Nebraska Lidar Download [Extra Quality]

Understanding how a bridge structure responds to various loads in a time and resource-efficient approach is vital in characterizing bridge health. Bridge health monitoring is an evaluation of the structural condition and performance, which optimizes limited transportation budgets by prioritizing the bridges that are in the most need for retrofit or replacement. Identifying and remedying issues will mitigate long-term problems and ensure that the bridge remains open to service for all legal loads. In contrast, health monitoring and load rating will determine if a bridge can only carry traffic up to a certain weight or speed, requiring a bridge load restriction. Bridge response monitored in the field can calibrate a finite element method model to produce more reliable load ratings and distribution factors for bridges. Conventional methods utilizing discrete sensors can be time-consuming and provide a limited view of the bridge response that varies throughout the structure. Full-field data provided by lidar proves to be a viable tool to display the entire bridge response using less time and resources than conventional methods. This thesis evaluates the use of lidar to characterize bridge deflection response under changing live and dead loads. Two bridge structures were monitored while a loaded triaxial truck was placed on the deck and the other two were monitored during a phased construction concrete deck pour. The four assessed bridges represent a wide variety of bridges, where in each case lidar was able to provide high-fidelity and full-field deflection shapes. For one of these bridge structures, an inverted tee girder bridge of 19.81-meter length, a numerical model was constructed using the known parameters. Using traditional finite element modeling techniques, the numerical model fell short of the physical bridge response under live loads. The numerical model demonstrated the lack of uniform displacement, which was highlighted and characterized in the lidar point clouds. The use of lidar for this bridge structure demonstrates the benefit of the full-field response as well as the simplicity in the load test procedure.

Post-Fire Damage Assessment of a Highway Bridge

Quantify and characterize damage current health state following vehicular file using ground-based lidar. Role: PI. Collaborators: Christine E. Wittich (co-PI). Status: Funded - Project Complete. Funding agency: Nebraska Department of Transportation (prime), HDR Inc. (indirect).

Nebraska Lidar Download

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This course, designed for those curious about what lidar is and why it is useful for management decisions, provides quick and flexible access to several topics needed to understand the lidar landscape. The course features engaging video and audio, optional knowledge checks, a final quiz with certificate, and assistive services for those with disabilities.

The Offices Of Perry J Zucker, Degreed Engineers, are a well-regarded forensic engineering firm with a strong national presence. Since our establishment in 1993, we have consistently demonstrated impressive success and a proactive approach in our field. Our dedicated team comprises of accomplished in-house engineers, technicians, and support staff, who specialize in addressing matters related to police lidar/laser speeding tickets and violations. Specifically, we proficiently handle cases pertaining to probable cause traffic stops and reasonable suspicion traffic stops.

The procedure involves a minimum of two police vehicles. Police vehicle one is a stationary vehicle equipped with a lidar/laser speed measuring device, specifically a handheld laser / lidar gun. The operator activates the speed measuring device, directing the laser beam towards the target vehicle. The beam reflects back to the lidar / laser device, and the police officer/state trooper reads the speed displayed on the digital readout, typically LED or LCD. They then communicate this information to a second police officer in vehicle two, who is positioned ahead, using a two-way radio. The pursuing police vehicle initiates a traffic stop on the target vehicle.

Carefully review all the documents and evidence that the officer intends to present during the trial. This may include data records and meta files. Police officers who issue lidar or laser speeding tickets are considered experts in using these devices, but not necessarily in their theoretical underpinnings. Therefore, the most logical defense strategy would be to engage an expert witness who is an engineer specializing in laser or lidar speed measuring devices. This expert should also possess knowledge in police training, vehicle characteristics, court rules, and hold certifications as a speed measuring device operator and instructor as well as a certified vehicle technician or mechanic. It is worth noting that trial by declaration (mail) is typically not very successful, especially in certain states.

