Thursday, 17 September 2015
Oklahoma F (Embassy Suites Hotel and Conference Center )
This paper presents our experience, in the south of Brazil, dealing with radar data analysis for single and dual polarimetric systems, using scientific visualization techniques to explore complex data and obtain an overview and better understanding of the phenomenon. In the past decade, the technology of polarimetric weather radars have been available to several meteorological operational centers throughout the world. The available information generated have, in many cases, increased more than five times, but we are still learning how to select the necessary information in real-time to apply in nowcasting activities. Traditionally, data visualization systems in meteorology maintained some standard user interfaces and features that have persisted for many years. Designers and developers of such systems have given little attention to issues relating to the interface, especially with regard to usability and also exploring new tools to interact and explore the data visually. On the other hand, considering the evolution of personal computers and mobile devices, with changes in habits and how users interact with those devices, it becomes important to study new ways to explore and visualize scientific data. We believe that the experience of viewing such data should be as natural, enjoyable and effective as possible and that it should provide the user with a fast and easy way to make a mental map of the spatial configuration of the data and, thus, get an improved perception of the observed phenomenon. The usability of a display system is an important issue because, when visually inspecting a set of complex data, it is important for the analyst to focus on the analysis and understanding of the data and the phenomenon rather than worry about how to use the system. This means that the analyst should not be distracted, looking for a feature in menus and submenus or performing numerous mouse clicks. A more efficient user interface allows the analyst to focus on research data to gain a better understanding of the phenomenon and thus be able to make decisions and reach to conclusions more quickly and effectively. In this work we used Python language, with Pygame and Numpy packages to develop an application for visualization and analysis of dual polarization weather radar data, focused on ease of use and speed of system response to user requests, in order to provide an effective means of exploitation and visual analysis of the data. We expect to develop and explore different features and tools to offer to the user a more pleasant and efficient experience. We used techniques for navigation and interaction inspired by systems of highly interactive nature, such as games and applications for mobile devices. We seek in this project a balance between productivity in the development and performance in the application. Using Numpy package, we were able to create algorithms for numerical processing with relative ease and with satisfactory performance for our purpose. It has a large collection of mathematical functions for working with arrays and matrices using optimized routines which are transparent to the programmer. Pygame libraries and modules provide an easier way to develop interactive visual systems, such as computer games, with libraries providing low-level access to the keyboard, mouse, joystick, 3D hardware via OpenGL, and 2D image buffer. Using this package it is possible to develop and test new ways to navigate and interact with weather radar data.
The main idea of our application is to obtain several radar products from a set of data available locally, interacting directly on a visual representation that serves as a basis for the interaction. In this way, the user can focus on the analysis of the data much faster and naturally, without the need to convert in advance the data through several processes before being able to view a specific variable or product. This work presents our experience in polarimetric weather radar data visualization and usage within an operational environment for nowcasting severe weather.
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