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The states of coupling between clouds and surface or boundary layer have been investigated much more extensively for marine stratocumulus clouds than for continental low clouds, partly due to more complex thermodynamic structures over land. A manifestation is a lack of robust remote sensing methods to identify coupled and decoupled clouds over land. Following the idea for determining cloud coupling over the ocean, we have generalized the concept of coupling and decoupling to low clouds over land, based on potential temperature profiles. Furthermore, by using ample measurements from lidar and a suite of surface meteorological instruments at the U.S. Department of Energy's Atmospheric Radiation Measurement Program's Southern Great Plains site from 1998 to 2019, we have developed a method to simultaneously retrieve the planetary boundary layer (PBL) height (PBLH) and coupled states under cloudy conditions during the daytime. The new lidar-based method relies on the PBLH, the lifted condensation level, and the cloud base to diagnose the cloud coupling. The coupled states derived from this method are highly consistent with those derived from radiosondes. Retrieving the PBLH under cloudy conditions, which has been a persistent problem in lidar remote sensing, is resolved in this study. Our method can lead to high-quality retrievals of the PBLH under cloudy conditions and the determination of cloud coupling states. With the new method, we find that coupled clouds are sensitive to changes in the PBL with a strong diurnal cycle, whereas decoupled clouds and the PBL are weakly related. Since coupled and decoupled clouds have distinct features, our new method offers an advanced tool to separately investigate them in climate systems.

Severe windstorms resulting from mesoscale convective systems (MCS) cause significant disruption to society, including widespread power outages, tree and structural damage, and transportation accidents that affect multi-state regions and metropolitan areas along their track. A derecho, defined as a long-lived, widespread severe convective windstorm, is composed of numerous downbursts (intense localized storm downdrafts) organized into clusters or families of clusters. Derechos can produce winds above hurricane force along a track that may exceed several hundred kilometers. Convective windstorm potential has been expressed as a grouping of stability parameters that are relevant for downburst generation. These include lower-to-mid-tropospheric and equivalent potential temperature (θe) lapse rates, vertical relative humidity differences, and the amount of convective available potential energy (CAPE) in the troposphere. One major environmental factor addressed in the generation of widespread severe convective winds is an elevated mixed layer and its associated instability that promotes powerful storm updrafts and downdrafts. Another critical factor is developing a rear-inflow jet into an MCS that channels unsaturated mid-tropospheric air into the leading convective storm line. Establishing an elevated, ascending front-to-rear flow originating from deep, moist convection, overlying a strong and deep outflow-induced cold pool has been found to generate and sustain a robust rear inflow jet. The North American Derecho of 29-30 June 2012 exhibits many classic progressive and serial derecho features. It remains as one of the highest-impact derecho-producing convective systems (DCS) over CONUS since 2000. This research effort enhances the understanding of the science of operational forecasting of severe windstorms through examples of employing new satellite and ground-based microwave and vertical wind profile data. During the track of the derecho from the upper Midwestern U.S. through the mid-Atlantic region on 29 June 2012, clear signatures associated with a severe MCS were apparent in polar satellite imagery, especially from the EPS METOP-A Microwave Humidity Sounder (MHS), Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager Sounder (SSMIS) and NASA TERRA Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, morning (descending node) and the evening (ascending node) METOP-A Infrared Atmospheric Sounding Interferometer (IASI) soundings are compared to soundings from surface-based Radiometrics Corporation MP-3000 series microwave radiometer profilers (MWRPs) along the track of the derecho system. The co-located IASI and MWRP soundings revealed a pre-convective environment that indicated a favorable volatile tropospheric profile for severe downburst wind generation. An important outcome of this study will be to formulate a functional relationship between satellite-derived parameters and signatures, and severe convective wind occurrence. In summary, a comprehensive approach to observational data analysis involves both surface- and satellite-based instrumentation. Because this approach utilizes operational products available to weather service forecasters, it can feasibly be used for monitoring and forecasting local-scale downburst occurrence within derecho systems, as well as larger-scale convective wind intensity associated with the entire DCS.

