Transcript Morphologic Investigation of Thunderstorm Initiates and GIS

Morphologic Investigation of Thunderstorm
Initiates and GIS Attributes with Testing for
Improved Operational Nowcasting of
Thunderstorms & their Severity in New Jersey
Dr. Paul Croft1, Alan Cope2,
Danielle Fadeski3, Alexis Ottati3, Jackie Parr3
Faculty Research Advisor 1
National Weather Service2
Undergraduate Student 3
To Improve…
Skill
Confidence
Precision
in
Convective
Forecasting
Why Improve the convective initiation forecast?
What We Forecast…
What Really Happens…
“…a 40% chance of showers and thunderstorms…”
Convective Objectives
 Determine Convective Initiation
patterns PHI CWA and nearby region
 Movement, Intensity and Coverage
 Use online database to assist in enhanced operational
forecasting of thunderstorm initiation, coverage, and
severity in real-time
 Establish operational archive and forecast database
http://hurri.kean.edu/~keancast/thunder/thunder.html
Data Collection & Methods
 Study Period: 2000 – 2010

10 Summer Seasons: June, July & August (one test season)
 Mapped daily radar every 3 hours between 12 UTC and 00 UTC

Recorded cell/area radar intensities of 30 & 50 dBz
Classification of each day and identify initiation locations/patterns
 500mb flow, surface synoptic pattern, and combinations of the two
Southwest Warm Front
Event
Contaminate
Classification of Convective
Activity for 1200 – 0000 UTC
Null
Event
Contaminate
Null
After
15 UTC
Before
15 UTC
No
Activity
Research to Operations: Thunder Dome
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

Preferred locations of initiation from archive
Empirical probabilities of occurrence developed
Critical threshold values and field patterns associated with activity
Discern an “E” from “C” or “Null” day with greater confidence
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
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
500 flow
Sfc Synoptic
Probabilities
Locations
 CDC
http://hurri.kean.edu/~keancast/thunder/thunder.html
Diagnostics
 Pattern of
parameters
 Causative
Factors
Building an Operational Conceptual Model
 Determine 500 mb flow type (e.g., West flow cases)
500 mb flow- WEST
Day Type
Sample Size
% Chance
Event
Contaminate
Null
79
63
68
38%
30%
32%
68% chance of initiation
to occur with West flow
Forecasting with Operational Conceptual Model
 Determine Surface Feature (e.g., Cold Front)
Surface Feature- COLD FRONT
Day Type
Sample Size
% Chance
Event
Contaminate
Null
85
78
25
45%
41%
13%
86% chance of initiation
to occur with surface
cold front
Applying the Operational Conceptual Model
 Using a combination (500mb+Surface Feature)
 e.g., West flow and Cold Front
West-Cold font
Day Type
Sample Size
% Chance
Event
Contaminate
Null
27
21
10
47%
36%
17%
preferred region
for initiation for
event cold front
and 500 mb flow
83% chance of initiation
with W-CF combination
Contaminate cold
front cases show
no preference for
initiation location
Use of Diagnostic Patterns/Thresholds…
32% Chance Event
PWAT Event
64% Chance Contaminate
PWAT Contaminate
4% Chance Null
PWAT Null
Operational Testing & Verification
Outline areas of
initiation; cells,
areas, or lines &
where for
severe
Indicate whether forecast day of
interest will be: E, C, N & if Severe
Date/Type: June 1, 2009/Event
How successful?
Time of Forecast
Obs/Predict
Event
Previous
Afternoon
2010
Success Rate
Contaminate88% Null
Event
7
Early
Morning 2010
3
79%
0
Contaminate
2
Early
Morning 2009
7
85%
0
Null
4
Early Morning
2009/2010
0
81%
4
1200 UTC
500 mb flow: NW
Number
sequence of
cell initiation
Sfc Pattern: High P

Severe: Yes


Student: Match location to
highest MOS POP axis &
compare with gridded/zone
Lightning Data
STP for Coverage
Severe versus Non-Severe
Develop a Lightning Climatology
Event Days, SW Flow
Event Days, NW Flow
Can break down hourly to show diurnal evolution…
Can assist in verification and determining coverage/impacts…
What’s the pattern in time?
Event Days, SW Flow
Event Days, NW Flow
What’s the Coverage of Convective Activity?
(short term forecasting: 0-6h & 6-12h)
 Storm Total Precipitation (STP)
Consider the first (12-18z) and second
(18-00z) halves of the day
 See progression/development of cells
after initiation locations
 Mapped values from website products
 0.1 inch signifies “likely” precipitation
related to day’s convection
 1.0+ inches suggest thunderstorm with
heavy rainfall and intensity/severity
 Composites of Coverage/Intensity
 Suggests greater risk regions
 Amounts and possible severe storms

What about probability/location of Severity?
48.5 % Severe
Half E-COLD create Severe Weather
25.6% Severe
One-fourth C-COLD create severe weather
Diagnosing Events: Non-Severe vs. Severe
Non-Severe
Severe
Omega at 700mb for Cold
Front EVENT days: 00-09
Non-Severe
Omega at 700mb for Cold Front
CONTAMINATE days: 00-09
Severe
GIS tie-in to Models & NDFD: Explaining Convection
 Use high resolution GIS-based grid with 1-km grid of study
region with details of the forecast region and locations
 Relate specific physiographic features in the area to the preferred
locations of convective initiation and its severity

GIS grid calculations focus on land use and land cover, elevation,
distance to coast, and slope and can be related to model output

Risk assessment and management; warning specificity & public
information statements; visualizations in time and space

Automation and animation for response planning/preparation
GIS Assisted
Convective Forecasting
If we know the
characteristics of the Grid
Box (Elevation, Land
Cover, Population, etc.)
If we know
the synoptic
regime &
500mb Flow
(SW CF, etc.)
Combine this information with CDC
composite variable or parameter
values (PWAT, Omega, etc.)
GIS Assisted Prediction of
Convective Initiation
characteristics, impacts, & risks
Summary & Conclusions
 Comprehensive Prediction of Convective Initiation
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


haveWhere,
the ability
to improve…
We know: Who, We
What,
How,
When, & Why of initiation
We can: Distinguish CoverageSkill
and Intensity/Severity
Confidence
Now Provide: Operational Products
Precision with Online Archive
Now Identify: Operational Conceptual
Model & Cause/Effect
in
Convective Forecasting
 Next: Refine, Enhance, Automate (GIS-based radar data)
 Future steps: GIS-grid assisted forecasting
 Future purposes: Risk assessment and management
Acknowledgements
Thanks to the Kean University Department of Geology & Meteorology Faculty & Staff, Student Majors, and Adam
Gonsiewski, undergraduate student of Millersville University for their assistance with this project.
This presentation was prepared by Kean University and the National Weather Service under a sub-award
with the University Corporation for Atmospheric Research (UCAR) under Cooperative Agreement with
the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce (DOC).