RII Track-2 FEC: Precise Regional Forecasting via Intelligent and Rapid Harnessing of National Scale Hydrometeorological Big Data

RII Track-2 FEC：通过智能快速利用国家规模水文气象大数据进行精确区域预报

• 批准号: 2019511
• 负责人: Nian-Feng Tzeng
• 金额: $ 500万
• 依托单位: University of Louisiana at Lafayette
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019511&HistoricalAwards=false
• 关键词: RII; Track; FEC; Precise; Regional

Global warming has emerged as a stark problem of national importance, as it results in more frequent extreme weather and climate events that cause rising economic loss and adverse societal impacts on numerous sectors, such as agriculture, transportation, water resource management, urban planning, among others. For better observations and numerical models on weather and climate parameters to improve forecasting accuracy, this project addresses precise regional forecasting via intelligent and rapid harness on national scale hydrometeorological Big Data. It aims to improve meteorological and hydrologic forecasts at target regions of interest by integrating massive atmospheric data sets with gathered surface data for finer temporal and spatial predictions, containing both fundamental research and experimental activities. Its solution approach is innovative by leveraging the actual gathered data as feedback to make prediction models generate better products with multiple near-term time horizons. Better regional prediction results from harnessing Big Data intelligently and rapidly via utilizing (1) a collection of proposed simple neural network models (called modelets) and (2) multiple accelerating methodologies developed or under development by the research team members. The modelet-based solutions for improving weather prediction spatially and temporally are applicable to all regions in the nation, with easy portability. They are being undertaken synergistically by jurisdictional collaboration across five universities in Louisiana, Alabama, and Kentucky, plus U.S. Geological Survey, enabling broad engagement at the frontiers of discovery and innovation in science and engineering related to accelerating data analytics, meteorology, and hydrology. Besides promoting the progress of science, this multidisciplinary project advances the national prosperity and welfare by curbing potential disruption due to global warming. The project also includes comprehensive efforts for (1) building future leadership through collaboration and supervision of junior investigators for their career advances, (2) enriching educational materials on the focused disciplines and strengthening student research to boost workforce development, and (3) aggressively recruiting and engaging underrepresented participants to support diversity.Better observations and numerical models on weather and climate parameters improve forecasting accuracy, able to suppress the uptrend in economic loss and societal impacts caused by disasters pertinent to extreme weather and climate events, as a result of global warming. This multidisciplinary project involves both fundamental research and experimental activities, built upon and expanding earlier work of team members in the disciplines of computer science and engineering, meteorology, hydrology, and electrical & computer engineering. It deals with the technical challenges of intelligent and rapid harness on national scale hydrometeorological Big Data for precise meteorological and hydrological forecasting regionally, with anticipated outcomes likely to push the frontiers of intelligent bigdata harness by NNs (neural networks) and of speedy data processing through various methodologies. Intelligent bigdata harness results from proper end-to-end simple NN models (called modelets), which are trained inventively by huge datasets obtained continuously from both near-ground observations (via Mesonet stations or water gauges) and geo-gridded predictions based on computing physical atmospheric equations (via the Weather Research and Forecasting model with High-Resolution Rapid Refresh). Various methodologies for accelerating bigdata harness are under development and to be evaluated thoroughly during the project years, including (1) effective computer system DRAM expansion, (2) execution resilience enhancement, and (3) high-compute density support by SC (stochastic computing)-based accelerators and by GPGPUs with desirable scheduling policies for modelet training and inference. The modelet-based solutions for precise regional forecasting via intelligent and rapid (PREFER) bigdata harness improve weather prediction spatially and temporally for wide applications to all regions in the nation, with easy portability to offer short-term and fine spatial resolution prediction. They aim to address the important problems of meteorological forecast applications (e.g., landfalling of severe thunderstorms or tropical systems), flood warning alert enhancement, backwater wetland storage capacity investigation for river flood mitigation, among others. This PREFER work inspires and nourishes cross-disciplinary research and jurisdictional collaboration across five universities in Louisiana, Alabama, and Kentucky, plus U.S. Geological Survey, helping to integrate research and education while advancing discovery and understanding in the scientific contents of interwoven project activities. It contains comprehensive efforts for (1) lifting career development of junior investigators to build future leadership, (2) enriching educational materials on the focused disciplines and supporting student research to spur workforce development, (3) engaging active participation from underrepresented groups, and (4) disseminating project outcomes and software tools widely.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

