Panel 4 Presentations – Enhanced Technologies for Weather Emergencies

ITS-NY 2012 SPRING FORUM April 12, 2012

Managing Weather-Related Events with ITS Technologies

PANEL 4 PRESENTATIONS “Enhanced Technologies for Weather Emergencies”

Panel Moderator: “The Realities of Disasters: What New “Integrating Weather and Dr. Camille Kamga, UTRC Decision Support Systems Must Transportation Information,” Consider,” Prof. Reza Khanbilvardi, CCNY Dr. Jose Holguin-Veras, Rensselaer Polytechnic Institute

“Precision Weather Modeling, Analytics and “Application of High Resolution Weather Visualization for Emergency Management,” Modeling and Damage Prediction at Con Anthony Praino, IBM Edison Emergency Management,” Carlos D. Torres, Con Edison

Photos by Matt Ficarra, ITS-NY Board Member and Photographer Extraordinaire 1

The Realities of Disasters: What New Decision Support Systems Must Consider

José Holguín-Veras, William H. Hart Professor, Director of the Center for Infrastructure, Transportation, and the Environment Acknowledgments  Other contributors:  Miguel Jaller, Noel Pérez, Lisa Destro, Tricia Wachtendorf  Research was supported by NSF:  NSF-RAPID CMMI-1034635 “Investigation on the Comparative Performance of Alternative Humanitarian Logistic Structures”  CMMI-0624083 “DRU: Contending with Materiel Convergence: Optimal Control, Coordination, and Delivery of Critical Supplies to the Site of Extreme Events”  CMS-SGER 0554949 “Characterization of the Supply Chains in the Aftermath of an Extreme Event: The Gulf Coast Experience”  "RAPID: Field Investigation on Post-Disaster Humanitarian Logistic Practices under Cascading Disasters and a Persistent Threat: The Tohoku Earthquake Disasters"

2 Humanitarian Logistics Research Group The group has pioneered the multidisciplinary study of post-disaster humanitarian logistics: Identified lessons learned from the largest disasters of recent times; Translated these lessons into policy recommendations; Shared these suggestions with disaster response agencies; Developed new paradigms of humanitarian logistic models that account for material convergence, deprivation costs and other unique features of post-disaster operations; Conducted detailed analyses of: Hurricane Katrina, the Port-au-Prince earthquake, the tornadoes in Joplin and Alabama, Hurricane Irene, the Tohoku disasters in Japan, among others.

3 Competing paradigms Relatively new field, difficult to characterize Lacks an accepted cannon, very different than normal, tough places to visit, isolated operations, few and far between (disasters/catastrophes), small professional community, lack of published accounts, transient and dynamic operations Complex behaviors Wide range of operations: Regular HL: Distribution of aid Post-Disaster HL: Relief distribution after a disaster Wide range of extreme events: Disasters Catastrophes  A qualitatively different response Differential scalability in response functions? 4 Multidimensionally Complex Phenomena

Technical Logisticians A really complex Engineers socio-technical process, with internal dynamics that cannot Post- be disentangled along Disaster disciplinary lines HL

Social Regular HL

Disaster Catastrophe

Scientists 5 Social Three essential components

A social network of individuals orchestrate the logistical operations A set technical activities are performed by the social network These social and technical activities are performed over a set of supporting systems (e.g., transportation, communication)

Weight

6 Our main focus (emphasis Two different environments on catastrophes) Wide Spectrum of Operations

Characte- Regular Humanitarian Post-Disaster Commercial Logistics ristic Logistics Humanitarian Logistics

Objective Minimization of private Minimization of social Minimization of social pursued (logistic) costs costs (logistic+deprivation) costs (logistic+deprivation)

Origination of Impacted by material Self-contained Mostly self-contained cargo flows convergence

Knowledge of Known with some Unknown/dynamic, lack of Uncertain demand certainty information/access to site

Decision ma- Structured interactions Structured interactions Non-structured interac- king structure controlled by few DMs controlled by few DMs tions, thousands of DMs

Periodicity / Repetitive, relative steady Repetitive, relative steady One in a lifetime events, volume flows, "large" volumes flows, "large" volumes large pulse

Supporting Stable, though not always Impacted and dynamically Stable and functional 7systems functional changing Disaster: Joplin, Missouri (50,000 residents)

