A prominent Saharan Air Layer (SAL) was detected over the Northern Atlantic from the West African Coast to the Caribbean Sea in 2007. Data was collected from the Aerosols and Ocean Science Expedition (AEROSE), which encountered a major dust outflow on 13 and 14 May 2007. These observational measurements came from onboard instrumentation and radiosondes that captured the dust-front event from 13 to 14 May 2007. Aerosol backscatter was confined within the Marine Boundary Layer (MBL), with layers detected up to 3 km. Aerosol Optical Depth (AOD) increased by one order of magnitude during the dust front, from 0.1 to 1. Downward solar radiation was also attenuated by 200 W/m2 and 100 W/m2 on the first and second days, respectively. A weaker gradient at and above 500 m from potential temperature profiles indicates a less-defined MBL, and an ambient air temperature of 26 °C on 14 May and 28 °C on 15 May were observed above 500 m, reinforcing the temperature inversion and static stability of the SAL. Subsequent days, clear and boundary-layer cloudy days were observed after the dust front. From 14 to 18 May, a Convective Inhibition (CIN) layer started to form at the top of the MBL, developing into a negative buoyancy from 17 to 23 May, and reinforcing the large-scale anticyclonic atmospheric conditions. These results show that the SAL acts as positive feedback on suppressing deep convection over the tropical Atlantic during this dust outflow and several days after its passage.

Forecasts by mid-2015 for a strong El Nino during winter 2015/16 presented an exceptional scientific opportunity to accelerate advances in understanding and predictions of an extreme climate event and its impacts while the event was ongoing. Seizing this opportunity, the National Oceanic and Atmospheric Administration (NOAA) initiated an El Nino Rapid Response (ENRR), conducting the first field campaign to obtain intensive atmospheric observations over the tropical Pacific during El Nino.The overarching ENRR goal was to determine the atmospheric response to El Nino and the implications for predicting extratropical storms and U.S. West Coast rainfall. The field campaign observations extended from the central tropical Pacific to the West Coast, with a primary focus on the initial tropical atmospheric response that links El Nino to its global impacts. NOAA deployed its Gulfstream-IV (G-IV) aircraft to obtain observations around organized tropical convection and poleward convective outflow near the heart of El Nino. Additional tropical Pacific observations were obtained by radiosondes launched from Kiritimati , Kiribati, and the NOAA ship Ronald H. Brown, and in the eastern North Pacific by the National Aeronautics and Space Administration (NASA) Global Hawk unmanned aerial system. These observations were all transmitted in real time for use in operational prediction models. An X-band radar installed in Santa Clara, California, helped characterize precipitation distributions. This suite supported an end-to-end capability extending from tropical Pacific processes to West Coast impacts. The ENRR observations were used during the event in operational predictions. They now provide an unprecedented dataset for further research to improve understanding and predictions of El Nino and its impacts.

This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).© 2018 American Meteorological Society.* Retired.Publisher's Note: On 19 September 2018 Figure 10 was revised to correctly identify the field program and credit the photographer.CORRESPONDING AUTHOR: Daniel Wolfe, dwolfe2115@gmail.com

During the early months of the 2015/2016 El Niño event, scientists led by the Earth System Research Laboratory's Physical Sciences Division conducted the National Oceanic and Atmospheric Administration's (NOAA's) El Niño Rapid Response (ENRR) field campaign. One component of ENRR involved in situ observations collected over the near-equatorial eastern–central Pacific Ocean. From 25 January to 28 March 2016, standard surface meteorology observations, including rainfall, were collected at Kiritimati Island (2.0° N, 157.4° E) in support of twice-daily radiosonde launches. From 16 February to 16 March 2016, continuous measurements of surface meteorology, sea surface temperature, and downwelling shortwave radiation were made by NOAA Ship Ronald H. Brown. These were largely done in support of the four to eight radiosondes launched each day as the ship travelled from Hawaii to TAO buoy locations along longitudes 140 and 125° W and then back to port in San Diego, California. The rapid nature of these remote field deployments led to some specific challenges in addition to those common to many surface data collection efforts. This paper documents the two deployments as well as the steps taken to evaluate and process the data. The results are two multi-week surface meteorology data products and one accompanying set of surface fluxes, all collected in the core of the eastern–central Pacific's extremely warm waters. These data sets, plus metadata, are archived at the NOAA's National Centers for Environmental Information (NCEI) and are free for public access: surface meteorology from Kiritimati Island (https://doi.org/10.7289/V51Z42H4); surface meteorology and some surface fluxes from NOAA Ship Ronald H. Brown (https://doi.org/10.7289/V5SF2T80; https://doi.org/10.7289/V58050VP).

