IBM Research | Weather Modelling Group

1. Introduction

Efforts to create generic visualizations, both content and interface, often fail when applied to operational forecasting. Such activities are typically composed of distinct tasks. Attempts to build generalized systems to address these tasks have been useful for research activities and applications development. However, they fare poorly in more mission-critical environments. Generic solutions, even when oriented toward weather, may lack sufficient focus to be effective for diverse forecasting tasks.

2. Task-Based Visualization

Consider three steps to defining visualization tasks:

Identification of user needs
Composition of design elements and interface actions
Establishing different techniques for various users

Tasks can be decomposed hierarchically by recognizing that the user's tasks are not the same as the visualization tasks. Hence, a given user may require one or more visualization tasks, and a specific technique may support more than one user task. Then consider goals for the user in visualizing (i.e., exploration, insight, presentation), with the need for:

Feature or event identification
Comparison or fusion of data from disparate sources
Decision support
Communication of results

Independent of the user goals is the definition of visualization tasks. These are graphical actions such as select, interact, animate, interrogate, etc. The actions are used for specific composition like browse, analyze or present. To test these ideas, they are applied to operational weather forecasting.

3. Operational Weather Forecasting

This work is an extension of the mesoscale weather forecasting project of the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) at the 1996 Centennial Olympic Games (Treinish and Rothfusz, 1997). It is a collaborative effort with NOAA Forecast System Laboratory (FSL). It provided a testbed for new visualization techniques, focused on meeting specific forecasting goals, yet affected by real, operational constraints.

3.1 Previous Work

The majority of visualization systems today for meteorology are typically designed from the perspective of "one size fits all". While there is variation among them, they individually provide one interface and style of visualization independent of the task supporting a single class of users. While such systems clearly can be successful, further efficiency in utilization is possible by recognizing that even a single user is likely to have more than one goal for visualization, and that there is more than one class of users.

Improvements in speed and effectiveness have significant impacts on operational forecasting, which is why weather agencies have invested in developing highly focused visualization tools. One example is the Advanced Weather Interactive Processing System (AWIPS) deployed by the NWS, which provides two-dimensional visualizations via its D2D subsystem (NWS, 1998). Conceptually similar tools are available from a plethora of other organizations worldwide. This category of traditional weather visualization tools is termed Class I. It consists of conventional representations of selected meteorological fields for analysis tasks by forecasters with minimal direct (graphical) interaction at a specific "layer", either the ground or at an isobaric level. Given a flat canvas for visualization design consistent with the display of a single layer, these tools can only show a few parameters simultaneously (e.g., overlaying wind as barbs or arrows, a scalar variable as line contours). Simplified versions of these representations have also been developed to support presentation to non-meteorologists, particularly for the media (e.g., Schroder, 1993). Figure 1 is an example of a typical visualization produced by Class I tools. It shows a precipitation forecast for August 4, 1996 at 8 pm EDT. Class I may also provide simple flip-book animation.

Large, three-dimensional volumes are typical of current acquired and computed data, for which improved facilities are required for timely assessment and utilization in forecasts. However, two-dimensional techniques dominate in operational settings, even though their use can be burdensome with such data because of the aforementioned mismatch between interface and users. There are a few exceptions to this situation. Chief among them is Vis-5D developed at the University of Wisconsin. It has a fixed user interface with specific visualization tools typically tied to graphics hardware on Unix workstations, with support for a single class of data. The implementation focuses on regularly gridded data, preferably compressed to byte precision to increase the speed of operation. This yields an highly interactive tool that maps well to many meteorological data sets (Hibbard et al, 1994). However, for forecasting tasks such as model assessment and dissemination, it can be too generic and does not have the ideal interface or content, because of its primary focus on analysis.

In contrast, Fraunhofer Institut für Graphische Datenverarbeitung (FIGD) has implemented independent systems operated by meteorologists that are focused on specific tasks. Their development has been in conjunction with the Deutscher Wetterdientst (DWD). The first was Triton, oriented toward generating two-dimensional visualizations for the non-meteorologist (Schroder, 1993). The second, TriVis, is based upon a related goal -- providing two- and three-dimensional visualizations for television broadcasts (Schroder and Lux, 1997). The third system, RASSIN, is designed to provide analysis facilities directly on the native grids of meteorological data (Lux and Fruhauf, 1998). The latter two systems are in use by DWD. The FIGD systems only share an underlying renderer.

The approach herein utilizes a natural coordinate system to provide a context for three-dimensional display and interaction. They provide representations of the atmosphere fully consistent with the data source that are registered with terrain and political boundary maps. Further, it uses correlative visualization, where each data set to be examined is processed independently and merging takes places at render time (Treinish, 1994). Both the design of the content and the choice of coordinate system has been dictated by the user task for conceptual and physical realizations.

Since color is a critical component, knowledge of human perception is applied via a rule-based advisory tool that is sensitive to the spatial frequency of data and the task (Rogowitz and Treinish, 1996). For example, noisy data such as wind speed are primarily mapped into luminance, while more smoothly varying data such as temperature are primarily mapped into opposing saturation pairs to impart an isomorphic or continuous representation. For moisture-related data, two colormaps are combined such that dry regions are mapped to brown ranging through yellow to green for modest values. At high levels, the data are mapped into blue, with decreasing luminance. For contouring, a segmented colormap with perceived ordinality is applied. For discrete three-dimensional representations (e.g., cloud surfaces), uniform but complementary colors are chosen to minimize the effects of color mixing. For direct volume rendering, this is extended with simultaneous mapping into luminance and opacity. Of course, some of these ideas are not directly apparent due to the monochromatic printing of these proceedings.

