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Doppler Weather Radar Algorithms

Doppler Weather Radar Algorithms. METR 4803 Kurt Hondl National Severe Storms Laboratory 28 April 2005. Basics of Radar Data. Assumptions Complete and uniform filling of the radar beam Standard refraction Observation Errors / Effects Calibration Number of samples / noise

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Doppler Weather Radar Algorithms

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  1. Doppler Weather Radar Algorithms METR 4803 Kurt Hondl National Severe Storms Laboratory 28 April 2005

  2. Basics of Radar Data • Assumptions • Complete and uniform filling of the radar beam • Standard refraction • Observation Errors / Effects • Calibration • Number of samples / noise • Antenna rotation rate • Beamwidth / sidelobes • Other Issues • Range and velocity aliasing • AP / clutter METR 4803 - Doppler Weather Radar Algorithms

  3. Doppler Weather Radar Observations • What can we see/detect with weather radars? • Storm cells and features • Thunderstorm structure, supercell, hook echoes • Precipitation, hail • Rotation • Mesocyclone, tornadic vortex signature (TVS) • Wind • Wind profile, 2D wind field METR 4803 - Doppler Weather Radar Algorithms

  4. Algorithm Basics • What are algorithms? • Automated methods to turn vast amounts of data into useful information • Why use algorithms? • NEXRAD – 14 MB of data every 5 minutes • Humans are very good at visual image processing • But human processing capacity is limited and subject to information overload and fatigue • And human processing varies by individuals METR 4803 - Doppler Weather Radar Algorithms

  5. The Use of Algorithms • Algorithms are intended to aid the human decision maker • Integrate information • Provide guidance • Be a “safety net” • Identify and rank all features • Let the meteorologist make the final warning decision METR 4803 - Doppler Weather Radar Algorithms

  6. More on Algorithms • Algorithm capabilities • Number crunching on streaming data • Must be able to process all data in a timely manner • Feature detection through image processing • Pattern vectors, texture, filters • Artificial intelligence • Expert systems, fuzzy logic, neural network, clustering • Reliable stores of feature characteristics • Allows access to trends of information METR 4803 - Doppler Weather Radar Algorithms

  7. More on Algorithms • Algorithm limitations • Algorithms only as good as the technique • Based on past observations • Simple techniques become complex • Desire to remove false alarms and improve detection efficiency • Most algorithms affected by noise in the data • Adaptable parameter settings • Allows “tuning” of the algorithms to meet needs of forecasters … but this changes performance • Detection vs prediction METR 4803 - Doppler Weather Radar Algorithms

  8. Algorithms Deal with Arrays of Data METR 4803 - Doppler Weather Radar Algorithms

  9. Scoring Algorithms • How to evaluate algorithm accuracy • Probability of Detection • POD = H / (H+M) • False Alarm Ratio • FAR = F / (H+F) • Critical Success Index • CSI = H / (H+F+M) • Lead time • RMS error or RMS difference H = forecast event that occurs M = occurrence of event that wasn’t forecast F = forecast event that doesn’t occur METR 4803 - Doppler Weather Radar Algorithms

  10. Where is the Storm? Tornado? METR 4803 - Doppler Weather Radar Algorithms

  11. What about now? METR 4803 - Doppler Weather Radar Algorithms

  12. Or now? METR 4803 - Doppler Weather Radar Algorithms

  13. Algorithm Examples

  14. NEXRAD Number Crunching Algorithms • Velocity Dealiasing • Composite Reflectivity • Vertically Integrated Liquid water content • Echo Tops • Quantitative Precipitation Estimation • VAD Wind Profile METR 4803 - Doppler Weather Radar Algorithms

  15. Velocity Dealiasing • Radial velocity observations of velocity outside the Nyquist interval will be aliased (folded) back into the Nyquist interval • Use radial continuity and look for large changes in radial velocity (approx 2*VNyq) • Noisy or non-continuous data present problems • Other techniques being developed • Use 2D information and other data METR 4803 - Doppler Weather Radar Algorithms

  16. Velocity Dealiasing Aliased Velocity Dealiased Velocity METR 4803 - Doppler Weather Radar Algorithms

  17. Velocity Dealiasing Aliased Velocity Dealiased Velocity METR 4803 - Doppler Weather Radar Algorithms

  18. CompRefl / VIL / ET • CompRefl: Maximum value of reflectivity at each 2D location from any elevation angle • Obscures some signatures • Used by forecasters to obtain motion (looping of images) • VIL: An integration of reflectivity with respect to height • Using reflectivity as a substitute for liquid water content • Converted to kg/m2 using a fudge factor • May be contaminated by hail • Echo Top: Altitude of the top of the 18 dBZ echo • Or 10 dBZ, or 0 dBZ • Assumes standard propagation • Height calculated from center of beam • Elevation angles dependent on scan strategy METR 4803 - Doppler Weather Radar Algorithms

