CSR: Small: System Research to Advance Real-time Dust Storm Forecasting

CSR：小型：推进实时沙尘暴预报的系统研究

• 批准号: 1117300
• 负责人: Songqing Chen
• 金额: $ 42.5万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117300&HistoricalAwards=false
• 关键词: CSR; Small; System; Research; Advance

This project anticipates significantly speeding up prediction of dust storms. The PIs have successful preliminary work in the area in collaboration with relevant domain experts at federal agencies such as NOAA and FEMA. The major challenges in this project are geospatial interoperability and format heterogeneity across data sources. The PIs speed up collection of high-volume continuous sensor data, run dust-storm prediction models, and disseminate detailed prediction data to users such as emergency managers. The approach to speeding-up the first task is based on reduction of data via data differencing. Challenges in choice of data differencing schemes include client-server load-balancing, diversity of geospatial data types (e.g. raster, vector), and possibility of errors/missing data in some snapshots, which may be magnified via difference techniques leading to errors in dust forecast. Speed up for prediction models is done by customizing caching and computation job scheduling to dust prediction models. For example, a location?s potential dust storm is predicted from properties of nearby places based on wind direction etc. To speed-up result dissemination, the project studies geospatial data access patterns and develops custom pre-fetching techniques to be able to serve a large number of concurrent users. The results of this project will benefit society by improving forecasting of dust storms. Custom techniques for data difference, caching, job-scheduling and pre-fetching may also benefit other societal applications such as weather prediction. It will also lead to curriculum development as well as training of graduate and undergraduate students.

该项目预计将大大加快沙尘暴的预测速度。私人投资机构与NOAA和FEMA等联邦机构的相关领域专家合作，在该领域开展了成功的初步工作。该项目的主要挑战是地理空间互操作性和数据源之间的格式异构性。PI加速收集大量的连续传感器数据，运行沙尘暴预测模型，并将详细的预测数据传播给用户，如应急管理人员。加速第一个任务的方法是基于通过数据差异减少数据。选择数据差异方案的挑战包括客户端-服务器负载平衡、地理空间数据类型(例如栅格、矢量)的多样性以及某些快照中错误/丢失数据的可能性，这些问题可能会通过导致沙尘预测错误的差异技术而放大。通过为预测模型定制缓存和计算作业调度来提高预测模型的速度。例如，根据风向等附近地点的属性预测位置-S潜在的沙尘暴。为了加快结果发布，该项目研究了地理空间数据访问模式，并开发了定制的预取技术，以能够服务于大量并发用户。该项目的成果将通过改进沙尘暴预报而造福社会。用于数据差异、缓存、作业调度和预取的定制技术也可能使其他社会应用程序受益，例如天气预报。它还将导致课程开发以及研究生和本科生的培训。