Doppler Weather Radar Algorithms
METR 4803Kurt HondlNational Severe Storms Laboratory28 April 2005
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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
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Doppler Weather Radar Observations
What can we see/detect with weather radars?Storm cells and features
Thunderstorm structure, supercell, hook echoesPrecipitation, hail
RotationMesocyclone, tornadic vortex signature (TVS)
WindWind profile, 2D wind field
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Algorithm Basics
What are algorithms?Automated methods to turn vast amounts of
data into useful informationWhy use algorithms?
NEXRAD – 14 MB of data every 5 minutesHumans are very good at visual image
processingBut human processing capacity is limited and
subject to information overload and fatigueAnd human processing varies by individuals
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The Use of Algorithms
Algorithms are intended to aid the human decision maker Integrate informationProvide guidanceBe a “safety net”
Identify and rank all features
Let the meteorologist make the final warning decision
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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
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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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Algorithms Deal with Arrays of Data
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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
Forecast
- - - - -
Occurrence
Yes No
Yes H M
No F Correct Nulls
H = forecast event that occurs
M = occurrence of event that wasn’t forecast
F = forecast event that doesn’t occur
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Where is the Storm? Tornado?
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What about now?
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Or now?
Algorithm Examples
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NEXRAD Number Crunching Algorithms
Velocity DealiasingComposite ReflectivityVertically Integrated Liquid water contentEcho TopsQuantitative Precipitation EstimationVAD Wind Profile
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Velocity Dealiasing
Radial velocity observations of velocity outside the Nyquist interval will be aliased (folded) back into the Nyquist intervalUse radial continuity and look for large
changes in radial velocity (approx 2*VNyq)Noisy or non-continuous data present problems
Other techniques being developedUse 2D information and other data
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Velocity Dealiasing
Aliased Velocity Dealiased Velocity
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Velocity Dealiasing
Aliased Velocity Dealiased Velocity
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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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QPE
R = 200 Z 1.6 (Marshall-Palmer formula)Many Z/R relationships used for different
environmentsConvective, stratiform, tropical
Accumulates/integrates rainfall over a period of time
Observations may be different than actual rainfall amounts in rain gagesLarge areal estimate vs point value
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VAD Wind Profile
Radial velocity at constant range & elevation varies azimuthally like a sine wavePhase & amplitude of sine wave used to
estimate wind direction and speedAssumes linearity of the wind fieldEstimates at different ranges/elevations
provides wind values at different altitudes
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VAD Wind Profile
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NEXRAD Feature Detection Algorithms
Storm Cell Identification and TrackingHail Detection AlgorithmMesocyclone Detection AlgorithmTVS Detection Algorithm
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NEXRAD Feature Detection Algorithms
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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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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.
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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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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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MDA Details
2.4o
0.5o
1.5o
Mesocyclone
Storm cloud
Cloud base
WSR-88D
Vertically associate 2D circulations.
Classify and DiagnoseClassify and Diagnose
Rule Bases (MESO, LOWTOP, Rule Bases (MESO, LOWTOP, WKCIRC, SHALLO) WKCIRC, SHALLO) Strength Rank, MSI Strength Rank, MSI
Neural Network Probabilities Neural Network Probabilities
Find Shear Segments and construct 2D circulations.
Storm-relative VelocityReflectivity
Mesocyclone
330o330o
100 km100 km
28.527.0
29.028.527.5
24.523.5
-26.5
20.5
23.0-25.5-22.5
-4.5 -20.0
-23.5-23.0
-27.0
24.026.0
21.5
22.0
19.514.515.5
27.024.5 28.0
29.5
26.527.528.0
-20.5
-20.5
-23.5
-15.0
-20.5
-12.0
-9.0
-8.5 -5.5 -5.5
-7.5
-22.0-22.0-25.0-25.0-19.5-19.5-11.0
-18.5
30.5
20.020.0
20.521.0
24.020.0
21.0
17.522.5
21.5 19.5
20.5
98.7599.0099.2599.5099.75
97.7598.0098.2598.50
Ran
ge (
km)
335.5o334.5o333.5o332.5o331.5o330.5o329.5o
Azimuth
Shear Segments
Mesocyclone
Track and display outputTrack and display output
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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
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30o
60o60o
50 km 50 km
TVS
30o
Shear segments24.5
-6.5-18.0-32.5-31.0-15.5-10.5
8.021.0
23.514.06.5R
ange
(km
)
Azimuth53.5o 54.5o
28.528.027.527.026.526.0
Reflectivity SRM Velocity
2.4o
0.5o
1.5o
Tornado
Storm cloud
Cloud base
WSR-88D
Check size/strength
Base Height: 0.5o or < .6 km AGL
Depth: >/= 1.5 km
Max. Vel. Diff.: Base and 3D
Find shear segments and construct 2D circulation features
Track and display output
Vertically associate 2D circulation signatures
TDA Details
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NetRad TDA/MDA
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Advanced Algorithms
Multi-Radar, Multi-Sensor AlgorithmsTake advantage of increases in
computational capacityForecast techniques are using inputs from
multiple sensorsAlgorithms 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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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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Multiple radars provide one answer
KMOB
KLIX
KJAN
MESO ID RANK MSI etc. etc.
31 8 4503
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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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Multi-Radar VIL ExampleMulti-Radar VIL Example
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Rotational shear Rotation tracks
Reflectivity Velocity
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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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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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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.
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Gridded Hail ProductsReflectivity (dBZ)Reflectivity (dBZ) Probability of Severe Hail (>19 mm dia)Probability of Severe Hail (>19 mm dia)
Maximum Expected Hail Size (mm)Maximum Expected Hail Size (mm) Two Hour Path of Max Hail Size (mm)Two Hour Path of Max Hail Size (mm)
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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.
Observed Reflectivity at T0
00 min00 min
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Motion Estimation
30 min30 min30 min30 min
Observed Reflectivity at T30 Forecast Reflectivity at T0+30
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Motion Estimation
60 min60 min60 min60 min
Observed Reflectivity at T60 Forecast Reflectivity at T0+60
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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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Quality Control Neural NetworkOriginal dBZ Original dBZ Radar-only QCNN Radar-only QCNN
Cloud Cover (Tsfc –
Tsat) Cloud Cover (Tsfc –
Tsat) Multi-sensor QCNN Multi-sensor QCNN
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Dual Polarization Hydrometeor Classification Algorithm
Fuzzy logic algorithm to classify hydrometeor types based on polarimetric data