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.)