Ruixin Yang

Performance‐improving techniques in web‐based GIS

WebGIS (also known as web‐based GIS and Internet GIS) denotes a type of Geographic Information System (GIS), whose client is implemented in a Web browser. WebGISs have been developed and used extensively in real‐world applications. However, when such a complex web‐based system involves the dissemination of large volumes of data and/or massive user interactions, its performance can become an issue. In this paper, we first identify several major potential performance problems with WebGIS. Then, we discuss several possible techniques to improve the performance. These techniques include the use of pyramids and hash indices on the server side to handle large images. To resolve server‐side conflicts originating from concurrent massive access and user interactions, we suggest clustering and multithreading techniques. Multithreading is also used to break down the long sequential, layer‐based data access to concurrent data access on the client side. Caching is suggested as a means to enhance concurrent data access for the same datasets on both the server and the client sides. The technique of client‐side dynamic data requests is used to improve data transmission. Compressed binary representation is implemented on both sides to reduce transmission volume. We also compare the performance of a prototype WebGIS with and without these techniques.

Multi-sensor studies of the Sumatra earthquake and tsunami of 26 December 2004

Multi sensor satellites are now capable of monitoring the globe during day and night and provide information about the land, ocean and atmosphere. Soon after the Sumatra tsunami and earthquake of 26 December 2004, multi‐sensors data have been analysed to study the changes in ocean, land, meteorological and atmospheric parameters. A pronounced changes in the ocean, atmospheric and meteorological parameters are observed while comparing data prior and after the Sumatra main event of 26 December 2004. These changes strongly suggest a strong coupling between land, ocean and atmosphere associated with the Sumatra event.

Numerical Simulations of the Impacts of the Saharan Air Layer on Atlantic Tropical Cyclone Development

Abstract In this study, the role of the Saharan air layer (SAL) is investigated in the development and intensification of tropical cyclones (TCs) via modifying environmental stability and moisture, using multisensor satellite data, long-term TC track and intensity records, dust data, and numerical simulations with a state-of-the-art Weather Research and Forecasting model (WRF). The long-term relationship between dust and Atlantic TC activity shows that dust aerosols are negatively associated with hurricane activity in the Atlantic basin, especially with the major hurricanes in the western Atlantic region. Numerical simulations with the WRF for specific cases during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) experiment show that, when vertical temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS) were assimilated into the model, detailed features of the warm and dry SAL, including the entrainment of dry air wrapping around the developing vortex, are well simulated....

Hyperspectral image assessment of oil‐contaminated wetland

In exploring the nature of hyperspectral data, this study has focused on one of its most challenging applications—oil spill detection—in order to uncover the potential limits of such data. The classification performance of conventional techniques can be improved by testing the accuracy of the existing classifiers using a ground data image as a reference. Moreover, a created prototype demonstrates how hyperspectral data can supplement information on environmental deterioration due to oil pollution, specifically the Patuxent River wetland at the Chesapeake Bay in Maryland. The data allow an assessment of the current state of wetland losses and habitat changes due to oil pollution of local waters and associated wetlands. Airborne Imaging Spectro‐Radiometer for Applications (AISA) hyperspectral imagery was used for this study and the results were derived using the Environment for Visualizing Images (ENVI) software. The use of different classifiers showed low accuracy and class overlap for many classes. Therefore, a ground data image was created using maximum likelihood (ML) classification to compare the results of several classifiers and to assess the accuracy of each technique. Using 2D scatter plots for selecting regions of interest yielded more accurate results than digitizing polygons for training samples. It allowed precise identification of grass stress and soil damaged by polluted water.