The 2015 Plains Elevated Convection at Night (PECAN) field campaign provided a wealth of intensive observations for improving understanding of interplay between the Great Plains low-level jet (LLJ), mesoscale convective systems (MCSs), and other phenomena in the nocturnal boundary layer. This case study utilizes PECAN ground-based Doppler and water vapor lidar and airborne water vapor lidar observations for a detailed examination of water vapor transport in the Great Plains. The chosen case, 11 July 2015, featured a strong LLJ that helped sustain an MCS overnight. The lidars resolved boundary layer moisture being transported northward, leading to a large increase in water vapor in the lowest several hundred meters above the surface in northern Kansas. A branch of nocturnal convection initiated coincident with the observed maximum water vapor flux. Radiosondes confirmed an increase in convective potential within the LLJ layer. Moist static energy (MSE) growth was generated by increasing moisture in spite of a temperature decrease in the LLJ layer. This unique dataset is also used to evaluate the Rapid Refresh (RAP) analysis model performance, comparing model output against the continuous lidar profiles of water vapor and wind. While the RAP analysis captured the large-scale trends, errors in water vapor mixing ratio were found ranging from 0 to 2 g kg−1 at the ground-based lidar sites. Comparison with the airborne lidar throughout the PECAN domain yielded an RMSE of 1.14 g kg−1 in the planetary boundary layer. These errors mostly manifested as contiguous dry or wet regions spanning spatial scales on the order of ten to hundreds of kilometers.

Accurate simulation of planetary boundary layer height (PBLH) is key to greenhouse gas emission estimation, air quality prediction, and weather forecasting. This paper describes an extensive performance assessment of several Weather Research and Forecasting (WRF) Model configurations in which novel observations from ceilometers, surface stations, and a flux tower were used to study their ability to reproduce the PBLH and the impact that the urban heat island (UHI) has on the modeled PBLHs in the greater Washington, D.C., area. In addition, CO2 measurements at two urban towers were compared with tracer transport simulations. The ensemble of models used four PBL parameterizations, two sources of initial and boundary conditions, and one configuration including the building energy parameterization urban canopy model. Results have shown low biases over the whole domain and period for wind speed, wind direction, and temperature, with no drastic differences between meteorological drivers. We find that PBLH errors are mostly positively correlated with sensible heat flux errors and that modeled positive UHI intensities are associated with deeper modeled PBLs over the urban areas. In addition, we find that modeled PBLHs are typically biased low during nighttime for most of the configurations with the exception of those using the MYNN parameterization, and these biases directly translate to tracer biases. Overall, the configurations using the MYNN scheme performed the best, reproducing the PBLH and CO2 molar fractions reasonably well during all hours and thus opening the door to future nighttime inverse modeling.

A network of automated weather stations (AWS) with ceilometers can be used to detect sky conditions, aerosol dispersion, and mixing layer heights, in addition to the routine surface meteorological parameters (temperature, pressure, humidity, etc.). Currently, a dense network of AWSs that observe all of these parameters does not exist in the United States even though networks of them with ceilometers exist. These networks normally use ceilometers for determining only sky conditions. Updating AWS networks to obtain those nonstandard observations with ceilometers, especially mixing layer height, across the United States would provide valuable information for validating and improving weather/climate forecast models. In this respect, an aerosol-based mixing layer height detection method, called the combined-hybrid method, is developed and evaluated for its uncertainty characteristics for application in the United States. Four years of ceilometer data from the National Weather Service Ceilometer Proof of Concept Project taken in temperate, maritime polar, and hot/arid climate regimes are utilized in this evaluation. Overall, the method proved to be a strong candidate for estimating mixing layer heights with ceilometer data, with averaged uncertainties of 237 ± 398 m in all tested climate regimes and 69 ± 250 m when excluding the hot/arid climate regime.

