Transcript Precision Weather Modelling, Analytics and Visualization for Emergency Management Anthony P. Praino, Lloyd A.

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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
© Copyright IBM Corporation 2012
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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
© Copyright IBM Corporation 2012
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Weather Model Configuration for New York
• Three 2-way nests at 18, 6, 2 km
horizontal resolution
• 42 vertical levels
• 84 hour runs twice daily
• NOAA NAM for background and
lateral boundary conditions
• Post NWP electrical distribution
outage prediction model
2 km
6 km
18 km
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Match the Scale of the Weather Model to Application Requirements
2km
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
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Short-Term Weather Event Prediction and Observation
Nowcasting (Sensors)
Forecasting (Modelling)
NWS / Commercial
Providers
Deep Thunder
Forecast for longerterm planning where
decisions require days
of lead time, but may
not have direct coupling
to business processes
Forecast for assetbased decisions to
manage weather event,
pre-stage resources
and labor proactively
Continental to
Local Scale
Global Scale
72-168
18-72
Remote
Fine-tune
approach
based upon
extrapolation
from Doppler
radar and
satellite
observations
In Situ
Near-real time revision
Local Scale
3
0
Time Horizon for a Local Weather Event (Hours of Lead Time)
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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
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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 weatherrelated 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
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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
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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
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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
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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
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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
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Deep Thunder Wind Forecast for Tropical Storm Irene:
Afternoon of 27 August 2011
Maximum Sustained Wind
Maximum Daily Gust
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Tropical Storm Irene Deep Thunder Impact Forecast
Estimated Outages per
Substation (Repair Jobs)
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)
© Copyright IBM Corporation 2012
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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
inches
Snapshot from NexRad KOKX at 6:30 AM EDT on August 8, 2007
© Copyright IBM Corporation 2012
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Flooding Estimate
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.
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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
 Coupled flooding model (see below)
Three-dimensional forecasted clouds with terrain surface and
precipitation overlaid with arrows for wind speed & direction
(above) and estimated surface runoff from heavy rainfall (below)
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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”
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Alerts from Deep Thunder within the
Intelligent Operations Center
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Backup
Slides
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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 4dimensional grid (e.g.,
latitude, longitude,
altitude, time)
Complementary to
observations (e.g.,
NWS weather stations)
Solution yields predictions of surface
and upper air
–Temperature, humidity, moisture
–Wind speed and direction
–Cloud cover and visibility
–Precipitation type and intensity
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Approach to Urban Flood Forecasting
Precipitation
Estimates
Weather Prediction and/or
Rainfall Measurements
Actual Flood Impacts
Analysis of Precipitation
Flood
Prediction
Refine Sensor Network
and Model Calibration
Model
Calibration
Impact
Estimates
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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:
Environmental
Analyze 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
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