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Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s−1. Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s−1. Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ∼0.1 m s−1, but some reached 0.3 m s−1. All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error. Significance Statement To evaluate model forecast accuracy using remote sensing instruments, it is important to understand the effects of measurement uncertainties due to differences in the methods of measurement and data processing techniques, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. In this study, three types of collocated remote sensing systems are used to quantify the impact of measurement variability on the magnitude of calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m.

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.

Low-powered commercially-available coherent Doppler lidar (CDL) provides continuous measurement of vertical profiles of wind in the lower troposphere, usually close to or up to the top of the planetary boundary layer. The vertical extent of these wind profiles is limited by the availability of scatterers, and thus varies substantially throughout the day and from one day to the next. This makes it challenging to develop continuous products that rely on CDL-observed wind profiles. In order to overcome this problem, we have developed a new method for wind profile retrievals from CDL that combines the traditional velocity-azimuth display (VAD) technique with optimal estimation (OE) to provide continuous wind profiles up to 3 km. The new method exploits the level-to-level covariance present in the wind profile to fill in the gaps where the signal to noise ratio of the CDL return is too low to provide reliable results using the traditional VAD method. Another advantage of the new method is that it provides the full error covariance matrix of the solution and profiles of information content, which more easily facilitates the assimilation of the observed wind profiles into numerical weather prediction models. This method was tested using CDL measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility. Comparison with the ARM operational CDL wind profile product and collocated radiosonde wind measurements shows excellent agreement (R2 > 0.99) with no degradation in results where the traditional VAD provided a valid solution. In the region where traditional VAD do not provide results, the OE wind speed has uncertainty of 4.5 m/s. As a result, the new method provides additional information over the standard technique and increases the effective range of existing CDL systems without the need for additional hardware.

The human pathogen Mycobacterium tuberculosis typically causes lung disease but can also disseminate to other tissues. We identified a M. tuberculosis (Mtb) outbreak presenting with unusually high rates of extrapulmonary dissemination and bone disease. We found that the causal strain carried an ancestral full-length version of the type VII-secreted effector EsxM rather than the truncated version present in other modern Mtb lineages. The ancestral EsxM variant exacerbated dissemination through enhancement of macrophage motility, increased egress of macrophages from established granulomas, and alterations in macrophage actin dynamics. Reconstitution of the ancestral version of EsxM in an attenuated modern strain of Mtb altered the migratory mode of infected macrophages, enhancing their motility. In a zebrafish model, full-length EsxM promoted bone disease. The presence of a derived nonsense variant in EsxM throughout the major Mtb lineages 2, 3, and 4 is consistent with a role for EsxM in regulating the extent of dissemination.

During mycobacterial infections, pathogenic mycobacteria manipulate both host immune and stromal cells to establish and maintain a productive infection. In humans, non-human primates, and zebrafish models of infection, pathogenic mycobacteria produce and modify the specialized lipid trehalose 6,6'-dimycolate (TDM) in the bacterial cell envelope to drive host angiogenesis toward the site of forming granulomas, leading to enhanced bacterial growth. Here, we use the zebrafish-Mycobacterium marinum infection model to define the signaling basis of the host angiogenic response. Through intravital imaging and cell-restricted peptide-mediated inhibition, we identify macrophage-specific activation of NFAT signaling as essential to TDM-mediated angiogenesis in vivo. Exposure of cultured human cells to Mycobacterium tuberculosis results in robust induction of VEGFA, which is dependent on a signaling pathway downstream of host TDM detection and culminates in NFATC2 activation. As granuloma-associated angiogenesis is known to serve bacterial-beneficial roles, these findings identify potential host targets to improve tuberculosis disease outcomes.

This work details a master oscillator power amplifier (MOPA) microjoule-class pulsed coherent Doppler lidar system configuration designed to measure line-of-sight wind velocities and backscatter intensity of atmospheric aerosols. The instrument is unique in its form factor. It consists of two physically separated modules connected by a 10 m umbilical cable. One module hosts the transceiver, which is composed of the telescope, transmit/receive (T/R) switch, and high-gain optical amplifier, and is housed in a small box (34.3 cm × 34.3 cm × 17.8 cm). The second module contains the data acquisition system and several electro-optical components. This form factor enables deployments on platforms that are otherwise inaccessible by commercial and research instruments of similar design. In this work, optical, electrical, and data acquisition components and configurations of the lidar are detailed and two example deployments are presented. The first deployment describes measurements of a controlled wildfire burn from a small aircraft to measure vertical plume dynamics and fire inflow conditions during summer in Florida. The second presents measurements of the marine boundary layer height and vertical velocity and variance profiles from the Research Vessel (R/V) Thomas Thompson. The new instrument has enabled greater flexibility in field campaigns where previous instruments would have been too costly or space prohibitive to deploy.

