Photo: Marc Tule
Applications for the "In-house" Weather Analyst
Weather affects a wide range of industries from energy to insurance
to commodities investing. As such, many organizations employ
"in-house" meteorologists and atmospheric scientists. Private sector
scientists are inevitably at the forefront of day-to-day corporate
activities and often lack the time and resources to build background
tools to make them more efficient and effective in their duties.
Some such tools are currently available but not customizable or
operationally supported. Existing tools are also scattered
throughout the world of cyberspace and difficult to locate. SPHEAR
proposes a centralized network of tools such as:
- Real-time performance diagnostics for the primary global,
mesoscale, and tropical forecasting models. Provide the
private-sector meteorologist with a suite of tools to analyze
model skill in real-time. Goal: help the "in-house"
meteorologist produce forecasts more efficiently and
- Tools to better understand the variables that lead to
hemispheric jet-stream patterns within and beyond the two-week
forecast window. Quantify which atmospheric and/or oceanic
signals have the most power to explain significant changes in
global atmospheric patterns within and beyond the two-week
forecast window. Goal: give the "in-house" meteorologist a suite
of products to better predict intra-seasonal weather patterns.
- Multi-model ensemble techniques for weather prediction and probabilistic forecast application. Weather forecast accuracy can be significantly improved using
multi-model ensembles. Probabilistic forecast output makes
industrial application more efficient. Goal: give the "in-house"
meteorologist methods for constructing and communicating
probabilistic temperature forecasts via multi-model ensembles.
Event, Hazard, and Industry Modeling
Private sector science teams often utilize vendor modeling services
and/or models from the government sector. SPHEAR's academic partners
pioneered the underlying research behind many of these models.
Vendor models are often fully proprietary closed systems and the
user has little ability to observe or adjust the internal workings.
Government models are often inflexible with no ability for user
customization. SPHEAR proposes the development of "open source"
models and modules to specifically address industry needs such as:
- Catastrophic Events Leading to Financial Loss. Develop synthetic catalogs of physical hazards; engineering and
damage functions; and financial loss components in an "open
source" environment so as to be fully customizable by the user.
Goal: provide industry a transparent, peer-reviewed, alternative
to vendor catastrophe models.
- Coupled Ocean/Atmosphere Model for Weekly to Seasonal Climate Prediction. Develop research models that emulate and extend the current NCEP
Coupled Forecast System. Goal: provide additional tools and data
toward seasonal and intraseasonal weather prediction.
- Model Business Impact from Weather Events. Direct relationships
exist between weather phenomena (such as temperature) and
industrial applications (such as energy demand). Goal: provide
models that convert primary weather inputs (such as rainy days
in summertime) to important business variables (such as
amusement park ticket sales).
Quantifying Extreme Events
How likely is a deadly heat wave this summer? How often do
catastrophic floods affect densely populated areas? What's the risk
of extreme temperatures in any given winter? What are the odds that
multiple severe hurricanes will move through crucial
energy-production regions in a given year? These are all regular
questions from business leaders to their staff. Yet methodologies
for constructing a robust climatology for such extreme events are
relatively unknown in the private sector. Further, the question as
to how oceanic and atmospheric cycles or climate change itself might
impact such events is unexplained. SPHEAR proposes to quantitatively
address issues such as:
- Temperature and Precipitation Extremes. Quantify the probability
that certain thresholds of extreme heat, extreme cold, or
extreme precipitation would be exceeded in any given year for
specified regions around the world. Further determine the
oceanic and/or atmospheric variables that condition the
base-line probability for such events. Goal: provide tools for
base-line risk assessment and real-time conditional probability
of extreme events.
- Event "Clustering" in Natural Hazards and Extreme Events. Determine whether winter storms, hurricanes, severe
thunderstorms, and/or earthquakes demonstrate grouping or
clustering characteristics. Goal: develop methodologies for
simulating such events in a risk modeling framework.
- Seasonal and Intraseasonal Hurricane Risk. Develop peer-reviewed
and scientifically accepted methodology for determining seasonal
and intraseasonal tropical cyclone risk. Goal: provide real-time
tropical cyclone risk assessment.
Contact the executive director of SPHEAR at email@example.com