8 Disaster: Joplin, Missouri (160 deaths)

9 Catastrophe: Minami Sanriku (19,170 residents)

10 Catastrophe: Minami Sanriku (10,000 missing)

ITS-Related Decision Support Systems

12 ITS that could help Global Positioning Systems: ITS Japan shared GPS tracks to allow agencies to infer which roads were passable Agreements with companies to get GPS data Robust communication systems Very tricky In 1995 Kobe earthquake only cell phones worked well; in 2001 WTC only Blackberry worked well; in 2012 Tohoku disasters, only 4G Ipads worked well Systems to support coordination Emerging coordination Use of the social media to improve coordination

13 Analysis capabilities Knowledge of demand /supply: Development of models to estimate immediate resource requirements (agent- and response-generated demands) Use of remote sensing to assess state of transportation networks, and locations of groups of survivors Systems to influence behavior: Panic/precautionary buying, control strategies Response to persistent threats Emergent social practices and response structures

14 Theory, Modeling Appropriate decision support tools: Routing models, Inventory allocation models based on DCs Dynamic control models to allocate resources to manage and physically control material convergence Planning of Points of Distribution Use of quasi-real time gathering of information about emerging donation drives to both quantify donation flows, and modify donation behavior Reverse logistics Supply pre-positioning accounting for DCs

15 Thanks

16 Monitoring Weather Impacts on Transportation

Dr. Reza Khanbilvardi NOAA-CREST / City University of New York; [email protected]

ITS-NY 2012 SPRING FORUM Hudson Valley Transportation Management Center, Hawthorne, New York Managing Weather-Related Events with ITS Technologies April 12, 2012 OUTLINE:

 How Do Weather Events Impact Roads?

 Weather Phenomena and Impacts

 Rain Impacts – Flood and Flash Flood

 How Remote Sensing Helps

 Data Acquisition Sources

 NOAA-CREST Case Study: The flood event in Iowa How Do Weather Impact Roads? Weather acts through visibility impairments, precipitation, high winds, and temperature extremes to affect driver capabilities, vehicle performance (i.e., traction, stability and maneuverability), pavement friction, roadway infrastructure, crash risk, traffic flow, and agency productivity (www.dot.gov).

Three different categories of weather impacts: 1. Roadways (Visibility distance , friction) 2. Traffic flow (Traffic speed , Accident risk) 3. Operational decisions (Traffic signal timing, Road treatment strategy) Weather Phenomena & Impacts: Major weather phenomena have certain negative impacts such as lane obstruction, ….

Road Weather Traffic Flow Roadway Impacts Operational Impacts Variables Impacts

Air temperature and Road treatment strategy N/A N/A humidity. (e.g., snow and ice control)

Lane obstruction (due Evacuation decision Wind speed to wind-blown snow, Accident risk support debris)

Fog Visibility distance Traffic speed Driver capabilities/behavior

Pavement temperature Infrastructure damage N/A Road treatment strategy

Pavement condition Pavement friction Traffic speed Traffic signal timing Evacuation decision Water level Lane submersion Travel time delay support

Precipitation Pavement friction Traffic speed Road treatment strategy (type, rate, start/end times) Visibility distance Accident risk Evacuation decision support

source: www.dot.gov Rainfall & Road Safety:

In term of weather and road safety most weather-related crashes happen on wet pavement and during rainfall:  75% on wet pavement.  47% during rainfall.  15% during snow or sleet.

Weather-Related Crash Annual Rates (Approximately) Statistics

707,000 crashes 11% of vehicle crashes 47% of weather-related crashes

52% of weather-related crash 330,200 persons injured 11% of crash injuries Rain injuries

46% of weather-related crash 3,300 persons killed 8% of crash fatalities fatalities

source: www.dot.gov Rainfall Impacts (Flood & Flash Flood)  A flash flood is a rapid rise of water along a stream or low-lying (e.g. urban area) .  Flash flood damage and most fatalities tend to occur in areas immediately adjacent to a stream or arroyo, due to a combination of heavy rain, rapid snowmelt.  Heavy rain falling on steep terrain can weaken soil and cause debris flow, damaging roads, and property.  Flash floods can be produced when slow moving or multiple thunderstorms occur over the same area. Flash Flood Risk in Roads: • Almost half of all flash flood fatalities occur in vehicles. • Water can erode the road bed, creating unsafe driving conditions. • Underpasses can fill rapidly with water, while the adjacent roadway remains clear. Courtesy: www.noaa.gov

Data Acquisition Sources:

 In Situ Data Collection (usually direct Observation)

