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Intelligent integration for nowcasting

Intelligent integration for nowcasting. Selected slides from a talk given at the 38 th Annual Congress of the Canadian Meteorological and Oceanographic Society. For the complete powerpoint file see:

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Intelligent integration for nowcasting

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  1. Intelligent integration for nowcasting Selected slides from a talk given at the 38th Annual Congressof the Canadian Meteorological and Oceanographic Society. For the complete powerpoint file see: A Fuzzy Logic-based Analog Forecasting System for Ceiling and Visibility, 38th Annual Congress of the Canadian Meteorological and Oceanographic Society, May 31-June 3, 2004, Edmonton, Alberta. http://arxt39.cmc.ec.gc.ca/~armabha/papers_and_presentations

  2. Future Role of Operational Meteorology Scientific and systematicforecast process Partnership with technology How?

  3. WeatherRadarNowcasts RAP, Thunderstorm Auto-Nowcasting, www.rap.ucar.edu/projects/nowcast GUI • IWS Design • Expert system development framework • Applies existing knowledge, techniques and algorithms • Achieves intelligent integration of all relevant, real-time data • Supports rapid development of useful, maintainable operational applications Intelligent Weather Systems (RAP/NCAR) 1 HumanInput(> 15 min) Real-TimeDataAlgorithms Real-Time DataPreprocessing Fuzzy LogicIntegrationAlgorithm Real-time Track SensorSystems QualityControl ProductGenerator User Model Track ModelOutputAlgorithms Data AssimilationMesoscale Model SelectiveClimatologicalInput 1. RAP, Intelligent Weather Systems, www.rap.ucar.edu/technology/iws/design.htm

  4. Satellite image Wind speed Humidity Humidity trend Chance of radiation fog (qualitative description) Intelligent Weather Systems (RAP/NCAR) 1 Fuzzy logic integration algorithm For example, a fuzzy rule for forecasting radiation fog: 2 If sky clear and wind light and humidity high and humidity increasing Then chance of radiation fog is high Human input  Decision For example, choice of data and fcst technique Fuzzy Rule Base Matrix of fuzzyrules coversspace ofall predictors System canrun continuouslyto give real-time,smart forecastquality control. For details,see examples. 3 1. RAP, Intelligent Weather Systems, www.rap.ucar.edu/technology/iws/design.htm 2. Jim Murtha, 1995: Applications of fuzzy logic in operational meteorology, Scientific Services and Professional Development Newsletter, Canadian Forces Weather Service, 42-54 3. Meteorological applications of fuzzy, http://chebucto.ca/Science/AIMET/applications

  5. Operational MeteorologyA Scientific and Systematic Forecast Process:a partnership with technology! 1 WORKSTATION SCRIBE/AVIPADS, etc. Decisions 1. Jim Abraham, 2004: Science-Operations Connection workshop, Meteorological Service of Canada, Toronto, 24-26 February 2004.

  6. Impendingproblem Bust “Smart Alert” Concept

  7. | | | | | | | | || | | | | | | | || | | | | | | | || | | | | | | | || | | | | | | | |…| | | | | | | | | | | | | | | | | || | | | | | | | || | | | | | | | || | | | | | | | || | | | | | | | |…| | | | | | | | | l l lll l llllll AMD TAF CYYT 270010Z 270024 1315KT 2SM -RA BR OVC006 TEMPO 0002 1/2SM -DZ FG OVC003 FM0200Z 14010KT 1/2SM -DZ FG OVC002 TEMPO 0224 1/4SM -DZ FG OVC001 RMK NXT FCST BY 06Z= Search Search Make St. John’s 100+603025201510987654321 FitLoose Tight CeilingVisibilityDirectionSpeedTime…Weather Wind Weather 00h 121501h 131402h 1412...12h 1408 00h R-L-01h R-L-02h L-...12h L- 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 Search Make Save Send

  8. DECISION SUPPORT SYSTEMS * official forecast Battleboard raises forecaster’s situational awareness GUI leverages forecaster’s actions FORECASTER(interacts, intervenes)awareness and knowledge ! actual trend 0 time Graphic interventionFirst resort Direct interventionLast resort HEADS-UPALERT &DISPLAY PRODUCTDISPLAY(editable) ACTUALWEATHERMAP(animated) GUIDANCEDISPLAY(satellite, NWP, etc.) MODELLEDWEATHERMAP(editable) DSS(interaction withintegration andprediction) POST-PROCESSING PRODUCTSinformation TRANSLATION DAdata NWPdata METAR MODEL-BASEDWEATHERELEMENTS RADAR REAL-TIMEOBSdata FORECAST INTEGRATION SATELLITE PRODUCTGENERATION UPPER AIR EXTRAPOLATION RAW, QC’dWEATHERdata AIknowledge USER PRODUCTSPECIFICATIONS • information • • special interests • cost-based decision-making models PROJECTEDOBS data and information• up-to-the-minute intelligent data fusion• abstract features• derived fields• intelligently composed “interest fields” MODELLEDWEATHER CLIMATEARCHIVEdata PREDICTION CONSISTENCYCHECKING VERIFICATION * Forecaster Workstation User Requirements Working Group meeting notes, 2000: Decision support systems for weather forecasting based on modular design, updated slightly for Aviation Tools Workshop in 2003.

  9. Decision Support Systems Design Generic: no-name, conceptual design that could link andintegrate the most useful elements of WIND, AVISA, MultiAlert,SCRIBE, FPA, URP, and so on in evolving WSP application, NinJo. Modular: shows where distinct sub-tools / agents can be developed. Working in this way, individual developers could work on isolatedsub-problems and anticipate how to plug their results into a larger shared system. As technology inevitably improves, improved modules can be easily installed and quickly implemented. User-centered: forecast decision support systems from forecaster's point of view, designed to increase situational awareness. Hybrid: combines complementary sources of knowledge, forecasters and AI, to increase the quality of input data and output information.Intelligent integration of data, information, and model output, anduse of adaptive forecasting strategies are intrinsic in this design.

