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

该项目预计将大大加快沙尘暴的预测。 PI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跨数据源的格式异构性。PI可以加速收集大量连续传感器数据，运行沙尘暴预测模型，并向应急管理人员等用户传播详细的预测数据。加速第一个任务的方法是基于通过数据差分减少数据。选择数据差异方案的挑战包括客户端-服务器负载平衡、地理空间数据类型的多样性（例如栅格、矢量）以及某些快照中错误/缺失数据的可能性，这些可能会通过差异技术放大，导致沙尘预报错误。通过为粉尘预测模型定制缓存和计算作业调度来加快预测模型的速度。例如，一个位置？的潜在的沙尘暴预测从附近的地方的属性，根据风向等，以加快结果传播，该项目研究地理空间数据的访问模式，并开发自定义预取技术，能够为大量的并发用户。该项目的成果将通过改善沙尘暴预报造福社会。用于数据差异、缓存、作业调度和预取的定制技术也可能有益于其他社会应用，例如天气预测。它还将导致课程开发以及对研究生和本科生的培训。

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.