 Remote Sensing Data Collection: - Aerial Photography - Satellite imagery & Observations - Radar (NEXTAD) Measurements

 Model Output Products Multi-Source Data Acquisition

TRMM NASA NOAA-M MW, IR EOS TMI, PR, VIRS VIS, NIR, IR POES-M MODIS VIS, NIR, IR SOUNDING ASTER, CERES

AMSU- Precipitable Water Wind Speed MW + Direction

Topography (Elevation & Slope)

Radar Surface Temperature Soil Moisture Gauge Vegetation Quantitative Precipitation Estimation and Forecasting (QPE, QPF):

QPE and QPF Meta data How get data products/prediction

Gauge data NWS COOP (~ 32000 stations) http://www.ncdc.noaa.gov

 NOAA-CMOPRH (8 km, 30 min http://cpc.ncep.noaa.gov resolution) Satellite  Precipitation estimation and nowcasting http://crest.ccny.cuny.edu/ (NOAA-CREST) data-and-products/  NCEP Stage II (hourly , 4-km)  NMQ, National Mosaic QPE (5 min, 250 http://emc.ncep.noaa.gov Radar m) Warning Decision Support System http://nmq.ou.edu (WDSS) to detect storm WRF-High Resolution Rapid Refresh NWP http://ruc.noaa.gov/hrrr/ (HRRR) (1 hour, 3 km)

Merge Rain gauge +Radar (NMQ)(hourly, 1km) http://nmq.ou.edu

Weather Related Data: Availability and Sources of weather related data information:

Weather Variables Observations Estimations/Predictions

Gauge/Radar-based Precipitation observations; Radar- and satellite- based (Rainfall/Snowfall) algorithms; NWP Satellite-based cloud properties

Flood Ground-based discharge Hydrological Models (Discharge) observations

Humidity/Fog Weather stations & satellite Atmospheric/mesoscale (Visibility) instruments Numerical models

Weather stations, Radar, & Atmospheric/mesoscale NWP Wind satellites sounding models

Pavement/Surface Ground Weather Stations & Satellite-based/NWP Models temperature Satellite Infrared info Ground-based Weather Atmospheric/mesoscale NWP Air Temperature Stations; models Satellite sounding Global Reliable Rain Gauge Map

(Weather Stations) Climatic National Data Center

(NCDC)

Rain Gauge Network Gauge Rain

Characteristics of Ground-based Data Station-based Data Products • Needs Field work • Rainfall (rain-gauges), Storm type • Direct or indirect Information • Snowfall & Snow Water Equivalent • Point-based Observations (SNOTEL & Snow Pillow) • Spatial (over land) and time limitation • Water discharge/Run off/Flood • Ground Truth/Reference Information • Temperature (min, max, mean) • Noises & Missing data • Humidity/Visibility & Air Pressure Airborne Remote Sensing Airborne Photography In airborne remote sensing, downward or sideward looking sensors are mounted on an aircraft to obtain images of the earth's surface. An advantage of airborne remote sensing, compared to satellite remote sensing, is the capability of offering very high spatial resolution images (20 cm or less). Y Z GPS Properties • Very High Spatial Resolution Data X X of Airborne • Creating 3-D model of the targets LASER Y Data • Aerial-based (continuous) Data Z • Digital Elevation/Terrain Model (DEM/DTM), using laser & GPS h Airborne • Snowpack depth/SWE (Gama sensor) GPS

Data • Slopes/Paths of Roads, Streams/Rivers R A • Products Water standing/Flood Z • Detecting Traffic, Accidents, … Y • Many more products & Applications OBJECT X How Remote Sensing Helps:  Remote sensors observations are used for weather monitoring and forecasting from local to global scales.  Remote sensing is used for quantitatively measuring atmospheric temperature and wind patterns, monitoring advancing fronts and storms (e.g., hurricanes, blizzards), imaging of water (i.e., oceans, lakes, rivers, soil moisture, vapor in the air, clouds, snow cover), estimating runoff and flood potential from thawing.  Remote sensing has shown a Promising performance to generate flood inundation map.