全球变暖已成为一个具有国家重要性的严峻问题，因为它导致极端天气和气候事件更加频繁，对农业、交通、水资源管理、城市规划等许多部门造成越来越大的经济损失和不利的社会影响。 为了更好地观测天气和气候参数并建立数值模型，以提高预报精度，该项目通过智能和快速利用国家级水文气象大数据来实现精确的区域预报。 它旨在通过将大量大气数据集与收集的地面数据相结合，进行更精确的时间和空间预测，改进目标感兴趣区域的气象和水文预报，其中包括基础研究和实验活动。 它的解决方案是创新的，利用实际收集的数据作为反馈，使预测模型生成具有多个近期时间范围的更好的产品。 更好的区域预测结果来自于智能和快速地利用大数据，通过利用（1）一组拟议的简单神经网络模型（称为modelets）和（2）研究团队成员开发或正在开发的多种加速方法。 基于模型的解决方案，以改善天气预报的空间和时间适用于全国所有地区，具有很好的可移植性。 它们正在通过路易斯安那州、亚拉巴马州和肯塔基州的五所大学以及美国地质调查局的司法合作进行协同合作，使人们能够广泛参与与加速数据分析、气象学和水文学相关的科学和工程领域的发现和创新。 除了促进科学的进步，这个多学科项目还通过遏制全球变暖造成的潜在破坏来促进国家的繁荣和福利。 该项目还包括以下综合努力：（1）通过合作和监督初级研究人员的职业发展来培养未来的领导力，（2）丰富重点学科的教育材料，加强学生研究以促进劳动力发展，以及（3）积极招募和吸引代表性不足的参与者，以支持多样性。提高预测准确性，能够抑制全球变暖导致的极端天气和气候事件相关灾害造成的经济损失和社会影响的上升趋势。 这个多学科项目涉及基础研究和实验活动，建立在计算机科学与工程，气象学，水文学和电气计算机工程学科的团队成员的早期工作的基础上。 它涉及智能和快速利用国家级水文气象大数据进行区域精确气象和水文预报的技术挑战，预期结果可能会推动NN（神经网络）智能大数据利用的前沿，并通过各种方法快速处理数据。 智能大数据利用来自适当的端到端简单NN模型（称为modelets），这些模型通过从近地面观测（通过Mesonet站或水位计）和基于计算物理大气方程的地理网格预测（通过具有高分辨率快速刷新的天气研究和预测模型）连续获得的巨大数据集进行创造性训练。 加速大数据利用的各种方法正在开发中，并将在项目年内进行彻底评估，包括（1）有效的计算机系统DRAM扩展，（2）执行弹性增强，以及（3）基于SC（随机计算）的加速器和GPGPU的高计算密度支持，以及用于模型训练和推理的理想调度策略。 基于模型的解决方案通过智能和快速（PREFER）大数据工具进行精确的区域预报，在空间和时间上改善天气预报，广泛应用于全国所有地区，易于移植，以提供短期和精细的空间分辨率预测。 它们旨在解决气象预报应用的重要问题（例如，强雷暴或热带系统的登陆）、洪水预警警报增强、为缓解河流洪水而进行的回水湿地蓄水能力调查等。 这项PREFER的工作激励和促进了路易斯安那州、亚拉巴马州和肯塔基州的五所大学以及美国地质调查局的跨学科研究和司法合作，有助于整合研究和教育，同时促进对交织在一起的项目活动的科学内容的发现和理解。 它包含以下全面努力：（1）提升初级调查员的职业发展，以建立未来的领导力，（2）丰富重点学科的教育材料，支持学生研究，以刺激劳动力发展，（3）吸引代表性不足的群体的积极参与，以及（4）广泛传播项目成果和软件工具。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

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• DOI: 10.1145/3583781.3590307
• 发表时间: 2023-06
• 期刊: Proceedings of the Great Lakes Symposium on VLSI 2023
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Platform-Oblivious Anti-Spam Gateway

• DOI: 10.1145/3485832.3488024
• 发表时间: 2021-12
• 期刊: Proceedings of the 37th Annual Computer Security Applications Conference
• 作者: Yihe Zhang;Xu Yuan;N. Tzeng

Devils in Your Apps: Vulnerabilities and User Privacy Exposure in Mobile Notification Systems

• DOI: 10.1109/dsn58367.2023.00017
• 发表时间: 2023-06
• 期刊: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
• 作者: Jiadong Lou;Xiaohan Zhang;Yihe Zhang;Xinghua Li;Xu Yuan;Ning Zhang

Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge

• DOI: 10.1109/ucc56403.2022.00012
• 发表时间: 2022-11
• 期刊: 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)
• 作者: S. Zobaed;Ali Mokhtari;J. Champati;M. Kourouma;M. Salehi

Stochastic Computing Design and Implementation of a Sound Source Localization System

• DOI: 10.1109/jetcas.2023.3243604
• 发表时间: 2023-03-01
• 期刊: IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS
• 影响因子: 4.6
• 作者: Schober, Peter;Estiri, Seyedeh Newsha;TaheriNejad, Nima
• 通讯作者: TaheriNejad, Nima