Inspired by the importance of Planetary Boundary Layer Heights (PBLH) for inferring Air Quality assessments and the disappointing performance of current weather forecasts of PBLH, this paper presents the proposed impact of using a Machine Learning derived PBLH (ML-PBLH) using ground-based Ceilometer observing systems. The PBLH is vital in air pollution studies to determine the extent of vertical mixing of pollutants (e.g., particles, trace gases, etc.). On the other hand, model forecasts of the nighttime collapse of the PBLH close to the surface, grossly underestimate the actual observations. We propose using machine learning methods, employing deep neural networks for denoising and image segmentation to detect PBLH from the Ceilometer backscatter signal from the LIDAR observations. The ML-PBLH detection algorithm shows promising early results when compared with conventional ground-based Ceilometer retrieval methods such as Wavelet methods used for LIDAR identification of PBLH under normal conditions. The use of conventional methods as well as the wavelet method for LIDAR backscatter retrievals is limited in the presence of dense clouds. However, the proposed machine learning approach is able to infer PBLH even under such conditions.

This paper describes the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) approach to managing the transition from the Vaisala RS92 to the Vaisala RS41 as the operational radiosonde. The goal of GRUAN is to provide long-term high-quality reference observations of upper-air essential climate variables (ECVs) such as temperature and water vapor. With GRUAN data being used for climate monitoring, it is vital that the change of measurement system does not introduce inhomogeneities to the data record. The majority of the 27 GRUAN sites were launching the RS92 as their operational radiosonde, and following the end of production of the RS92 in the last quarter of 2017, most of these sites have now switched to the RS41. Such a large-scale change in instrumentation is unprecedented in the history of GRUAN and poses a challenge for the network. Several measurement programs have been initiated to characterize differences in biases, uncertainties, and noise between the two radiosonde types. These include laboratory characterization of measurement errors, extensive twin sounding studies with RS92 and RS41 on the same balloon, and comparison with ancillary data. This integrated approach is commensurate with the GRUAN principles of traceability and deliberate redundancy. A 2-year period of regular twin soundings is recommended, and for sites that are not able to implement this, burden-sharing is employed such that measurements at a certain site are considered representative of other sites with similar climatological characteristics. All data relevant to the RS92–RS41 transition are archived in a database that will be accessible to the scientific community for external scrutiny. Furthermore, the knowledge and experience gained regarding GRUAN's RS92–RS41 transition will be extensively documented to ensure traceability of the process. This documentation will benefit other networks in managing changes in their operational radiosonde systems. Preliminary analysis of the laboratory experiments indicates that the manufacturer's calibration of the RS41 temperature and humidity sensors is more accurate than for the RS92, with uncertainties of  K for the temperature and  % RH (RH: relative humidity) for the humidity sensor. A first analysis of 224 RS92–RS41 twin soundings at Lindenberg Observatory shows nighttime temperature differences  K between the Vaisala-processed temperature data for the RS41 (TRS41) and the GRUAN data product for the RS92 (TRS92-GDP.2). However, daytime temperature differences in the stratosphere increase steadily with altitude, with TRS92-GDP.2 up to 0.6 K higher than TRS41 at 35 km. RHRS41 values are up to 8 % higher, which is consistent with the analysis of satellite–radiosonde collocations.

