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The Fires, Asian, and Stratospheric Transport–Las Vegas Ozone Study (FAST-LVOS) was conducted in May and June of 2017 to study the transport of ozone (O3) to Clark County, Nevada, a marginal non-attainment area in the southwestern United States (SWUS). This 6-week (20 May–30 June 2017) field campaign used lidar, ozonesonde, aircraft, and in situ measurements in conjunction with a variety of models to characterize the distribution of O3 and related species above southern Nevada and neighboring California and to probe the influence of stratospheric intrusions and wildfires as well as local, regional, and Asian pollution on surface O3 concentrations in the Las Vegas Valley (≈ 900 m above sea level, a.s.l.). In this paper, we describe the FAST-LVOS campaign and present case studies illustrating the influence of different transport processes on background O3 in Clark County and southern Nevada. The companion paper by Zhang et al. (2020) describes the use of the AM4 and GEOS-Chem global models to simulate the measurements and estimate the impacts of transported O3 on surface air quality across the greater southwestern US and Intermountain West. The FAST-LVOS measurements found elevated O3 layers above Las Vegas on more than 75 % (35 of 45) of the sample days and show that entrainment of these layers contributed to mean 8 h average regional background O3 concentrations of 50–55 parts per billion by volume (ppbv), or about 85–95 µg m−3. These high background concentrations constitute 70 %–80 % of the current US National Ambient Air Quality Standard (NAAQS) of 70 ppbv (≈ 120 µg m−3 at 900 m a.s.l.) for the daily maximum 8 h average (MDA8) and will make attainment of the more stringent standards of 60 or 65 ppbv currently being considered extremely difficult in the interior SWUS.

The Fires, Asian, and Stratospheric Transport-Las Vegas Ozone Study (FAST-LVOS) was conducted in May and June of 2017 to study the transport of ozone (O3) to Clark County, Nevada, a marginal non-attainment area in the Southwestern U.S. (SWUS). This 6-week (20 May–30 June 2017) field campaign used lidar, ozonesonde, aircraft, and in-situ measurements in conjunction with a variety of models to characterize the distribution of O3 and related species above southern Nevada and neighbouring California, and to probe the influence of stratospheric intrusions, wildfires, and local, regional, and Asian pollution on surface O3 concentrations in Las Vegas and the surrounding area. In this paper, we describe the FAST-LVOS campaign and present case studies illustrating the influence of different transport processes on background O3 and air quality attainment in the SWUS. The measurements found elevated O3 layers above Las Vegas on more than 75 % (35 of 45) of the sample days, and show that entrainment of these layers contributed to mean 8-h average background O3 concentrations of 50–55 parts-per-billion by volume (ppbv) across southern Nevada. These background concentrations constitute 70–80 % of the current U.S. National Ambient Air Quality Standard (NAAQS) of 70 ppbv, and illustrate some of the challenges facing air quality managers tasked with O3 attainment in the SWUS during late spring and early summer. The companion paper by Zhang et al. (2020) describes the use of the AM4 and GEOS-Chem global models to estimate the impacts of transported O3 on surface air quality in the Southwestern U.S. and Intermountain West during the FAST-LVOS campaign.

Nocturnal low-level jets (LLJs) have been documented at many places around the world, particularly across the Great Plains of the United States. Nocturnal LLJs significantly affect the regional weather and climate, as well as several industrial activities. While the LLJ has been extensively studied, little attention has been paid to the relationship between the evolution of the LLJ, its properties, and the boundary-layer structure. Here, observations collected in northern Oklahoma, USA are used to analyze the characteristics and evolution of the LLJ during conditions of weak and strong turbulence, respectively, coinciding with strongly and weakly stable nocturnal boundary layers. Additionally, the relationship between the LLJ and the temperature profile under these different conditions is examined. Three case studies are used during conditions of weak and strong turbulence, followed by statistics computed from a larger dataset of LLJs. When turbulence is weak, the LLJ is shown to reach peak intensity later near sunrise, while a maximum in the intensity is reached about six hours after sunset in the highly turbulent boundary layer. Furthermore, the LLJ tends to stay constant with height when turbulence is maintained throughout the night, while the LLJ tends to follow isentropic surfaces when vertical mixing is suppressed. When the LLJ follows isentropic surfaces, changes in the wind speed are related to the stability modification.