Ground-based Doppler-lidar instrumentation provides atmospheric wind data at dramatically improved accuracies and spatial/temporal resolutions. These capabilities have provided new insights into atmospheric flow phenomena, but they also should have a strong role in NWP model improvement. Insight into the nature of model errors can be gained by studying recurrent atmospheric flows, here a regional summertime diurnal sea breeze and subsequent marine-air intrusion into the arid interior of Oregon–Washington, where these winds are an important wind-energy resource. These marine intrusions were sampled by three scanning Doppler lidars in the Columbia River basin as part of the Second Wind Forecast Improvement Project (WFIP2), using data from summer 2016. Lidar time–height cross sections of wind speed identified 8 days when the diurnal flow cycle (peak wind speeds at midnight, afternoon minima) was obvious and strong. The 8-day composite time–height cross sections of lidar wind speeds are used to validate those generated by the operational NCEP–HRRR model. HRRR simulated the diurnal wind cycle, but produced errors in the timing of onset and significant errors due to a premature nighttime demise of the intrusion flow, producing low-bias errors of 6 m s−1. Day-to-day and in the composite, whenever a marine intrusion occurred, HRRR made these same errors. The errors occurred under a range of gradient wind conditions indicating that they resulted from the misrepresentation of physical processes within a limited region around the measurement locations. Because of their generation within a limited geographical area, field measurement programs can be designed to find and address the sources of these NWP errors.

The California Fire Dynamics Study (CalFiDE) is a collaborative effort between the Chemical Sciences Laboratory (CSL) at National Oceanic and Atmospheric Administration (NOAA), Wildfire Interdisciplinary Research Center (WIRC) at San Jose State University (SJSU) and the University of Nevada Reno (UNR), to investigate wildfire dynamics in Northern California in late Summer and Fall 2022. Further information on the instrumentation and scope of this study can be found in the Valero et al. short paper. During CalFiDE, the contributing organizations plan to deploy four Doppler lidar systems in addition to infrared imaging and chemical measurements to quantify the fire dynamics and atmospheric coupling of large scale wildfires. These systems will be installed on a range of mobile platforms including; a Twin Otter aircraft, pickup trucks, and trailers. The mobile ground based installations allow researchers to rapidly deploy the systems to areas of interest during the study, while the aircraft installation will cover a larger range of spatial scales. Systems will operate in scanning modes to quantify 3D winds, vertical stares to resolve fine scale vertical velocities, and coordinated scans where the measurement volumes of nearby systems are overlapped to provide high resolution measurements of 3D winds. The coordinated scanning technique will be used to target fine scale features within fire plumes such as helical updrafts, counter-rotating vortices, and inflow/outflow dynamics. Collectively these lidar observations will provide validation data sets for simulated fire-generated winds.

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.

Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s−1 and RMSEs of 2-3 m s−1. Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.

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.

Documentation is essential for communicating care between credentialed nutrition and dietetics practitioners and other health care providers. A validated tool that can evaluate quality documentation of the Nutrition Care Process (NCP) encounter, including progress on outcomes is lacking. The aim of the NCP Quality Evaluation and Standardization Tool (QUEST) validation study is to revise an existing NCP audit tool and evaluate it when used within US Veterans Affairs in all clinical care settings. Six registered dietitian nutritionists revised an existing NCP audit tool. The revised tool (NCP-QUEST) was analyzed for clarity, relevance, and reliability. Eighty-five documentation notes (44 initial, 41 reassessment) were received from eight volunteer Veterans Affairs sites. Five of six registered dietitian nutritionists participated in the interrater reliability testing blinded to each other's ratings; and two registered dietitian nutritionists participated in intrarater reliability reviewing the same notes 6 weeks later blinded to the original ratings. Results showed moderate levels of agreement in interrater reliability (Krippendorff’s α = .62 for all items, .66 for total score, and .52 for quality category rating). Intrarater reliability was excellent for all items (α = .86 to .87 for all items; .91 to .94 for total score and.74 to .89 for quality category rating). The NCP-QUEST has high content validity (Content Validity Index = 0.78 for item level, and 0.9 for scale level) after two cycles of content validity review. The tool can facilitate critical thinking, improved linking of NCP chains, and is a necessary foundation for quality data collection and outcomes management. The NCP-QUEST tool can improve accuracy and confidence in charting.

