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: A Fuzzy Logic-based Analog Forecasting System for Ceiling and Visibility, 38 th 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_pres entations
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: - PowerPoint PPT Presentation
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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.
• Applies existing knowledge, techniques and algorithms
• Achieves intelligent integration of all relevant, real-time data
• Supports rapid development of useful, maintainable operational applications
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
Intelligent Weather Systems (RAP/NCAR) 1
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
Satellite image
Wind speed
Humidity
Humidity trend
Chance of radiation fog(qualitative description)
W1
low med hilow
W2 medhi
Fuzzy Rule Base
Matrix of fuzzyrules coversspace ofall predictors
System canrun continuouslyto give real-time,smart forecastquality control.
For details,see examples. 3
Human input
DecisionFor example, choice of
data and fcst technique
Operational MeteorologyA Scientific and Systematic Forecast Process:
* 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.
DECISION SUPPORT SYSTEMS *
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.
Hybrid Forecast Decision Support SystemsHybrid 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/awr2. Research Applications Program, http://www.rap.ucar.edu3. Norbert Driedger, 2004, personal communication.4. Cobel, 1-D model, http://www.rap.ucar.edu/staff/tardif/COBEL
Hybrid Forecast Decision Support SystemsAWRP 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
1. Gerry Wiener, personal communication, July 2003.2. Intelligent Weather Systems, RAP, NCAR, http://www.rap.ucar.edu/technology/iws3. 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/nowcast5. 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.
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.
National C&V Forecast System
DATABASE OF FORECAST COMPONENT PERFORMANCE VS WEATHER CONDITION.
FORECASTCOMPONENT WEIGHTS BASED ON PERFORMANCE DATABASE.
Eta ModelAugments RUC in CONUS and will support subsequent Alaska product
RUC20 C & V values derived from forecast hydrometeor and humidity fields.
PersistenceStatically carries forward current C & V conditions.
CurrentDisplay: NCV web, ADDS,
Cockpit, Other.
Forecast of Ceiling, Visibility & Flight
Category on RUC Grid
FY 04
Improved C&V TranslationExperimental use of data mining for improved translation.
Obs-Based TechniquesFirst trials of forecasts from historical data using obs inputs.
Rule-Based MethodsPractical forecast methods from operations for targeted locale.
Future
COBEL Column ModelColumn model with initialfocus on fog and low cloud in NE.
Others TBD. HybridsFuture methods focused on C & V.
Feedback LoopUsing FY03-04 Mods
Hybrid Forecast Decision Support Systems
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.pdf2. 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
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.
Fuzzy Logic at Research Applications Program, NCAR
A graphical illustration to fuzzy logic, http://www.mcu.motsps.com/lit/tutor/fuzzy/fuzzy.html
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
Fuzzy logic
A graphical illustration to fuzzy logic, http://www.mcu.motsps.com/lit/tutor/fuzzy/fuzzy.html
How Rules Relate to a Control SurfaceA 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
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.
Three Dimensional Control Surface
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.