Profiles of the sensible heat flux are key to understanding atmospheric-boundary-layer (ABL) structure and development. Based on temperature profiling by a remotely-piloted aircraft system (RPAS), the Small Unmanned Meteorological Observer (SUMO) platform, during the Boundary Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign, 108 heat-flux profiles are estimated using a simplified version of the prognostic equation for potential temperature $$\theta $$θ that relates the tendency in $$\theta $$θ to the flux divergence over the time span between two consecutive flights. We validate for the first time RPAS-based heat-flux profiles against a network of 12 ground-based eddy-covariance stations (2–60 m above ground), in addition to a comparison with fluxes from a manned aircraft and a tethered balloon, enabling the detailed investigation of the potential and limitations related to this technique for obtaining fluxes from RPAS platforms. We find that appropriate treatment of horizontal advection is crucial for obtaining realistic flux values, and present correction methods specific to the state of the ABL. Advection from a mesoscale model is also tested as another correction method. The SUMO heat-flux estimates with appropriate corrections compare well with the reference measurements, with differences in the performance depending on the time of day, since the evening period shows the best results (94$$\%$$% within the spread of ground stations), and the afternoon period shows the poorest results (63$$\%$$% within the spread). The diurnal cycle of the heat flux is captured by the SUMO platform for several days, with the flux values from the manned aircraft and tethered balloon coinciding well with those from the SUMO platform.

Annually and seasonally averaged wind profiles from three Doppler lidars were obtained from sites in the Columbia River basin of east-central Oregon and Washington, a major region of wind-energy production, for the Second Wind Forecast Improvement Project (WFIP2) experiment. The profile data are used to quantify the spatial variability of wind flows in this area of complex terrain, to assess the HRRR–NCEP model’s ability to capture spatial and temporal variability of wind profiles, and to evaluate model errors. Annually averaged measured wind speed differences over the 70-km extent of the lidar measurements reached 1 m s−1 within the wind-turbine rotor layer, and 2 m s−1 for 200–500 m AGL. Stronger wind speeds in the lowest 500 m occurred at sites higher in elevation, farther from the river, and farther west—closer to the Cascade Mountain barrier. Validating against the lidar data, the HRRR model underestimated strong wind speeds (>12 m s−1) and, consequently, their frequency of occurrence, especially at the two lowest-elevation sites, producing annual low biases in rotor-layer wind speed of 0.5 m s−1. The RMSE between measured and modeled winds at all sites was about 3 m s−1 and did not degrade significantly with forecast lead time. The nature of the model errors was different for different seasons. Moreover, although the three sites were located in the same basin terrain, the nature of the model errors was different at each site. Thus, if only one of the sites had been instrumented, different conclusions would have been drawn as to the major sources of model error, depending on where the measurements were made.

Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current Earth system models to capture and simulate land–atmosphere feedback. To facilitate its realistic simulation in next-generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land–atmosphere (L–A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable.Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive datasets for characterizing L–A feedback independently of any model input. Several new applications are introduced, such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain; the testing of Monin–Obukhov similarity theory and turbulence parameterizations; the direct measurement of entrainment fluxes; and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE dataset are introduced. We envision the initiation of innovative L–A feedback studies in different climate regions to improve weather forecast, climate, and Earth system models worldwide.

