© Copyright IBM Corporation 2012 1 Precision Weather Modelling, Analytics and Visualization for...
-
Upload
gunnar-barns -
Category
Documents
-
view
217 -
download
0
Transcript of © Copyright IBM Corporation 2012 1 Precision Weather Modelling, Analytics and Visualization for...
© Copyright IBM Corporation 2012
1
Precision Weather Modelling,
Analytics and Visualization for
Emergency Management
Anthony P. Praino, Lloyd A. Treinish, James P. CiprianiIBM Thomas J. Watson Research Center
Yorktown Heights, NY
© Copyright IBM Corporation 2012
2
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
3
Weather Model Configuration for New York
2 km
6 km
18 km
• 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
© Copyright IBM Corporation 2012
4
Match the Scale of the Weather Model to Application Requirements
Capture the geographic characteristics that affect weather (horizontally, vertically, temporally)Ensure that the weather forecasts address the features that matter to the business
2km
2km
Central Park
Weather Station
© Copyright IBM Corporation 2012
5
Short-Term Weather Event Prediction and Observation
Nowcasting (Sensors)
Deep Thunder Remote
Near-real time revision Fine-tune approach based upon extrapolation from Doppler radar and satellite observations
Forecast for asset-based decisions to manage weather event, pre-stage resources and labor proactively
Forecasting (Modelling)
NWS / Commercial Providers
Forecast for longer-term planning where decisions require days of lead time, but may not have direct coupling to business processes
Time Horizon for a Local Weather Event (Hours of Lead Time)3 018-7272-168
Continental to Global Scale
Local Scale
In Situ
Local Scale
© Copyright IBM Corporation 2012
6
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
Approach
© Copyright IBM Corporation 2012
7
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
© Copyright IBM Corporation 2012
8
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
9
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
© Copyright IBM Corporation 2012
10
28 August 2011: Hurricane Irene New York City Metro AreaSustained 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
11
Deep Thunder New York Forecast for Tropical Storm Irene
Visualization of Clouds, Wind and Precipitation, including Rain Bands
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
© Copyright IBM Corporation 2012
12
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
© Copyright IBM Corporation 2012
13
Deep Thunder Wind Forecast for Tropical Storm Irene:Afternoon of 27 August 2011
Maximum Sustained Wind Maximum Daily Gust
13
© Copyright IBM Corporation 2012
14
Tropical Storm Irene Deep Thunder Impact ForecastEstimated Outages per Substation (Repair
Jobs)
Likelihood (Probability) of a Range of Repair Jobs per Substation
(Right) High Severity (> 100 Jobs)
(Left) Moderate Severity (51 to 100 Jobs)
Actual Number of Repair Jobs per Substation Area
(Total = 1953)
© Copyright IBM Corporation 2012
15
Example Event and Forecast: New York City Severe Thunderstorms – 07 August 2007On 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
16
Flooding Estimate for August 8, 2007Intense 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.
© Copyright IBM Corporation 2012
17
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)
© Copyright IBM Corporation 2012
18
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”
© Copyright IBM Corporation 2012
19
Alerts from Deep Thunder within the Intelligent Operations Center
© Copyright IBM Corporation 2012
20
Backup
Slides
© Copyright IBM Corporation 2012
21
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., 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
© Copyright IBM Corporation 2012
22
Approach to Urban Flood Forecasting
PrecipitationEstimates
Flood Prediction
ImpactEstimates
ModelCalibration
Refine Sensor Network and Model CalibrationActual Flood Impacts
Weather Prediction and/or Rainfall Measurements
Analysis of Precipitation
23
Environmental
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: 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
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: 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
IBM Intelligent Operations Center (IOC) for Transportation, Cities, Utilities, etc.