EPJ Web Conf., Volume 237, 2020 The 29th International Laser Radar Conference (ILRC 29), This study focuses on the distribution of ozone (O₃) concentration near the Chesapeake Bay, USA (hereafter CB) by integrating observations and model simulations. The motivation of this work is to understand reasons causing the horizontal and vertical distribution of pollutants (mainly O₃) near the CB. The O₃ exceedance over the CB happens very frequently during summer and the Maryland Department of Environment intends to find out the reasons in order to make policy-related decision. The observation data used in this study are from the Ozone Water-Land Environmental Transition Study-2 (OWLETS-2) field campaign, including observations from O₃ lidar, Doppler wind lidar, ozonesonde. The mesoscale model employed is Weather Research and Forecasting model coupled with chemistry (WRF-Chem) version 3.9.1. The anthropogenic emission dataset is from National Emission Inventory 2011 (NEI-2011), including various emission species, e.g., CO, NOₓ, SO₂, NH₃, PM₂.₅, PM₁₀, etc. The meteorological initial and boundary conditions are from the Northern American Regional Reanalysis (NARR) dataset, which is a high-resolution combined model and assimilated dataset from the National Centers for Environmental Prediction (NCEP). There are several findings of this study based on the model simulations and ground-based observations. Actually, at the beginning of study, we considered two different versions of anthropogenic emissions from NEI-2005 and NEI-2011 developed by the Environment Protection Agency (EPA). EPA added the anthropogenic emissions over CB from boats and ships while updating from NEI-2005 to NEI-2011. For model performance evaluation, we employed AirNow surface hourly O₃ mixing ratio diurnal variation and compared it with model simulations. For instance, at Essex site near Baltimore City, observed O₃ has a strong diurnal variation, with minimum (25 ppbv) just after sunrise (05:00 EST), and with maximum (75 ppbv) around afternoon (15:00 EST). Even the model simulation has a good agreement with the observation, it underestimates the mean O₃ mixing ratio by about 15-20 ppbv. Both the surface and 700 mb level horizontal spatial distribution of O₃ indicate the higher O₃ concentration over the north-middle CB, with surface O₃ mixing ratio of 40-50 ppbv and 700 mb level O₃ mixing ratio of 60 ppbv, which means the surface O₃ was lifted up after production. The vertical profiles of wind of both model and Doppler wind lidar match very well, indicating that the model captured the vertical variation of wind. However, the vertical profiles of O₃ from model simulation, ozonesonde, and O₃ lidar suggests that model simulation underestimated the O₃ from surface to 4.5 km. In addition, the model simulation captured the vertical mixing of O₃ from surface to 2 km, while misses the O₃ variation above 2 km. In order to study the influence of bay breeze on the O₃ small scale transport, three vertical cross sections through the CB from west to east at the northern, middle, and southern CB. The results show that higher O₃ concentration above the CB. The bay breeze over the southern CB is stronger than the northern CB. The planetary boundary layer height over the CB is dramatically lower than the surrounding land in the day, which contributes to the surface higher O₃ concentration over the CB.

This paper describes the GRUAN-wide approach to manage the transition from the Vaisala RS92 to the Vaisala RS41 as the operational radiosonde. The goal of the GCOS Reference Upper-Air Network (GRUAN) is to provide long-term high-quality reference observations of upper air Essential Climate Variables (ECVs) such as temperature and water vapor. With GRUAN data being used for climate monitoring, it is vital that the change of measurement system does not introduce inhomogeneities in to the data record. The majority of the 27 GRUAN sites were launching the RS92 as their operational radiosonde, and following the end of production of the RS92 in the last quarter of 2017, most of these sites have now switched to the RS41. Such a large-scale change in instrumentation is unprecedented in the history of GRUAN and poses a challenge for the network. Several measurement programmes have been initiated to characterize differences in biases, uncertainties and noise between the two radiosonde types. These include laboratory characterization of measurement errors, extensive twin sounding studies with RS92 and RS41 on the same balloon, and comparison with ancillary data. This integrated approach is commensurate with the GRUAN principles of traceability and deliberate redundancy. A two-year period of regular twin soundings is recommended, and for sites that are not able to implement this burden sharing is employed, such that measurements at a certain site are considered representative of other sites with similar climatological characteristics. All data relevant to the RS92-RS41 transition are archived in a database that will be accessible to the scientific community for external scrutiny. Furthermore, the knowledge and experience gained about GRUAN's RS92-RS41 transition will be extensively documented to ensure traceability of the process. This documentation will benefit other networks in managing changes in their operational radiosonde systems. Preliminary analysis of the laboratory experiments indicates that the manufacturer's calibration of the RS41's temperature and humidity sensors is more accurate than for the RS92; with uncertainties of

