## 2 Related Work  
A few geovisual analytical tools dealing with climate data have been developed. In the past, climate data were processed and analyzed in standalone computing resources [18] using scientific packages, such as \"NumPy\" [19] and \"PyClimate\" [20]. Besides the well-developed packages, scientists might  
Figure 1: ModelE data volume. also develop their specialized statistical analysis scripts such as anomaly trend analysis using R to achieve particular research purposes [21]. Although the analytical packages have plenty of professional analysis functions, they do have several limitations: (1) Packages written in various languages such as R, Python, FORTRAN and C are not interoperable. Researchers have to spend considerable amount of time on translating packages; (2) Except for some well-developed packages (e.g., NumPy), analytical tools designed by particular scientists are not shared with others. This circumstance leads to repeated developments of similar analytical functions, which is time consuming. The accuracy of analytical functions produced by developers without good scientific training is not guaranteed [22]; (3) Generic functions on data management and data processing like regridding, data format conversion, and metadata editing are missing. Data preprocessing is necessary before employing the packages for analysis. In addition, since software packages are launched on standalone computers, data analysts have to spend time on transferring data to a local machine where tool is installed when dealing with large distributed data. This process requires large data storage, large network bandwidth and enough computing resources on personal computing facilities. In order to overcome the deficiencies of standalone applications, analytical packages that integrate functions for data processing, visualization and analysis were developed. For example, the Climate Data Analysis Tool (CDAT) [23] is a set of utilities designed for climate research on large volume data sets. CDAT provides capabilities including: (1) management and remote access of data sets; (2) data preprocessing such as regridding and format conversion; (3) functions for advanced statistical and numerical analysis; and (4) multiple 2D or 3D data visualization, for example, ViSUS system [24]. As a component of geovisual analytical tools, a graphical interface is provided to users to interactively invoke CDAT functions. Without the interface, users have to conduct analysis by typing Python commands. However, despite strong data processing and analytical capabilities, CDAT did not address the limitations discussed above related to launching packages on standalone computers. Also, CDAT requires users to install multiple packages on their machines. In addition, these highly professional tools require users to have knowledge of climatology and some programming skills. The similar deficiencies have also been identified in Ultra Volume-CDAT (UV-CDAT). With the popularity of Web 2.0 [25], online systems should be used for geovisual analytics for climate data. A web-based visualization platform developed by Sun [26] offers management and 3D rendering of distributed climate data from different vendors. Simple map operations and statistical plots like line plot of time series are supported through web browsers. Open Statistics eXplorer-platform [27], another web-based geovisual analytical system, provides a good interactive graphical data representation [15]. The system enables dynamic querying over graphs and linkages between different maps and statistical plots. Users can customize map symbolization through interface as well. Such web-based systems partially address the deficiencies of using local machines to process climate data by integrating all data processes in a single point. Data analysts no longer need to transfer data and develop geovisual analytical functions.