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.

To advance the understanding of meteorological processes in offshore coastal regions, the spatial variability of wind profiles must be characterized and uncertainties (errors) in NWP model wind forecasts quantified. These gaps are especially critical for the new offshore wind energy industry, where wind profile measurements in the marine atmospheric layer spanned by wind turbine rotor blades, generally 50–200 m above mean sea level (MSL), have been largely unavailable. Here, high-quality wind profile measurements were available every 15 min from the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL)’s high-resolution Doppler lidar (HRDL) during a monthlong research cruise in the Gulf of Maine for the 2004 New England Air Quality Study. These measurements were compared with retrospective NWP model wind forecasts over the area using two NOAA forecast-modeling systems [North American Mesoscale Forecast System (NAM) and Rapid Refresh (RAP)]. HRDL profile measurements quantified model errors, including their dependence on height above sea level, diurnal cycle, and forecast lead time. Typical model wind speed errors were ∼2.5 m s−1, and vector-wind errors were ∼4 m s−1. Short-term forecast errors were larger near the surface—30% larger below 100 m than above and largest for several hours after local midnight (biased low). Longer-term, 12-h forecasts had the largest errors after local sunset (biased high). At more than 3-h lead times, predictions from finer-resolution models exhibited larger errors. Horizontal variability of winds, measured as the ship traversed the Gulf of Maine, was significant and raised questions about whether modeled fields, which appeared smooth in comparison, were capturing this variability. If not, horizontal arrays of high-quality, vertical-profiling devices will be required for wind energy resource assessment offshore. Such measurement arrays are also needed to improve NWP models.

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.

Evaluation of model skill in predicting winds over the ocean was performed by comparing retrospective runs of numerical weather prediction (NWP) forecast models to shipborne Doppler lidar measurements in the Gulf of Maine, a potential region for U.S. coastal wind farm development. Deployed on board the NOAA R/V Ronald H. Brown during a 2004 field campaign, the high-resolution Doppler lidar (HRDL) provided accurate motion-compensated wind measurements from the water surface up through several hundred meters of the marine atmospheric boundary layer (MABL). The quality and resolution of the HRDL data allow detailed analysis of wind flow at heights within the rotor layer of modern wind turbines and data on other critical variables to be obtained, such as wind speed and direction shear, turbulence, low-level jet properties, ramp events, and many other wind-energy-relevant aspects of the flow. This study will focus on the quantitative validation of NWP models’ wind forecasts within the lower MABL by comparison with HRDL measurements. Validation of two modeling systems rerun in special configurations for these 2004 cases—the hourly updated Rapid Refresh (RAP) system and a special hourly updated version of the North American Mesoscale Forecast System [NAM Rapid Refresh (NAMRR)]—are presented. These models were run at both normal-resolution (RAP, 13 km; NAMRR, 12 km) and high-resolution versions: the NAMRR-CONUS-nest (4 km) and the High-Resolution Rapid Refresh (HRRR, 3 km). Each model was run twice: with (experimental runs) and without (control runs) assimilation of data from 11 wind profiling radars located along the U.S. East Coast. The impact of the additional assimilation of the 11 profilers was estimated by comparing HRDL data to modeled winds from both runs. The results obtained demonstrate the importance of high-resolution lidar measurements to validate NWP models and to better understand what atmospheric conditions may impact the accuracy of wind forecasts in the marine atmospheric boundary layer. Results of this research will also provide a first guess as to the uncertainties of wind resource assessment using NWP models in one of the U.S. offshore areas projected for wind plant development.

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.

Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.

