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Collaborative Research: Unfolding the Link between Forest Canopy Structure and Flow Morphology: A Physics-based Representation for Numerical Weather Prediction Simulations

合作研究：揭示森林冠层结构与流动形态之间的联系：数值天气预报模拟的基于物理的表示

基本信息

批准号：
1712530
负责人：
Chad Higgins
金额：
$ 8.46万
依托单位：
Oregon State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2020-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712530&HistoricalAwards=false
关键词：
Collaborative Research Unfolding Link between

项目摘要

Numerical weather prediction models are becoming indispensable for severe weather and air quality forecasts as well as for managements in river flows, wind energy, agriculture, and food security. Model results of near-surface weather parameters are routinely supplied to end-users either directly or with statistical post-processing. To produce more reliable numerical weather predictions, two main issues must be addressed: (i) numerical resolution and (ii) representation of the near-surface conditions that shape the local-weather. While advances in computation are enabling a better representation of real world conditions in numerical weather prediction models, representing the detailed vegetated canopy-atmosphere interactions remains a challenge. This is partially the result of canopy heterogeneities present at different scales whose variability significantly affects the near-surface region of the atmosphere. This near-surface region is host to a myriad of relevant meteorological processes such as fog, frost, dew, and turbulence in general, which if not accounted for, can distort weather forecasts. To tackle this limitation of current numerical weather prediction models, the focus of this research project is on understanding and quantifying the effect of vegetated canopy heterogeneities and developing new methodologies to properly account for them within numerical weather prediction models. This will be achieved through the synergy of wind tunnel measurements and high-resolution numerical simulations. With the acquired data, new canopy representations will be formulated that expand upon traditional relationships currently used in numerical weather prediction models such that the spatiotemporal variability of the flow in vegetated canopies can be well captured. This research project will improve the understanding and representation of the canopy-atmosphere interactions on a heterogeneous canopy cover. A better representation of canopies within numerical weather prediction models will lead to more accurate weather forecasts. This research project also provides learning experiences to graduate students and involves underrepresented undergraduate students in the STEM fields. The PIs lay out a plan that involves collaborations with the REFUGES program at University of Utah to increase the number of underrepresented minorities in academia.

数值天气预报模式在恶劣天气和空气质量预报以及河流流量、风能、农业和粮食安全管理方面正变得不可或缺。近地表天气参数的模式结果通常直接或经统计后处理后提供给最终用户。为了产生更可靠的数值天气预报，必须解决两个主要问题：(i)数值分辨率和（ii）影响当地天气的近地表条件的表示。虽然计算技术的进步使数值天气预报模型能够更好地代表真实世界的条件，但要详细地表示植被冠层与大气的相互作用仍然是一个挑战。这在一定程度上是由于不同尺度上的冠层不均匀性，其变异显著影响大气近地表区域。这个近地表区域是无数相关气象过程的宿主，如雾、霜、露和一般的湍流，如果不加以考虑，可能会扭曲天气预报。为了解决当前数值天气预报模型的这一局限性，本研究项目的重点是理解和量化植被冠层异质性的影响，并开发新的方法来适当地在数值天气预报模型中考虑它们。这将通过风洞测量和高分辨率数值模拟的协同作用来实现。利用获得的数据，将在数值天气预报模型中使用的传统关系的基础上制定新的冠层表示，从而可以很好地捕获植被冠层中流量的时空变异性。本研究项目将提高对非均质冠层上冠层-大气相互作用的认识和表征。数值天气预报模式若能更准确地反映冠层，天气预报便会更准确。该研究项目还为研究生提供学习经验，并涉及STEM领域代表性不足的本科生。PIs制定了一项计划，其中包括与犹他大学的难民项目合作，以增加学术界代表性不足的少数民族的数量。