As the 2015/2016 El Niño was gathering strength in late 2015, scientists at the Earth System Research Laboratory's Physical Sciences Division proposed and led the implementation of the National Oceanic and Atmospheric Administration's (NOAA's) El Niño Rapid Response (ENRR) Field Campaign. ENRR observations included wind and thermodynamic profiles of the atmosphere over the near-equatorial eastern central Pacific Ocean, many of which were collected from two field sites and transmitted in near-real time for inclusion in global forecasting models. From 26 January to 28 March 2016, twice-daily rawinsonde observations were made from Kiritimati (pronounced Christmas) Island (2.0° N, 157.4° E; call sign CXENRR). From 16 February to 16 March 2016, three to eight radiosondes were launched each day from NOAA Ship Ronald H. Brown (allocated call sign WTEC) as it travelled southeast from Hawaii to service Tropical Atmosphere Ocean (TAO) buoys along longitudes 140 and 125° W and then north to San Diego, California. Both the rapid and remote nature of these deployments created particular difficulties in collecting and disseminating the soundings; these are described together with the methods used to reprocess the data after the field campaign finished. The reprocessed and lightly quality-controlled data have been put into an easy-to-read text format, qualifying them to be termed Level 2 soundings. They are archived and freely available for public access at NOAA's National Centers for Environmental Information (NCEI) in the form of two separate data sets: one consisting of 125 soundings from Kiritimati (https://doi.org/10.7289/V55Q4T5K), the other of 193 soundings from NOAA Ship Ronald H. Brown (https://doi.org/10.7289/V5X63K15). Of the Kiritimati soundings, 94 % reached the tropopause and 88 % reached 40 hPa, while 89 % of the ship's soundings reached the tropopause and 87 % reached 40 hPa. The soundings captured the repeated advance and retreat of the Intertropical Convergence Zone (ITCZ) at Kiritimati, a variety of marine tropospheric environments encountered by the ship, and lower-stratospheric features of the 2015–2016 QBO (quasi-biennial oscillation), all providing a rich view of the local atmosphere's response to the eastern central Pacific's extremely warm waters during the 2015/16 El Niño.

Vertical profiles of 3-D wind velocity are retrieved from triple range-height-indicator (RHI) scans performed with multiple simultaneous scanning Doppler wind lidars. This test is part of the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign carried out at the Boulder Atmospheric Observatory. The three wind velocity components are retrieved and then compared with the data acquired through various profiling wind lidars and high-frequency wind data obtained from sonic anemometers installed on a 300 m meteorological tower. The results show that the magnitude of the horizontal wind velocity and the wind direction obtained from the triple RHI scans are generally retrieved with good accuracy. However, poor accuracy is obtained for the evaluation of the vertical velocity, which is mainly due to its typically smaller magnitude and to the error propagation connected with the data retrieval procedure and accuracy in the experimental setup.

Abstract. 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.

To assess current remote-sensing capabilities for wind energy applications, a remote-sensing system evaluation study, called XPIA (eXperimental Planetary boundary layer Instrument Assessment), was held in the spring of 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. Several remote-sensing platforms were evaluated to determine their suitability for the verification and validation processes used to test the accuracy of numerical weather prediction models.The evaluation of these platforms was performed with respect to well-defined reference systems: the BAO's 300 m tower equipped at six levels (50, 100, 150, 200, 250, and 300 m) with 12 sonic anemometers and six temperature (T) and relative humidity (RH) sensors; and approximately 60 radiosonde launches.In this study we first employ these reference measurements to validate temperature profiles retrieved by two co-located microwave radiometers (MWRs) as well as virtual temperature (Tv) measured by co-located wind profiling radars equipped with radio acoustic sounding systems (RASSs). Results indicate a mean absolute error (MAE) in the temperature retrieved by the microwave radiometers below 1.5 K in the lowest 5 km of the atmosphere and a mean absolute error in the virtual temperature measured by the radio acoustic sounding systems below 0.8 K in the layer of the atmosphere covered by these measurements (up to approximately 1.6–2 km). We also investigated the benefit of the vertical velocity correction applied to the speed of sound before computing the virtual temperature by the radio acoustic sounding systems. We find that using this correction frequently increases the RASS error, and that it should not be routinely applied to all data.Water vapor density (WVD) profiles measured by the MWRs were also compared with similar measurements from the soundings, showing the capability of MWRs to follow the vertical profile measured by the sounding and finding a mean absolute error below 0.5 g m−3 in the lowest 5 km of the atmosphere. However, the relative humidity profiles measured by the microwave radiometer lack the high-resolution details available from radiosonde profiles. An encouraging and significant finding of this study was that the coefficient of determination between the lapse rate measured by the microwave radiometer and the tower measurements over the tower levels between 50 and 300 m ranged from 0.76 to 0.91, proving that these remote-sensing instruments can provide accurate information on atmospheric stability conditions in the lower boundary layer.

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.