  10. Hybrid Forecast Decision Support Systems Hybrid forecast system development is a current direction of the Aviation Weather Research Program (AWRP) 1 and the Research Applications Program (RAP), 2 NCAR (the main organizers of AWRP R&D). “If a statistical / analog forecast disagrees with a model forecast, or if different sensors disagree about how C&V are measured, what should we do about it? Fuzzy logic could simulate how humans might apply confidence factors to different pieces of information in different scenarios.” 3 AWRP Terminal Ceiling and Visibility Product Development Team (PDT) project, Consensus Forecast System, a combination of: • COBEL, a physical column model 4 • Statistical forecast models, local and regional • Satellite statistical forecast model 1. Aviation Weather Research Program, http://www.faa.gov/aua/awr 2. Research Applications Program, http://www.rap.ucar.edu 3. Norbert Driedger, 2004, personal communication. 4. Cobel, 1-D model, http://www.rap.ucar.edu/staff/tardif/COBEL

  11. Hybrid Forecast Decision Support Systems AWRP National Ceiling and Visibility PDT research initiatives: 1 • Data fusion: intelligent integration of output of various models, observational data, and forecaster input using fuzzy logic 2, 3 • Data mining, C5.0 pattern recognition software for generating decision trees based on data mining, freeware by Ross Quinlan (http://www.rulequest.com), like CART • Analog forecasting using Euclidean distance development of daily climatology for 1500+ continental US (CONUS) sites • Incorporate AutoNowcast of weather radar in 2004-2005 4 • Incorporate satellite image cloud-type classification algorithms 5 1. Gerry Wiener, personal communication, July 2003. 2. Intelligent Weather Systems, RAP, NCAR, http://www.rap.ucar.edu/technology/iws 3. Shel Gerding and William Myers, 2003: Adaptive data fusion of meteorological forecast modules, 3rd Conference on Artificial Intelligence Applications to Environmental Science, AMS. 4. AutoNowcast, http://www.rap.ucar.edu/projects/nowcast 5. Tag, Paul M., Bankert, Richard L., Brody, L. Robin. 2000: An AVHRR Multiple Cloud- Type Classification Package. Journal of Applied Meteorology: Vol. 39, No. 2, pp. 125-134.

  12. Hybrid Forecast Decision Support Systems 1. Herzegh, P. H., Bankert, R. L., Hansen, B. K., Tryhane, M., and Wiener, G., 2004: Recent progress in the development of automated analysis and forecast products for ceiling and visibility conditions, 20th Conference on Interactive Information and Processing Systems, American Meteorological Society.

  13. Fuzzy Logic at Research Applications Program, NCAR According to Richard Wagoner, Deputy Director at Research Applications Program (“Technology Transfer Program”), NCAR: 1 • NCAR / RAP is now a “continuous set theory” [fuzzy set theory] development center. • Over 90% of systems developed use fuzzy logic [FL] as the intelligence integrator. [ … P.S. It is now 100% 2 ] • [FL offers] unprecedented fidelity and accuracy in systems development. • Automatic FL-based systems now compete with human forecasts. 1. Richard Wagoner, 2001: Background briefing on post processing data fusion technology at NCAR, online presentation, http://www.rap.ucar.edu/general/press/presentations/wagoner_21feb2001.pdf 2. John K. Williams, 2004: Introduction to Fuzzy Logic as Used in the NCAR Research Applications Program, Artificial Intelligence Methods in Atmospheric and Oceanic Sciences: Neural Networks, Fuzzy Logic, and Genetic Algorithms, Short Course, American Meteorological Society, 10-11 January 2004, Seattle, WA. ftp://ftp.rap.ucar.edu/pub/AMS_AI_ShortCourse/Williams_AMS_ShortCourse_11Jan2004.pdf

  14. Fuzzy logic Since we can assign numeric values to linguistic expressions, it follows that we can also combine such expressions into rules and evaluate them mathematically. A typical fuzzy logic rule might be: If temperature is warm and pressure is low then set heat to high A graphical illustration to fuzzy logic, http://www.mcu.motsps.com/lit/tutor/fuzzy/fuzzy.html

  15. How Rules Relate to a Control Surface A fuzzy associative matrix (FAM) can be helpful to be sure you are not missing any important rules in your system. Figure shows a FAM for a control system with two inputs, each having three labels. Inside each box you write a label of the system output. In this system there are nine possible rules corresponding to the nine boxes in the FAM. The highlighted box corresponds to the rule: If temperature is warm and pressure is low then set heat to high A graphical illustration to fuzzy logic, http://www.mcu.motsps.com/lit/tutor/fuzzy/fuzzy.html

  16. Three Dimensional Control Surface The input to output relationship is precise and constant. Many engineers were initially unwilling to embrace fuzzy logic because of a misconception that the results were not repeatable and approximate. The term fuzzy actually refers to the gradual transitions at set boundaries from false to true. A graphical illustration to fuzzy logic, http://www.mcu.motsps.com/lit/tutor/fuzzy/fuzzy.html

  17. Intelligent integration for nowcasting For more information, see: A Fuzzy Logic-based Analog Forecasting System for Ceiling and Visibility, 38th Annual Congress of the Canadian Meteorological and Oceanographic Society, May 31-June 3, 2004, Edmonton, Alberta. http://arxt39.cmc.ec.gc.ca/~armabha/papers_and_presentations

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