Technical Report An Interactive Iso-Surface Based 3D Weather Radar Data Visualization Package Using VisAD for WDSSII (Jianting Zhang, Last Modified 2/5/2004) Abstract... 2 1 Introduction... 3 2 Overviews of Related Issues... 3 2.1 3D Data Visualization... 3 2.2 Weather Radar Data... 5 2.3 Data Interpolation for Weather Radar Data Volume... 6 3 VisAD and its Related Features for 3D Visualization... 7 4 Design and Implementation... 8 5 Conclusion and Future Work Directions... 17 References... 18 1
Abstract In this report, we describe the design and implementation of an iso-surface based 3D weather radar visualization package using VisAD for WDSSII. We begin with discussions of 3D data visualization, the characteristics of weather radar data and issues in interpolation of weather radar data volume in order to use the marching cube technique to generate iso-surfaces using VisAD. After introducing VisAD and its related features for 3D visualization, we describe our design and implementation and illustrate the results using snapshots. Finally we provide conclusion and future work directions. We observe that although it is convenient to use commercial or open source software, such as VisAD, to generate iso-surfaces using their implementations of marching cube technique, applying them to curvilinear weather radar data often incur computation and storage overheads and interpolation errors. In the companioning report (Efficient Generation of Iso-Surfaces from Volumetric Radar Data Using Marching Cube Technique), we describe our work on efficient generation of iso-surfaces from volumetric radar data by implementing the marching cube technique which works directly on radar space without needing interpolation. This report mostly servers for documentation purposes. 2
1 Introduction Visualizing weather radar data is very useful for meteorologists to discovery weather patterns. Many existing radar data display packages only provide 2D visualization functionality. The WDSSII system provides 3D visualization capability by plotting pixels in all radar elevation scan images as 3D scatter points. WDSSII allows specifying a 3D box or a cross section plane and the heights of 3D scatter points can be compared against the scales specified by the gridded box or plane. It also allows compare the values of the points along the sides of the 3D box or the plane. In this report, we describe the design and implementation of an iso-surface based 3D weather radar visualization package using VisAD for WDSSII. We hope that the isosurface based weather radar visualization can provide a new tool for domain experts to explore radar data and make better decisions. 2 Overviews of Related Issues 2.1 3D Data Visualization The three basic options for displaying a three-dimensional data set on a twodimensional computer screen are (Schroeder, 1997): 1) Slicing the data set with a crosssectional plane. WDSSII has provided this function. Different from traditional slicing techniques which display the cross section in a 2D setting, WDSSII display the supported cross sections (3D box or a plane) also in a 3D setting. 2) Surface rendering. Surfaces of the objects in the data set are extracted and represented as triangles, which are typically rendered by a hardware accelerated renderer. 3) Volume rendering. Assign transparency and/or color to data items in the data set and then view the entire set from any angle. While not as powerful as volume rendering, surface rendering is widely used because it is 3
relatively fast compared to volume rendering and allows us to create images for a wide variety of data and objects. In this study we focus on surface rendering and iso-surface based visualization in particular. There are two major types of 3D data set, gridded and irregular. The topology of a gridded 3D data set is implicit, i.e., the neighbors of a data point can be retrieved by advancing the array indices associated with a data point. For example, the 6 neighbors of a data point (i,j,k) are (i+1,j,k), (i-1,j,k), (i,j-1,k), (i,j+1,k), (i,j,k-1), (i,j,k+1). Note that the data points in a gridded data sets does not need to be evenly distributed in a coordinate system. For irregular data set, the topology has to be explicitly specified which consumes more computational resources in generating iso-surfaces. The resolution of data set plays an important role in both visual effects and data organization. It is desirable to visualize most detailed information for better visual effects. However, it might beyond the scope of current computer technologies in terms of rendering speed and the manageable size of the data set. Although pre-generate the isosurfaces in a very fine resolution (the flat scheme) and query the iso-surfaces in the Region of Interest (ROI) might improve visualization speed, the computation and storage demands of the materialization might be prohibitive. A multi-resolution scheme might reduce the problem to a certain extent, however, the algorithms under the scheme is often very complex and the resolution hierarchy might be ad-hoc which does not fit in the practical needs very well. In this study, we propose to use a simple interactive scheme for meteorologist to solve their domain specific problems. The scheme allows a user to specify the space and resolution parameters one thinks to be the best for visualizing a 4
