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Abstract This study documents the capabilities of the StreamSonde, a lightweight (24 g) instrument manufactured by Skyfora that measures atmospheric temperature, pressure, humidity, and wind velocity. Unique features of the StreamSonde are its wind speed accuracy enabled by a dual-band Global Navigation Satellite System (GNSS) receiver, the ability to vary the terminal fall velocity, a theoretical maximum communication distance between the instrument and the deployment aircraft of 250 km, and the ability to simultaneously operate up to eight instruments (50 in the future). Skyfora’s GNSS receiver receives signals on two bands from U.S. global positioning system (GPS) (L1/L5), European Galileo (E1/E5a), and Chinese BeiDou (B1I/B2a) satellites to calculate the wind speed. The combination of dual GNSS and lower terminal fall velocity results in more accurate wind retrievals than from single-band GPS potentially allowing us calculate turbulence quantities, especially near the surface. StreamSondes were launched as dropsondes from the NOAA P-3 aircraft in both clear-air low-wind testing environments and in Hurricane Nigel (2023). The pressure, temperature, humidity (in clear air), and derived wind velocity collected by the StreamSonde compare favorably to the widely used RD41 dropsonde that was developed at the National Center for Atmospheric Research (NCAR) and is manufactured by Vaisala. At coreleased drops in Hurricane Nigel, mean absolute differences between RD41 dropsondes and StreamSondes are generally below 1°C for air temperature, 1.5 m s⁻¹for wind speed, and 6° for wind direction. The benefits of using the StreamSonde instrument along with planned improvements to the platform are discussed. Significance StatementThis study presents proof of concept for operational deployment of a new, lightweight atmospheric profiler called the StreamSonde in a tropical cyclone. It uses advanced positioning technology to accurately measure three-dimensional wind velocity, has an adjustable terminal velocity, and can be deployed in “swarms” of sensors that have up to eight (50 in the future) instruments simultaneously active. The versatility of this emerging technology makes it useable for many meteorological applications. 

Abstract Active machine learning is widely used in computational studies where repeated numerical simulations can be conducted on high performance computers without human intervention. But translation of these active learning methods to physical systems has proven more difficult and the accelerated pace of discoveries aided by these methods remains as yet unrealized. Through the presentation of a general active learning framework and its application to large-scale boundary layer wind tunnel experiments, we demonstrate that the active learning framework used so successfully in computational studies is directly applicable to the investigation of physical experimental systems and the corresponding improvements in the rate of discovery can be transformative. We specifically show that, for our wind tunnel experiments, we are able to achieve in approximately 300 experiments a learning objective that would be impossible using traditional methods. 

Abstract This is the second of a two-part study that explores the capabilities of a mesoscale atmospheric model to reproduce the near-surface wind fields in hurricanes over land. The Weather Research and Forecasting (WRF) Model is used with two planetary boundary layer parameterizations: the Yonsei University (YSU) and the Mellor–Yamada–Janjić (MYJ) schemes. The first part presented the modeling framework and initial conditions used to produce simulations of Hurricane Wilma (2005) that closely reproduced the track, intensity, and size of its wind field as it passed over South Florida. This part explores how well these simulations can reproduce the winds at fixed points over land by making comparisons with observations from airports and research weather stations. The results show that peak wind speeds are remarkably well reproduced at several locations. Wind directions are evaluated in terms of the inflow angle relative to the storm center, and the simulated inflow angles are generally smaller than observed. Localized peak wind events are associated with vertical vorticity maxima in the boundary layer with horizontal scales of 5–10 km. The boundary layer winds are compared with wind profiles obtained by velocity–azimuth display (VAD) analyses from National Weather Service Doppler radars at Miami and Key West, Florida; results from these comparisons are mixed. Nonetheless the comparisons with surface observations suggest that when short-term hurricane forecasts can sufficiently predict storm track, intensity, and size, they will also be able to provide useful information on extreme winds at locations of interest. 

ABSTRACT: This paper explores the use of cyber-physical systems (CPS) for optimal design in wind engineering. The approach combines the accuracy of physical wind tunnel testing with the ability to efficiently explore a solution space using numerical optimization algorithms. The approach is fully automated, with experiments executed in a boundary layer wind tunnel (BLWT), sensor feedback monitored by a high-performance computer, and actuators used to bring about physical changes in the BLWT. Because the model is undergoing physical change as it approaches the optimal solution, this approach is given the name “loop-in-the-model” testing. The building selected for this study is a low-rise structure with a parapet wall of variable height. Parapet walls alter the location of the roof corner vortices, alleviating large suction loads on the windward facing roof corner and edges and setting up an interesting optimal design problem. In the BLWT, the model parapet height is adjusted using servo-motors to achieve a particular design. The model surface is instrumented with pressure taps to measure the envelope pressure loading. The taps are densely spaced on the roof to provide sufficient resolution to capture the change in roof corner vortex formation. Experiments are conducted using a boundary BLWT located at the University of Florida Natural Hazard Engineering Research Infrastructure (NHERI) Experimental Facility. The proposed CPS approach enables the optimal solution to be found quicker than brute force methods, in particular for complex structures with many design variables. The parapet wall provides a proof-of-concept study with a single design variable that has a non-monotonic influence on a structure’s wind load. This study focuses on envelope load effects, seeking the parapet height that minimizes roof and parapet wall suction loading. Implications are significant for more complex structures where the optimal solution may not be obvious and cannot be reasonably determined with traditional experimental or computational methods. KEYWORDS: Cyber-physical systems, optimization, boundary-layer wind tunnel, parapet wall, NHERI