Information technology implementation for a distributed data system serving Earth scientists: seasonal to interannual ESIP

We address the implementation of a distributed data system designed to serve Earth system scientists. A consortium led by George Mason University has been funded by NASAs Working Prototype Earth Science Information Partner (WP-ESIP) program to develop, implement, and operate a distributed data and information system. The system will address the research needs of seasonal to interannual scientists whose research focus includes phenomena such as El Nino, monsoons and associated climate studies. The system implementation involves several institutions using a multitiered client-server architecture. Specifically the consortium involves an information system of three physical sites, GMU, the Center for Ocean-Land-Atmosphere Studies (COLA) and the Goddard Distributed Active Archive Center, distributing tasks in the areas of user services, access to data, archiving, and other aspects enabled by a low-cost, scalable information technology implementation. The project can serve as a model for a larger WP-ESIP Federation to assist in the overall data information system associated with future large Earth Observing System data sets and their distribution. The consortium has developed innovative information technology techniques such as content based browsing, data mining and associated component working prototypes; analysis tools particularly GrADS developed by COLA, the preferred analysis tool of the working seasonal to interannual communities; and a Java front-end query engine working prototype.

Managing scientific metadata using XML

We present our XML-based Distributed Metadata Server (Dimes) - which comprises a flexible metadata model, search software, and a Web-based interface - to support multilevel metadata access, and introduce two prototype systems. Our Scientific Data and Information Super Server (SDISS), which is based on Dimes and GDS, solves accurate data-search and outdated data-link problems by integrating metadata with the data systems. On the implementation front, we combine independent components and open-source technologies into a coherent system to dramatically extend system capabilities. Obviously, our approach can be applied to other scientific communities, such as bioinformatics and space science.

Association Rule Data Mining Applications for Atlantic Tropical Cyclone Intensity Changes

AbstractThis study applies a data mining technique called association rule mining to the analysis of intensity changes of North Atlantic tropical cyclones (TCs). The “best track” data from the National Hurricane Center and the Statistical Hurricane Intensity Prediction Scheme databases were stratified into tropical depressions, tropical storms, and category 1–5 hurricanes based on the Saffir–Simpson hurricane scale. After stratification, the seven resulting groups of TCs plus two additional aggregation groups were further separated into intensifying, weakening, and stable TCs. The analysis of the stratified data for preprocessing revealed that faster northward storm motion (the meridional component of storm motion) favors tropical storm intensification but does not favor the intensification of hurricanes. Intensifying tropical storms are more strongly associated with a higher convergence in the upper atmosphere (200-hPa relative eddy momentum flux convergence) than weakening tropical storms, while intensi...

A pyramid data model for supporting content-based browsing and knowledge discovery

Remote sensing from space can provide global and continuous observations. The associated measurement data need to be stored and studied to understand the Earth system processes. The ability of interactive content-based browsing, i.e., browsing or searching the content to narrow-down the interesting portions of data sets prior to actually accessing or ordering full data sets, is highly desirable for any Earth science data information system. However the large volumes of archived and future Earth science remote sensing data are clearly a serious challenge for an interactive browsing process. In this paper a pyramid data model is introduced to support interactive content-based browsing and knowledge discovery for a wide variety of Earth science remote sensing data sets. By using multi-level precomputation and robust nonparametric approximation procedures, the interactive browsing performance can be enhanced greatly. An initial implementation and testing of this data model has been carried out through our research prototype system, Virtual Domain Application Data Center (VDADC). Future implementations are planned for our Seasonal to Interannual Earth Science Information Partner (SIESIP) project.

Value Range Queries on Earth Science Data via Histogram Clustering

Remote sensing data as well as ground-based and model output data about the Earth system can be very large in volume. On the other hand, in order to use the data efficiently, scientists need to search for data based on not only metadata but also actual data values. To answer value range queries by scanning very large volumes of data is obviously unrealistic. This article studies a clustering technique on histograms of data values on predefined cells to index the cells. Through this index system, the so-called statistical range queries can be answered quickly and approximately together with an accuracy assessment. Examples of using this technique for Earth science data sets are given in this article.

Anomalous cold water detected along Mid‐Atlantic Coast

In July 2003, anomalous cold water along the mid-Atlantic coast affected local tourism and fishing. The cold water interfered with tuna fishing, and for 2 to 3 weeks, rockfish generally found during the fall were present in the area [Kelly, 2003]. Satellite data, buoy observations, and weather maps were analyzed to investigate the cause of this cold water event. The results show that the increasing westerly and southerly winds that resulted from approaching cold fronts may have induced upwelling away from and along the mid-Atlantic coast. This, combined with the southward advection of cold sea water from the North Atlantic Ocean, might have caused the anomalous cold water along the coast.