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The CARDS System Description and
Algorithms
CAnadian Radar Decision Support
Paul JoeMeteorological Service of Canada
Outline
• Introduction
• Requirements / Issues
• The CARDS Solution
• Algorithms, Products, Functionality
• Example of Usage
Introduction
TITAN = Thunderstorm Initiation, Analysis and Nowcasting (NCAR “free*”)
WDSS II = Warning Decision Support System (NSSL “free*” )
CARDS = Canadian Radar Decision Support (EC “free**”)
•*Download from web
•** Discuss
Introduction
• Operational system of the Meteorological Service of Canada
• Single radar processing systems for multiple uses
• In transition, being integrated with forecaster workstation (NinJo)
The Severe Warning Challenge
• Specificity of information is needed to be effective– Time/duration, Location, Type of Event
• Distinguish between severe and non-severe, • And tornadic and non-tornadic thunderstorms.• Looking for the rare event, many types of severe storms• Large forecast area • Work Load, Efficiency
3,000,000 km2
Thunderstorm locations and reported severe weather
The Rare Event
Yellow and white= events
Green = thunderstorms
100 km
High Level Requirements
An expert can…• Recognise patterns• Detect anomalies• Keep the big picture (situational awareness)• Understand the way things work• Relate past, present, and future events• Pick up on very subtle differences• Observe opportunities, able to improvise• Address their own limitations
The system design must enable this!
Using Algorithm Approch
•Not an automated answer!•Individual algorithms are configured to have high POD
–but results in high FAR•Combination of algorithms:
–support each other to reduce the FAR–create leverage points for further inquiry–support use of the conceptual model–support expert decision-making
An algorithm searches the data for relevant patterns (spatial or temporal).
Enabling Expertise
• Can not do anything if only the answer is provided!– This will make anyone dumb!– Self-fulfilling prophesy
• Must be able to “access or drill down” to the underlying data
Recall Manual Analysis Process..…
We want to mimic this – but quickly• High reflectivity• Echo top• Shapes• Gradients of
reflectivity• Trends • Movement• Flair echo/Hail in
dual-pol
• Relationships – Updraft Tilt– Weak Echo Regions
(WER)– Bounded WER– Location– Echotop - Gradient
• Rotation• Divergence• Convergence
Cell ViewCell View to access to data/products
CAPPI’s
Echo Top
gradient
hail
VIL
Time historyAutomated XSECT
Algorithms Approach
•Not the answer! but …•Create “Leverage” Points•Support your Conceptual Model•Support Decision Making
Algorithm
• A set of computer procedures or steps
• Attempts to match human visual/pattern recognition skills
• Software that identifies a feature in the data that represents a meteorological feature (e.g., a thunderstorm cell, a cell track)
Products/Algorithms(configurable)
• CAPPI (many)• MAXR• Height of MAXR• EchoTop• VIL, Downdraft, Hail Size• Reflectivity Gradient• PPI’s• Radial Velocity• Spectral Width• Corrected Reflectivity• Precipitation Accumulations• Composites of various products• Interactive Cross-sections• Algorithm Ensemble Product• Cell Views• Storm Cell Identification Table
• Cell Identification– average and max value– locations
• Bounded Weak Echo Region• Mesocylone, downburst, gust• Cell Properties
– Echotop, VIL, Hail Size– See Product List
• Automatic Cross-sections• Tracking, Simple Nowcast• Multi-radar algorithm merge• Rank Weight
– Color Coding• Sorted Rank• Cross-correlation Tracking
– Point Forecast
Need for “Leverage” Points Algorithms Where is the rotation/Tornado Vortex Signature?
