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Applications of Fuzzy Logic Used at the NCAR Research Applications Program

The 84 th American Meteorological Society Annual Meeting, Seattle, 10-11 January 2004 Short Course on Artificial Intelligence Methods in Atmospheric and Oceanic Sciences: Neural Networks, Fuzzy Logic, and Genetic Algorithms.

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Applications of Fuzzy Logic Used at the NCAR Research Applications Program

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  1. The 84th American Meteorological Society Annual Meeting, Seattle, 10-11 January 2004 Short Course on Artificial Intelligence Methods in Atmospheric and Oceanic Sciences: Neural Networks, Fuzzy Logic, and Genetic Algorithms Applications of Fuzzy Logic Used at the NCAR Research Applications Program Cathy Kessinger NCAR/RAP With contributions from: David Albo, Ben Bernstein, Scott Ellis, Shel Gerding, Cathy Kessinger, Cory Morse, Cindy Mueller, Bob Sharman, Andrew Weekley, John Williams, and others AMS Short Course on AI Methods

  2. Research Applications Program • Program within NCAR, as opposed to a division • Matrix across divisions • Technology transfer for NCAR • Historically slanted toward aviation community • FAA sole funding source for years • Broadening fiscal base last 10 years AMS Short Course on AI Methods

  3. Research Applications Program • Basic and applied/directed research • Summer and winter storms • Continental and oceanic • Cloud scale to synoptic scale • Full range of meteorological instruments • Numerical models • Fuzzy logic, data fusion techniques important component AMS Short Course on AI Methods

  4. Categories of Algorithms • Improving data quality • Detection/diagnosis of current weather phenomena • Forecasting weather phenomena AMS Short Course on AI Methods

  5. Improving Data Quality • Radar Echo Classifier (REC) • Doppler radars • NCAR Improved Moment Algorithm (NIMA) • Profilers • Intelligent Outlier Detection Algorithm (IODA) • Time series data from surface sensors AMS Short Course on AI Methods

  6. Detection/Diagnosis of Current Weather Phenomena • Microburst Automatic Detection (MAD) • Doppler radar • Particle classification using S-Band polarization radar measurements (PID) • Dual polarimetric radar, sounding • Current Icing Potential (CIP) • Multiple sensors and numerical model AMS Short Course on AI Methods

  7. Forecasting Weather Phenomena • Thunderstorm AutoNowcast System (ANC) • Multiple sensors and numerical models • Graphical Turbulence Guidance (GTG) • Renamed; ITFA is the old name • Numerical model • RAP real-time algorithm output available • Aviation Digital Data Service (ADDS) • http://adds.aviationweather.gov • Provides analysis (0hr) and forecasts • ADDS supported by NWS and FAA AMS Short Course on AI Methods

  8. Algorithms for Improving Data Quality AMS Short Course on AI Methods

  9. Radar Echo Classifier (REC)for WSR-88D AMS Short Course on AI Methods

  10. Radar Echo Classifier • Consists of four algorithms • Anomalously propagated (AP) ground clutter detection algorithm (APDA) • Precipitation detection algorithm (PDA) • Sea clutter detection algorithm (SCDA) • Insect clear air detection algorithm (ICADA) • The APDA is operational in the WSR-88D • Used for quality control (ground clutter removal) in radar-derived rainfall algorithm AMS Short Course on AI Methods

  11. Reflectivity Radial Velocity Reflectivity Precipitation Reflectivity AP Clutter WSR-88D data contaminated with ground clutter Ground clutter creates errors in radar-derived rainfall Goal of REC is to identify and separate clutter return from precipitation return AMS Short Course on AI Methods

  12. Schematic of REC Swf Sw FEATURE GENERATION FUZZY LOGIC ENGINE (Data Fusion) APPLY THRESHOLD (Defuzzification) INPUT OUTPUT Z V W Mean Median StdDev Texture VertDif Spin w w Final Product T w w Apply membership functions Apply weights Compute Interest Field AMS Short Course on AI Methods