USGS Inundation map for flooding of June, 2008 for the White River at Spencer, Indiana Remote Sensing Applications Importance & Applications of Remote Sensing: • Improve our ability to inventor and manage the earth’s resources; • Quickly provide information about the environment; • Monitor changes in environment; • Natural disaster assessment that can become faster and better without extensive labor; • Meteorological improvements (e.g., nowcasting and forecasting rainfall/ snowfall, and Flood warning) (Weather sensors require Higher time resolution); • Oceanographic monitoring (Ocean viewing sensors require greater sensitivity because of much lower signal); • Land-change studies (need higher spatial resolution); • Agricultural related managements (e.g., Crop yield and watering schedules); • Geologic studies; • Climate change studies (e.g., predicting wet/dry seasons/years). Satellite-based Products & Propertied Properties of Satellite-based Observations: • Indirect data Collection • Areal/Continuous data Collection • Large Special Coverage • No Spatial Limitations • Apace Platforms

Satellite-based Products: •Cloud properties (top temperature, …) •Cloud mask (convertible to precipitation) •Storm detection •Temperature (skin, SST, ..) •Precipitation (rainfall/snowfall) estimates/forecasts/nowcasts •Wind velocity & direction •Flooding •Humidity Satellite-based Observing Technology Remote Sensing is defined as the science and art of obtaining information about the properties of natural targets by a sensor/instrument that is not in direct physical contact with the investigating target. Orbits of Satellites: Near Polar Orbits A near polar orbit is one with the orbital plane inclined at a small angle with respect to the earth's rotation axis, and is able to cover nearly the whole earth surface in a repeat cycle. Geostationary Orbit: The Geostationary satellite appears stationary with respect to the Earth's surface. Geostationary orbits enable a satellite to always view the

same area on the earth.

850 km, km, 850

–

Satellites

Satellites Satellites

& 15 15 & min)

twice / twice day)

Geostationary

(H ≈ 36,000 ≈ (H km,

(H ≈ 700 700 ≈ (H Low/Polar Orbiting Low/Polar Sensing Electromagnetic Spectrum Sensors/Instruments: •Active Sensors: generate and transmit a signal toward the target & receive a turned signal after its interaction with the target (e.g., Radar, Lidar) •Passive Sensors: receive a natural/sun’s signal from the target which is reflected or emitted by the target (e.g., Camera, Radiometer, eye)

Visible (VIS) Near Infrared (NIR) Thermal Infrared (IR) Microwave (MW)

VIS = 0.4 m – 0.7 m NIR = 0.7 m – 1.3 m SWIR = 1.3 m – 5 m Thermal IR = 5 m – 16 m MW = 1mm – 1 m Radar (NEXRAD) Observing

Scanning: System & Blockage Every single volume scan, radar beam scans 360° horizontally at several elevation angles, takes a few minutes (5 to 10 minutes) to complete the sequence of elevation angles.

Blockage: Underestimated Precipitation

Blockage a Radar Coverage over the U.S. (Radar Characteristics and Products) Properties of Radar-based Data: • High temporal resolution (5 min) Frequency of Rainfall, for 1998 – 2000

• Indirect data Collection • Areal/Continuous data Collection • Spatial coverage limitation (blockage & only land coverage) • Noises Ground-based Radar Products: • Storm Detection & Tracking, • Precipitation (rainfall/snowfall) estimation (NCEP NEXRAD Stages II, III, and IV; NSSL-Q2; NMQ (National Mosaic QPE) • Precipitation Nowcasting & Warning Decision Support System (WDSS) • Wind velocity & directions, • 3-D model of storm structure Radar-based Observed Layers of Storm Structure Radar-based 3-D Storm Structure

2 Storms September 25, 1997 at 00:45 (UTC) Hurricane Nora, over the S.W. U.S., Latitude (Degrees, N) Latitude (Degrees, N) 23 24 25 26 27 28 29 30 31 32 33 N 23 24 25 26 27 28 29 30 31 32 33 N

117 117 116 115 114 113 112 111 110W 117 117 116 115 114 113 112 111 110W NEXRAD NEXRAD Image GOES IR Image IR GOES

Longitude (Degrees, Longitude W) Radar Radar vs. Satellite Imagery

200 220 240 260 280 300 No-Data 0 15 30 45 60

Brightness Temperature (K) Radar Rainfall (mm/h)

Hurricane Hurricane Katrina, Louisiana, August August 29, 2005, at 09:00 UTC Latitude (Degrees, N) Latitude (Degrees, N) 25 27 29 31 23 35 25 27 29 31 23 35 Satellite Rainfall Satellite

NEXRAD NEXRAD Image Longitude (Degrees, Longitude W)