During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign, which was carried out at the Boulder Atmospheric Observatory (BAO) in spring 2015, multiple-Doppler scanning strategies were carried out with scanning wind lidars and Ka-band radars. Specifically, step–stare measurements were collected simultaneously with three scanning Doppler lidars, while two scanning Ka-band radars carried out simultaneous range height indicator (RHI) scans. The XPIA experiment provided the unique opportunity to compare directly virtual-tower measurements performed simultaneously with Ka-band radars and Doppler wind lidars. Furthermore, multiple-Doppler measurements were assessed against sonic anemometer data acquired from the meteorological tower (met-tower) present at the BAO site and a lidar wind profiler. This survey shows that – despite the different technologies, measurement volumes and sampling periods used for the lidar and radar measurements – a very good accuracy is achieved for both remote-sensing techniques for probing horizontal wind speed and wind direction with the virtual-tower scanning technique.

The eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) field campaign took place in March through May 2015 at the Boulder Atmospheric Observatory, utilizing its 300 m meteorological tower, instrumented with two sonic anemometers mounted on opposite sides of the tower at six heights. This allowed for at least one sonic anemometer at each level to be upstream of the tower at all times and for identification of the times when a sonic anemometer is in the wake of the tower frame. Other instrumentation, including profiling and scanning lidars aided in the identification of the tower wake. Here we compare pairs of sonic anemometers at the same heights to identify the range of directions that are affected by the tower for each of the opposing booms. The mean velocity and turbulent kinetic energy are used to quantify the wake impact on these first- and second-order wind measurements, showing up to a 50 % reduction in wind speed and an order of magnitude increase in turbulent kinetic energy. Comparisons of wind speeds from profiling and scanning lidars confirmed the extent of the tower wake, with the same reduction in wind speed observed in the tower wake, and a speed-up effect around the wake boundaries. Wind direction differences between pairs of sonic anemometers and between sonic anemometers and lidars can also be significant, as the flow is deflected by the tower structure. Comparisons of lengths of averaging intervals showed a decrease in wind speed deficit with longer averages, but the flow deflection remains constant over longer averages. Furthermore, asymmetry exists in the tower effects due to the geometry and placement of the booms on the triangular tower. An analysis of the percentage of observations in the wake that must be removed from 2 min mean wind speed and 20 min turbulent values showed that removing even small portions of the time interval due to wakes impacts these two quantities. However, a vast majority of intervals have no observations in the tower wake, so removing the full 2 or 20 min intervals does not diminish the XPIA dataset.

To assess current capabilities for measuring flow within the atmospheric boundary layer, including within wind farms, the U.S. Department of Energy sponsored the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign at the Boulder Atmospheric Observatory (BAO) in spring 2015. Herein, we summarize the XPIA field experiment, highlight novel measurement approaches, and quantify uncertainties associated with these measurement methods. Line-of-sight velocities measured by scanning lidars and radars exhibit close agreement with tower measurements, despite differences in measurement volumes. Virtual towers of wind measurements, from multiple lidars or radars, also agree well with tower and profiling lidar measurements. Estimates of winds over volumes from scanning lidars and radars are in close agreement, enabling the assessment of spatial variability. Strengths of the radar systems used here include high scan rates, large domain coverage, and availability during most precipitation events, but they struggle at times to provide data during periods with limited atmospheric scatterers. In contrast, for the deployment geometry tested here, the lidars have slower scan rates and less range but provide more data during nonprecipitating atmospheric conditions. Microwave radiometers provide temperature profiles with approximately the same uncertainty as radio acoustic sounding systems (RASS). Using a motion platform, we assess motion-compensation algorithms for lidars to be mounted on offshore platforms. Finally, we highlight cases for validation of mesoscale or large-eddy simulations, providing information on accessing the archived dataset. We conclude that modern remote sensing systems provide a generational improvement in observational capabilities, enabling the resolution of finescale processes critical to understanding inhomogeneous boundary layer flows.

The central pathogen-immune interface in tuberculosis is the granuloma, a complex host immune structure that dictates infection trajectory and physiology. Granuloma macrophages undergo a dramatic transition in which entire epithelial modules are induced and define granuloma architecture. In tuberculosis, relatively little is known about the host signals that trigger this transition. Using the zebrafish-Mycobacterium marinum model, we identify the basis of granuloma macrophage transformation. Single-cell RNA-seq analysis of zebrafish granulomas as well as analysis of Mycobacterium tuberculosis-infected macaques reveal that, even in the presence of robust type 1 immune responses, countervailing type 2 signals associate with macrophage epithelialization. We find that type 2 immune signaling, mediated via stat6, is absolutely required for epithelialization and granuloma formation. In mixed chimeras, stat6 acts cell-autonomously within macrophages, where it is required for epithelioid transformation and incorporation into necrotic granulomas. These findings establish the signaling pathway that produces the hallmark structure of mycobacterial infection.

