Power Delivery & MarketsDistribution Advisory CouncilFebruary 13, 2007

Storm Modeling

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Coastal Ocean Observation Lab http://marine.rutgers.edu/cool

Scott Glenn, Rich Dunk, Louis Bowers

Coastal Ocean Observation Lab

Coastal Observation and Prediction Sponsors:

Weather Forecast Benchmarking and Plant Damage Modeling for

Public Utilities

Public Service Electric & Gas http://www.pseg.com

John Carlson, Wayne WittmanPublic Service Electric & Gas

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Agenda

• Executive Summary – What Have We Done So Far?

• What is our weather forecast/storm management approach?

• Weather Forecast Modeling

• Future Plans and how EPRI can help.

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Executive Summary

• Cooperative research project between Rutgers University and Public Service Electric & Gas

• Operational meteorological forecasts and “alerts” since October 2004.

• Operation of high-resolution Weather Research Forecast (RU-WRF) model since 2005.

• Storm plant damage forecast model development 2005-2006.

• Overall severe storm forecast validation of 82.9% from October 2004 through December 2006 out of 602 events.

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Tasks

• RU-WRF model operation

• Operational weather forecast tailored to PSE&G delivered daily via protected website by 7 AM. Highly detailed forecast for days 1 and 2. General overall synopsis for forecast days 3 through 7.

• Issuance of Severe Weather “Alerts” during inclement weather conditions transmitted to PSE&G personnel via email and protected website.

• Experimental plant damage forecast transmitted to PSE&G personnel via email at the initiation of severe weather alerts.

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Rutgers Weather Forecast Process

• Forecasts issued by Senior Meteorologist and upper-level undergraduate students.

• Model has state-of-the-art physics and microphysics packages, high-resolution sea surface temperatures (SST), and local mesoscale meteorological observations.

• Forecasters use RU-WRF model in combination with various other forecast models, analyses, ocean products, etc. to formulate forecast.

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Student Teaching Teams

• Rutgers forecast teams consists of Masters Level Senior Meteorologist and 3-6 upper-level undergraduate students.

• Provides real-world forecast experience for college students.

• A total of 23 students have participated in the project since October 2004.

• Graduating students have gone on to careers in private sector, federal government, and broadcast meteorology.

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Modeling Introduction

• WRF: Weather Research Forecast– Run 3x daily– Duration out 24 to 120 hours

• Climatological “Model”– Statistical correction to WRF

model data using observations.

• Plant Damage Model– Prognostic equations to predict PSE&G

equipment loss during severe weather events. Equations developed using historical damage reports and archived meteorological observations. Input to model is RU-WRF forecast severe storm parameters.

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Alert Criteria

• Severe Thunderstorms Possible– Severe thunderstorms are expected within the first forecast period or are presently occurring.

• Heavy Rainfall Alert– Heavy rainfall amounts or rates are expected or occurring.

• Significant Wet Snowfall Accumulations– Greater than 3 inches of wet snow is expected to fall, and stick, especially on branches and power lines.

Significant Dry Snowfall Accumulations– Greater than 5 inches of dry snow is expected to fall.

Significant Icing Alert– Non-trivial accumulation of freezing rain is expected, or greater than 0.1 inches of sleet.

• Excessive Heat Alert– Air temperatures and/or heat indices exceed 90° F for a period greater than 3 hours, or if temperatures and/or heat

indices max at or above 95° F for any length of time. •

Excessive Cold Alert– Air temperatures go below 15° F for a period greater than 3 hours, or if temperatures go below 10° F for any length of

time. Also used when wind chills go below 0° F for any extended period of time. •

Excessive Wind Alert– Sustained winds exceed 25 MPH for a period greater than 3 hours, or if wind gusts above 30 MPH at any time.

• Frequent Lightning Strikes Possible– Frequent cloud-to-ground lightning is possible during the next 12 to 24 hour period.

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Verification

• Severe weather forecasts are verified using strict criteria determined crucial by PSE&G.

• Plant Damage forecasts are verified using actual plant damage data.

• Model performance used to adjust model in future upgrades.

Alert Justified Unjustified Total Accuracy

Southern 120 27 147 81.60%

Central 128 21 149 85.90%

Metropolitan 126 27 153 82.40%

Palisades 125 28 153 81.60%

Total 499 103 602 82.90%

Severe Weather Alert

Verification Statistics

Oct 2004-Dec 2006

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Damage

• Plant Damage prognostic equations developed from PSE&G storm damage database (2003-2006).

• Plant Damage data compared to available weather observations (13 locations in NJ) to develop equations.

• Plant Damage forecast transmitted via email to PSE&G personnel.

• Forecast verified and accuracy used to periodically update equations.

Date DIST_NO Pole Wire Tree Explosion Part Power

H_L Volt

01/01/03 Central 1 5 3 1 12 1

01/01/03 Palisades 2 6 1 1 10 1

01/01/03 Metropolitan 1 1 1 1 6 1

01/01/03 Southern 1 7 1 0 11 0

SOUTHERN DIVISION

ALERTS: Excessive Wind Alert

COVERAGE: WIDESPREAD

DAMAGE CALLS: 75

POLES: 7

WIRES: 23

TREE: 28

EXPLOSION: 0

PART POWER: 12

HIGH_LOW VOLTAGE: 5

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Case Study: High-Resolution Plant Damage Prediction

• High-resolution modeling of local-area specific forecast problems.

– Topographically enhanced wind flows, e.g. Watchung Mountains in NE NJ.

– Rain/Ice/Snow Line prediction using high-resolution SST.

– Worst case scenario Hurricane damage.

– Rainshadow effect and localized flooding.

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Case Study: Tropical Storm Ernesto September 1-3, 2006

RU-WRF provided the best real-time forecast of Tropical Storm Ernesto

after landfall.

Used by Researchers, by Regional, State & Local Managers, by Power

Companies, by Agriculture Extension.

The most significant difference with operational models was improved

physics.

This is a common storm track for the Mid-Atlantic States.

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Case Study: Tropical Storm Ernesto September 1-3, 2006

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Limitations

• There is no such thing as a perfect forecast.

• Plant Damage model effectiveness tied to availability of relevant data and model verification.

• Small-scale phenomena still not well simulated by weather forecast models, e.g. isolated summertime severe thunderstorms.

• Rare and extreme events may not be well forecast by plant damage model equations.

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Next Steps

• Benchmarking Study – Best Weather Practices

• Unified Prediction Program - Common Alert Criteria

• Development of Plant Damage Model

– Collect data• Plant Damage

• Weather Observations

– Develop Equations

– Test Model

• Final Goal: A best practices forecasting approach to weather management that can be applied to any electric utility.

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End