A Halo Photonics Stream Line XR Doppler lidar has been deployed for the Indianapolis Flux Experiment (INFLUX) to measure profiles of the mean horizontal wind and the mixing layer height for quantification of greenhouse gas emissions from the urban area. To measure the mixing layer height continuously and autonomously, a novel composite fuzzy logic approach has been developed that combines information from various scan types, including conical and vertical-slice scans and zenith stares, to determine a unified measurement of the mixing height and its uncertainty. The composite approach uses the strengths of each measurement strategy to overcome the limitations of others so that a complete representation of turbulent mixing is made in the lowest km, depending on clouds and aerosol distribution. Additionally, submeso nonturbulent motions are identified from zenith stares and removed from the analysis, as these motions can lead to an overestimate of the mixing height. The mixing height is compared with in situ profile measurements from a research aircraft for validation. To demonstrate the utility of the measurements, statistics of the mixing height and its diurnal and annual variability for 2016 are also presented. The annual cycle is clearly captured, with the largest and smallest afternoon mixing heights observed at the summer and winter solstices, respectively. The diurnal cycle of the mixing layer is affected by the mean wind, growing slower in the morning and decaying more rapidly in the evening with lighter winds.

Mobile radar platforms designed for observation of severe local storms have consistently pushed the boundaries of spatial and temporal resolution in order to allow for detailed analysis of storm structure and evolution. Digital beamforming, or radar imaging, is a technique that is similar in nature to a photograwphic camera, where data samples from different spaces at the same range are collected simultaneously. This allows for rapid volumetric update rates compared to radars that scan with a single narrow beam. The Atmospheric Imaging Radar (AIR) is a mobile X-band (3.14-cm wavelength) imaging weather radar that transmits a vertical, 20° fan beam and uses a 36-element receive array to form instantaneous range–height indicators (RHIs) with a native beamwidth of 1° × 1°. Rotation in azimuth allows for 20° × 90° volumetric updates in under 6 s, while advanced pulse compression techniques achieve 37.5-m range resolution. The AIR has been operational since 2012 and has collected data on tornadoes and supercells at ranges as close as 6 km, resulting in high spatial and temporal resolution observations of severe local storms. The use of atmospheric imaging is exploited to detail rapidly evolving phenomena that are difficult to observe with traditional scanning weather radars.

Doppler lidars with a high pulse repetition frequency are susceptible to range ambiguities from clouds and other distant targets with large backscatter. These range ambiguities degrade the quality of wind profiles and other calculated or retrieved variables. Here, a technique to detect and remove range-folded returns from the moment observations is proposed and described. A simplified method of removing erroneous wind observations from range-folded returns is also detailed. Through detection and removal of range-folded echoes, the quality and accuracy of wind profiles are shown to improve.

Since turbulence measurements from Doppler lidars are being increasingly used within wind energy and boundary-layer meteorology, it is important to assess and improve the accuracy of these observations. While turbulent quantities are measured by Doppler lidars in several different ways, the simplest and most frequently used statistic is vertical velocity variance (w′2) from zenith stares. However, the competing effects of signal noise and resolution volume limitations, which respectively increase and decrease w′2, reduce the accuracy of these measurements. Herein, an established method that utilises the autocovariance of the signal to remove noise is evaluated and its skill in correcting for volume-averaging effects in the calculation of w′2 is also assessed. Additionally, this autocovariance technique is further refined by defining the amount of lag time to use for the most accurate estimates of w′2. Through comparison of observations from two Doppler lidars and sonic anemometers on a 300 m tower, the autocovariance technique is shown to generally improve estimates of w′2. After the autocovariance technique is applied, values of w′2 from the Doppler lidars are generally in close agreement (R2 ≈ 0.95 − 0.98) with those calculated from sonic anemometer measurements.