The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. The effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.

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.

Density currents (i.e., cold pools or outflows) beneath marine stratocumulus clouds are characterized using 30 days of ship-based observations obtained during the 2008 Variability of American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx) in the southeast Pacific. An air density increase criterion applied to the Improved Meteorological (IMET) sensor data identified 71 density current front, core (peak density), and tail (dissipating) zones. The similarity in speeds of the mean density current propagation speed (1.8 m s−1) and the mean cloud-level advection relative to the surface layer wind (1.9 m s−1) allowed drizzle cells to deposit elongated density currents in their wakes. Scanning Doppler lidar captured prefrontal updrafts with a mean intensity of 0.91 m s−1 and an average vertical extent of 800 m. Updrafts were often surmounted by low-lying shelf clouds not connected to the overlying stratocumulus cloud. The observed density currents were 5–10 times thinner and weaker than typical continental thunderstorm cold pools. Nearly 90% of density currents were identified when C-band radar estimated areal average rain rates exceeded 1 mm day−1 over a 30-km diameter. Rather than peaking when rain rates were highest overnight, density current occurrence peaks between 0600 and 0800 local solar time when enhanced local drizzle co-occurred with shallow subcloud dry and stable layers. The dry layers may have contributed to density current formation by enhancing subcloud evaporation of drizzle. Density currents preferentially occurred in a large region of predominantly open cells but also occurred in regions of closed cells.

Wind turbine wakes in the atmosphere are three-dimensional (3D) and time dependent. An important question is how best to measure atmospheric wake properties, both for characterizing these properties observationally and for verification of numerical, conceptual, and physical (e.g., wind tunnel) models of wakes. Here a scanning, pulsed, coherent Doppler lidar is used to sample a turbine wake using 3D volume scan patterns that envelop the wake and simultaneously measure the inflow profile. The volume data are analyzed for quantities of interest, such as peak velocity deficit, downwind variability of the deficit, and downwind extent of the wake, in a manner that preserves the measured data. For the case study presented here, in which the wake was well defined in the lidar data, peak deficits of up to 80% were measured 0.6–2 rotor diameters (D) downwind of the turbine, and the wakes extended more than 11D downwind. Temporal wake variability over periods of minutes and the effects of atmospheric gusts and lulls in the inflow are demonstrated in the analysis. Lidar scanning trade-offs important to ensuring that the wake quantities of interest are adequately sampled by the scan pattern, including scan coverage, number of scans per volume, data resolution, and scan-cycle repeat interval, are discussed.

To effectively address climate change, aggressive mitigation policies need to be implemented to reduce greenhouse gas emissions. Anthropogenic carbon emissions are mostly generated from urban environments, where human activities are spatially concentrated. Improvements in uncertainty determinations and precision of measurement techniques are critical to permit accurate and precise tracking of emissions changes relative to the reduction targets. As part of the INFLUX project, we quantified carbon dioxide (CO 2 ), carbon monoxide (CO) and methane (CH 4 ) emission rates for the city of Indianapolis by averaging results from nine aircraft-based mass balance experiments performed in November-December 2014. Our goal was to assess the achievable precision of the aircraft-based mass balance method through averaging, assuming constant CO 2 , CH 4 and CO emissions during a three-week field campaign in late fall. The averaging method leads to an emission rate of 14,600 mol/s for CO 2 , assumed to be largely fossil-derived for this period of the year, and 108 mol/s for CO. The relative standard error of the mean is 17% and 16%, for CO 2 and CO, respectively, at the 95% confidence level (CL), i.e. a more than 2-fold improvement from the previous estimate of ~40% for single-flight measurements for Indianapolis. For CH 4 , the averaged emission rate is 67 mol/s, while the standard error of the mean at 95% CL is large, i.e. ±60%. Given the results for CO 2 and CO for the same flight data, we conclude that this much larger scatter in the observed CH 4 emission rate is most likely due to variability of CH 4 emissions, suggesting that the assumption of constant daily emissions is not correct for CH 4 sources. This work shows that repeated measurements using aircraft-based mass balance methods can yield sufficient precision of the mean to inform emissions reduction efforts by detecting changes over time in urban emissions.

