Visualization Techniques
for Applications of High-Resolution Numerical Weather Models
Lloyd A. Treinish and
Zaphiris D. Christidis
IBM Thomas J. Watson Research
Center
Yorktown Heights, NY 10598
1. Introduction
Visualization is
a method of computing by which the enormous bandwidth and processing power
of the human visual (eye-brain) system becomes an integral part of extracting
knowledge from complex data. In that regard, our previous work has discussed
methods of appropriate mapping of user goals to the design of pictorial
content by considering both the underlying data characteristics and the
(human) perception of the visualization (Treinish,
1999). However, the scaling of traditional data sources and introduction
of new applications challenges the effectiveness of conventional visualization
methods. Consider, for example, rapid execution (e.g., 10 to 30 times faster
than real-time) of mesocale weather models operating at cloud-scale resolution.
Earlier we illustrated that there is a mismatch between this rate of data
generation and the ability to utilize the model results with traditional
two-dimensional techniques (Treinish
and Rothfusz, 1997). Introduction of three-dimensional visualization
is only a partial solution because typical methods can easily fail to capture
the salient characteristics of such simulations.
2. Approach
The resolution of
the visualization must match that of the scale of the model to build usable
products that are perceptually coherent. We have determined that realization
needs to be based upon the integration of all computational nests with
high-resolution topography in a three-dimensional cartographic coordinate
system and sequencing consistent with the internal time step of the computation.
The choice of realization geometry is also affected by the resolution of
the data so that perceptual artifacts do not dominate the presentation,
especially in animation (Treinish, 1999).
We have looked at problems with visualization of these model results for
typical meteorological operations as well as applications in other domains.
3. Vector Field Realization
The choice of realization
methods is particularly important for vector fields (e.g., predicted winds).
We have used several techniques for wind velocity, which are pseudo-colored
by speed. For ground level data, we typically drape the results over a
topographic surface. A continuous colormap is used ranging from a deep
violet or green, for example, for very calm winds to white for the maximum
speed. Such a luminance-based colormap is perceptually isomorphic for data
with relatively high spatial frequency (Rogowitz
and Treinish, 1998). Vector arrows are a common technique, but we create
them with fixed size to eliminate misleading motion cues during animation.
Similar problems would also occur in animation if wind barbs are employed,
which are best suited for static displays. Fixed size arrow glyphs are
illustrated in Figure 1. It shows the result of a mesoscale forecast generated
by the Regional Atmospheric Modeling System (RAMS). The domain is at 6.5
km resolution in a region roughly 650 x 650 km in extent to include Hawaii
(cf., Snook et al, 1998). Output from RAMS every 10 minutes of forecast
time are generated for a browsing visualization (Treinish,
1999).
Figure 1. Predicted Winds Visualized
as Arrow Glyphs with Temperature Contours and a Terrain Surface.
The image shows a terrain map as
a deformed surface, pseudo-colored by color-filled contour bands of predicted
temperature overlaid with coastline maps for 7 PM local time on March 4,
1999. This approach generates a textured field, which is effective at showing
gross predicted atmospheric movement. The visualization is consistent with
prevailing wind patterns in Hawaii. However, this structure is disturbed
on the lee side of the big island of Hawaii and to a lesser extent, Maui.
An animation of a similar visualization
illustrates this effect.
Unfortunately, this realization
technique does not capture sufficient details to interpret these apparent
features. Instead, we introduce streamlines with directional arrows. Although
visually more complex they are superior at capturing fronts, convergence
zones, vortices, etc. An example result for the same forecast period is
shown in Figure 2. The temperature data are now shown as a continuous field
as better contrast with the wind visualization. A perceptually isomorphic
colormap is used between opposing saturation pairs for such low-spatial
frequency data.
Figure 2. Predicted
Winds Visualized as Streamlines with a Terrain Surface Colored by Temperature.
The results show more
fine structure, but there are gaps, especially in regions that potentially
are interesting as implied in Figure 1. The problem is that a conventional
approach toward steady-state streamline generation was used. In particular,
the domain was uniformly sampled to define seed points for integration.
While generic, it fails to capture salient high-resolution features. This
is a well-known issue in flow visualization utilized in aerospace computational
fluid dynamics (e.g., Helman and Hesselink, 1990). A key problem in these
applications is the understanding of the topology of the underlying vector
field. Among the more important characteristics of the topology are the
location of critical points (i.e., where the velocity field vanishes) and
tangent curves, which connect these points. To first order, we can identify
the location of the critical points by the eigenvectors and eigenvalues
of the divergence of the velocity. In particular, we use a sampling
of the locations where the divergence is zero as seed points for integration.