- - 92 92 92 92 - - 90 90 90 90 - - 88 88 88 88 - - 86W 86W

NoData NoData 80 80 80 20 40 60 20 40 60 0 0

Satellite Rainfall (mm/hr) Radar Rainfall (mm/hr) ended Rio Missing

Grande

12 Satellite Rainfall Satellite NEXRAD NEXRAD Image

radar 00

UTC, River

coverage

Basin, / 24 / 2000

over

for

24 the h

CREST Weather Station

Ground-Based Remote Sensing Instrumentation @ The City College of New York CREST Instruments/Observations & Products CREST Instruments: • Weather stations (at top of NAC building), Lehman College, City Tech. in Brooklyn, • Satellite Receiver Station, on the roof of NAC building, since 12/18/2007, • Lidar generator/transmitter (station & mobile) • Ground-based station using remote sensing sensor for “Soil moisture” and “Snowpack depth” measurements; and snow depth;

CREST Weather Station Second Second stage:

Satellite Receiver Station Receiver Satellite (Data Acquisition Unit) Acquisition (Data

NAC building roof

CREST Observations & Products CREST Products: • CREST SRS observations: - MODIS products, including: cloud mask and cloud properties, aerosol concentration and optical properties, vegetation and land surface cover, surface temperature over oceans and land, ocean color & concentration of chlorophyll-a).

- GEOS imagery data & products including: images of the Earth’s surface and cloud cover derived from radiation samples of the Earth and Atmosphere • Weather instrument observations including (temperature, relative humidity, rainfall, pressure, …) • Emissivity; • Radar-satellite merged rainfall products for radar gap areas; • Cloud nowcasts, by running RDT (Rapid Developed Thunderstorm) algorithm operationally. CREST Related Projects CREST Projects: • Ensuring Nationwide Accessibility to Hydrometeorological Data - Flood monitoring during the 2008 Iowa flood using AMSR-E data • Cloud Nowcasting using RDT algorithm; • Multi-source rainfall product to generate rainfall for for radar gap areas; CREST Case Study: The Flood Event in Iowa Several regions particularly in Iowa were affected by a 500 year flood which was classified as the worst in the history of the region. Major damages and large inundated areas related to these floods have been recorded. Applying Extrapolation

Extrapolation is based on RDT cloud lifecycles study.

Red: previous Yellow: current Green: extrapolated The RDT model in New York Data from direct broadcast, 15 min refresh rate

http://air.ccny.cuny.edu/ Conclusions… • Rainfall and consequences such as flood and flash flood are very important for national safety. • Improvement on weather forecasting will enhance transportation (roads, traffic, and driving conditions) • Remote sensing significantly can improve monitoring weather-related phenomena. • Flood inundation map helps to closely look at flooded regions. • High temporal resolution radar and/or GOES products are useful for short term precipitation prediction/nowcasting & warning • Radar precipitation can initiate storm that satellite data cannot, • Satellite observations (AMSR-E, MODIS,…) are strong tool for flood monitoring. • PRVI detects anomalies in soil moisture and can therefore be used in flood and discharge monitoring. 1

Precision Weather Modelling, Analytics and Visualization for Emergency Management

Anthony P. Praino, Lloyd A. Treinish, James P. Cipriani IBM Thomas J. Watson Research Center Yorktown Heights, NY

Precision Weather Modelling, Analytics and Visualization for Emergency Management

. Problem: weather-sensitive business operations are often reactive to short-term (few hours to a few days), local conditions (city, county, state) due to unavailability of appropriate predicted data at this scale – Energy, transportation, agriculture, insurance, broadcasting, sports, entertainment, tourism, construction, communications, emergency planning and disaster warnings . Solution: application of reliable, affordable, weather and impact models for predictive and proactive decision making and operational planning – Numerical weather forecasts coupled to business processes models – Products and operations customized to business problems – Competitive advantage -- efficiency, safety, security and economic & societal benefit

Road Weather Applications

. More precise predictions of the location and timing of severe weather (e.g., thunderstorms, strong winds, heavy snow and rain, freezing temperatures, fog) could help recover the multi-billion dollar annual cost of weather- related delays on and damage to roads in the U.S., by enabling the following: – Transportation officials could initiate recovery plans for both operations and traffic management before weather-induced disruptions actually occur – The public, commercial transportation companies, schools and emergency services could better plan for how and when they would travel – Highway supervisors could more efficiently schedule, staff and equip for deicing and snow removal operations during the winter

Match the Scale of the Weather Model to Application Requirements

2km

Central Park Weather Station

.Capture the geographic characteristics that affect weather (horizontally, vertically, temporally) .Ensure that the weather forecasts address the features that matter to the business

