Overview of Achievements October 2001 October 2003 Adrian Raftery, P.I. MURI Overview Presentation, 17 October 2003 c 2003 Adrian E.

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1 MURI Project: Integration and Visualization of Multisource Information for Mesoscale Meteorology: Statistical and Cognitive Approaches to Visualizing Uncertainty, Overview of Achievements October 2001 October 2003 Adrian Raftery, P.I. MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 1

2 Project Goals Develop statistical methods for assessing uncertainty in numerical weather predictions Develop ways of visualizing and communicating this uncertainty to users Develop tools for users to implement the methods Project Team: Statisticians, atmospheric scientists, psychologists, and visualization specialists from the University of Washington MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 2

3 Summary of Achievements Expanded UW mesoscale ensemble forecast system and results Calibrated probabilistic forecasting methods developed New verification concepts and tools developed Observation and evaluation of how naval forecasters use model information completed New Web tools developed: Expanded UW Ensemble website MUM (MURI Uncertainty Monitor) Shared data archive data extraction tools Intense between-group interaction and interdisciplinary learning Many students, papers and presentations The springboard for a highly productive next 3 years MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 3

4 UW Mesoscale Ensemble System Development of an operational mesoscale ensemble system Representation of initial condition uncertainty with 8 analyses from different major operational forecast centers Representation of physics uncertainty through perturbations to the mesoscale model New ensemble members through mirroring, a new idea due to Tony Eckel Demonstrated a robust skill-spread relationship MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 4

5 Grid-Based Bias Correction Much literature on bias correction at observation sites, but little on grid-based bias correction. Grid-based bias correction cannot use observations at the point at which the correction is being made. Three different approaches developed by MURI researchers Sliding time window at each grid point, with bias estimated from comparing forecast with verifying analysis (not observations): 14 day window found to perform well. Substantial reduction in RMSE (up to 30%) Global method using observations, and modern nonlinear regression with ACE, CART and Bayesian model selection: Independent variables: Functions of latitude, longitude, elevation, distance from ocean, land use. Fitted using 2001 temperature data for Pacific Northwest. Evaluated using 2002 data. 12% reduction in RMSE. MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 5

6 Local method using recent neighboring observations, defined as close to the grid point spatially and in terms of land use and elevation: Good performance with a 20-day sliding window, and windows of 1 degree latitude and longitude, 500m elevation, and broad land use categories 25% reduction in RMSE. Combination of global and local methods using regression: 26% reduction in RMSE. Small improvement over local method, but global component allows good estimates when there are few observational assets Open question: Comparison and combination of analysis-based and observation-based approaches MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 6

7 Statistical Assessment of Uncertainty Development of three calibrated probabilistic forecasting methods, possibly for the first time BMA (Bayesian Model Averaging): Optimally combining an ensemble of deterministic forecasts from different MM5 initializations with probabilistic error distributions to produce a calibrated and sharp predictive probability distribution of a future meteorological quantity CHMOS (Conditionally Heteroscedastic Model Output Statistics): Extends standard MOS by using the predictive distribution from regression and allowing for nonconstant variance to yield a calibrated probability distribution of a future meteorological quantity GOP (Geostatistical Output Perturbation Method): Probabilistic forecasting of entire fields: Combining a deterministic forecast with a spatial statistical model of a random field to produce a calibrated probability distribution of an entire field at a future time point MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 7

8 Bayesian Model Averaging A principled statistical method for combining probabilistic forecasts from different models and analyses using weights that reflect their relative skills, yielding both a probabilistic forecast (PDF of future weather quantities and events), and a deterministic forecast, and honoring the spread-skill relationship Provides a theoretical explanation of a paradox: Why do ensembles show a spread-skill relationship, thus providing useful predictions of forecast skill, and yet are uncalibrated? Can be represented as an analytic function, or as an equally weighted ensemble of any desired size The weights provide a basis for selecting ensemble members We established good training period lengths: 20 days for temperature; 40 days for sea-level pressure MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 8

9 BMA Results The BMA predictive distribution is much better calibrated than the raw ensemble It is also sharp, with prediction intervals that were 62% shorter than those produced by climatology in an out-of-sample evaluation The BMA deterministic forecast had an RMSE that was: 55% better than climatology 11% better than the best single forecast 6% better than the ensemble mean MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 9

10 CHMOS: Conditionally Heteroscedastic Model Output Statistics Easy-to-implement linear postprocessing techniques for mesoscale ensembles: EMOS: ensemble model output statistics CHMOS: conditionally heteroscedastic model output statistics MEMOS: mean-based EMOS [uses only ensemble mean as predictor] MCHMOS: mean-based CHMOS [uses only ensemble mean as predictor] Good properties: calibrated sharp CHMOS and MCHMOS honor the spread-error relationship quick and dirty approximation to BMA MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 10

11 CHMOS Results In a cross-validation study for SLP in the Puget Lowland (January-March 2000), and for the test set, CHMOS shows 12% reduction of MAE 24% reduction of log/ignorance score over raw (smoothed) ensemble output in true forecast mode for 2-m temperature in the Puget Lowland (Spring 2000), MEMOS shows up to 80% reduction of log/ignorance score over raw (smoothed) ensemble output in true forecast mode for SLP in the Puget Lowland (Spring 2000), CHMOS shows up to 1% reduction of MAE 35% reduction of ignorance over raw (smoothed) ensemble output MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 11

