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

数值天气预报模型正成为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中不可或缺的模型。近地表天气参数的模型结果通常直接或通过统计后处理提供给最终用户。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。虽然计算的进步使数值天气预报模式能够更好地表示真实世界的条件，但表示植被覆盖的树冠-大气相互作用的细节仍然是一个挑战。这在一定程度上是由于不同尺度上的树冠不均匀造成的，其变异性严重影响了大气近地表区域。这一近地表地区是无数相关气象过程的宿主，如雾、霜、露和湍流，如果不考虑这些过程，可能会扭曲天气预报。为了克服现有数值预报模式的局限性，本研究的重点是了解和量化植被冠层异质性的影响，并开发新的方法在数值天气预报模式中适当地考虑这些影响。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。利用所获得的数据，将制定新的冠层表示法，以扩展目前在数值天气预报模式中使用的传统关系，以便能够很好地捕捉植被覆盖的冠层中流动的时空变化。这一研究项目将提高对非均匀树冠覆盖上的树冠-大气相互作用的理解和表达。在数值天气预报模式中更好地表示树冠将导致更准确的天气预报。该研究项目还为研究生提供学习经验，并涉及STEM领域中代表性不足的本科生。PI制定了一项计划，涉及与犹他大学的避难所计划合作，以增加学术界代表不足的少数族裔的数量。

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.