This paper presents a case study of a strong low-level jet (LLJ) that was observed about 20 km off the coast of Ocean City, Maryland, during a measurement campaign in the summer of 2013. Doppler wind lidar observations offshore, together with analyses of 4-km WRF Model data and NARR data, are used to reconstruct the forcing mechanisms that led to the growth and rapid collapse of the jet offshore as well as to differentiate the forcing mechanisms resulting in an LLJ farther inland. It was observed that the LLJ over the mid-Atlantic coastal plain decreased gradually throughout the early morning hours relative to the LLJ along the coastal ocean as a downslope wind moved eastward from the Appalachian Mountains. The forcing of the LLJ was a result of both thermal and mechanical mechanisms linked to the topography, while synoptic forcing from an approaching cold front led to a downslope wind. Data from a wind profiler near Cambridge, Maryland, also showed an LLJ, but forced by different regional conditions, emphasizing the difficulties of inferring wind conditions offshore from onshore observations. The sudden breakdown of the jet offshore appears to have been a result of an interaction with a downslope wind from the Appalachian Mountains. This particular case study highlights the 1) importance of both large-scale and regional forcing, 2) impact that topographical forcing farther inland had on offshore wind, and 3) different responses in the wind profile as a downslope wind moved across the mid-Atlantic region.

Thirty southerly low‐level jet (LLJ) events were observed during the Plains Elevated Convection at Night (PECAN) field campaign in the Great Plains region of the United States during summer 2015. Here we present Doppler lidar wind data from three PECAN instrumentation sites to explore characteristics of LLJs and the boundary layer as well as some of the heterogeneities possible within the wind field of a LLJ. Southerly LLJs were observed on 66% of nights at the southwestern site (Greensburg, KS) but only 56% and 53% of nights at the eastern and northern sites, respectively (Hesston and Ellis, KS). The northernmost site had a relative abundance of weaker jets or nonjet conditions due to fronts or convective systems that only affected part of the observation domain. Plotting mean wind fields of each LLJ type reveals that the strongest LLJs tend to develop under very similar conditions but begin to show variability in wind profile evolution after several hours. A robust mixed layer height retrieval algorithm is used to investigate the interplay between the jets and the turbulent convective boundary layer, showing that stronger LLJs are preceded by deeper afternoon mixed layers and often have a later decoupling of mixing between the upper convective mixed layer and the near‐surface layer. Only the strongest LLJs generated a shallow mixing layer overnight. Comparing jet strength and direction to pristine nocturnal convection initiation shows that the strongest southerly LLJs yielded the most pristine nocturnal convection initiation events per night, and the pristine nocturnal convection initiation occurred farther north.

The performance of twenty GCMs that participated in the Coupled Model Intercomparison Phase 5 (CMIP5) is evaluated at Sterling, Virginia, by comparing model outputs with radiosonde observational dataset and reanalysis dataset. We evaluated CMIP5 models in their ability to simulate wind climatology, seasonal cycle, interannual variability, and trends at the pressure levels from 850 hPa to 30 hPa. We also addressed the question of the number of years required to detect statistically significant wind trends using radiosonde wind measurements. Our results show that CMIP5 models and reanalysis successfully reproduced the observed climatological annual mean zonal wind and wind speed vertical distribution. They also capture the observed seasonal zonal, meridional, and wind speed vertical distribution with stronger (weaker) wind during the winter (summer) season. However, there is some disagreement in the magnitude of vertical profiles among CMIP5 models, reanalysis, and radiosonde observation. Overall, the number of years to obtain statistically significant trend decreases with increasing pressure level except for upper troposphere. Although the vertical profile of interannual variability of CMIP5 models and reanalysis agree with the radiosonde observation, the wind trend is not statistically significant. This indicates that detection of trends on local scale is challenging because of small signal-to-noise ratio problems.

Water vapor advection is an important diagnostic parameter for initiation and sustenance of convective systems. This variable has traditionally been explored with balloon soundings or models, but lidar provides a unique opportunity to continuously resolve spatial and temporal evolution of water vapor transport with relatively high resolution. In this study we utilize co-located Doppler wind lidars and water vapor lidars (DIAL and Raman) at multiple sites to investigate water vapor advection. A case of nocturnal convection in the Great Plains is analyzed with these observations and compared to the North American Mesoscale Forecast System (NAM) operational forecast model.

