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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.
http://arxt39.cmc.ec.gc.ca/~armabha/papers_and_presentations
Future Role of Operational Meteorology
Scientific and systematicforecast process
Partnership with technology
How?
Fuzzy LogicIntegrationAlgorithm
ProductGenerator
User
HumanInput
(> 15 min)
SelectiveClimatological
Input
Real-TimeData
Algorithms
ModelOutput
Algorithms
Data AssimilationMesoscale Model
Real-Time DataPreprocessing
QualityControl
SensorSystems
1. RAP, Intelligent Weather Systems, www.rap.ucar.edu/technology/iws/design.htm
Intelligent Weather Systems (RAP/NCAR) 1
WeatherRadar
Nowcasts
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
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
Decision
For example, choice of
data and fcst technique
Operational MeteorologyA Scientific and Systematic Forecast Process:
a partnership with technology! 1
Technology Meteorologist
Observation Sat, radar, awos… Reports from public
Analyses 4DVAR, AI… Pattern recognition
Diagnoses RDP, AI… Conceptual models
Prognoses GEM, EPS, UMOS… Science, experience,training
Products/Services
PerformanceMeasures
1. Jim Abraham, 2004: Science-Operations Connection workshop, Meteorological Service of Canada, Toronto, 24-26 February 2004.
WORKSTATIONSCRIBE/AVIPADS, etc. Decisions
“Smart Alert” Concept
Impendingproblem
Bust
0 1 2 3 4 5 6 87 9 10 11 12232221
100+603025201510987654321
Search
St. John’s
| | | | | | | | || | | | | | | | || | | | | | | | || | | | | | | | || | | | | | | | |
…| | | | | | | | |
Make
Save Send
WeatherWind
CeilingVisibilityDirectionSpeedTime…Weather
FitLoose Tight| | | | | | | | || | | | | | | | || | | | | | | | || | | | | | | | || | | | | | | | |
…| | | | | | | | |
00h 121501h 131402h 1412...12h 1408
00h R-L-01h R-L-02h L-...12h L-
Search
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 Make
INTEGRATION
CLIMATEARCHIVE
data
PRODUCTDISPLAY(editable)
HEADS-UPALERT &DISPLAY
ACTUALWEATHER
MAP(animated)
GUIDANCEDISPLAY(satellite,
NWP, etc.)
FORECASTER(interacts, intervenes)
awareness and knowledge
PREDICTION
UPPER AIR
SATELLITE
METAR
REAL-TIMEOBSdata
RAW, QC’dWEATHER
data
MODELLEDWEATHER
NWPdata
PRODUCTGENERATION
PRODUCTSinformation
MODELLEDWEATHER
MAP(editable)
DSS(interaction withintegration and
prediction)
PRODUCTSPECIFICATIONS
CONSISTENCYCHECKING
TRANSLATION
FORECAST
EXTRAPOLATION
PROJECTEDOBS
AIknowledge
USER
MODEL-BASEDWEATHERELEMENTS
VERIFICATION
0 time
official forecast
actual trend
!
Graphic interventionFirst resort
Direct interventionLast resort
data and information• up-to-the-minute intelligent data fusion• abstract features• derived fields• intelligently composed “interest fields”
RADAR
DAdata
information• special interests• cost-based decision-making models
POST-PROCESSING
Battleboard raises forecaster’s situational awareness
GUI leverages forecaster’s actions
* 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 and
integrate 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 isolated
sub-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, and
use of adaptive forecasting strategies are intrinsic in this design.
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
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.
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 Model
Augments RUC in CONUS and will support subsequent Alaska product
EXPERT SYSTEM-BASED FORECAST MERGE PROCESS(Weighted Simple Additive Model)
RUC20
C & V values derived from forecast hydrometeor and humidity fields.
Persistence
Statically 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 Translation
Experimental use of data mining for improved translation.
Obs-Based Techniques
First trials of forecasts from historical data using obs inputs.
Rule-Based Methods
Practical forecast methods from operations for targeted locale.
Future
COBEL Column ModelColumn model with initialfocus on fog and low cloud in NE.
Others TBD. Hybrids
Future 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.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
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 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
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.
http://arxt39.cmc.ec.gc.ca/~armabha/papers_and_presentations