12 Forecasting Fields: The Geostatistical Output Perturbation (GOP) Method Calibrated sharp probabilistic forecasting method Honors the spatial characteristics of the error field Simulates an ensemble of forecast fields using geostatistical error models and fast random field simulation methods Contours look rougher than meteorologists are used to MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 12

13 Example of GOP Simulations 01/12/ /12/2002 Latitude Latitude Longitude Longitude 01/12/ /12/2002 Latitude Latitude Longitude Longitude MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 13

14 Verification Established principle of maximizing sharpness subject to calibration Developed a theoretical framework for verifying probabilistic forecasts Developed concepts of probabilistic calibration, exceedance calibration, and marginal calibration The concept of marginal calibration was adopted at 2002 NCAR verification workshop Developed verification tools such as the marginal calibration plot Established results about scoring systems (e.g., showed that the probability score is improper) MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 14

15 Rapid Mesoscale Forecast Verification (MVT) Developed an automated user interface tool for rapid mesoscale forecast verification (MVT) allowing rapid and accurate selection of fields for verification. MVT allows selection, data access, and setup of a verification case in minutes, as compared to hours to days Investigation of rapid image processing algorithms revealed a combination of algorithms that resulted in a 30-fold increase in computational verification speed with essentially no degradation (< 1% in accuracy). MVT technology allows consideration of phase and timing errors in automated verification of grid forecasts, an attribute not routinely considered in automated verification schemes. MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 15

16 Human Factors How are naval forecasters using model information now? Completed work qualitative observations statistical evaluations Hypothetical uses of probabilistic information: Work in progress: Experiments using meteorology students Evaluation of alternative techniques for display: Experiments planned. MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 16

17 Human Factors: Experiments Carried Out Conducted forecaster observations at NAMOC, Norfolk, VA, NAS Whidbey, and NOAA Developed verbal protocol coding scheme appropriate for weather forecasting Conducted verbal protocol study on 6 forecasters at NAS Whidbey, and at NOAA Conducted uncertainty questionnaire study at NAS Whidbey (joint APL/Psych) Conducted uncertainty products experiment at UW (joint APL/Psych/Atmos) Conducted lens model analysis of NAS Whidbey wind forecasts (190 TAFs) Analyzed comparative accuracy of forecasts by MM5 alone and by forecasters using MM5 MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 17

18 Visualization and Data Sharing Expanded operational UW Ensemble website Developed an on-line prototype interface for visualizing uncertainty: the MURI Uncertainty Monitor (MUM): will facilitate more rapid assessment of model uncertainty produces new types of model performance and forecast uncertainty visualizations Developed a web-based data extraction tool for research and operational post-processing Developed a Java Web Start tool for mesoscale verification analyses Jeff Baars, jointly appointed between the Atmos and Stat groups, works on data sharing and verification MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 18

19 Transitions and Leveraging of MURI Research ONR Code 322 funded research on Mesoscale Verification, Jan03-Dec06 WA Sea Grant funded research for system to deliver environmental information to Puget Sound boaters, Feb04-Jan07 Army SBIR announcement on Portraying Uncertainty in Battlefield Weather Situation Awareness Presentation to Commander, Naval METOC Command, March 03 Briefing by Doug Brown (Army) to NATO working group Battlearea Meteorological Systems and Support, Nov 02. Great interest reported. Considerable contact with Whidbey Island Naval Air Station Forecasters Invited talk by P.I. to Army Applied Statistics Conference, Oct 03 MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 19

20 Major Student Involvement 5 undergraduate students supported (2 in Stat, 3 at APL) 9 graduate students supported (2 in Atmos, 5 in Stat, 2 in Psych) MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 20

21 Intense Between-Group Collaboration 6 one-day all-muri meetings: August 2001, January 2002, May 2002, October 2002, April 2003, October 2003 Active MURI list: muri@stat.washington.edu MURI web site: Attendance by all groups at Mass s weather forecasting classes Attendance by all groups at Gneiting and Raftery s course on Inference for Deterministic Simulation Models MURI visualization working group MURI verification working group MURI Data committee involving members from all groups MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 21

22 Joint attendance at conferences, e.g.: Verification Workshop at NCAR, July 2002 Pacific Northwest Weather Workshop, March 2003 Mesoscale Ensemble Workshop, Monterey, July 2003 UW Mesoscale Verification Workshop, organized by Cliff Mass, July 2003 Ensemble Forecasting Workshop, Québec, Sept 2003 American Meteorological Society Annual Meeting, Jan 2004 (including 3 talks by statisticians) Joint visits to Whidbey NAS (esp. Psych APL) Regular APL-Psych meetings (every two weeks) Many other ad hoc inter-group meetings MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 22

23 Quantitative Summary: FY 2002 Refereed publications 15 Invited presentations 7 Contributed presentations 12 Honors 4 (FY 2003 report not yet available) MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 23

24 Summary of Achievements New ensemble forecast system and results Calibrated probabilistic forecasting methods developed New verification concepts and tools developed Observation and cognitive task analysis of how naval forecasters use models completed Web-based Uncertainty visualization, data extractor and data-sharing tools developed Major student involvement Already, a range of potential transitions Intense between-group interaction and learning MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 24

25 Next Steps: Prototypes and Going Operational Continue, complete and publish current ensemble, probabilistic forecasting, bias correction, verification, human factors and visualization work Practical implementation of probabilistic forecasting products Experimentation in a quasi-operational setting on the Web (UW Ensemble Web site and APL s MUM) Going operational! MURI Overview Presentation, 17 October 2003 c 2003 Adrian E. Raftery 25