As natural gas extraction and use continues to increase, the need to quantify emissions of methane (CH4), a powerful greenhouse gas, has grown. Large discrepancies in Indianapolis CH4 emissions have been observed when comparing inventory, aircraft mass-balance, and tower inverse modeling estimates. Four years of continuous CH4 mole fraction observations from a network of nine tower-based cavity ring-down spectrometers measuring atmospheric CH4 mole fractions at 39 to 136 m above ground as part of the Indianapolis Flux Experiment (INFLUX) are utilized to investigate four possible reasons for the abovementioned inconsistencies: (1) differences in definition of the city domain, (2) a highly temporally variable and spatially non-uniform CH4 background, (3) temporal variability in CH4 emissions, and (4) the presence of unknown CH4 sources. Reducing the Indianapolis urban domain size to be consistent with the inventory domain size decreases the CH4 emission estimation of the inverse modeling methodology by about 35 % and thereby lessens the discrepancy by bringing total city flux within an error range of one of the inventories. Nevertheless, the inverse modeling estimate still remains about 40 % higher than the inventory value. Hourly urban background CH4 mole fractions are shown to be heterogeneous and temporally variable. Statistically significant, long-term biases in background mole fractions of 2–5 ppb are found from single point observations from most wind directions. Random errors in single point background mole fractions observed for a few hours are 20–30 ppb, but decrease substantially when data are averaged over multiple days. Boundary layer budget estimates suggest that Indianapolis CH4 emissions did not change significantly when comparing 2014 to 2016. However, it appears that CH4 emissions may follow a diurnal cycle with daytime emissions (12–16 LST) approximately twice as large as nighttime emissions (20–5 LST). The strongest CH4 source in Indianapolis is the South Side Landfill. Other point sources, perhaps leaks from the natural gas distribution system, are localized and transient, and do not appear to be a consistently large source of CH4 emissions in Indianapolis. Long-term averaging, spatially-extensive upwind mole fraction observations, mesoscale atmospheric modeling of the regional emissions environment, and careful treatment of the times of day and areal representation of emission estimates is recommended for precise and accurate quantification of urban CH4 emissions.

The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE)- and National Oceanic and Atmospheric Administration (NOAA)-funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign that took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 km × 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.

Several factors cause lidars to measure different values of turbulence than an anemometer on a tower, including volume averaging, instrument noise and the use of a scanning circle to estimate the wind field. One way to avoid the use of a scanning circle is to deploy multiple scanning lidars and point them toward the same volume in space to collect velocity measurements and extract high-resolution turbulence information. This paper explores the use of two multi-lidar scanning strategies, the tri-Doppler technique and the virtual tower technique, for measuring 3-D turbulence. In summer 2013, a vertically profiling Leosphere WindCube lidar and three Halo Photonics Streamline lidars were operated at the Southern Great Plains Atmospheric Radiation Measurement site to test these multi-lidar scanning strategies. During the first half of the field campaign, all three scanning lidars were pointed at approximately the same point in space and a tri-Doppler analysis was completed to calculate the three-dimensional wind vector every second. Next, all three scanning lidars were used to build a ‘virtual tower’ above the WindCube lidar. Results indicate that the tri-Doppler technique measures higher values of horizontal turbulence than the WindCube lidar under stable atmospheric conditions, reduces variance contamination under unstable conditions and can measure high-resolution profiles of mean wind speed and direction. The virtual tower technique provides adequate turbulence information under stable conditions but cannot capture the full temporal variability of turbulence experienced under unstable conditions because of the time needed to readjust the scans. Copyright © 2016 John Wiley & Sons, Ltd.

Recent warming of the Arctic has motivated assessments of methane (CH4) release from the North Slope Region of Alaska (NSRA). This study examines the contributions of thermogenic emissions from the Prudhoe Bay Oil Field (PBOF) to the elevated concentrations of atmospheric CH4 observed across the NSRA. We report high precision atmospheric measurements of CH4 and ethane (C2H6) within and downwind of the PBOF. Biogenic CH4 emissions, due to methanogenic processes within the Arctic tundra, are not co-emitted with C2H6. We show that the thermogenic gas emanating from oil and gas extraction point sources contains on average 1 mol of C2H6 for every 16 mol of CH4. We use a mass balance approach to estimate total emissions of thermogenic CH4 from two days in the summer of 2016 and find 2–5 times greater emissions than the sum of all sources in the PBOF reported to the EPA Greenhouse Gas Reporting Program in 2016. Although higher than reported, these emissions are much smaller than estimates of CH4 emissions from other oil and natural gas production areas in the US, and they make a very small contribution to total CH4 emissions from the North Slope.