Short-Term Weather Event Prediction and Observation

Forecasting (Modelling) Nowcasting (Sensors)

NWS / Commercial Deep Thunder Remote In Situ Providers Forecast for longer- Forecast for asset- Fine-tune Near-real time revision term planning where based decisions to approach decisions require days manage weather event, based upon of lead time, but may pre-stage resources extrapolation not have direct coupling and labor proactively from Doppler to business processes radar and satellite observations

Continental to Local Scale Local Scale Global Scale 72-168 18-72 3 0 Time Horizon for a Local Weather Event (Hours of Lead Time)

Approach .It is not about weather but integrating forecasts into decision making to optimize business processes .“You don't get points for predicting rain. You get points for building arks.” (Former IBM CEO, Lou Gerstner) .For example, the operation of an electric or water utility or a city government can be highly sensitive to local weather conditions .What is the potential to enable proactive allocation and deployment of resources (people and equipment) to mitigate damage, and minimize time for restoration? –Ability to predict specific events or combination of weather conditions and their impact that can disrupt infrastructure –Rather than monitor a storm, stage resources at the right place and time prior to the event to minimize the impact (i.e., plan not react) –Sufficient spatial and temporal precision, and lead time to reduce the uncertainty in decision making –Integration with end user business applications (i.e., analytics and visualization) –Delivery as a service tailored for the geographic, throughput and dissemination requirements of the client

© Copyright IBM Corporation 2012 7 29 October 2011 “Surprise” . Classic nor'easter leading to heavy snow in the north eastern US, except for the date, which led to significant new records for snow totals . Snow was widespread, wet and heavy, with totals over 2 feet in some areas, damaging millions of trees . Wind gusts up to 50-60 mph were recorded . Electric utility and transportation systems were widely disrupted (over 2 million homes lost power) Reported Snowfall

© Copyright IBM Corporation 2012 8 Deep Thunder Prediction of 29 October “Surprise” Snow . Good agreement in snow totals, geographic distribution, and start and stop times . Initiated with data from 0800 EDT on 10/28 with results available 18 hours before snow began

28 August 2011: Hurricane Irene New York City Metro Area .Sustained winds 40 to 52 mph with gusting 60 to 90 mph and heavy rains (over 10” in some areas) .Innumerable downed trees and power lines, and local flooding and evacuations .Electricity service lost to about 1M residences and businesses (half of CT) .Widespread disruption of transportation systems (e.g., road and bridge closures, airport and rail delays) .Others forecasted storm as Category 1 or 2 but actually tropical storm at landfall .Hence, expectation of much greater impacts of wind, and far less impact from heavy rainfall

© Copyright IBM Corporation 2012 10 Deep Thunder New York Forecast for Tropical Storm Irene . Fourth of six operational forecasts covering the event confirming the earlier forecast of tropical storm not hurricane strength at landfall and showing the track to the north . Heavy rainfall predicted with similar distribution to reported rainfall

Visualization of Clouds, Wind and Precipitation, including Rain Bands

Deep Thunder New York Forecast for Tropical Storm Irene: Afternoon of 27 August 2011

.Initiated with data from 0800 EDT on 8/27 with results available in the late afternoon .Shows rainfall beginning in parts of New York City in the evening on 8/27 and ending the afternoon of 8/28 .Sustained winds in parts of New York City well below hurricane strength

Deep Thunder Wind Forecast for Tropical Storm Irene: Afternoon of 27 August 2011 Maximum Sustained Wind Maximum Daily Gust

Actual Number of Repair Jobs per Substation Area (Total = 1953)

Likelihood (Probability) of a Range of Repair Jobs per Substation

(Right) High Severity (> 100 Jobs)

(Left) Moderate Severity (51 to 100 Jobs)

Example Event and Forecast: New York City Severe Thunderstorms – 07 August 2007 .On August 8, 2007, New York City area became an epicenter of a Mesoscale Convective System (MCS) with rainfall exceeding three inches in less than two hours in some areas .The subway system was partially closed due to flooding, streets were impassable, about 2.3 million people and numerous businesses were affected .Available operational forecasts did not predict this event, as a result area agencies and businesses were unprepared .Rainfall started just before 0600 EDT and lasted about two hours .Total rainfall ranged from 1.4 to 4.2 Snapshot from NexRad KOKX at 6:30 AM EDT on August 8, 2007 inches

© Copyright IBM Corporation 2012 Flooding Estimate15 for August 8, 2007 .Intense localized cells and flash flooding in Queens (and Brooklyn) .Rainfall estimates from Deep Thunder forecast initialized at 2000 EDT on 7 Aug 2007 was used in a GIS-based hydrology model to examine flooding patterns and impact on urban infrastructure

Hillside Ave Flooding. “August 8, 2007: Storm Report.” Metropolitan Transportation Authority, 9/20/2007, page 23.