Several techniques are implemented for surface wind velocity, which are pseudo-colored by wind speed draped over the topographic surface. Vector arrows of fixed size are used to eliminate misleading motion cues during animation and to show gross atmospheric movement. In contrast, streamlines with directional arrows, although visually more complex, are superior at capturing fronts, convergence zones, vortices etc. On the other hand, waving flags that point in the direction of the wind have been effective to illustrate wind motion to the non-meteorologist. They can be either rigid or furled (i.e., straight at maximum speed and draped against a flag pole in the absence of wind).

The task decomposition leads to three other classes of visualization, each of which are described below. In each case, the user has a set of tools to design a visualization and interact with selected data. The available techniques are limited to focus on specific tasks, which are then supported by relatively simple interfaces.

Class II can be viewed as a superset of Class I by including enhancements into three dimensions and the ability to leverage modern workstation hardware. Its focus is for analysis by forecasters, particularly to support data comparison. Because the appearance of the visualizations may be complex, direct manipulation is provided. 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 source. 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 in figure 2. Four scalar fields are visualized in the stereographic coordinates for December 5, 1995 at 11:00 pm EST. The primary features are precipitable water as the height of a shaded, deformed surface, pseudo-colored by temperature, and marked with the mean sea level pressure at discrete locations. Color-coded relative humidity contours at 10% intervals are overlaid along with streamlines of wind. The wind direction is indicated by arrows and the speed by color. The surface is also draped with local coastline (black), state boundaries (magenta) and river (blue) maps. For example, a correlation is visible between moderate levels of precipitable water and humidity with high temperature in south-central Georgia, and lower levels of precipitable water, wind and temperature along the coast. The effectiveness of this approach to examining multiple two-dimensional fields is further illustrated via time-based animation.

Class III enables forecasters to create qualitative three-dimensional representations for both interactive investigation and production of animation via browsing. The consumers may or may not be specialists but the interactive user is likely to be a meteorologist. Thus, the results may be suitable for media and public dissemination to support the explanation of specific forecasts. The visualizations have a simplified appearance to utilize pattern r ecognition for general understanding as well as feature identification. It requires high-resolution data (temporally and spatially) to enable a coherent presentation. Figure 3 shows a result from such an application, which enables interaction in geographic-altitude coordinates. The ability to create both time-based and key-frame (flyover) animations is available.

A three-dimensional representation of predicted cloud structure is shown 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 familiar 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. 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 shaft and the region of relatively heavy precipitation is quite clear. The surface is also overlaid with vector arrows of surface wind velocity, color-coded by speed. 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. The visualization can be viewed in more detailed through interacting with a VRML representation of its geometry after simplification, or an image-based rendering via PanoramIX.

The browser enables tracking of simulations during execution. Instead of direct interprocessor communication between the visualization and simulation, a custom file format was designed with a set of specialized readers. To further minimize the volume of data and latency in interaction, several parameters were computed within the application.

5.3 Class IV: Three-dimensional analysis

Class IV provides analysis, viewing, interrogation and interaction tools with standard products designed for AWIPS . This class is similar to the visualization tasks addressed by the aforementioned Vis-5D and RASSIN packages, but with greater emphasis on direct manipulation and the introduction of new realization methods. Figure 4 shows a result from such an application, which enables interrogation in a geographic-pressure coordinate system annotated with an axes box and base maps. One may probe the volume for specific values at selected locations within the data set.

The upper air three-dimensional wind velocity is visualized via interactive marking of geographic locations for virtual
soundings within the model atmosphere. At each location, a vertical profile is extruded through the atmosphere, which is realized as a tube. The wind velocity along the profile is shown by a set of vector arrows that point in the direction of the wind. Horizontal speed at these points is indicated by the color and length of the arrows. Optionally, the locations on each virtual wind profiler can be used for seed particles for particle advection, which is realized as streamlines. These lines, which are also pseudo-colored by horizontal speed, indicate the instantaneous direction of the modelled wind from these locations. These techniques are best utilized in an interactive session, where the various methods and variables can be altered, data interrogation, and geometrically transformed in order to analyze the results of the simulation. However, some of these advantage can be seen through animation.

Figure 4. Class IV Visualization.

A suite of tools invoked through c-shell scripts on a UNIX workstation or shortcut icons on a WIntel workstation provides the facilities for visualization Classes II, III and IV. 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 IBM Visualization Data Explorer (DX) (Abram and Treinish, 1995).

DX is a portable, general-purpose software package for visualization and analysis. It employs a client-server architecture with an extended data-flow execution model. A generic toolkit was used to avoid having to implement a graphics and computational infrastructure. Unlike traditional meteorological graphics, DX enables the use of modern workstation hardware equipped with three-dimensional graphics accelerators and is parallelized for symmetric multi-processors. 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, 1995). F urther, such a toolkit is extensible to allow development to be focused on meteorological data and tasks, and reuse of tools between applications with similar user interface components. This simplifies training of users to employ the applications with different content matched to separate tasks. It also reduces the cost of development and maintenance, and enables more rapid iterative refinement with or adaptation to new users.

These tools are part of an integrated system that provides operational mesoscale numerical weather prediction in a wide variety of environments. It evolved from the aforementioned experiments to support forecasting at the 1996 Olympic Games (Snook et al, 1998). Additional information about the system as well as numerous visualization examples are available on-line at http://www.research.ibm.com/weather.