Accurate three-dimensional information of wind flow fields can be an important tool in not only visualizing complex flow but also understanding the underlying physical processes and improving flow modeling. However, a thorough analysis of the measurement uncertainties is required to properly interpret results. The XPIA (eXperimental Planetary boundary layer Instrumentation Assessment) field campaign conducted at the Boulder Atmospheric Observatory (BAO) in Erie, CO, from 2 March to 31 May 2015 brought together a large suite of in situ and remote sensing measurement platforms to evaluate complex flow measurement strategies. In this paper, measurement uncertainties for different single and multi-Doppler strategies using simple scan geometries (conical, vertical plane and staring) are investigated. The tradeoffs (such as time–space resolution vs. spatial coverage) among the different measurement techniques are evaluated using co-located measurements made near the BAO tower. Sensitivity of the single-/multi-Doppler measurement uncertainties to averaging period are investigated using the sonic anemometers installed on the BAO tower as the standard reference. Finally, the radiometer measurements are used to partition the measurement periods as a function of atmospheric stability to determine their effect on measurement uncertainty. It was found that with an increase in spatial coverage and measurement complexity, the uncertainty in the wind measurement also increased. For multi-Doppler techniques, the increase in uncertainty for temporally uncoordinated measurements is possibly due to requiring additional assumptions of stationarity along with horizontal homogeneity and less representative line-of-sight velocity statistics. It was also found that wind speed measurement uncertainty was lower during stable conditions compared to unstable conditions.

Several errors occur when a traditional Doppler beam swinging (DBS) or velocity–azimuth display (VAD) strategy is used to measure turbulence with a lidar. To mitigate some of these errors, a scanning strategy was recently developed which employs six beam positions to independently estimate the u, v, and w velocity variances and covariances. In order to assess the ability of these different scanning techniques to measure turbulence, a Halo scanning lidar, WindCube v2 pulsed lidar, and ZephIR continuous wave lidar were deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers.Results indicate that the six-beam strategy mitigates some of the errors caused by VAD and DBS scans, but the strategy is strongly affected by errors in the variance measured at the different beam positions. The ZephIR and WindCube lidars overestimated horizontal variance values by over 60 % under unstable conditions as a result of variance contamination, where additional variance components contaminate the true value of the variance. A correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the u and v variance components. The correction method reduced WindCube variance estimates by over 20 % at both the Oklahoma and Colorado sites under unstable conditions, when variance contamination is largest. This correction method can be easily applied to other lidars that contain a vertical beam position and is a promising method for accurately estimating turbulence with commercially available lidars.

This paper presents an overview of the Lower Atmospheric Boundary Layer Experiment (LABLE), which included two measurement campaigns conducted at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site in Oklahoma during 2012 and 2013. LABLE was conducted as a collaborative effort between the University of Oklahoma (OU), the National Severe Storms Laboratory, Lawrence Livermore National Laboratory (LLNL), and the ARM program. LABLE can be considered unique in that it was designed as a multiphase, low-cost, multiagency collaboration. Graduate students served as principal investigators and took the lead in designing and conducting experiments aimed at examining boundary layer processes. The main objective of LABLE was to study turbulent phenomena in the lowest 2 km of the atmosphere over heterogeneous terrain using a variety of novel atmospheric profiling techniques. Several instruments from OU and LLNL were deployed to augment the suite of in situ and remote sensing instruments at the ARM site. The complementary nature of the deployed instruments with respect to resolution and height coverage provides a near-complete picture of the dynamic and thermodynamic structure of the atmospheric boundary layer. This paper provides an overview of the experiment including 1) instruments deployed, 2) sampling strategies, 3) parameters observed, and 4) student involvement. To illustrate these components, the presented results focus on one particular aspect of LABLE: namely, the study of the nocturnal boundary layer and the formation and structure of nocturnal low-level jets. During LABLE, low-level jets were frequently observed and they often interacted with mesoscale atmospheric disturbances such as frontal passages.