The resultant streamlines are thus, an approximation of tangent curves.
To ensure consistency in this steady-state calculation for animation, this
determination is done for each time step. An application of this approach
for the previously used forecast period is shown in Figure 3.
Figure 3. Wind
Streamlines as Tangent Curves Using Critical Point Determination.
The technique is clearly
superior at capturing detailed features from the high-resolution forecast.
In this case, the structure of the wind field on the lee side of the islands
is now clear. This morphology is due to vortex shedding of the fluid flow
past mountains not unlike what can happen behind a wing or an engine nacelle
for a jet aircraft in flight. The results are particularly compelling when
animated
by time. This technique looks promising for the visualization of other
high-resolution orographic effects on predicted winds.
An improved initial
representation of predicted winds enables one to identify features of potential
interest for further analysis. We have introduced several additional approaches
to study the data in greater detail. The first is to incorporate both spatial
and temporal integration as streaklines. Another is to examine other predicted
fields generated by the model in conjunction with wind data. Figures 4
and 5 show examples of this idea, fusing multiple variables into a consistent
representation (Treinish, 1999). They are
for the same forecast period depicted in Figures 1, 2 and 3. Figure 4 illustrates
a surface variable (precipitable water) for display as pseudo-colored filled
contour bands, which are overlaid on a topographic map. Coastlines (black)
are draped on the surface. An upper air variable (relative humidity) is
displayed via surface extraction.
Figure 4. Three-Dimensional
Wind Streamlines with Surface and Upper Air Data.
The surface at 90%
is requested in translucent white, which corresponds roughly to a cloud
boundary. Another field (vertical wind speed) has been selected to show
as a vertical slice, which is pseudo-color contoured. The location of the
slice was chosen to include the area at the lee of Hawaii as identified
in Figure 3. The upper air wind data can be seen along two vertical profiles,
which are specified interactively to study this same area. The direction
of the model wind field along these "virtual soundings" is shown via vector
arrows and streamribbons. 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 marked 2 can
help illustrate the upper air characteristics of the predicted vortex shedding
described earlier. This can be further illustrated in animation.
Figure 5 shows another approach
in which five different surface variables have been selected in a combined
visualization. Precipitable water is shown as pseudo-color. Wind velocity
is illustrated as streamlines, colored by speed, using the same technique
as in Figure 3. Colored line contours of cloud top heights in increments
of 1000 m are shown. Each of these planar representations are deformed
vertically by mean sea level pressure to create a shaded surface. A coastline
map (black) is draped on the surface. Finally, temperature values at discrete
locations are also shown by value on the surface. One can see the distinctions
between the leeward and windward sides of the islands in this representation,
particularly for the large island of Hawaii. The effect of the vortex shedding
on cloud heights is also apparent, especially in animation.
Figure 5. Integration of Wind Streamlines
with Other Surface Variables.
4. Applications
We have extended
our earlier work for situations where high-resolution models can be utilized
in variety of decision-making efforts such as emergency planning, energy
production, airline operations, risk assessment, etc. These applications
imply the coupling of weather simulations with other models, analyses and
data. To enable effective assessment and appropriate decisions, focused
visualizations must be designed to integrate these distinct data sources,
yet still be driven by user goals. This leverages our past efforts in providing
uniform access to a diversity of data by preserving their underlying fidelity
despite variations in sampling and coordinate systems (Treinish,
1994). In many cases, the resultant visualizations do not show
forecasts of weather phenomena directly but the derived properties, which
are influenced by weather. These ideas are illustrated by two examples.
4.1 Demographics
In many applications,
the design of a visualization should be based upon the correlation of weather
prediction with other sources of data relevant to decision making. This
is illustrated in Figure 6, which shows the relationship between demographic
data and a forecast generated by RAMS. The domain in this example is 800
x 800 km at 8 km resolution centered over Dallas.
Figure 6. Correlation of a weather
forecast with demographic data.
The forecast was
generated in real-time during the 1999 AMS
conference in the exhibition area. In this case, the visualization
is a simple two-dimensional map, which shows a set of glyphs, colored by
median house value. The glyphs are located at the centroid of the area
associated with zip codes in the forecast domain. These locations are only
marked on the map when a set of conditions on house value, population and
predicted wind speed are met, as indicated in the control panel widget.
The demographic data are derived from available census information (http://tiger.census.gov).
The user is free to interactively set these thresholds and animate the
results in time corresponding to the weather simulation in hourly steps.