  19. QPE • R = 200 Z 1.6 (Marshall-Palmer formula) • Many Z/R relationships used for different environments • Convective, stratiform, tropical • Accumulates/integrates rainfall over a period of time • Observations may be different than actual rainfall amounts in rain gages • Large areal estimate vs point value METR 4803 - Doppler Weather Radar Algorithms

  20. VAD Wind Profile • Radial velocity at constant range & elevation varies azimuthally like a sine wave • Phase & amplitude of sine wave used to estimate wind direction and speed • Assumes linearity of the wind field • Estimates at different ranges/elevations provides wind values at different altitudes METR 4803 - Doppler Weather Radar Algorithms

  21. VAD Wind Profile METR 4803 - Doppler Weather Radar Algorithms

  22. NEXRAD Feature Detection Algorithms • Storm Cell Identification and Tracking • Hail Detection Algorithm • Mesocyclone Detection Algorithm • TVS Detection Algorithm METR 4803 - Doppler Weather Radar Algorithms

  23. NEXRAD Feature Detection Algorithms METR 4803 - Doppler Weather Radar Algorithms

  24. Storm Cell Identification & Tracking • Identifies cell centroids using pattern vectors • Searches for relative maxima in reflectivity data • Works better with filtered data • Correlates centroids across time to determine past locations of the same feature • Uses past locations and linear regression to estimate speed and direction of motion (and to forecast locations) METR 4803 - Doppler Weather Radar Algorithms

  25. Storm Cell Identification and Tracking (SCIT) • Searches for “gate runs” (segments) using multiple reflectivity thresholds (30, 35, 40,...60 dBZ) on each elevation scan. • Correlates “gate runs” into 2D “features” and extracts cores from multiple reflectivity threshold information. METR 4803 - Doppler Weather Radar Algorithms

  26. Uses reflectivity structure to detect hail Uses empirical formula obtained from hundreds of reported hail events and associated radar signatures Vertical integration of reflectivity Uses altitude of 0o and -20o C temperature levels Detects hail aloft … before it falls to the ground Estimates produced Probability of any size hail Probability of severe hail (>0.75 inches) Maximum expected size of hail Hail Detection Algorithm METR 4803 - Doppler Weather Radar Algorithms

  27. Uses pattern vectors to detect radial velocity differences across radials (shear) at a constant range Groups 1D shear vectors into 2D and 3D sets Expert system then classifies detected signatures Circ, CPLT, MESO Neural network to calculate the probability of a tornado associated with the mesocyclone Only cyclonic signatures are detected What is a shear vector? Mesocyclone Detection Algorithm (MDA) METR 4803 - Doppler Weather Radar Algorithms

  28. Find Shear Segments and construct Vertically associate 2D circulations. 2D circulations. Reflectivity Storm-relative Velocity o 2.4 330o 330o o Storm cloud 1.5 Mesocyclone Mesocyclone 100 km 100 km o 0.5 WSR-88D 99.75 -4.5 -20.0 -20.5 22.0 21.5 19.5 17.5 Mesocyclone Cloud base 99.50 -9.0 -22.0 -22.5 23.0 23.5 22.5 20.0 99.25 -12.0 -22.0 -25.5 24.0 24.5 24.0 20.5 99.00 -20.5 -25.0 -26.5 26.0 27.0 27.0 21.0 Shear Segments 98.75 -15.0 -25.0 -27.0 24.5 28.0 28.5 20.5 Range (km) 98.50 -7.5 -18.5 -23.5 21.5 29.5 30.5 21.0 98.25 -8.5 -19.5 -23.5 19.5 28.0 29.0 20.5 98.00 -5.5 -19.5 -23.0 14.5 27.5 28.5 20.0 97.75 -5.5 -11.0 -20.5 15.5 26.5 27.5 20.0 329.5o 330.5o 331.5o 332.5o 333.5o 334.5o 335.5o Azimuth Track and display output MDA Details Classify and Diagnose 4 Rule Bases (MESO, LOWTOP, WKCIRC, SHALLO) 4 Strength Rank, MSI 4 Neural Network Probabilities METR 4803 - Doppler Weather Radar Algorithms

  29. Similar technique to MDA Shear must be from adjacent azimuths Shear must be at lowest elevation angle to be a TVS Classifies signatures as Elevated TVS or TVS TVS Detection Algorithm (TDA) METR 4803 - Doppler Weather Radar Algorithms