Our understanding of the distribution of heat and water in the atmosphere still shows critical gaps on all temporal and spatial scales. This is mainly due to a lack of accurate measurements of water vapor and temperature profiles -hereafter called thermodynamic (TD) profiles -withhigh vertical and temporal resolution, especially in the lower troposphere. Accurate, high temporal-spatial resolution observations of TD profiles are essential for improving weather forecasting and re-analyses, for studying land-atmosphere feedback processes and for improving model parameterizations of land-surface and turbulent transport processes in the Atmospheric Boundary Layer. These observational gaps can be addressed with a new active remote sensing system in space based on the Raman lidar technique. Combining vibrational and rotational Raman backscatter signals, simultaneous measurements of water vapour and temperature profiles and a variety of derived variables are possible with unprecedented vertical and horizontal resolution, especially in the lower troposphere. This is the key concept of ATLAS, which was submitted in March 2018to the European Space Agency in response to the Call for Earth Explorer-10 Mission Ideas in the frame of ESAEOEP. An assessment of the expected performance of the systemand the specifications of the different lidar sub-systems has been performed based on the application ofan analytical simulation model for space-borne Raman lidar systems. Results from the simulations and technical aspects of the proposed mission will beillustrated at the conference.

The 28th International Laser Radar Conference (ILRC 28), Rain evaporation significantly contributes to moisture and heat cloud budgets. In this paper, we illustrate an approach to estimate the median volume raindrop diameter and the rain evaporation rate profiles from dual-wavelength lidar measurements. These observational results are compared with those provided by a model analytical solution. We made use of measurements from the multi-wavelength Raman lidar BASIL.

The Water Vapor Variability-Satellite/Sondes (WAVES) 2006 field campaign provided a contiguous 5-day period of concentrated high-resolution measurements to examine finescale boundary layer phenomena under the influence of a summertime subtropical high over the mid-Atlantic region that is characterized by complex geography. A holistic analytical approach to low-level wind observations was adopted to identify the low-level flow structures and patterns of evolution on the basis of airmass properties and origination. An analysis of the measurements and the other available observations is consistent with the classic depiction of the daytime boundary layer development but revealed a pronounced diurnal cycle that was categorized into three stages: (i) daytime growth of the convective boundary layer, (ii) flow intensification into a low-level jet regime after dusk, and (iii) interruption by a downslope wind regime after midnight. The use of the field campaign data allows for the differentiation of the latter two flow regimes by their directions with respect to the orientation of the Appalachian Mountains and their airmass origins. Previous studies that have investigated mountain flows and low-level jet circulations have focused on regions with overt geographic prominence, stark gradients, or frequent reoccurrences, whereby such meteorological phenomena exhibit a clear signature and can be easily isolated and diagnosed. The results of this study provide evidence that similar circulation patterns operate in nonclassic locations with milder topography and atmospheric gradients, such as the mid-Atlantic region. The new results have important implications for the understanding of the mountain-forced flows and some air quality problems during the nocturnal period.

The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night. To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings.

Mesoscale convective systems are high impact convectively driven weather systems that contribute large amounts to the precipitation daily and monthly totals at various locations globally. As such, an understanding of the lifecycle, characteristics, frequency and seasonality of these convective features is important for several sectors and studies in climate studies, agricultural and hydrological studies, and disaster management. This study explores the applicability of graph theory to creating a fully automated algorithm for identifying mesoscale convective systems and determining their precipitation characteristics from satellite datasets. Our results show that applying graph theory to this problem allows for the identification of features from infrared satellite data and the seamlessly identification in a precipitation rate satellite-based dataset, while innately handling the inherent complexity and non-linearity of mesoscale convective systems.

This paper addresses the use of unmanned aerial systems (UAS) to carry out atmospheric data collection and studies. An important area of research is the study of the chemistry and physics of Earth's planetary boundary layer (PBL). The PBL, also known as the atmospheric boundary layer (ABL), is the lowest part of the atmosphere and its behavior is directly influenced by its contact with the planetary surface. Sampling of the PBL is performed in a timely and periodic manner. Currently, sensors and uncontrollable balloons are used to obtain relevant data and information. This method is cumbersome and can be ineffective in obtaining consistent environmental data. This paper proposes the use of autonomous UAS’ to study the atmosphere in an effort to improve the efficiency and accuracy of the sampling process. The UAS setup and design is provided, and preliminary data collection information is shared.