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.

The objective of the Indianapolis Flux Experiment (INFLUX) is to develop, evaluate and improve methods for measuring greenhouse gas (GHG) emissions from cities. INFLUX’s scientific objectives are to quantify CO2 and CH4 emission rates at 1 km2 resolution with a 10% or better accuracy and precision, to determine whole-city emissions with similar skill, and to achieve high (weekly or finer) temporal resolution at both spatial resolutions. The experiment employs atmospheric GHG measurements from both towers and aircraft, atmospheric transport observations and models, and activity-based inventory products to quantify urban GHG emissions. Multiple, independent methods for estimating urban emissions are a central facet of our experimental design. INFLUX was initiated in 2010 and measurements and analyses are ongoing. To date we have quantified urban atmospheric GHG enhancements using aircraft and towers with measurements collected over multiple years, and have estimated whole-city CO2 and CH4 emissions using aircraft and tower GHG measurements, and inventory methods. Significant differences exist across methods; these differences have not yet been resolved; research to reduce uncertainties and reconcile these differences is underway. Sectorally- and spatially-resolved flux estimates, and detection of changes of fluxes over time, are also active research topics. Major challenges include developing methods for distinguishing anthropogenic from biogenic CO2 fluxes, improving our ability to interpret atmospheric GHG measurements close to urban GHG sources and across a broader range of atmospheric stability conditions, and quantifying uncertainties in inventory data products. INFLUX data and tools are intended to serve as an open resource and test bed for future investigations. Well-documented, public archival of data and methods is under development in support of this objective.

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.

The diurnal cycle of marine stratocumulus in cloud-topped boundary layers is examined using ship-based meteorological data obtained during the 2008 Variability of American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx). The high temporal and spatial continuity of the ship data, as well as the 31-day sample size, allows the diurnal transition in degree of coupling of the stratocumulus-topped boundary layer to be resolved. The amplitude of diurnal variation was comparable to the magnitude of longitudinal differences between regions east and west of 80°W for most of the cloud, surface, and precipitation variables examined. The diurnal cycle of precipitation is examined in terms of areal coverage, number of drizzle cells, and estimated rain rate. East of 80°W, the drizzle cell frequency and drizzle area peaks just prior to sunrise. West of 80°W, total drizzle area peaks at 0300 local solar time (LST), 2–3 h before sunrise. Peak drizzle cell frequency is 3 times higher west of 80°W compared to east of 80°W. The waning of drizzle several hours prior to the ramp up of shortwave fluxes may be related to the higher peak drizzle frequencies in the west. The ensemble effect of localized subcloud evaporation of precipitation may make drizzle a self-limiting process where the areal density of drizzle cells is sufficiently high. The daytime reduction in vertical velocity variance in a less coupled boundary layer is accompanied by enhanced stratification of potential temperature and a buildup of moisture near the surface.

Numerically modeled turbulence simulated by the Advanced Research Weather Research and Forecasting Model (ARW) is evaluated with turbulence measurements from NOAA’s high-resolution Doppler lidar on the NOAA Research Vessel Ronald H. Brown during the Variability of the American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study—Regional Experiment (VOCALS-Rex) field program. A nonprecipitating nocturnal marine stratocumulus case is examined, and a nudging technique is applied to allow turbulence to spin up and come into a statistically stationary state with the initial observed cloud field. This “stationary” state is then used as the initial condition for the subsequent free-running simulation. The comparison shows that the modeled turbulence is consistently weaker than that observed. For the same resolution, the turbulence becomes stronger, especially for the horizontal component, as the length of the horizontal domain increases from 6.4 to 25.6 km. Analysis of the power spectral density shows that, even for the largest domain, the horizontal component of the turbulence is limited by the upper limit of the domain size; supporting evidence from past studies is provided. Results suggest that convergence is expected for (i) energy spectra of turbulence with a sufficiently large domain and (ii) liquid water path with an adequately large domain and fine resolution. Additional tests are performed by changing momentum advection and turning off subgrid-scale diffusion. These exhibit more significant changes in turbulence characteristics compared to the sensitivity to domain size and resolution, suggesting that the model behavior is essentially established by the configuration of the model dynamics and physics and that the simulation only gradually improves when domain size and resolution are increased.