certain meteorological phenomena through a fine-tuning process, i.e., to generate isosurfaces from a coarse resolution to a fine resolution interactively. 2.2 Weather Radar Data A volume of a radar data set consists of several elevation scans. Each elevation scan is characterized by an elevation angle and each pixel in an elevation image is characterized by two parameters: Azimuth and Range. The relationship between the Cartesian coordinates and the radar polar coordinates is as follows (assuming ideal cone space for a radar data set): X=gate_no*gate_width*cos(elevation)*cos(90-azimuth) Y= gate_no*gate_width*cos(elevation)*sin(90-azimuth) Z=gate_no*gate_width*sin(elevation) A typical volume of radar sets consisting 9 elevation scans (VCP 21 mode) are shown in Fig 1.The number of radials for all the elevation scans remains roughly the same, however, the number of gates along the radials of the elevation scans vary greatly: they are 460, 356, 336, 268,216,176,110,85,70 respectively (each unit represents one kilometer). Thus we can see that the radar image pixels distributed unevenly in the 3D Cartesian space. This imposes some difficulties in efficient iso-surface based 3D visualization. We have done experiments on generating iso-surfaces by modeling radar data set into both Gridded data set and Irregular data set. We found that the visual effects of the generated iso-surfaces are generally better for the former than that of the latter. We decided to adopt the Gridded data model, which requires interpolation of the values of data points in the grid from the image pixels of the elevation scans in a radar data set volume. 5
06.00 04.30 03.35 00.50 01.45 09.90 14.60 19.50 02.40 Fig. 1 The Nine Elevation Scans in a Volume of Radar Data Set 2.3 Data Interpolation for Weather Radar Data Volume There are several well-established interpolation methods, such as Gaussian splatting, Shepard s method and several Kriging methods in geo-statistics. However, they are all essentially weighting methods that combine neighboring data points based on the distances between the data points whose values are already known and the data point whose value to be interpolated. Determining the number of neighboring nodes to be used in the interpolation and the complex weighting functions could be computationally prohibitive for interactive visualization. In this implementation, we use a domain-specific nearest neighbor method. Given the coordinates of a grid point in the Cartesian space (latitude, longitude height), we first compute the range, azimuth and elevation of beam that would observe the point as described in (Doviak, 1992). The calculated elevation scan angle might be either between two of the nine elevation scan angles (which again could be either within the threshold ranges (ele- ele, ele+ ele) or not), or greater than the 6
19.95, the largest of the nine elevation scan angles. If the calculated elevation scan angle is within one of such range, the elevation scan will be chosen. If the calculated elevation scan angle is within both of such ranges of the two neighboring elevation scans, the one has smaller deviation will be chosen. Finally, if an elevation scan is chosen, the value of the pixel that has the calculated range, azimuth and chosen elevation scan angle will be assigned to the point to be interpolated. Otherwise the data point to be interpolated will be assigned to no data. Unlike the traditional nearest neighbor method that finds the nearest point in 3D space, the method we use is essentially one-dimensional nearest neighbor one (along elevation scans dimension). 3 VisAD and its Related Features for 3D Visualization VisAD is a Java software package developed at the Space Science and Engineering Center of the University of Wisconsin Madison and designed for numerical visualization of distributed objects (Hibbard, 2002). VisAD uses mathematical types to express numerical data organizations in terms of their primitive elements. By reducing them to primitive elements, VisAD s data model can be applied to a wide variety of numerical data which enables widespread sharing of numerical data and computations across the Internet. In order to visualize various data organizations definable via VisAD s mathematical types, the system s data displays defined by mappings from primitive numerical and text types to primitive display types. For example, to visualize the reflectivity of a radar volume, we need to define the following mappings: Latitude->x_axis Logitude->y_axis Height-> z_axis 7
By using the mapping of (Reflectivity-> IsoContour), only the iso-surface of the specified iso-value is generated and displayed from the reflectivity data set. If we use the mapping of (Reflectivity-> RGBA) instead, then the data set will be displayed as scatter points. If we use both of the mappings, then the iso-surface will be generated and colored according to the defined color map. From user perspective, one only needs to specify the data set and the mappings and VisAD will automatically invoke functions for different data types to generate iso-surfaces. VisAD support two major data types for iso-surface based visualization, the Gridded3DSet and Irregular3DSet. For Gridded3DSet, the marching cube algorithm is applied to generate iso-surfaces. For Irregular3DSet, one of Delauny triangulation algorithms is first applied before generating iso-surfaces. As discussed in Section 2.2, we will adopt grid data model and use the marching cube technique to generate the isosurfaces. 