The National Weather Service (NWS) is investigating the potential of utilizing the Automatic Surface Observing System’s (ASOS) cloud base height indicator, the Vaisala CL31 ceilometer, to profile aerosols in the atmosphere. Field test sites of stand-alone CL31 ceilometers have been established, primarily, around the Washington DC metropolitan area, with additional systems in southwest USA and Puerto Rico. The CL31 PBL project examines the CL31 data collected for data quality, mixing height retrieval applicability, and its compliment to satellite data. This paper reviews the topics of the CL31 data quality and mixing height retrieval applicability.

The 28th International Laser Radar Conference (ILRC 28), Results from the PECAN (Plains Elevated Convection at Night) campaign are discussed. In particular, the utility of simple backscatter lidars/ceilometers in quantifying atmospheric dynamics parameters and variables as well as evolution of the lower tropospheric dynamics are made. Cases of bore wave dynamics and the potential of these events in lofting of low level, moist, airmass and its consequence in thunderstorm initiation are made. A suite of thermodynamic profiling instruments are combined and compared to describe and visualize lower tropospheric dynamic evolution.

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.

The NSF-funded REU summer program in the Department of Physics & Astronomy at Howard University provided cutting-edge research opportunities in Computational Nanophysics, Experimental Nanophysics, Laser Spectroscopy, Atmospheric Physics and Superstring Theory to six undergraduate students recruited from across the U.S. The REU students were engaged in challenging research projects under the supervision of seasoned mentors across a variety of stimulating physics sub-disciplines that included: (1) computation-intensive surface nanophysics of condensed phase systems focused on the adsorption of gases in Metal-Organic Frameworks (MOFs); (2) experimental measurements using light scattering techniques on gels and polymers in the condensed phase; (3) experimental laser spectroscopy with special emphasis on Raman spectral measurements on tungsten oxide nanolayer deposited on a silicon substrate; (4) observation-based and modeling-intensive atmospheric physics project for developing better understanding of wind lidar performance under various aerosol/cloud loading and relative humidity scenarios in the U.S. and regional ozone and aerosols modeling and analysis of data recorded in West Africa; and (5) a cross-disciplinary project involving quantum theory, supersymmetry, graph theory, encryption, and super-commutative algebra and algebraic geometry with applications to string theory. Each student learned a multitude of relevant techniques related to their research projects, with the vision of teaching and nurturing knowledge, both theoretical and experimental, that will be useful throughout their academic careers both in their major discipline and in interdisciplinary research as a whole. Raman Spectroscopy, 3D Physics Modeling, Monte Carlo Simulations, Algebraic Geometry and Graph Theory are some of the techniques that the students learned that illustrate the importance of physics research in general and have wide-ranging applications in interdisciplinary studies. In addition, the students participated in field trips to the University of Maryland (visit coinciding with NanoDay), Georgetown University (cleanroom tour and research presentations), NASA Goddard Space Flight Center (visit coinciding with Science Jamboree Day), and Smithsonian Museums (coinciding with evening fireworks viewing on the Mall on July 4). The REU students gave midterm and final research presentations and submitted a research paper in refereed journal format at the end of their internship. A comprehensive assessment of the REU program was conducted by an independent project evaluator.

In this study, the combined effect of the Atlantic Multidecadal Oscillation (AMO) and El Nino Southern Oscillation (ENSO) on the Lake Chad (LC) level variability is explored. Our results show that the lake level at the Bol monitoring station has a statistically significant correlation with precipitation (R2 = 0.6, at the 99.5% confidence level). The period between the late 1960s and early 1970s marked a turning point in the response of the regional rainfall to climatic drivers, thereby severely affecting the LC level. Our results also suggest that the negative impact of the cold phase of AMO on Sahel precipitation masks and supersedes the positive effect of La Nina in the early the 1970s. The drop in the size of LC level from 282.5 m in the early 1960s to about 278.1 m in 1983/1984 was the largest to occur within the period of study (1900–2010) and coincides with the combined cold phase of AMO and strong El Nino phase of ENSO. Further analyses show that the current warm phase of AMO and increasing...