doppler weather radar algorithms metr 4803 kurt hondl national severe storms laboratory 28 april...

Doppler Weather Radar Algorithms

METR 4803Kurt HondlNational Severe Storms Laboratory28 April 2005

28 April 2005 METR 4803 - Doppler Weather Radar Algorithms 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


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


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


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


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


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


Algorithms Deal with Arrays of Data


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


Where is the Storm? Tornado?


What about now?


Or now?

Algorithm Examples


NEXRAD Number Crunching Algorithms

Velocity DealiasingComposite ReflectivityVertically Integrated Liquid water contentEcho TopsQuantitative Precipitation EstimationVAD Wind Profile


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


Velocity Dealiasing

Aliased Velocity Dealiased Velocity


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


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


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


VAD Wind Profile


NEXRAD Feature Detection Algorithms

Storm Cell Identification and TrackingHail Detection AlgorithmMesocyclone Detection AlgorithmTVS Detection Algorithm


NEXRAD Feature Detection Algorithms


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)


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.


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


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?


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


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


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


NetRad TDA/MDA


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


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


Multiple radars provide one answer

KMOB

KLIX

KJAN

MESO ID RANK MSI etc. etc.

31 8 4503


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.).


Multi-Radar VIL ExampleMulti-Radar VIL Example


Rotational shear Rotation tracks

Reflectivity Velocity


Multi-Doppler Wind Analysis

View of the same vortex from multiple radars Simulated

radar data from a storm-scale numerical model


Multi-Doppler Wind Analysis

Multi-Doppler analysis provides 2D wind vectors in real-time Wind vectors

computed from simulated radar data


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.


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)


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


Motion Estimation

30 min30 min30 min30 min

Observed Reflectivity at T30 Forecast Reflectivity at T0+30


Motion Estimation

60 min60 min60 min60 min

Observed Reflectivity at T60 Forecast Reflectivity at T0+60


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).


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


Dual Polarization Hydrometeor Classification Algorithm

Fuzzy logic algorithm to classify hydrometeor types based on polarimetric data

doppler weather radar algorithms metr 4803 kurt hondl national severe storms laboratory 28 april...

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