4 Design and Implementation The package is developed within WDSSII architecture. It is capable of reading the distributed XML index file implemented in WDSSII. An Index structure is generated from an index file for further process which consists multiple index records, each index record is the index for a virtual volume. The package supports both SAX and DOM based XML index files. Supporting SAX based XML index file processing could reduce the index processing time by one or two orders than that of DOM based method when the index file is big. A user may indicate a time window (specified by starting time and ending time) and only the portion of the index records (corresponding to virtual volumes) will be used to retrieve actual data for visualization. A Java TreeMap structure is used to 8
speed up the temporal range queries. The index records together with the initial parameters of data space will be used for interactive visualization as described below. The main interface is shown in Fig. 2. The implementation makes use of the overlapping structure of virtual volumes. Suppose the virtual volume at time t is v t, it will consist the following elevation scans (00.50, 01.45, 02.40, 03.35, 04.30, 06.00, 09.90, 14.60, 19.50), where the last elevation scan 19.50 is the latest arriving data that stamps the time of the virtual volume. The virtual volume at time t+1 will be v t+1 that consists of elevation scans of (01.45, 02.40, 03.35, 04.30, 06.00, 09.90, 14.60, 19.50,00.50) where the 00.50 elevation scan in the previous virtual volume is replaced by a newly arrived 00.50 elevation scan which also stamps the time of the virtual volume. It is easy to see that the overlap between the two consecutive virtual volumes is 8/9. We set up a cache to hold the most recently used elevation scans. Once the cache is filled with a virtual volume, only one elevation scan is needed to read from the disk and the rest 8 elevation scans can be reused. When the cache is full, the oldest one is discarded. For a cache that can hold N elevation scans and is used fully, no data reading is needed when a user works on N-8 consecutive virtual volumes, either back or forth. 9
Coordinate System Iso-surface 2D-Index map Time setting Multimedia output controls Display controls Iso-value setting Space setting Fig. 2 The Main Interface 10
There are three independent groups of parameters need to be set before displaying iso-surfaces of a radar data set: time, space and iso-value. The range of time parameter is determined by the specified time window. A user may change the current time parameter (corresponding to a virtual volume) either by dragging along the slider bar or choose from the drop down list (Fig. 3). The two GUI controls will reflect one of another to indicate the correct time indication. When the time parameter changes, the 2D index map which corresponds to the virtual volume of the time and the min/max values in the isovalue control group will change correspondingly. Since a virtual volume may consist multiple elevation scans, a user may choose any of them as the 2D index map to specify space parameters as described below. While the time parameter indicated by the slider is in long integer format to check with the time indicated in the XML index records, yyyymmdd-hhmmssz format is used in the captions of dropdown list to be more intuitive. The interface of setting space parameters and the related operations are shown in Fig. 4. The space parameters consist three sub-groups of parameters for each of the three dimensions. For the latitude and longitude dimension, we need to specify the center, range and resolution (labeled as num). For the height dimension, we need to specify the base, range and resolution (labeled as num). The 2D index map can be used to specify the center and range in latitude and longitude dimensions interactively when user drag the two corner points of the work space indication box over the index map. To reflect the changes of the center and range in latitude and longitude dimensions input by user from the text fields, a user must hit set map button to redraw the work space indication box. A user may hit 11
full range button to restore to initially work space setting. When the desired work space parameters are set, hitting update iso-surface button will draw an iso-surface of the sub-space (specified by the space parameters) of the current virtual volume (specified by the time parameter) using the iso-value specified by the iso-surface controls as to be described below. Total number of virtual volumes in the data set Current Sequence Number Current time in long integer format (<time> tag in code_index.xml) Current time in yyyymmdd-hhmmssz format (<params> tag in code_index.xml) Expanded Dropdown List Update Fig. 3 Time Setting Interface 12
Expanded Dropdown list Update Update Centers of work space (in degree, degree, meter respectively) Ranges of work space (in degree, degree, meter respectively) Update Resolutions of work space Update iso-surfaces using current time/iso-value settings Fig. 4 Index Map and Space Setting Interface 13
