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Measuring boundary layer stratification, wind shear, and turbulence remains challenging for wind resource assessment. In particular, larger eddy scales have the greatest impact on turbine load fluctuations, and there are few in situ methods to observe them adequately. Satellite remote sensing using synthetic aperture radar (SAR) is an alternative approach. In this study, eddy‐related signatures in 704 high‐resolution images are related to stratification through a bulk Richardson number () measured by a buoy near Martha's Vineyard, the US epicenter of offshore wind. Variations in SAR‐observed atmospheric boundary layer eddies, or lack of them, correspond to specific regimes. Accounting for strong vertical wind shear, typically under stable stratification, is critical for energy production and turbine loads, and SAR directly identifies these conditions by the absence of energetic eddies. SAR also provides a regional climatology of atmospheric stratification for offshore wind assessment, complementing other observations, and with potential application worldwide.

 

Eddy covariance (EC) air–sea CO₂flux measurements have been developed for large research vessels, but have yet to be demonstrated for smaller platforms. Our goal was to design and build a complete EC CO₂flux package suitable for unattended operation on a buoy. Published state-of-the-art techniques that have proven effective on research vessels, such as airstream drying and liquid water rejection, were adapted for a 2-m discus buoy with limited power. Fast-response atmospheric CO₂concentration was measured using both an off-the-shelf (“stock”) gas analyzer (EC155, Campbell Scientific, Inc.) and a prototype gas analyzer (“proto”) with reduced motion-induced error that was designed and built in collaboration with an instrument manufacturer. The system was tested on the University of New Hampshire (UNH) air–sea interaction buoy for 18 days in the Gulf of Maine in October 2020. The data demonstrate the overall robustness of the system. Empirical postprocessing techniques previously used on ship-based measurements to address motion sensitivity of CO₂analyzers were generally not effective for the stock sensor. The proto analyzer markedly outperformed the stock unit and did not require ad hoc motion corrections, yet revealed some remaining artifacts to be addressed in future designs. Additional system refinements to further reduce power demands and increase unattended deployment duration are described.

 

Advances in perception for self-driving cars have accel- erated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions. Yet, to achieve the high safety re- quirement, these perceptual systems must operate robustly under a wide variety of weather conditions including snow and rain. In this paper, we present a new dataset to enable robust autonomous driving via a novel data collection pro- cess — data is repeatedly recorded along a 15 km route un- der diverse scene (urban, highway, rural, campus), weather (snow, rain, sun), time (day/night), and traffic conditions (pedestrians, cyclists and cars). The dataset includes im- ages and point clouds from cameras and LiDAR sensors, along with high-precision GPS/INS to establish correspon- dence across routes. The dataset includes road and object annotations using amodal masks to capture partial occlu- sions and 3D bounding boxes. We demonstrate the unique- ness of this dataset by analyzing the performance of base- lines in amodal segmentation of road and objects, depth estimation, and 3D object detection. The repeated routes opens new research directions in object discovery, contin- ual learning, and anomaly detection. Link to Ithaca365: https://ithaca365.mae.cornell.edu/