  13. APDA Membership Functions Median Radial Velocity Median Spectrum Width 1 1 “Texture” of Reflectivity Standard Deviation of Radial Velocity 0 0 “Spin” of Reflectivity F) Spin 100 0 50 G) Sign “Sign” -10 -0.6 0 0.6 10 Feature fields are calculated over a “local area” AMS Short Course on AI Methods

  14. Radar Moment Fields Reflectivity Radial Velocity Spectrum Width AP ground clutter Moment fields are input into REC AMS Short Course on AI Methods

  15. APDA Final Output APDA Feature Fields APDA Interest Fields APDA APDA Threshold (Brown = highest interest, Blue = lowest interest) Texture of dBZ Radial Velocity Spectrum Width Spin of dBZ Std Dev Velocity AMS Short Course on AI Methods

  16. end APDA as Threshold Before After Reflectivity Radial Velocity APDA is operational on WSR-88D radars AMS Short Course on AI Methods

  17. NCAR Improved Moment Algorithm (NIMA)for Profilers AMS Short Course on AI Methods

  18. Profilers Stacked Spectra Display • Traditional signal processing computes the moments by selecting the peak spectral component • Profiler Online Program, or POP • Spectral contamination can lead to errors • NIMA developed for use at airports in high clutter environments • Operational at the Juneau, AK airport and the Hong Kong Chep Lap Kok airport AMS Short Course on AI Methods

  19. Meteorological Issues Weather Signal g-i g0 g+i Contoured Stacked Spectra Display • Wind profiling radars produce Doppler velocity data often contaminated by • Ground clutter • Radio Frequency Interference (RFI) • Point Targets (aircraft and birds) • Noise • Bad moments give bad winds and potential false alarms of wind shear RFI POP Moments Ground Clutter Point Targets AMS Short Course on AI Methods

  20. AMS Short Course on AI Methods

  21. NIMA Results end POP Moments NIMA Moments NIMA Moments POP Moments AMS Short Course on AI Methods

  22. Intelligent Outlier Detection Algorithm (IODA)for Time-series Data AMS Short Course on AI Methods

  23. IODA Introduction Example data signatures of sensor failure modes IODA assigns a confidence value to each data point (dark blue = high confidence, dark red = low confidence) AMS Short Course on AI Methods

  24. Cluster Analysis Time Domain Lag Domain • IODA performs cluster analysis to group data points • Time domain • Lag domain AMS Short Course on AI Methods

  25. IODA Results end Confidence values are color-coded Dark blue = high confidence Dark red = low confidence AMS Short Course on AI Methods

  26. Detection/Diagnosis of Current Weather Events AMS Short Course on AI Methods

  27. next Microburst Automatic Detection (MAD) A microburst is a small-scale downdraft that is an aviation hazard at low altitudes Microbursts have been responsible for a number of aircraft accidents AMS Short Course on AI Methods

  28. Meteorological Problem • Microburst Characteristics • Divergent outflow (headwind then tailwind for aircraft) • Near a storm • Characteristic size and shape • Not too big, not too small • Individual microbursts are roughly circular • Can have a line microbursts • Characteristic lifetime (minutes) AMS Short Course on AI Methods

  29. MAD Algorithm • Algorithm Characteristics • Divergent shear is the principle indicator of a microburst • Being near a storm enhances interest • Being near an earlier microburst location enhances interest • Areas of high interest that “look like a microburst” determine current detection AMS Short Course on AI Methods

  30. Radial Shear Algorithm Radial Velocity Median filter Compute shear, convert to interest ShearInterest AMS Short Course on AI Methods

  31. Storm Algorithm map mask Dilate&Clump StormMask StormInterest Reflectivity AMS Short Course on AI Methods

  32. Building Microburst Regions StormInterest ShearInterest StormMask Tracked MB Combined Interest Build Microburst Region (next slide) AMS Short Course on AI Methods

