Transcript Customization of a Mesoscale Numerical Weather Prediction System for Energy & Utility Applications Anthony P.

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
http://www.research.ibm.com/weather/DT.html
Customization of a Mesoscale Numerical
Weather Prediction System for
Energy Industry Applications
Background and motivation
Architecture and implementation
Customization for energy applications
–Energy Distribution
–Energy Generation
Discussion, conclusions and future work
Background and Motivation
Estimated 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 Architecture
Sufficiently 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 manner
Visualization integrated into all components
Weather Data
Pre-processing
Synoptic
Model
Processing
Post-processing
and Tracking
RS/6000 SP
ETA
NOAAPORT Data Ingest
FCST
Boundary
Conditions
Other Input Products
Advanced
Visualization
Weather Server
Data Explorer
NCEP Forecast Products
Satellite Images
Other NWS Data
http://www...
Analysis
Initial
Conditions
Forecast
Modelling
Systems
Analysis
Observations
Data Assimilation
Cloud-Scale Model
Custom Products
for Business
Applications
and
Traditional
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
Transmission
New York State
Transmission
System
–Color-contoured to
show forecasted
temperature
–Available in 10 minute
intervals from each 24hour 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 Forecasting
Simple
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
Summary
Deep 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.)