Leverage = “look at me”
Forecasters need to maintain situational awareness:#1 problem of missed warnings but which cell is the dangerous one? NO NEED FOR SINGLE RADAR PRODUCTS! But…
Forecasters must be able to diagnose the salient features to make a warning decision
Severe Storm Features
-Large cell with strong elevated reflectivity (MAXR>45 dBZ)
-Tall (high echo top)
-Hail
-Low level Reflectivity gradients under highest echo tops
-Weak Echo Region
-Hook/Kidney beam shape
-Mesocyclones
-Downdrafts
Codifying the Lemon Technique through Cell Views
Some of the Algorithms
•Hail•Downdraft Algorithm•Storm Classification Identification and Tracking•Ranking Storms
Products/Algorithms(configurable)
• CAPPI (many)• MAXR• EchoTop• VIL, WDraft, Hail Size• Reflectivity Gradient• PPI’s• Radial Velocity• Spectral Width• Corrected Reflectivity• Precipitation Accumulations• Composites of various products• Interactive Cross-sections• Algorithm Ensemble Product• Cell Views• Storm Cell Identification Table
• Cell Identification– average and max value– locations
• Bounded Weak Echo Region• Mesocylone, downburst, gust• Cell Properties
– Echotop, VIL, Hail Size– See Product List
• Automatic Cross-sections• Tracking, Simple Nowcast• Multi-radar algorithm merge• Rank Weight
– Color Coding• Sorted Rank• Cross-correlation Tracking
– Point Forecast
S2K Hail Products
• Polarimetric, BOM/MSC, WDSS• BOM/Treloar Empirical Algorithm
– Uses height of 50 dBZ echo, VIL and freezing level
• WDSS– Uses height diff of freezing level and 45 dBZ top,
VIL, hail kinetic energy (fn of dBZ), temperature profile
– Probability of severe hail– SHI
WDSS HDA Probability of Hail (POH)
• Estimate the probability of any size hail associated with a storm
• H45 = Height of the 45 dBZ echo AGL (km)
• H0 = Height of the melting level AGL (km)
Based on data from a Swiss hail suppression experiment Based on data from a Swiss hail suppression experiment
-> Δ H
HDA Severe Hail Index (SHI)
• Vertically Integrated Liquid (VIL) (Emphasis given to lower dBZ)
– To remove “hail contamination”
• Hailfall Kinetic Energy (E) (Emphasis given to higher dBZ and those dBZ above the melting layer)
E = 5 x 10-6 x 100.084Z x W(Z)
– W(Z) = 0 for Z < 40 dBZ– W(Z) linearly interpolated for 40
dBZ > Z > 50 dBZ– W(Z) = 1 for Z > 50 dBZ
• Weighted by thermodynamic profile
– Obtained manually from nearby sounding, or
– Obtained automatically from mesoscale model analysis
• Greater temporal and spatial resolution
•
• Prob. Of Severe Hail (POSH; dia > 1.9 cm) and Max. Estimated Hail Size (MEHS) derived from SHI (Witt et al. 1998)
• Weighted by thermodynamic profile
– Obtained manually from nearby sounding, or
– Obtained automatically from mesoscale model analysis
• Greater temporal and spatial resolution
•
• Prob. Of Severe Hail (POSH; dia > 1.9 cm) and Max. Estimated Hail Size (MEHS) derived from SHI (Witt et al. 1998)
HDA Severe Hail Index (SHI)
WT(H)
SHI = 0.1WT(Hi) Ei HiSHI = 0.1WT(Hi) Ei Hi
NN
ii
S2K ComparisonAverage Hail Size
POL CARDS WDSS
OBS
• Polarimetric, BOM/MSC, WDSS• CARDS/BOM/Treloar Empirical Algorithm
– Uses height of 50 dBZ echo, VIL and freezing level
• WDSS– Uses height diff of freezing level and 45 dBZ top, VIL,
hail kinetic energy (fn of dBZ), temperature profile– Probability of severe hail– SHI
• What is the truth? Do you want to just reduce the CSI or do you want high POD? What is the relationship to your forecast product?
0
2
4
6
8
10
12
0300 0330 0400 0430 0500 0530 0600 0630
Time (UTC)
Dia
met
er (
cm) Max
AveObs
CARDS Hail Size Time SequenceNov 3 Case
0
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4
6
8
10
12
0300 0330 0400 0430 0500 0530 0600 0630
Time (UTC)
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me
ter
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Harold Brooks
MAX
Ave
WDSS Probability of Hail
0
20
40
60
80
100
0300 0330 0400 0430 0500 0530 0600 0630
Time (UTC)
Pro
ba
bil
ity
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Harold Brooks
WDSS Max Hail Size
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0300 0330 0400 0430 0500 0530 0600 0630
Time (UTC)
Dia
me
ter
(cm
)
Harold Brooks
WDraft ProductSpeed of the outflow
• Theoretically based on work by Emmanuel• Empirically adapted by Stewart, OU• VIL -> downdraft strength -> outflow strength• Earlier warning than just the surface divergence
product• Uses volume scan reflectivity data
Gust Potential Algorithm
• Outflow Speed
W = (20.63 VIL – 3.125 x 10-6 H2 )1/2
W = outflow speed m/s
H = Echotop height
• Threshold = 10 m/s
Example
Courtesy of Isztar Zawadzki
Microburst from radial velocity at surfaceIndication of Strong Gust
TIME Increasing
Surface Doppler
Reflectivity Based
Cell Ranking
• Objective: – find the most dangerous and strongest
storm– Reduce FAR of individual high POD
algorithms
• Algorithm: use cell properties to compute a single metric – rank weight
• Sort the rank weights to find the strongest storm
Table Rankings:
Rank = Circulation(f*109) + (10*Size + 10*POSH)*105 + 10*Damaging Wind Index
WDSS Ranking
CARDS Cell Analysis SummaryStorm Classification Identification Table
Rank Wt = ∑ αi vi
Storm Number
Rank (order)
Rank Wt (severity)
Category (X)
DownDraft (m/s)
BWER (ht)
Meso Shear
Hail Size
VIL/VIL density
Max dbZ
EchoTop Ht
Speed
Rank Weight
• A parameter to numerically summarize the various attributes of the cell object
• α is an empirical coefficient that normalizes and scales the parameter v by severity
• Normalization done by categorizing the parameter
Rank Wt = ∑ αi vi
Storm Rank Weight
Each parameter is categorized on a scale from 0 to 4 (normalizing)
Rank Weight is the average of the categorized values.
