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

2. METR 4803 - Doppler Weather Radar Algorithms
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
28 April 2005
METR 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
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METR Doppler Weather Radar Algorithms

4. METR 4803 - Doppler Weather Radar Algorithms
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
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METR Doppler Weather Radar Algorithms

5. METR 4803 - Doppler Weather Radar Algorithms
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
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METR Doppler Weather Radar Algorithms

6. METR 4803 - Doppler Weather Radar Algorithms
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
28 April 2005
METR Doppler Weather Radar Algorithms

7. METR 4803 - Doppler Weather Radar Algorithms
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
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METR Doppler Weather Radar Algorithms

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

9. METR 4803 - Doppler Weather Radar Algorithms
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
Forecast
Occurrence
Yes No H M F
Correct Nulls
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METR Doppler Weather Radar Algorithms

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

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

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

15. METR 4803 - Doppler Weather Radar Algorithms
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
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METR Doppler Weather Radar Algorithms

16. METR 4803 - Doppler Weather Radar Algorithms
Velocity Dealiasing
Aliased Velocity
Dealiased Velocity
28 April 2005
METR Doppler Weather Radar Algorithms

17. METR 4803 - Doppler Weather Radar Algorithms
Velocity Dealiasing
Aliased Velocity
Dealiased Velocity
28 April 2005
METR Doppler Weather Radar Algorithms

18. METR 4803 - Doppler Weather Radar Algorithms
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
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METR Doppler Weather Radar Algorithms

19. METR 4803 - Doppler Weather Radar Algorithms
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
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METR Doppler Weather Radar Algorithms

20. METR 4803 - Doppler Weather Radar Algorithms
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
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METR Doppler Weather Radar Algorithms

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

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

23. NEXRAD Feature Detection Algorithms
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METR 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)
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METR 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.
28 April 2005
METR Doppler Weather Radar Algorithms

26. Hail Detection Algorithm
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
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METR Doppler Weather Radar Algorithms

27. Mesocyclone Detection Algorithm (MDA)
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?
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METR Doppler Weather Radar Algorithms

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

29. TVS Detection Algorithm (TDA)
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
28 April 2005
METR Doppler Weather Radar Algorithms

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

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

32. METR 4803 - Doppler Weather Radar Algorithms
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
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METR Doppler Weather Radar Algorithms

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

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

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

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

40. METR 4803 - Doppler Weather Radar Algorithms
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.
28 April 2005
METR Doppler Weather Radar Algorithms

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

42. METR 4803 - Doppler Weather Radar Algorithms
Motion Estimation
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.
00 min
Observed Reflectivity at T0
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METR Doppler Weather Radar Algorithms

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

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

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

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