  30. Reflectivity SRM Velocity o 2.4 30 30 o o 50 km 50 km o Storm cloud 1.5 60 o 60 o TVS o 0.5 WSR-88D Tornado Cloud base Shear segments 24.5 Range (km) 28.5 -6.5 8.0 53.5 54.5 o o 28.0 -18.0 21.0 Azimuth 27.5 -32.5 27.0 -31.0 23.5 26.5 -15.5 14.0 26.0 -10.5 6.5 Check size/strength 4 Base Height: 0.5 or o < .6 km AGL 4 Depth: >/= 1.5 km 4 Max. Vel. Diff.: Base and 3D TDA Details Find shear segments and construct 2D circulation features Vertically associate 2D circulation signatures Track and display output METR 4803 - Doppler Weather Radar Algorithms

  31. NetRad TDA/MDA METR 4803 - Doppler Weather Radar Algorithms

  32. Advanced Algorithms • Multi-Radar, Multi-Sensor Algorithms • Take advantage of increases in computational capacity • Forecast techniques are using inputs from multiple sensors • Algorithms also making use of multiple radars and other sensors to provide a more complete look at the storm and to fill in data gaps METR 4803 - Doppler Weather Radar Algorithms

  33. Data from adjacent radars are available to fill in the cone-of-silence Complete multi-radar data used for: VIL, POSH, MEHS Multiple Radar SSAP METR 4803 - Doppler Weather Radar Algorithms

  34. KJAN KLIX KMOB Multiple radars provide one answer METR 4803 - Doppler Weather Radar Algorithms

  35. Combining Data from Multiple Radars • Mosaic data from multiple radars to create a 3D Cartesian lat/lon/ht grid. • Uses time-weighting and inverse distance weighting schemes. • Can also advect older data when running motion estimator (later slide). • Run algorithms on continuously-updating 3D grids: • 3D reflectivity field for VIL, echo top, LRM, hail • 3D velocity derivative fields for vortex (rotation) and wind shift (convergence) detection • Easy to integrate other sensor information (NSE, satellite, lightning, etc.). METR 4803 - Doppler Weather Radar Algorithms

  36. Multi-Radar VIL Example METR 4803 - Doppler Weather Radar Algorithms

  37. Reflectivity Velocity Rotational shear Rotation tracks METR 4803 - Doppler Weather Radar Algorithms

  38. Multi-Doppler Wind Analysis • View of the same vortex from multiple radars • Simulated radar data from a storm-scale numerical model METR 4803 - Doppler Weather Radar Algorithms

  39. Multi-Doppler Wind Analysis • Multi-Doppler analysis provides 2D wind vectors in real-time • Wind vectors computed from simulated radar data METR 4803 - Doppler Weather Radar Algorithms

  40. Gridded Hail Products • A new paradigm in hail information delivery • Improves public service by giving them geo-spatial information on hail size versus a simple yes/no. • Geospatial info also facilitates improved verification. • Coupled with NSSL motion estimation algorithm, capability exists to predict short-term hail swaths. METR 4803 - Doppler Weather Radar Algorithms

  41. Reflectivity (dBZ) Probability of Severe Hail (>19 mm dia) Two Hour Path of Max Hail Size (mm) Maximum Expected Hail Size (mm) Gridded Hail Products METR 4803 - Doppler Weather Radar Algorithms

  42. Sophisticated technique using statistical segmentation and error analysis. Can be used on dBZ, IR satellite, VIL, lightning density, etc. Produces high-resolution motion field that can be used to predict hail, precipitation, rotation, lightning, etc. Motion Estimation 00 min Observed Reflectivity at T0 METR 4803 - Doppler Weather Radar Algorithms

  43. Motion Estimation 30 min 30 min Observed Reflectivity at T30 Forecast Reflectivity at T0+30 METR 4803 - Doppler Weather Radar Algorithms

  44. Motion Estimation 60 min 60 min Observed Reflectivity at T60 Forecast Reflectivity at T0+60 METR 4803 - Doppler Weather Radar Algorithms

  45. Quality Control Neural Network • QCNN uses multiple-sensor information to segregate precipitation echoes from non-precipitation echoes: • Non-precipitating clear-air return • Ground Clutter • Anomalous Propagation (AP) • Chaff • Resulting clean “precipitation” field used as input to other applications (MDA, TDA, QPE) • Lowers the number of False Alarms • Two stages: • Radar-only (texture statistics from all three moments, vertical profiles) • Radar, satellite, and surface temperature (for additional “cloud cover” product). METR 4803 - Doppler Weather Radar Algorithms

  46. Quality Control Neural Network Radar-only QCNN Original dBZ Cloud Cover (Tsfc – Tsat) Multi-sensor QCNN METR 4803 - Doppler Weather Radar Algorithms

  47. Dual Polarization Hydrometeor Classification Algorithm • Fuzzy logic algorithm to classify hydrometeor types based on polarimetric data METR 4803 - Doppler Weather Radar Algorithms

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