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

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

Classification of Convective Activity for 1200 – 0000 UTC

Event Contaminate Null

After15 UTC

Before15 UTC

No Activity

Southwest Warm Front

Event Contaminate Null

Research to Operations: Thunder Dome

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

500 flow Sfc

Synoptic Probabiliti

es Locations

CDC Diagnostics

Pattern of parameters

Causative Factors

http://hurri.kean.edu/~keancast/thunder/thunder.html

Building an Operational Conceptual Model

Determine 500 mb flow type (e.g., West flow cases)

500 mb flow- WEST

Day Type Event Contaminate Null

Sample Size 79 63 68

% Chance 38% 30% 32%

68% chance of initiationto occur with West flow

Forecasting with Operational Conceptual Model

Determine Surface Feature (e.g., Cold Front)

Surface Feature- COLD FRONT

Day Type Event Contaminate Null

Sample Size 85 78 25

% Chance 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 Event Contaminate Null

Sample Size 27 21 10

% Chance 47% 36% 17%

83% chance of initiation with W-CF combination

preferred region for initiation for event cold front and 500 mb flow

Contaminate cold front cases show no preference for initiation location

Use of Diagnostic Patterns/Thresholds…

PWAT Event PWAT Contaminate PWAT Null

32% Chance Event 64% Chance Contaminate 4% Chance Null

Operational Testing & Verification

Student: Match location to highest MOS POP axis & compare with gridded/zone

Number sequence of cell initiation

Outline areas of initiation; cells,

areas, or lines & where for

severeDate/Type: June 1, 2009/Event

Indicate whether forecast day of interest will be: E, C, N & if Severe

1200 UTC

500 mb flow: NW

Sfc Pattern: High P

Severe: Yes

Obs/Predict Event Contaminate Null

Event 7 3 0

Contaminate 2 7 0

Null 4 0 4

How successful?

Lightning Data STP for Coverage Severe versus Non-Severe

Time of Forecast Success Rate

Previous Afternoon 2010 88%

Early Morning 2010 79%

Early Morning 2009 85%

Early Morning 2009/2010 81%

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 Severe

Omega at 700mb for Cold Front CONTAMINATE days: 00-09

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 AssistedConvective 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 We know: Who, What, Where, How, When, & Why of initiation We can: Distinguish Coverage and Intensity/Severity Now Provide: Operational Products with Online Archive Now Identify: Operational Conceptual Model & Cause/Effect

Next: Refine, Enhance, Automate (GIS-based radar data)

Future steps: GIS-grid assisted forecastingFuture 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).