IBM Deep Thunder and the Integrated Command Center in Rio de Janeiro Mitigating the impact of severe weather events is the top priority for the client to enable effective planning and response to emergencies

. 48-hour forecast updated every 12 hours at 1 km resolution with the physics for the urban environment, sub-tropical micro-meteorology and complex topography . Disseminated via a web portal at the client site through specialized visualizations Three-dimensional forecasted clouds with terrain surface and . Coupled flooding model (see below) precipitation overlaid with arrows for wind speed & direction (above) and estimated surface runoff from heavy rainfall (below)

Summary . High-resolution physical weather modelling can provide significant value in predicting environmental impacts at a local as well as regional scales . A key aspect is the customization of the models for specific applications coupled with the decision making . Visualization is critical for decision making by people and the workflow required . Integration with other models as well as existing infrastructure enables actionable, proactive behavior . Positive stakeholder as well as economic and societal benefits can be realized in the application of the end-user-focused methodology . Future work will focus on coupling and integrating models for specific applications and enabling broader solutions within an “Integrated Operations Center”

Alerts from Deep Thunder within the Intelligent Operations Center

Backup

Slides

© Copyright IBM Corporation 2012 20 What is Weather Modelling? .A mathematical model that describes the physics of the atmosphere –The sun adds energy, gases rise from the surface, convection causes winds .Numerical weather prediction is done by solving the equations of these models on a 4- dimensional grid (e.g., .Solution yields predictions of surface latitude, longitude, and upper air altitude, time) –Temperature, humidity, moisture .Complementary to –Wind speed and direction observations (e.g., –Cloud cover and visibility NWS weather stations) –Precipitation type and intensity

Approach to Urban Flood Forecasting

Precipitation Estimates

Weather Prediction and/or Analysis of Precipitation Rainfall Measurements Flood Prediction

Refine Sensor Network Actual Flood Impacts and Model Calibration

Model Calibration Impact Estimates

© Copyright IBM Corporation 2012 IBM Intelligent Operations Center (IOC) for Transportation, Cities, Utilities, etc. Integrating the most repeatable best practice patterns: Leveraging information: . Citywide visibility across entire networks (utilities, transportation, water) and city services to improve incident response . Create insights from data to build a safer, more efficient and more accountable place to live and conduct business . Gain real-time and system wide visibility of traffic and transit networks . Create awareness of significant events and problem areas Anticipating problems: .EnvironmentalAnalyze traffic performance to alleviate congestion . Identify patterns and anticipate incidents impacting traffic congestion and transit schedules enabling improvement strategies . Increase efficiency and deliver situational awareness to first responders using predictive analytics . Uncover hidden connections faster, deliver timely and actionable results to protect citizens Coordinating resources: . Centralize monitoring and transit arrival prediction to improve the travelers’ experience . With traffic prediction and pro-active traffic management, reduce citizen aggravation and negative commercial impact . Ensure consistent service & better informed commuters with vehicle arrival prediction 22 Application of High Resolution Weather Modeling and Damage Prediction at Con Edison

Hudson Valley Transportation Management Center

Carlos D. Torres Consolidated Edison of New York, Inc. Emergency Management April 12, 2012

ON IT Agenda

• Overview • Weather Model • Impact Model • Applications • Challenges • Future Work

ON IT Overview Con Edison Service Territory

Con Edison Co. of New York

• 3.2 million electric customers • 1.0 million gas customers • 1,800 steam customers • 709 MW of regulated generation

Orange and Rockland • 300,000 electric customers • 130,000 gas customers

3 3 ON IT Con Edison Co. of New York Electric System

• 604 square miles • 3.3 million customers = 9.1 million people • 2.4 million customers are in Networks • System is 87% underground and 13% overhead • 62 area substations – 2,270 primary feeders • 95,600 miles underground cable • 36,800 miles of overhead cable • 209,300 poles • 49,800 overhead transformers 39,000 underground transformers • 4 4

