formal attention to the real possibility of OPERATIONAL Predictability ensemble prediction at ECMWF Workshop on Predictability In the Medium Range and Extended Range, 1986 GLOBAL EPS 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

ENSEMBLE FORECASTING Give Me Odds

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: 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 NET RESULT - ENHANCE UTILITY/VALUE OF NWP FOR VIRTUALLY ALL APPLICATIONS USE TO: Ascertain most likely deterministic prediction Confidence in deterministic forecast Same, plus identifying relative likelihood of alternative scenarios maximum informationFull probability distribution – maximum information NOT NECESSARILY SKILL

NOGAPS ECMWFMRF UKMET Providing EPS = Acceptance and Use /

RUN TO RUN MODEL CONTINUITY GOOD BAD TODAY’S DAY 3YESTERDAY’S DAY 4 NEW 84 HR MRF OLD 108 HR MRF NEW 96 HR ECMWF OLD 120 HR ECMWF WHICH SOLUTION IS PREFERRED?  DETERMINISTIC THINKING

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) STEEP LEARNING CURVE FOR NEW PARADIGM:NO A- PRIORI BEST SINGLE OUTCOME “DETERMINISM IS DEAD” ?? NOT YET; BUT…

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)

Schematic of how a probabilistic model forecast can be used for risk-based decision-making. 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). P1 P2 P3 P4. P N Model F(P) Compare T with Tcr Take appropriate action Pcr T P

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) necessary, “ Probability Forecasts are particularly useful, even necessary, to reliably provide early warnings of extreme weather events” TO AVOID, E.G.

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

FOR FORECASTERS, ANOTHER …

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 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 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 and relevant (MUST BE USER SPECIFIC AND USER FRIENDLY)  USER FEEDBACK ESSENTIAL

Products: Stamp maps

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); formal But, no further formal consideration 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  SPAGHETTI CHARTS: now one of the most recognizable and symbolic products of EPS

TYPES OF PRODUCTS SPAGHETTI CHARTS MEAN/SPREAD PROBABILITIES STORM TRACKS CLUSTERS VERTICAL PROFILES METEOGRAMS ENSEMBLE DERIVED MOS

010519/0000V63 SREFX-CMB; LIFTED INDEX PROB 0F < -4

5-Day ECMWF Ensemble Prediction of Typhoon Rusa

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

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

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

CONVEYING UNCERTAINTY TO PUBLIC VIA MEDIA - A CRITICAL LINK (see IABM.org) Bob Ryan

MON TUES WED THURS FRI % THIS

INCONSISTENT WITH PREDICTABILITY LIMITS NOT THIS

Visualizing Uncertainty in Mesoscale Meteorology APL Verification Methodology 21 May 2002 Scott Sandgathe

Questions ??

SPAGHETTI CHART

What a mess!!

END

BACKUP SLIDES

EVEN EXPERTS CAN HAVE A BAD DAY

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) 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!!!). – But But: Forcaster judgement could mitigate

Why we need ensembles – Deal with uncertainties in analyses and model formulation – But: Requires tradeoffs when computer resources limited (e.g., model resolution) – But But: 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

ADDITIONAL WEB SITES WITH ENSEMBLE PRODUCTS AND INFORMATION Univ of Utah: model_ens.html FNMOC: Canada: index.html CDC: ens.html State College:

ENSEMBLE FORECASTING Give Me Odds

EVEN EXPERTS CAN HAVE A BAD DAY

YOUR FORECAST HAS A 30% CHANCE OF BEING 70% CORRECT CHANCE OF BEING 70% CORRECT

Uncertain location of incoming western trough From CDC web site: Uncertain amplitude of eastern trough 3-day forecast from 00 UTC 11/2/01, spaghetti diagram for ensemble global

SOME NCEP ENSEMBLE MEMBERS OFFER A MORE “DIGGY” CENTRAL U.S. TROF THAN THE OPERATIONAL MRF..BUT NOT AS STRONG AS THE ECMWF “SPAGHETTI” DIAGRAM DAY3 564 DM CONTOUR NCEP ENSEMBLE