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Customization of a Mesoscale Numerical Weather Prediction System for Energy & Utility Applications Anthony P. Praino and Lloyd A. Treinish Deep Computing Institute IBM Thomas J. Watson Research Center Yorktown Heights, NY, USA {lloydt, apraino}@us.ibm.com - PowerPoint PPT Presentation
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Customization of a Mesoscale Numerical Weather Prediction System for Energy &
Utility Applications
Anthony P. Praino and Lloyd A. Treinish Deep Computing Institute
IBM Thomas J. Watson Research CenterYorktown Heights, NY, USA
{lloydt, apraino}@us.ibm.com
http://www.research.ibm.com/weather/DT.html
Customization of a Mesoscale Numerical Weather Prediction
System for Energy Industry ApplicationsBackground and motivation
Architecture and implementation
Customization for energy applications
–Energy Distribution
–Energy Generation
Discussion, conclusions and future work
Background and MotivationEstimated impact of weather on all types of energy & sanitary service across all geographic and temporal scales in the US is ~$230B/year–$ 0.1B to $1B per year for US energy industry related to poor temperature forecasts
Weather-sensitive utility operations are often reactive to short-term (3 to 36 hours), local conditions (city, county, state) due to unavailability of appropriate predicted data at this scale
Mesoscale (cloud-scale) NWP has shown "promise" for years as a potential enabler of proactive decision making for both economic and societal value
Background and Motivation
Despite the "promise" of cloud-scale NWP–Can models be coupled to weather sensitive business problems to demonstrate real value?
–Can a practical and usable system be implemented at reasonable cost?
Evaluate concept via implementations in several location around the country.
New York domain has the longest operational history–Operational end-to-end infrastructure and automation with focus on HPC, visualization and system integration
–Forecasts to 1 km resolution for metropolitan area with 3 to 21 hours lead time
–Prototype applications with actual end users
Model Forecast Domains
Triply nested telescoping grids
Modelling code derived from highly modified version of non-hydrostatic RAMS
Explicit, full cloud microphysics
Typically, one or two 24-hour runs per day
NAM-212/215 via NOAAport for lateral boundaries nudged every 3 hours
NAM-212/215 for initial conditions after isentropic analysis
Implementation and ArchitectureSufficiently fast (>10x real-time), robust, reliable and affordable
–E.g., 1.5 hours (42x375MHz Power3), 2.0 hours (24x375MHz Power3)–Focus on HPC, visualization, system integration and automation
Ability to provide usable products in a timely mannerVisualization integrated into all components
Pre-processing
ProcessingPost-
processing and Tracking
Weather Data
Analysis
Initial Condition
s
Synoptic Model
Boundary Conditions
Analysis
http://www...
Data Explorer
AdvancedVisualization
RS/6000 SP
Weather Server
Cloud-Scale ModelData Assimilation
ETA
Other Input Products
FCST
NCEP Forecast ProductsSatellite ImagesOther NWS Data
Observations
NOAAPORT Data Ingest
Forecast Modellin
g Systems
Custom Products for
Business Applications
andTraditional Weather Graphics
Visualization Component
Traditional meteorological visualization is typically driven by data for analysis -- inappropriate for energy utility applications
Timely usability of cloud-scale NWP results requires–Understanding of how weather data need to be used for end users–Identification of user goals, which are mapped to visualization tasks–Mapping of data to visualization tasks–Users have limited control over content (targeted design) and simple interaction–Products designed in terms relevant for user
Wide range of generic capabilities needed–Line plots to 2d maps to 3d animations -- but customized–Assessment, decision support, analysis and communications–Automated (parallelized) generation of products for web dissemination–Highly interactive applications on workstations
Example Customizations for Utility Operations
Distribution operations
Generation operations
Electricity TransmissionNew York State Transmission System–Color-contoured to show forecasted temperature
–Available in 10 minute intervals from each 24-hour Deep Thunder forecast at 4 and 1 km resolution
–Can be used to estimate transmission efficiency
–115 kV and above
Map also shows–State and county boundaries
–Major cities
Example -- Electricity Demand ForecastingSimple
estimated load–f(t,T,H) -- color and height–Scaled by capacity–Generator data from Georgia Power–Deep Thunder forecast
Map shows–Heat index–State & county boundaries–Major cities–Generating plants
Emergency Planning for Severe Winds
Geographic correlation of demographic and forecast data
Map shows
–Zip code locations colored by wind-induced residential building damage
–Constrained by value, population and wind damage above thresholds
SummaryDeep Thunder is an integrated system that is
–Usable forecasts are available automatically, in a timely, regular fashion–Illustrates the viablity of cloud-scale weather modelling to provide more precise forecasts of severe weather–Can be customized for different business applications and processes for safety, economic benefit and efficiency
Continued research and development–Improving quality of forecasts as well as product delivery–Adaptation of other research efforts to support operational applications–Multiple model forecast domains as platforms for development and collaboration
Future work–Adaptation and evaluation to other geographic areas–Enhanced workstation and web-based visualization, model tracking/steering and interactivity for both decision support and analysis–Improved computational performance and throughput–Extensions to still other models and data products–Customized interfaces, products and packaging for other applications (e.g., emergency planning, aviation, surface transportation, broadcast, insurance, agriculture, etc.)
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