These thresholds can be augmented to include other relevant demographic,
customer or property data. Essentially, they represent a simple method
to specify a query into various static and dynamic data sets, which are
then used to constrain a visual integration for display and interaction.
Such an application may be useful for planning purposes by an insurance
company or deployment of repair crews by a utility. An example animation
is also available for viewing.
4.2 Energy Load Forecasting
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 in Figure
7. It shows a map of Georgia with forecasted heat indices at 8 km resolution
generated by RAMS. 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.
Figure 7. Correlation
of a weather forecast with load prediction.
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 (Robinson, 1996). 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 (Chang
and Yi, 1998). The function is scaled by the rated power plant capacity
using published data (http://www.georgiapower.com/newsroom/plants.asp).
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.
5. Implementation
Figures 1 through
5 were generated by the visualization component of an integrated operational
mesoscale numerical weather prediction system. Additional information about
the system as well as numerous visualization examples are available at
http://www.research.ibm.com/weather.
They present a user interface based upon XWindow/Motif for indirect interaction
and OpenGL for direct three-dimensional interaction in cartographic coordinates.
They have been implemented with Visualization Data Explorer (DX) (Abram
and Treinish, 1995).
DX is a portable, general-purpose
software package for visualization and analysis. A generic toolkit was
used to avoid having to implement a graphics and computational infrastructure.
Unlike traditional meteorological graphics, DX is parallelized for multiprocessor
workstations and can utilize three-dimensional graphics accelerators. DX
is built upon an unified data model that enables these applications to
operate directly on the native grids without transformation or compression,
which preserves the fidelity during visualization (Treinish,
1994). F urther, such a toolkit is extensible to allow development
to be focused on meteorological data, applications and tasks, and reuse
of tools between applications with similar user interface components. Hence,
new applications as illustrated in Figures 6 and 7 required no additional
infrastructure development.
6. Conclusions and Future Work
Adaptation of on-going
work in computational flow visualization looks promising in applications
to high-resolution numerical weather models. As the complexity of the underlying
meshes increases to accommodate orographic and other effects in detail,
we will utilize additional methods for improved global representation such
as line integral convolution and critical point analysis for tangent curve
classification. The latter may indicate more precisely regions of separation
or attachment of predicted wind fields.
The visualization of applications
of mesoscale modeling have benefited from a focus on specialized interfaces
and tools matched to user goals and underlying visualization tasks. We
believe that this idea can be extended to other application areas such
as agriculture, aviation, emergency planning, etc. Within any given application,
incorporation of additional and more complex data sets will also be addressed.
Our goal is to develop simple interfaces and useful visual fusion.
7. References
- Abram, G. and L.
Treinish. An
Extended Data-Flow Architecture for Data Analysis and Visualization.
Proceedings of the IEEE Visualization 1995 Conference, October 29-November
3, 1995, Atlanta, GA, pp. 263-270. (DX is now available as open source
software via http://www.research.ibm.com/dx
and http://www.opendx.org)
- Chang, C. S. and M. Yi. Real-Time
Pricing Related Short-Term Load Forecasting. Proceedings of
the Energy Management and Power Delivery 1998 Conference, March 1998,
Singapore, pp. 411-416.
- Helman, J. L. and L. Hesselink.
Surface Representations of Two- and Three-Dimensional Fluid Flow Topology.
Proceedings of the IEEE Visualization 1990 Conference, October 1990,
San Francisco, pp. 6-13.
- Robinson, P. J.
Modeling Utility
Load and Temperature Relationships for Use with Long-Lead Forecasts.
Journal of Applied Meteorology, 36, no. 5, May 1997, pp. 591-598.
- Rogowitz, B. and
L. Treinish. Data
Visualization: The End of the Rainbow. IEEE Spectrum,
35, no.12, December 1998, pp. 52-59.
- Snook, J. S., P. A. Stamus, J.
Edwards, Z. Christidis, J. A. McGinley. Local-Domain Mesoscale
Analysis and Forecast Model Support for the 1996 Centennial Olympic Games.
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- Treinish, L. Visualization
of Disparate Data in the Earth Sciences. Computers in Physics,
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- Treinish, L. Creating
Effective Visualizations for Operational Weather Forecasting. Proceedings
of the 15th IIPS Conference, January 10-15, 1999, Dallas, pp. 517-520.
- Treinish, L. and L. Rothfusz.
Three-Dimensional
Visualization for Support of Operational Forecasting at the 1996 Centennial
Olympic Games.
Proceedings of the Thirteenth International Conference
on Interactive Information and Processing Systems for Meteorology, Oceanography
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lloydt@watson.ibm.com
