A Framework for Moving Sensor Data Query and Retrieval of Dynamic Atmospheric Events

Abstract

Abstract. One challenge in Earth science research is the accurate and efficient ad-hoc query and retrieval of Earth science satellite sensor data based on user-defined criteria to study and analyze atmospheric events such as tropical cyclones. The problem can be formulated as a spatiotemporal join query to identify the spatio-temporal location where moving sensor objects and dynamic atmospheric event objects intersect, either precisely or within a user-defined proximity. In this paper, we describe an efficient query and retrieval framework to handle the problem of identifying the spatio-temporal intersecting positions for satellite sensor data retrieval. We demonstrate the effectiveness of our proposed framework using sensor measurements from QuikSCAT (wind field measurement) and TRMM (precipitation vertical profile measurements) satellites, and the trajectories of the tropical cyclones occurring in the North Atlantic Ocean in 2009.

Manifold Learning for Multivariate Variable-Length Sequences With an Application to Similarity Search

“…Using each of these two tropical cyclones separately, one can further perform similarity search to identify two groups of similar tropical cyclones for further analysis. By integrating the query described earlier in this section into a satellite data retrieval system [36], one can retrieve satellite data based on the output from the query for analysis [37].…”