Differing boundary/mixed-layer height measurement methods were assessed in moderately-polluted and clean environments, with a focus on the Vaisala CL51 ceilometer. This intercomparison was performed as part of ongoing measurements at the Chemistry And Physics of the Atmospheric Boundary Layer Experiment (CAPABLE) site in Hampton, Virginia and during the 2014 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign that took place in and around Denver, Colorado. We analyzed CL51 data that were collected via two different methods (BLView software, which applied correction factors, and simple terminal emulation logging) to determine the impact of data collection methodology. Further, we evaluated the STRucture of the ATmosphere (STRAT) algorithm as an open-source alternative to BLView (note that the current work presents an evaluation of the BLView and STRAT algorithms and does not intend to act as a validation of either). Filtering criteria were defined according to the change in mixed-layer height (MLH) distributions for each instrument and algorithm and were applied throughout the analysis to remove high-frequency fluctuations from the MLH retrievals. Of primary interest was determining how the different data-collection methodologies and algorithms compare to each other and to radiosonde-derived boundary-layer heights when deployed as part of a larger instrument network. We determined that data-collection methodology is not as important as the processing algorithm and that much of the algorithm differences might be driven by impacts of local meteorology and precipitation events that pose algorithm difficulties. The results of this study show that a common processing algorithm is necessary for LIght Detection And Ranging (LIDAR)-based MLH intercomparisons, and ceilometer-network operation and that sonde-derived boundary layer heights are higher (10-15% at mid-day) than LIDAR-derived mixed-layer heights. We show that averaging the retrieved MLH to 1-hour resolution (an appropriate time scale for a priori data model initialization) significantly improved correlation between differing instruments and differing algorithms.

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.

Abstract 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.

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 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.

This paper examines the performance of satellite sounder atmospheric vertical moisture profiles under tropospheric conditions encompassing moisture contrasts driven by convection and advection transport mechanisms, specifically Atlantic Ocean Saharan air layers (SALs), tropical Hadley cells, and Pacific Ocean atmospheric rivers (ARs). Operational satellite sounder moisture profile retrievals from the Suomi National Polar-Orbiting Partnership (SNPP) NOAA Unique Combined Atmospheric Processing System (NUCAPS) are empirically assessed using collocated dedicated radiosonde observations (raobs) obtained from ocean-based intensive field campaigns. The raobs from these campaigns provide uniquely independent correlative truth data not assimilated into numerical weather prediction (NWP) models for satellite sounder validation over oceans. Although ocean cases are often considered "easy" by the satellite remote sensing community, these hydro meteorological phenomena present challenges to passive sounders, including vertical gradient discontinuities (e.g., strong inversions), as well as persistent uniform clouds, aerosols, and precipitation. It is found that the operational satellite sounder 100-layer moisture profile NUCAPS product performs close to global uncertainty requirements in the SAL/Hadley cell environment, with biases relative to raob within 10% up to 350 hPa. In the more difficult AR environment, bias relative to raob is found to be within 20% up to 400 hPa. In both environments, the sounder moisture retrievals are comparable to NWP model outputs, and cross-sectional analyses show the capability of the satellite sounder for detecting and resolving these tropospheric moisture features, thereby demonstrating a near-real-time forecast utility over these otherwise raob-sparse regions.

Tropospheric O 3 has been decreasing across much of the eastern U.S. but has remained steady or even increased in some western regions. Recent increases in VOC and NO x emissions associated with the production of oil and natural gas (O&NG) may contribute to this trend in some areas. The Northern Front Range of Colorado has regularly exceeded O 3 air quality standards during summertime in recent years. This region has VOC emissions from a rapidly developing O&NG basin and low concentrations of biogenic VOC in close proximity to urban‐Denver NO x emissions. Here VOC OH reactivity (OHR), O 3 production efficiency (OPE), and an observationally constrained box model are used to quantify the influence of O&NG emissions on regional summertime O 3 production. Analyses are based on measurements acquired over two summers at a central location within the Northern Front Range that lies between major regional O&NG and urban emission sectors. Observational analyses suggest that mixing obscures any OPE differences in air primarily influenced by O&NG or urban emission sector. The box model confirms relatively modest OPE differences that are within the uncertainties of the field observations. Box model results also indicate that maximum O 3 at the measurement location is sensitive to changes in NO x mixing ratio but also responsive to O&NG VOC reductions. Combined, these analyses show that O&NG alkanes contribute over 80% to the observed carbon mixing ratio, roughly 50% to the regional VOC OHR, and approximately 20% to regional photochemical O 3 production.

We describe a new open-path aerosol extinction cavity ringdown spectrometer (CRDS), which measures extinction coefficients as aerosol is drawn transversely through the optical cavity. With no inlet tubing, particle losses in the open-path CRDS due to impaction of coarse particles or evaporation of highly humidified particles in transfer lines are minimized, improving aerosol extinction measurements in dusty and humid environments. This report presents the key elements of the new open-path CRDS design as well as comparisons with a conventional closed-path CRDS and data obtained during a field study at NOAA's instrumented 300m tower in Erie, Colorado. The open-path CRDS's 1 sigma detection limit for one second averaged data was 0.05 Mm(-1), comparable to the limit of our closed-path CRDS.Copyright 2015 American Association for Aerosol Research