Although nocturnal convection is common in the Great Plains, nocturnal CI (NCI) remains challenging to forecast, especially in the absence of surface boundaries (Wilson and Roberts 2006; Reif and Bluestein 2018; Weckwerth et al. 2019). This is due in part to the paucity of routine thermodynamic profiling in the PBL and the limited resolution of satellite observations within the lowest few kilometers of the atmosphere (Kahn et al. 2011; Steinke et al. 2015; Weckwerth et al. 2019). Previous studies based on radiosonde and surface data or reanalysis datasets reveal the LLJ patterns already discussed but are limited in their ability to resolve some important details of the spatiotemporal evolution of the LLJ throughout its domain (e.g., Whiteman et al. 1997; Song et al. 2005; Walters et al. 2008, 2014). Targeted observations have been utilized over the years to improve our understanding of LLJs, MCSs, NCI, and their interplay. Wind, water vapor, and elastic backscatter lidars have been some of the key tools in these advanced studies, as they can continuously profile a variety of atmospheric properties from ground-based and airborne platforms. A case study of airborne differential absorption lidar (DIAL) data assimilation by Wulfmeyer et al. (2006) demonstrated major improvement to quantitative precipitation forecasting in the Great Plains. Airborne water vapor lidar has also proven useful in profiling the sharp moisture gradients of drylines and bore waves without steady-state assumptions (e.g., Koch et al. 2008; Bergmaier et al. 2014; Grasmick et al. 2018; Johnson et al. 2018; Lin et al. 2019). Tollerud et al. (2008) presented the first study examining submesoscale features of LLJ moisture transport using airborne DIAL and wind lidars. They found the lidar measurements to differ from interpolated dropsonde values by up to 25%, and argued for the importance of lidar-resolved small-scale circulations near the sharp moisture gradient at PBL or LLJ top. Schfler et al. (2010) used airborne water vapor and wind lidars over Europe (without an LLJ) to evaluate ECMWF forecast model performance, showing a model wet bias of 17%. Passive ground-based sensors such as the atmospheric emitted radiance interferometer (AERI; Turner and Lhnert 2014) have also contributed to studies of LLJ thermodynamics and interactions with the stable boundary layer (Bonin et al. 2015; Toms et al. 2017; Johnson et al. 2018; Lin et al. 2019).

PECAN featured six fixed ground sites (FPs) for automated and intensive operations, three research aircraft, several ground-based mobile observation platforms, and support from preexisting observation networks. This study utilizes observations primarily from two FPs and a lidar onboard the NASA DC-8 aircraft. The two FPs were named FP1 in the southern part of the PECAN domain and FP3 in the northern part of the domain, 317 km northwest of FP1. FP1 was at the Atmospheric Radiation Measurement (ARM) Southern Great Plains central facility (Sisterson et al. 2016). FP3 was in Ellis, Kansas. These FP locations and the DC-8 flight track are shown in Fig. 1. FP1 and FP3 were chosen because they are well-separated along the LLJ track and had complete data availability. No other sites had both autonomous wind and water vapor lidars.

For all lidar versus RAP comparisons, the finer spatial resolution (i.e., lidar) profile was linearly interpolated to the coarser resolution (i.e., RAP) profile. Likewise for moisture flux calculation from wind and water vapor lidar, the finer resolution was interpolated to the coarser.

The highest water vapor mixing ratios produced by RAP were generally in the lowest few hundred meters at night, partially below the minimum range of the lidars. This range limitation omits the lowest one to two vertical levels of RAP from comparison. While these lowest levels should be of general interest for a more complete picture, further examination is beyond the scope of this study.

When combined carefully, lidar observations of moisture and wind from multiple ground sites and an airborne platform provide continuously sampled profiles of the moisture flux across scales. Figure 4 shows the wind and water vapor lidar observations at both ground sites, alongside RAP output for comparison. The southern site FP1 was far upwind of the MCS, well outside the domain of Fig. 3. FP3 was only slightly upwind of the MCS, at the edge of the region of low-level moisture convergence.

Now that the ground-based and airborne lidar observations have been shown and discussed independently, an analysis of moisture flux (defined as the product of the previously examined r and horizontal wind speed) will be presented, also tying in parameters of convective potential and evolution of the MCS. Moisture flux calculation is limited to the ground sites with both Doppler and water vapor lidars.

This research has improved our understanding of LLJ moisture transport and its connections to convection and convective potential. The unprecedented detail and coverage of the lidar observations surpasses the only other lidar-based LLJ moisture transport study in the literature (Tollerud et al. 2008), and the direct ties to an MCS and NCI in this case are aligned with the PECAN campaign research questions. Comparison to RAP analysis identified the structure and magnitude of model water vapor errors in the domain, and this knowledge can potentially be applied to inform RAP usage in meteorological studies or future efforts toward model improvements. While this study was limited in scope of model analysis, more in-depth model comparisons could benefit from characterizing model spatial variability, and from assessing model forecasts. e24fc04721