Distributed Multidimensional Analysis of Battlespace Weather

Weather is a critical factor in the success or failure of a wide variety of military operations. Therefore, the ability to accurately predict the weather and efficiently incorporate these predictions into operational planning and execution represents a significant advantage over any adversary. Weather situational awareness is hampered by the size of the data sets, particular as ensemble forecasting methods are used to generate statistical distributions over multiple model runs. To address the need for improved multidimensional weather data storage and access within the military decision making process, we propose to develop Meta-information-Enhanced Multidimensional Meteorological Online Repositories for the net-centric Enterprise (MEMMOR-E). This approach consists of three primary elements. To streamline data storage and dissemination we will design a parameterized approach to wavelet transformations for fusing raw weather data. To decrease the processing burden and speed up the analysis process within end-user applications/services, we will research and experiment with the latest multidimensional data optimization technologies, including coalescing and temporal and spatial cube blocking methods. Finally, we will design and evaluate a process that controls these optimization technologies by assessing the end-user’s information and meta-information requirements and correlating those requirements with the data and meta-data available from sources.