Integration of the UNIDATA Scientific Data Management System Into the Undergraduate Meteorology Curriculm

将购置计算机硬件和软件，以运行由裁研所项目中心开发的气象数据查阅和分析科学数据管理系统。SDM系统是实现该项目的主要目标--将计算机化数据分析方法纳入基恩学院气象学课程--的基本要素。今天的气象学学生需要这些方法的经验，他们需要有效地获取当前数据的手段，以及在不久的将来可以获得的新类型的信息(即NEXTRAD、剖面仪和改进的卫星能力)。这些课程修订是必要的，以使我们的本科课程适应气象学领域不断发展的技术发展。课程修订将包括使用计算机化数据分析的新教学方法，以及开发一系列案例研究和学生练习，以加强新的方法。在修订气象学课程的同时，还将评估其对学生学习的影响。课程目标的评估将通过各种标准手段，以及作为基恩学院现有课程评估工作的一部分，通过专门设计的评估工具来完成。除了课程目标之外，SDM系统还将为教师在神经网络方法、局部预报和中尺度建模方面的研究以及学生研究项目提供更好的数据获取途径。它还将增强我们与该地区当地高中和其他大学共享数据的能力。

Computer hardware and software will be acquired to run the Scientific Data Management (SDM) system for meteorological data access and analysis developed by the UNIDATA Project Center. The SDM system is an essential element in achieving the major objective of this project - the incorporation of the methodologies of computerized data analysis into the meteorology curriculum at Kean College. Today's meteorology students need experience with these methodologies, and they require the means for efficient access to current data as well as new types of information to be available in the near future (i.e. NEXTRAD, profilers, and improved satellite capabilities). These curriculum revisions are necessary to adapt our undergraduate program to the evolving technological developments in the field of meteorology. The curriculum revisions will include new approaches to teaching using computerized data analysis and the development of a range of case studies and student exercises to reinforce the new methodologies. Coexisting with the revision of the meteorology curriculum will be evaluation and assessment of its impact on student learning. Evaluation of the curriculum objectives will be accomplished through a variety of standard means, as well as through specially designed assessment instruments as part of the existing program assessment effort at Kean College. In addition to the curriculum objectives, the SDM system will provide improved access to data for faculty research in neural network approaches to local forecasting and mesoscale modeling, as well as for student research projects. It will also enhance our ability to share data with local high schools and other colleges in the area.