Engineering topics
Publications and source records attributed to Newsom, Rob.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
The AWAKEN Dual-Doppler Lidar (ADDLidar) campaign involved the deployment of two Halo XR+ scanning Doppler lidars at AWAKEN sites A5 and A7. Virtual tower measurements were performed from 12 November 2022 until 17 October 2023.
Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k -means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.
We report turbulence intensity (TI) is often used to quantify the strength of turbulence in wind energy applications and serves as the basis of standards in wind turbine design. Thus, accurately characterizing the spatiotemporal variability in TI should lead to improved predictions of power production. Nevertheless, turbulence measurements over the ocean are far less prevalent than over land due to challenges in instrumental deployment, maintenance, and operation. Atmospheric models such as mesoscale (weather prediction) and large-eddy simulation (LES) models are commonly used in the wind energy industry to assess the spatial variability of a given site. However, the TI derivation from atmospheric models has not been well examined. An algorithm is proposed in this study to realize online calculation of TI in the Weather Research and Forecasting (WRF) model. Simulated TI is divided into two components depending on scale, including sub-grid (parameterized based on turbulence kinetic energy (TKE)) and grid resolved. The sensitivity of sea surface temperature (SST) on simulated TI is also tested. An assessment is performed by using observations collected during a field campaign conducted from February to June 2020 near the Woods Hole Oceanographic Institution Martha's Vineyard Coastal Observatory. Results show that while simulated TKE is generally smaller than the lidar-observed value, wind speed bias is usually small. Overall, this leads to a slight underestimation in sub-grid-scale estimated TI. Improved SST representation subsequently reduces model biases in atmospheric stability as well as wind speed and sub-grid TI near the hub height. Large TI events in conjunction with mesoscale weather systems observed during the studied period pose a challenge to accurately estimating TI from models. Due to notable uncertainty in accurately simulating those events, this suggests summing up sub-grid and resolved TI may not be an ideal solution. Efforts in further improving skills in simulating mesoscale flow and cloud systems are necessary as the next steps.
The Doppler lidar (DL) is an active remote-sensing instrument that provides range- and time-resolved measurements of the line-of-sight component of air velocity (i.e., radial velocity) and attenuated aerosol backscatter. The DL operates in the near-infrared and is sensitive to backscatter from atmospheric aerosol, which are assumed to be ideal tracers of atmospheric wind fields. The DL works by transmitting short pulses of infrared laser light into the atmosphere. Atmospheric aerosols scatter a small fraction of that light energy back to the transceiver, where it is collected and recorded as a time-resolved signal. From the delay between the outgoing pulse and the backscattered signal, the instrument infers the distance to the scattering volume. Coherent detection is used to measure the Doppler frequency shift of the backscatter signal. This is accomplished by mixing the backscatter signal with a reference laser beam (i.e., local oscillator) of known frequency. The onboard signal processor then determines the Doppler frequency shift from the spectrum of the mixed signal. The Doppler frequency shift and thus the radial air velocity is determined from the peak of the Doppler spectrum. The attenuated backscatter is determined from the energy content of the Doppler spectra. The DL provides accurate measurements of radial velocity in regions of the atmosphere where aerosol concentrations are high enough to ensure good signal-to-noise ratio. Thus, valid data are usually limited to the atmospheric boundary layer where aerosol is ubiquitous. Valid measurements can also be obtained in elevated aerosol layers or in optically thin clouds above the boundary layer. Most of the ARM DLs have full upper-hemispheric scanning capability, enabling 3D mapping of turbulent flows in the atmospheric boundary layer. With the scanner pointed vertically, the DL provides height- and time-resolved measurements of vertical velocity.
Refer to attached documentation.