The nocturnal stable boundary layer (SBL) can generally be classified into the weakly stable boundary layer (wSBL) and very stable boundary layer (vSBL). Within the wSBL, turbulence is relatively continuous, whereas in the vSBL, turbulence is intermittent and not well characterized. Differentiating characteristics of each type of SBL are still unknown. Herein, thermodynamic and kinematic data collected by a suite of instruments in north central Oklahoma in autumn 2012 are analyzed to better understand both SBL regimes and their differentiating characteristics. Many low-level jets were observed during the experiment, as it took place near a climatological maximum. A threshold wind speed, above which bulk shear-generated turbulence develops, is found to exist up to 300 m. The threshold wind speed must also be exceeded at lower heights (down to the surface) in order for strong turbulence to develop. Composite profiles, which are normalized using low-level jet scaling, of potential temperature, wind speed, vertical velocity variance, and the third-order moment of vertical velocity ( $$\overline{w'^3}$$ ) are produced for weak and moderate/strong turbulence regimes, which exhibit features of the vSBL and wSBL, respectively. Within the wSBL, turbulence is generated at the surface and transported upward. In the vSBL, values of vertical velocity variance are small throughout the entire boundary layer, likely due to the fact that a strong surface inversion typically forms after sunset. The temperature profile tends to be approximately isothermal in the lowest portions of the wSBL, and it did not substantially change over the night. Within both types of SBL, stability in the residual layer tends to increase as the night progresses. It is thought that this stability increase is due to differential warm air advection, which frequently occurs in the southern Great Plains when southerly low-level jets and a typical north–south temperature gradient are present. Differential radiative flux divergence also contributes to this increase in stability.

The structure function is often used to quantify the intensity of spatial inhomogeneities within turbulent flows. Here, the Small Multifunction Research and Teaching Sonde (SMARTSonde), an unmanned aerial system, is used to measure horizontal variations in temperature and to calculate the structure function of temperature at various heights for a range of separation distances. A method for correcting for the advection of turbulence in the calculation of the structure function is discussed. This advection correction improves the data quality, particularly when wind speeds are high. The temperature structure-function parameter $$C_T^2$$ can be calculated from the structure function of temperature. Two case studies from which the SMARTSonde was able to take measurements used to derive $$C_T^2$$ at several heights during multiple consecutive flights are discussed and compared with sodar measurements, from which $$C_T^2$$ is directly related to return power. Profiles of $$C_T^2$$ from both the sodar and SMARTSonde from an afternoon case exhibited generally good agreement. However, the profiles agreed poorly for a morning case. The discrepancies are partially attributed to different averaging times for the two instruments in a rapidly evolving environment, and the measurement errors associated with the SMARTSonde sampling within the stable boundary layer.

Small-scale turbulent fluctuations of temperature are known to affect the propagation of both electromagnetic and acoustic waves. Within the inertial-subrange scale, where the turbulence is locally homogeneous and isotropic, these temperature perturbations can be described, in a statistical sense, using the structure-function parameter for temperature, \(C_{T}^2\). Here we investigate different methods of evaluating \(C_{T}^2\), using data from a numerical large-eddy simulation together with atmospheric observations collected by an unmanned aerial system and a sodar. An example case using data from a late afternoon unmanned aerial system flight on April 24 2013 and corresponding large-eddy simulation data is presented and discussed.

