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At least 37 records · Page 2

Wind Plant Flow Physics and Power Performance in Complex Environments: Cooperative Research and Development (Final Report)

Cornell University will partner with NLR on the topic of wind farm wake effects to improve understanding of interactions between complex atmospheric flows, terrain, and wind turbine wakes and plant efficiency. Wind plant flow simulation tools will also be validated. The work performed will help improve wind farm modeling by analyzing data, applying models, designing and performing experiments to acquire additional wind farm data, and develop better models.

17 WIND ENERGY↗

Satellite-based evaluation of AeroCom model bias in biomass burning regions

Global models are widely used to simulate biomass burning aerosol (BBA). Exhaustive evaluations on model representation of aerosol distributions and properties are fundamental to assess health and climate impacts of BBA. Here we conducted a comprehensive comparison of Aerosol Comparisons between Observations and Models (AeroCom) project model simulations with satellite observations. A total of 59 runs by 18 models from three AeroCom Phase-III experiments (i.e., biomass burning emissions, CTRL16, and CTRL19) and 14 satellite products of aerosols were used in the study. Aerosol optical depth (AOD) at 550 nm was investigated during the fire season over three key fire regions reflecting different fire dynamics (i.e., deforestation-dominated Amazon, Southern Hemisphere Africa where savannas are the key source of emissions, and boreal forest burning in boreal North America). The 14 satellite products were first evaluated against AErosol RObotic NETwork (AERONET) observations, with large uncertainties found. But these uncertainties had small impacts on the model evaluation that was dominated by modeling bias. Through a comparison with Polarization and Directionality of the Earth’s Reflectances measurements with the Generalized Retrieval of Aerosol and Surface Properties algorithm (POLDER-GRASP), we found that the modeled AOD values were biased by –93 % to 152 %, with most models showing significant underestimations even for the state-of-the-art aerosol modeling techniques (i.e., CTRL19). By scaling up BBA emissions, the negative biases in modeled AOD were significantly mitigated, although it yielded only negligible improvements in the correlation between models and observations, and the spatial and temporal variations in AOD biases did not change much. For models in CTRL16 and CTRL19, the large diversity in modeled AOD was in almost equal measures caused by diversity in emissions, lifetime, and the mass extinction coefficient (MEC). We found that in the AeroCom ensemble, BBA lifetime correlated significantly with particle deposition (as expected) and in turn correlated strongly with precipitation. Additional analysis based on Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol profiles suggested that the altitude of the aerosol layer in the current models was generally too low, which also contributed to the bias in modeled lifetime. Modeled MECs exhibited significant correlations with the Ångström exponent (AE, an indicator of particle size). Comparisons with the POLDER-GRASP-observed AE suggested that the models tended to overestimate the AE (underestimated particle size), indicating a possible underestimation of MECs in models. The hygroscopic growth in most models generally agreed with observations and might not explain the overall underestimation of modeled AOD. Our results imply that current global models contain biases in important aerosol processes for BBA (e.g., emissions, removal, and optical properties) that remain to be addressed in future research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R2) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.

36 MATERIALS SCIENCE↗

Characterization of wind turbine flow through nacelle-mounted lidars: a review

This article provides a comprehensive review of the most recent advances in the planning, execution, and analysis of inflow and wake measurements from nacelle-mounted wind Doppler lidars. Lidars installed on top of wind turbines provide a holistic view of the inflow and wake characteristics required to characterize and optimize wind turbine performance, carry out model validation and calibration, and aid in real-time control. The need to balance the enhanced capabilities and limitations of lidars compared to traditional anemometers inspired a broad variety of approaches for scan design and wind reconstruction, which we discuss in this review. We give particular emphasis to identifying common guidelines and gaps in the available literature with the aim of providing an exhaustive picture of the state-of-the-art techniques for reconstructing wind plant flow using nacelle-mounted lidars.

17 WIND ENERGY↗

Change Detection Applications in the Earth Sciences Using UAS-Based Sensing: A Review and Future Opportunities

Over the past decade, advancements in collection platforms such as unoccupied aerial systems (UAS), survey-grade GNSS, sensor packages, processing software, and spatial analytical tools have facilitated change detection analyses at an unprecedented resolution over broader spatial and temporal extents and in environments where such investigations present challenges. These technological improvements, coupled with the accessibility and versatility of UAS technology, have pushed the boundaries of spatial and temporal scales in geomorphic change detection. As a result, the cm-scale analysis of topographic signatures can detect and quantify surface anomalies during geomorphic evolution. This review focuses on the use of UAS photogrammetry for fine spatial (cm) and temporal (hours to days) scale geomorphic analyses, and it highlights analytical approaches to detect and quantify surface processes that were previously elusive. The review provides insight into topographic change characterization with precise spatial validations applied to landscape processes in various fields, such as the cryosphere and geosphere, as well as anthropogenic earth processes and national security applications. This work sheds light on previously unexplored aspects of both natural and human-engineered environments, demonstrating the potential of UAS observations in change detection. Our discussion examines the emerging horizons of UAS-based change detection, including machine learning and LIDAR systems. In addition, our meta-analysis of spatial and temporal UAS-based observations highlights the new fine-scale niche of UAS-photogrammetry. This scale advancement sets a new frontier in change detection, offering exciting possibilities for the future of land surface analysis and environmental monitoring in the field of Earth Science.

Andresen, Christian G.↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