Task-Specific Visualization Design

(Deep Thunder -- Weather Visualization)

Three-Dimensional Browsing

Browsing enables the creation of qualitative three-dimensional representations for both interactive investigation and production of animation.  The consumers may or may not be specialists but the interactive user is likely to have domain expertise.  For numerical weather prediction, the focus is placed on surface conditions and precipitation, which are of greatest interest for general forecasting.  The visualizations are composed of a set of simplified qualitative techniques to utilize pattern recognition for general understanding as well as feature identification that serve two purposes: 1) gross assessment of the data and 2) source material potentially suitable for public dissemination to support the explanation of specific forecasts (e.g., media, World-Wide-Web, etc.).  They are presented in a geographic coordinates consistent with the data source -- cartographically projected horizontally and terrain-following (i.e., true height) vertically.  The techniques require high-resolution data (temporally and spatially) to enable a coherent presentation.

The above example shows a three-dimensional representation of predicted cloud structure as translucent, white isosurfaces of cloud water density at 10-5 kg/kg for August 4, 1996 at 9:00 pm EDT.  The cloud surfaces are registered with a terrain map overlaid with coastline (black) and state (white) boundary maps in a terrain-following, stereographic grid, where the cities of Atlanta and Savannah are marked.  This representation can effectively show gross atmospheric motion and potential distribution of moisture.  The terrain is pseudo-colored by total precipitation to indicate where and how much rainfall is predicted by the model, where heavy rainfall is shown as blue puddles.  Translucent, cyan isosurfaces in the interior of the clouds are forecast radar reflectivities at a threshold of 25 dBz, approximating rain shafts.  The correspondence between the rain shafts and the regions of relatively heavy precipitation is quite clear.  The surface is also overlaid with vector arrows of surface wind velocity, color-coded by speed.  The results were computed at 8 km horizontal resolution and are shown for the time during the closing ceremonies of the 1996 Centennial Olympic Games in Atlanta.  The model, as illustrated through three-dimensional visualization, correctly predicted thunderstorm activity in the vicinity of Atlanta, but NOT over the city itself.  This is quite evident through animation.  Hence, forecasters were able to give Olympic officials an "all clear" for the Closing Ceremonies despite thunderstorms in the area.  The visualization can be viewed in more detailed through interacting with image-based rendering below.

04-Aug-1996 - 21:00 EDT

Surface Total Precipitation, Cloud Water Density at 1.0e-5 kg/kg and Reflectivity at 25.0 dbZ

<BR><FONT FACE="Arial Black">Sorry, but PanoramIX is not available for your browser. PanoramIX is available for Microsoft Internet Explorer versions 3 and 4, and Netscape Navigator versions 3 and 4, on Windows 95, Windows NT/x86, and PowerMac systems.</FONT> <BR>


  • Wait for images to load.
  • Look around:  Drag with left mouse button or use arrow keys
  • Look and zoom:  Drag with right mouse button or use shift-arrow keys
  • Press the "l" (el) key to force vertical orientation.

  • Alternatively, interact with the same visualization with a VRML browsing of its geometry after simplification.  The simplification via volume tolerance is illustrated in the image below.  Each geometric component is individually simplified using different criteria.  The total volume is reduced from 343K triangles on the left to about 40K on the right, enabling web-based transmission.

    Two-1/2-dimensional analysis

    One set of methods for analysis of multiple two-dimensional fields focuses on data comparison tasks.  As a result, up to five parameters may be visualized simultaneously.  These two-dimensional variables may be any combination of surface or upper air layers from the same or different sources.  They may be illustrated redundantly by applying multiple techniques (e.g., color and height).  The variables and techniques can be independently selected interactively.  An example is shown below for a weather forecast initiated on January 13 at 0Z UTC (6 PM CST on January 12).  Five different surface variables have been selected in this combined visualization.  Mean sea level pressure is shown as pseudo-color.  Wind velocity is illustrated as streamlines with directional arrows arrows, colored by speed.  Colored line contours of relative humidity in increments of 10% are shown.  These planar representations are deformed vertically by lifted index to create a shaded surface.  A coastline map (black) and state boundaries (white) are draped on the surface.  Finally, temperature values at discrete locations are also shown by value on the surface.  Any of the surface and upper air fields available from the model can be visualized with any of these methods.  Since lifted index can be used to indicate the relative instability in the atmosphere, the peak in its surface illustrates where the front is located.  This representation is very effective, especially in animation, of showing the motion of a front.

    Three-dimensional analysis
    Three-dimensional methods 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 Data

    The correlation of weather prediction with other sources of data is relevant to decision making and other commercial applications of precision weather forecasting.  This idea is illustrated in the image above, which shows the relationship between demographic data and the results of a numerical weather prediction.  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 domain for a forecast model.  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 user is free to set these thresholds and animate the results in time corresponding to the weather simulation in hourly time 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, particularly via 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.