We present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) technique continuously assimilates meteorological observations from various observing systems into the transport modeling system, and is coupled to the high resolution CO2 emission product Hestia to simulate the atmospheric mole fractions of CO2. For the Indianapolis Flux Experiment (INFLUX) project, we evaluated the impact of assimilating different meteorological observation systems on the linearized adjoint solutions and the CO2 inverse fluxes estimated using observed CO2 mole fractions from 11 out of 12 communications towers over Indianapolis for the Sep.-Nov. 2013 period. While assimilating WMO surface measurements improved the simulated wind speed and direction, their impact on the planetary boundary layer (PBL) was limited. Simulated PBL wind statistics improved significantly when assimilating upper-air observations from the commercial airline program Aircraft Communications Addressing and Reporting System (ACARS) and continuous ground-based Doppler lidar wind observations. Wind direction mean absolute error (MAE) decreased from 26 to 14 degrees and the wind speed MAE decreased from 2.0 to 1.2 m s–1, while the bias remains small in all configurations (< 6 degrees and 0.2 m s–1). Wind speed MAE and ME are larger in daytime than in nighttime. PBL depth MAE is reduced by ~10%, with little bias reduction. The inverse results indicate that the spatial distribution of CO2 inverse fluxes were affected by the model performance while the overall flux estimates changed little across WRF simulations when aggregated over the entire domain. Our results show that PBL wind observations are a potent tool for increasing the precision of urban meteorological reanalyses, but that the impact on inverse flux estimates is dependent on the specific urban environment.

Measurements of turbulence are essential to understand and quantify the transport and dispersal of heat, moisture, momentum, and trace gases within the planetary boundary layer (PBL). Through the years, various techniques to measure turbulence using Doppler lidar observations have been proposed. However, the accuracy of these measurements has rarely been validated against trusted in situ instrumentation. Herein, data from the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) are used to verify Doppler lidar turbulence profiles through comparison with sonic anemometer measurements. For 17 days at the end of the experiment, a single scanning Doppler lidar continuously cycled through different turbulence measurement strategies: velocity–azimuth display (VAD), six-beam scans, and range–height indicators (RHIs) with a vertical stare.Measurements of turbulence kinetic energy (TKE), turbulence intensity, and stress velocity from these techniques are compared with sonic anemometer measurements at six heights on a 300 m tower. The six-beam technique is found to generally measure turbulence kinetic energy and turbulence intensity the most accurately at all heights (r2 ≈ 0.78), showing little bias in its observations (slope of ≈ 0. 95). Turbulence measurements from the velocity–azimuth display method tended to be biased low near the surface, as large eddies were not captured by the scan. None of the methods evaluated were able to consistently accurately measure the shear velocity (r2 = 0.15–0.17). Each of the scanning strategies assessed had its own strengths and limitations that need to be considered when selecting the method used in future experiments.

Wind energy encounters challenges due to variability in the wind resource. The paper presents the wind flow variability from Doppler lidar measurements and NWP models forecasts in the complex terrain of Columbia River Gorge.

Recently, there has been an increase in use of Unmanned Aerial Systems (UASs) as platforms for conducting fundamental and applied research in the lower atmosphere due to their relatively low cost and ability to collect samples with high spatial and temporal resolution. Concurrent with this development comes the need for accurate instrumentation and measurement methods suitable for small meteorological UASs. Moreover, the instrumentation to be integrated into such platforms must be small and lightweight. Whereas thermodynamic variables can be easily measured using well-aspirated sensors onboard, it is much more challenging to accurately measure the wind with a UAS. Several algorithms have been developed that incorporate GPS observations as a means of estimating the horizontal wind vector, with each algorithm exhibiting its own particular strengths and weaknesses. In the present study, the performance of three such GPS-based wind-retrieval algorithms has been investigated and compared with wind estimates from rawinsonde and sodar observations. Each of the algorithms considered agreed well with the wind measurements from sounding and sodar data. Through the integration of UAS-retrieved profiles of thermodynamic and kinematic parameters, one can investigate the static and dynamic stability of the atmosphere and relate them to the state of the boundary layer across a variety of times and locations, which might be difficult to access using conventional instrumentation.