There are two other buttons, although whose functionality does not logically belong to setting space parameters, might be best explained here. The color iso-surface checkbox button, when checked/un checked, will display the iso-surface either in RGBA colors or in gray. The latter option generates an iso-surface faster and consumes less computational resources. The auto loop button will loop through all the virtual volumes starting from the current one to generate iso-surfaces over time which might help user to understand the evolution of meteorological phenomena. The three buttons, update iso-surface, color iso-surface, auto loop share the similarity that they all use the currently space parameter setting, which is the primary reason that we put them together and explain here. Compared with setting of time and space parameters, redraw iso-surface due to change of iso-value is much quicker. The reason is that the former requires re-generate data sets according to the specified parameters which could be computationally very expensive (such as reading data from disk to memory, pruning unnecessary data and interpolating data points). The interface of setting iso-value is shown in Fig. 5. The interface consists of a slider bar and a text input field. The slider bar allows a user to specify a certain amount of iso-values depending on the resolution of the pointing device and the pre-set range of the slider bar. The text input field allows a user to input an arbitrary iso-value. As in setting time parameter, the slider bar and the text input box reflects one another. A user may first drag over the slider bar to overview the iso-surfaces of different iso-values. Then one can input iso-values of interest using the text input box. An advantage of the slider bar is that it does not allow values outsider of the min/max 14
value range which could be wrongly input in the text input box. By combing the two GUI controls, the implementation allows setting iso-values efficiently and effectively. Drag Update Text Input Update Iso-value update Fig. 5. Iso-value Setting Interface Although this package gives user flexibilities to generate iso-surfaces from different data sources using various time/space/iso-value parameters and visualize them from different view angles and zoom scales, the generated iso-surfaces are volatile by nature. Also both the data and the software might be proprietary and complex and thus it might be difficult to regenerate them. We believe that it will be very useful for users to be able to save the generated iso-surfaces into generic multimedia formats which can be redisplayed in popular software packages, such as image viewers and video players. We implement three functions in this package to achieve the goal using Java Advanced Imaging (JAI), Java 3D (on which VisAD is based) and Java Media Framework (JMF). 15
The interface is shown in Fig. 6. The Screen Snapshot button allows user to save whatever displayed in the current window, including both the iso-surface and the GUIs, into a JPEG image. By including the GUI part in the image, we can tell the parameters used in generating the iso-surface. The Save IsoSurface button allows a user to save only the generated iso-surface as a jpeg image. By skipping GUI, less storage space is required and also there might be a better visual effect. The Video Output button allows a user to save the iso-surfaces over the time window as an Apple QuickTime movie. The interface of displaying the generated movie in RealOne player is shown in the right side of Fig. 6. By plugging more video encoders into JMF, it is possible to generate videos in some other formats. Note that the name of the generated images and videos are based on the system time in long integer format. Fig. 6 Multimedia Output Interface 16
5 Conclusion and Future Work Directions In this report we discussed the background, design and implementation of an isosurface based radar data visualization package. The package is built on top of the popular scientific data visualization software VisAD and cross-platform Java technologies. This Java based package shares the data formats and the distributed indexes used in WDSSII. It can be a useful tool for meteorologist to explore weather patterns in 3D. The multimedia output capability allows viewing the generated iso-surfaces offline easily. For future work, we plan to improve efficiency of iso-surface rendering which includes both grid interpolation and iso-surface extraction from the interpolated gridded data set. We also plan to take uncertainty analysis for the generated iso-surfaces into consideration. It might be also interesting to integrate other types of 2D or 3D weather data into the package, such as precipitation, temperature, wind and lighting. Finally a promising direction might be to generate domain-specific MPEG-7 documents along with the video outputs to allow sharing visualization and analytical results among domain experts and general users. 17
References 1. Will Schroeder, Hen Martin, Bill Lorensen, The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, 2 nd Edition, Prentice Hall PTR, 1997 2. Richard J. Doviak, Dusan S. Zrnic, Doppler Radar and Weather Observation, Academic Press INC, 1992 3. W. Hibbard, C. Rueden, S. Emmerson, T. Rink, D. Glowacki, T. Whittaker, D. Fulker and J. Anderson, Java Distributed Objects for Numerical Visualization in VisAD, Communications of the ACM, 45(4), 2002, 160-170 18