Parameters are configurable.
Used to determine a numeical value for sorting.
Computer Hardware
• Server– S2K - Single dual processor, 600 Mhz, 1Gbyte RAM,
2 x 18 Gbyte Hard drive, Linux PC– MSC – Linux Cluster, central node for data ingest and
science processing, secondary nodes for product/image creation
• Client– S2K/MSC - PC to run Netscape or Java Application
for the “Interactive Viewer” to access and display the products
Generic Approach
• Conceptual Model• Data (2D or 3D, 1D or mxD)• Translate Conceptual Model to a Data/Sensor
Model• Define an Interest Field• Define a detection threshold• Search for elements exceeding threshold, grow
in the various dimenstions
Pattern Recognition Algorithm Glossary 1
• “Interest” Field– A grid of data of a parameter related to the “object”– 2D or 3D or …– Polar or cartesian or …
• Pattern Vector Element– a single grid point that exceeds a “threshold value”
• Pattern Vector– a contiguous line of pattern vector elements exceeding a threshold value– 1 dimensional
• 2D Feature– a contiguous set of pattern vectors - 2 dimensional
• Height Associated Feature (or 3D Feature)– a set of 2D features at different heights (in practice)
• Time Associated Feature (or 4D Feature)– a “tracked” Height Associated Feature
Glossary 2
• Weather Object– a Feature that satisfies constraints, rules, filters,
classifications, thresholds, etc – interpreted as a possible meteorological concept– Eg cell, mesocyclone, microburst, area of hail, area of
lightning
• Storm or Cell Attribute– an property of a storm– e.g. average value, max value, area, % of positive
strikes, etc
Glossary 3
• Field– a two dimensional array of a (radar or
derived) parameter– eg PPI of reflectivities, echotop heights
• Template– a two dimensional area defined by the extents
of the pattern vectors of a feature– Subset of a field
Basic Approach e.g. Thunderstorm Identification
• Thunderstorm = cell • Cell definition
– contiguous area of reflectivity (the Interest Field) above a certain threshold
– Could be from a PPI, a CAPPI, MAXR or VIL, lightning, hail from polarimetric radar, satellite or …
• Define Threshold
• Objective: – Identify individual cells– Determine their location– Determine the footprint (perimeter)– Compute properties
Interest Fields
• Cells – CAPPI, MAXR, VIL
• Mesocyclone – azimuthal shear
• Microburst – radial shear
• Hail – hail size field
Thresholds
• Cells – fixed (45 dBZ), multiple (25, 30, 35, 40 dBZ), adaptable (displacement from peak)
• Mesocyclones – 2m/s/km, -2 m/s/km
• Microbursts - -2m/s/km
• Hail – 0.1 cm
Detection/Classify
• Try to find all potential features (rare events)– High probability of detection
• Reduce high false alarms– Consistency with other features– Use other properties, shape, location– Forecaster
The Interest FieldMAX R = 2D Projection of 3D Data
9.0 km
7.0 km
3.0 km
1.5 km
CAPPI
MAXR
45 dBZ
30 dBZ
WDSS ii
CARDS
Region Growing Algorithm
• Find contiguous pixels/bins of data that exceed a threshold
• Terminology– Element -> pattern vector -> 2D feature– Pixels -> line of pixels -> pixel areas
Feature (2D)Cell = Feature = Group of Pattern Vectors
•Specify a dBZ threshold•Find Pattern Vectors•Collate PV’s into a Feature
MAXR field
+ + + +
Cell Properties
• Use footprint defined by cell identification on MAXR or VIL or …
• Use another interest field and find max, average and their locations– E.g. echotop, wdraft, hail, etc
• Can then plot the locations or use to automatically determine the cross-section points.
Summary
• Brief description of the CARDS system• Setup for high probability of detection, results in high
false alarm rate• Use the combination of algorithm outputs to determine
the most intense storms.• Fuzzy logic storm ranking• Rapid access to products• \Assume expert user
– Maintain situational awareness– Provide guidance/leverage products, drill down to data, hint at
where and what to look for in detail, require forecaster for final decision making