  33. Region Build Algorithm --Rules Combined Interest Storm Mask Tracked Microburst Apply rules: (C>0.5 and S>0.9) or (C>0.0 and S>0.9 and T>0) or (C>0.5 and T>0) Microburst Regions Clump, filter based on size and shape AMS Short Course on AI Methods

  34. TDWR Case Study from 25 June 2001 end Denver, Colorado Radial Velocity White contours indicate microburst location AMS Short Course on AI Methods

  35. next Current Icing Potential (CIP) AMS Short Course on AI Methods

  36. The Meteorological Problem Identify where inflight icing (supercooled liquid water) is likely to exist Assign attributes such as icing severity and type SLW associated with: clouds precipitation in-flight temperature range (~0 to –20oC or so) upward vertical velocity other* == * There are many causes and precursors AMS Short Course on AI Methods

  37. The Algorithm: CIP Philosophy Different sources of information provide clues as to where icing IS and IS NOT present Weather scenarios govern details of mapping functions Create a system that mimics manual forecast techniques Map “interest” in certain icing-related wx parameters Apply fields using a physically-based, situational approach Available on ADDS, also with Forecast Icing Potential (FIP) AMS Short Course on AI Methods

  38. The CIP Algorithm AMS Short Course on AI Methods

  39. Integrate, apply by situation Model Radar SFC Lightning PIREPs SAT Fuse data for each RUC grid column CLOUDY? ICING=0 NO YES PHYSICAL SITUATION/STRUCTURE 1 cloud no PCP 2+ cloud layers 1 cloud w/PCP classical CTT gradient T-storms Sub-situations for each, apply data appropriately. Add SLW, VV, PIREPs ICING & SLD POTENTIALS (0-100) AMS Short Course on AI Methods

  40. CIP – Maximum Icing Vertical Slice Composite AMS Short Course on AI Methods

  41. CIP Results: Verification end AMS Short Course on AI Methods

  42. next Particle Classification Using S-Band Polarization Radar Measurements AMS Short Course on AI Methods

  43. Polarimetric Measurements Large Raindrop Horizontal axis larger than vertical axis Small Raindrop Horizontal and vertical axes are equal • Polarimetric variables depend on particle axis ratio and refractive index. A combination of measurements can be used to identify particles AMS Short Course on AI Methods

  44. Applications for Particle Classification • Radar data quality • Ground clutter • Second trip • Detection of hail • Improve rainfall estimates • Severe weather warning • Cloud/precipitation physics • Operational interpretation AMS Short Course on AI Methods

  45. Particle Classification Inputs • Radar measurements used in algorithm • Reflectivity, Z • Differential reflecticity, ZDR • KDP • rHV • Linear depolarization ratio, LDR • Temperature – from sounding/aircraft • Derived quantities • s(FDP) • s(V) • s(ZDR) AMS Short Course on AI Methods

  46. Particle Classification rain Reflectivity 1 hail 0 35 45 55 65 ZDR Membership functions 1 0 LDR -1 0 1 2 1 0 -30 -25 -20 -15 P = membership value Q = weighted sum for each particle classification Example classifier for rain and hail AMS Short Course on AI Methods

  47. 2-D Membership Functions 15 categories of particle type Overlapping boundaries between types AMS Short Course on AI Methods

  48. Reflectivity STEPS tornadic supercell 29 June 2000 Eastern Colorado Differential Reflectivity, Zdr Particle Classification Range-Height cross section AMS Short Course on AI Methods

  49. Mesoscale Alpine Program (MAP), Italy 1999 end ~ Aircraft location Reflectivity Linear Depolarization Ratio 20 40 ~ 4.6 mm RICE – no icing occurred PMS – crystals and aggregates Radar – dry snow Differential Reflectivity, Zdr Particle Classification snow Wet Rain Dry snow Irregular ice AMS Short Course on AI Methods Oriented ice

  50. Forecasting of Weather Phenomena AMS Short Course on AI Methods

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