Accurate three-dimensional information of wind flow fields can be an important tool in not only visualizing complex flow, but also understanding the underlying physical processes and improving flow modeling. However, a thorough analysis of the measurement uncertainties is required to properly interpret results. The XPIA (eXperimental Planetary boundary layer Instrumentation Assessment) field campaign conducted at the Boulder Atmospheric Observatory (BAO) in Erie, CO from 2 March–31 May 2015 brought together a large suite of in-situ and remote sensing measurement platforms to evaluate complex flow measurement strategies. In this paper, measurement uncertainties for different single and multi-Doppler strategies are investigated. The tradeoffs (such as time/space resolution vs. spatial coverage) among the different measurement techniques are evaluated using co-located measurements made near the BAO tower. Sensitivity of the single/multi Doppler measurement uncertainties to averaging period are investigated using the sonic anemometers installed on the BAO tower as the standard reference. Finally, the radiometer measurements are used to partition the measurement periods as a function of atmospheric stability to determine their effect on measurement uncertainty. It was found that with increase in spatial coverage and measurement complexity, the uncertainty in the wind measurement also increased. For multi-Doppler techniques, the increase in uncertainty for temporally uncoordinated measurements is possibly due to requiring additional assumptions of stationarity and/or horizontal homogeneity. It was also found that wind speed measurement uncertainty was lower during stable conditions compared to unstable conditions.

The evolution of the lower portion of the planetary boundary layer is investigated using the Small Multifunction Research and Teaching Sonde (SMARTSonde), an unmanned aerial vehicle developed at the University of Oklahoma. The study focuses on the lowest 200 m of the atmosphere, where the most noticeable thermodynamic changes occur during the day. Between October 2010 and February 2011, a series of flights was conducted during the evening hours on several days to examine the vertical structure of the lower boundary layer. Data from a nearby Oklahoma Mesonet tower was used to supplement the vertical profiles of temperature, humidity, and pressure, which were collected approximately every 30 min, starting 2 h before sunset and continuing until dusk. From the profiles, sensible and latent heat fluxes were estimated. These fluxes were used to diagnose the portion of the boundary layer that was most affected by the early evening transition. During the transition period, a shallow cool and moist layer near the ground was formed, and as the evening progressed the cooling affected an increasingly shallower layer just above the surface.

The 28th International Laser Radar Conference (ILRC 28), A low-level jet (LLJ) is a prominent wind speed peak in the lower troposphere. Nocturnal LLJs have been shown to transport and mix atmospheric constituents from the residual layer down to the surface, breaching quiescent nocturnal conditions due to high wind shear. A new fuzzy logic algorithm combining turbulence and aerosol information from Doppler lidar scans can resolve the strength and depth of this mixing below the jet. Conclusions will be drawn about LLJ relations to turbulence and mixing.

Recently, there has been an increase in use of Unmanned Aerial Systems (UASs) as platforms for conducting fundamental and applied research in the lower atmosphere due to their relatively low cost and ability to collect samples with high spatial and temporal resolution. Concurrent with this development comes the need for accurate instrumentation and measurement methods suitable for small meteorological UASs. Moreover, the instrumentation to be integrated into such platforms must be small and lightweight. Whereas thermodynamic variables can be easily measured using well aspirated sensors onboard, it is much more challenging to accurately measure the wind with a UAS. Several algorithms have been developed that incorporate GPS observations as a means of estimating the horizontal wind vector, with each algorithm exhibiting its own particular strengths and weaknesses. In the present study, the performance of three such GPS-based wind-retrieval algorithms has been investigated and compared with wind estimates from rawinsonde and sodar observations. Each of the algorithms considered agreed well with the wind measurements from sounding and sodar data. Through the integration of UAS-retrieved profiles of thermodynamic and kinematic parameters, one can investigate the static and dynamic stability of the atmosphere and relate them to the state of the boundary layer across a variety of times and locations, which might be difficult to access using conventional instrumentation.