Real-time storm detection and weather forecast activation through data mining and events processing

Abstract

Each year across the USA, destructive weather events disrupt transportation and commerce, resulting in both loss of lives and property. Mitigating the impacts of such severe events requires innovative new software tools and cyberinfrastructure through which scientists can monitor data for specific severe weather events such as thunderstorms and launch focused modeling computations for prediction and forecasts of these evolving weather events. Bringing about a paradigm shift in meteorology research and education through advances in cyberinfrastructure is one of the key research objectives of the Linked Environments for Atmospheric Discovery (LEAD) project, a large-scale, interdisciplinary NSF funded project spanning ten institutions. In this paper we address the challenges of making cyberinfrastructure frameworks responsive to realtime conditions in the physical environment driven by the use cases in mesoscale meteorology. The contribution of the research is two-fold: on the cyberinfrastructure side, we propose a model for bridging between the physical environment and e-Science 1 workflow systems, specifically through events processing systems, and provide a proof of concept implementation of that model in the context of the LEAD cyberinfrastructure. On the algorithmic side, we propose efficient stream mining algorithms that can be carried out on a continuous basis in real time over large volumes of observational data.

Data Mining Methods to Generate Severe Wind Gust Models

“…The ANN node works by simulating a large number of interconnected simple processing units that resemble abstract versions of neurons. The ANNs are powerful general function estimators and they require minimal statistical or mathematical knowledge to train or apply [27]. The "Quick" method used with the Kumeu weather data is based on a feed-forward back-propagation network with the topology (number and configuration of nodes in the input, hidden and output layers) along with the input and output fields.…”

Introduction to data mining for sustainability

“…Forecasting storm, particularly mesocyclones, has been investigated using neural networks, clustering, and image processing based on weather data and simulations (Li et al 2008). The main achievement is the connection between data acquisition and data mining, in that data can trigger the data analysis.…”

Mapping workflow resource requests for bandwidth efficiency in data centers

“…The mapping technique therefore needs to take into account the lifetime of each task in the workflow. Further, while several applications are flexible in terms of bandwidth demands, i.e., the bandwidth available on the links may vary during the execution of the applications, the applications such as detecting gravitational-waves [3], weather forecasting [8], predicting earthquakes [5] need guaranteed bandwidth to ensure the application performance, e.g., meet the execution deadline. We are interested in the latter case where workflow applications require a guaranteed bandwidth, making the problem of mapping workflow resource requests in data centers more challenging for cloud providers to obtain the efficiency.…”