ON IT Orange & Rockland Electric System

• 1,350 square miles • 300,000 customers = 750,000 people • No customers are in Networks • System is 31% underground and 69% overhead • 80 substations – 251 primary feeders • 1,700 miles underground cable • 4,295 miles of overhead cable • 210,000 poles • 73,000 overhead transformers

5 5 ON IT Con Edison Co. of New York Gas System

• 1.1 million customers = 4 million people • 135 miles transmission piping • 4,330 miles of distribution piping • 293 million dekatherms annual throughput

6 6 ON IT Orange & Rockland Gas System

• 130,000 customers = 350,000 people • 1,800 miles of gas pipeline • 80 miles transmission piping • 1720 miles distribution piping • 26 million dekatherms annual throughput 7 7 ON IT Con Edison Co. of New York Steam System

• 1,800 customers • 105 miles steam pipe 74th Street • 27 billion pounds of steam heat Ravenswood - 59th Street - hot water

- air conditioning 60th Street

- sterilization East River Station - cooking

- humidification Manhattan

Brooklyn B.N.Y.C.P. Plant Queens Hudson Avenue

8 8 ON IT Overview

• Partnered with IBM in 2006 on Deep Thunder (DT) project • Response to audit for poor storm performance • Targeted weather information – Specific to Con Edison – Utilize high resolution weather model – Investigate link between weather and impact – Improve preparation and response

ON IT Weather Model

• 2km resolution forecast • 24/84hr forecast – – 2x daily (8am,8pm EDT) • Temperature (dry/wet bulb), wind speed and precipitation • Content available via web browser  Java script movies  Data tables  Charts 18 km 6 km 2 km • Email alert system Deep Thunder Domain

ON IT Weather Model

Synoptic scale models cover the entire High resolution models are northern hemisphere location specific NYC(all our forecast services use these) • Best resolution is 12km • T-Storms,NOAA sea12km breeze weather go undetectedmodel Deep Thunder 2 km weather model

ON IT Weather Model

• Daily maximum gust forecast – DT weather model outputs sustained wind speed only – Statistical forecast of gust speed – Input to impact model – Customization of data points

Gust Forecast 12

ON IT Impact Model

Impact Model Design

Sustained Deep Thunder wind data Gust Weather Model Calculation Forecast

Precip data

Impact Model # of Jobs

ON IT Impact Model

Damage Model Database Weather Factor Inputs  Historical job ticket data  Rainfall  Historical weather data  Wind gust  Soil Moisture (last 2 weeks rainfall)  Leaf coverage

ON IT Impact Model • Historical database contains 6 years of storm data Year Storms 2004 3 Type # of Type Rain 10 2005 6 2006 3 Wind 10 2007 4 Nor'easter 11 2008 15 Tropical/Remnants of 3 2009 25 Thunderstorm 22 Total 56 Notable Events

 2004 Remnants of Ivan  2008 Severe T-Storms, Tornado  2006 Remnants of Ernesto  2008 Tropical Storm Hanna  2007 Severe T-storms  2009 Severe T-Storms, Downburst 15

ON IT Applications – Weather Model Steam Distribution

• Intense rains can cause flooding of steam system – Inspect flood prone manholes – Respond to vapor complaints – Perform rain patrols • Mobilization – Internal crews – Contractor crews – Vapor inspectors

16 ON IT Applications – Impact Model Electric Operations

Westchester County • Uses DT weather model data as input • Output # of jobs per substation

• Probabilistic - Quantifies uncertainty

• Email alert system • Predictive & Nowcast “mode”

 Predictive = forecast data  Nowcast = real time observations

Deep Thunder Damage Model 17

ON IT Applications – Impact Model Electric Operations

Storm Weather Est. # Category Conditions Jobs

• Minor thunderstorms 1 • Brief gusty winds 30-110 Upgraded • Minor wet snow

• Severe thunderstorms 2 • Moderate winds 110-550 Serious • Low end Nor'easter • Moderate wet snow

• Large Nor'easter 3 • Tropical System 550+ Full Scale • Major wet snow

ON IT Challenges

• Data quality – Weather observations  Spatial distribution  Quality of data  Data outages – Outage data  Determining what is a “storm” outage

ON IT Challenges

• Forecast confidence – Visualization development – Verification techniques • Changing customer habits – Get operators to use it • Verification • Cost/Benefit

ON IT Future Work

• Create process for incorporating new events • Incorporate “black swan” storms • Enhance verification methodology • Wet snow and ice? • On-demand version with manual inputs

ON IT Questions?

ON IT