for analysis, viewing, interrogation and interaction tools are presented
in geographic coordinates consistent with the data source -- cartographically
projected horizontally but at standard pressure levels vertically.
Since these presentations can be visually complex even after application
of complementary colormaps, facilities to interrogate and estimate data
values are provided. The notion of a virtual met-station is
introduced -- a realization of direct manipulation as a graphical analogue
for a simulated atmosphere to the type of instrumentation that would be
used to observe the real atmosphere. An example is shown below from
the same forecast as the previous image, where the geographic-pressure
coordinate system is annotated with an axes box and base maps.
A surface variable (pressure) has been selected
for display as pseudo-colored filled contour bands, which are overlaid
on a topographic map. Any of the surface variables produced by the
model may be presented in this fashion. Coastlines (black), state
boundaries (white) and rivers (blue) are draped on the surface. An
upper air variable (relative humidity) has been selected for display via
surface extraction. The surface at 75% is requested in translucent
tan, which corresponds roughly to a cloud boundary. Another field
(temperature) has been selected to show as a vertical slice, which is pseudo-color
contoured. Any of the three-dimensional fields available from the
model can be visualized with either of these methods. The upper air
wind data can be seen along two vertical profiles, which are specified
interactively, and via streamribbons. The direction of the model
wind field along these "virtual sounding" is shown via vector arrows.
Both the arrows and ribbons are pseudo-colored by horizontal wind speed.
The length of the arrows also corresponds to the horizontal speed.
Points along the profile are used as seeds for the streamribbon integration.
Each profile is realized as a pseudo-colored tube, which is contoured by
the variable selected for isosurface realization (i.e., relative humidity).
The visualization for the profile toward the center of the domain can help
illustrate the three dimensional effects of the front moving through the
area as shown earlier, especially in animation.
Integration with Other Models
In other cases, the results
from the simulation need to be coupled to other models. In agriculture,
that could include an hydrological model to evaluate the effect of runoff
from predicted rainfall to help understand the impact on the application
of pesticides and fertilizer or planting of new seed. In the energy
industry, it would typically be input to a model used to predict load on
a power-generation facility or transmission lines for efficient running
of the facility or for trading. This idea is illustrated below.
It shows a map of Georgia with forecasted heat indices at 8 km resolution.
Major cities and locations of the generators owned and operated by Georgia
Power, the local electricity utility, are shown by name. Each power
plant location is also marked with a pin. The height and color of
the pin indicate an estimated load. An animation
of this visualization for a 24-hour simulation at 10-minute time steps
illustrates the temporal and geographic variation of predicted load.
The load is computed interactively
as a function of temperature and time of day from a simple model. The temperature
dependence is based upon a polynomial approximation of the relationship
between historical data of power demand and weather observations. The temporal
variation is based upon a spline fit of hourly electricity requirements
for mid-week days in an urban tropical environment, which is consistent
with other results in the literature. The function is scaled by the rated
power plant capacity using published data. Heat index is used as a more
accurate measure of demand than simply temperature. The weather model results
are interpolated at each time step to the location of each of the power
plants. In the interactive application, the user has the ability to select
the type of power plant (fossil, hydroelectric and/or nuclear), what data
to show on the map (e.g., weather, geographic or other customer/demographic)
and to query individual power plants. The results of the query include
the predicted load at each time step (as fine as every 10 minutes) as well
as a plot of predicted load over 24 hours with weather data at that location.