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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Remote sensing of planetary boundary layer from ground and space: Structure, thermodynamics, and boundary layer clouds

The planetary boundary layer (PBL) is a critical interface between Earth's surface and atmosphere, influencing atmospheric convection, weather patterns, and air quality. Recognized by the 2017 National Academies of Sciences, Engineering, and Medicine Earth Science Decadal Survey as an Incubation Targeted Observable, high-quality and effective observation of the PBL has become a priority. This paper reviews surface-based and satellite remote sensing techniques for characterizing PBL features, including PBL height (PBLH), boundary layer thermodynamics, turbulence, and boundary layer clouds (BLCs). These elements are treated as interconnected aspects of the PBL system, while recognizing that different instruments retrieve different physical manifestations of the PBL. The review summarizes recent advances and limitations in Micro-Pulse Lidar (MPL), Doppler lidar, Raman lidar, Differential Absorption Lidar (DIAL), ceilometers, wind profilers, GNSS Radio Occultation, radar, and hyperspectral sounders. These observations help trace dynamic processes within the PBL and link PBL structure to broader weather and climate processes. The review also highlights persistent observational gaps over oceans, remote land, and polar regions, where continuous surface-based profiling is sparse. By comparing instrument capabilities, practical limitations, and interpretation issues, this review emphasizes the need for integrated remote sensing approaches and careful definition of the retrieved PBL quantities.

Boundary layer clouds↗
DOE OSTI2023

Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements

Abstract. Cloud droplet number concentration (Nd) is crucial for understanding aerosol–cloud interactions (ACI) and associated radiative effects. We present evaluations of four ground-based Nd retrievals based on comprehensive datasets from the Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The Nd retrieval methods use ARM ENA observatory ground-based remote sensing observations from a micropulse lidar, Raman lidar, cloud radar, and the ARM NDROP (Droplet Number Concentration) value-added product (VAP), all of which also retrieve cloud effective radius (re). The retrievals are compared against aircraft measurements from the fast cloud droplet probe (FCDP) and the cloud and aerosol spectrometer (CAS) obtained from low-level marine boundary layer clouds on 12 flight days during summer and winter seasons. Additionally, the in situ measurements are used to validate the assumptions and characterizations used in the retrieval algorithms. Statistical comparisons of the probability distribution function (PDF) of the Nd and cloud re retrievals with aircraft measurements demonstrate that these retrievals align well with in situ measurements for overcast clouds, but they may substantially differ for broken clouds or clouds with low liquid water path (LWP). The retrievals are applied to 4 years of ground-based remote sensing measurements of overcast marine boundary layer clouds at the ARM ENA observatory to find that Nd (re) values exhibit seasonal variations, with higher (lower) values during the summer season and lower (higher) values during the winter season. The ensemble of various retrievals using different measurements and retrieval algorithms such as those in this paper can help to quantify Nd retrieval uncertainties and identify reliable Nd retrieval scenarios. Of the retrieval methods, we recommend using the micropulse lidar-based method. This method has good agreement with in situ measurements, less sensitivity to issues arising from precipitation and low cloud LWP and/or optical depth, and broad applicability by functioning for both daytime and nighttime conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluation of Four Ground-based Retrievals of Cloud Droplet Number Concentration in Marine Stratocumulus with Aircraft In Situ Measurements

Cloud droplet number concentration (N d ) is crucial for understanding aerosol-cloud interactions (ACI) and associated radiative effects. We present evaluations of four ground-based N d retrievals based on comprehensive datasets from the Atmospheric Radiation Measurements (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The N d retrieval methods use ARM ENA observatory ground-based remote sensing observations from a Micropulse lidar, Raman lidar, cloud radar, and the ARM NDROP Value-added Product (VAP), all of which also retrieve cloud effective radius (r e ). The retrievals are compared against aircraft measurements from the Fast-Cloud Droplet Probe (FCDP) and the Cloud and Aerosol Spectrometer (CAS) obtained from low-level marine boundary layer clouds on 12 flight days during summer and winter seasons. Additionally, the in situ measurements are used to validate the assumptions and characterizations used in the retrieval algorithms. Statistical comparisons of the probability distribution function (PDF) of the N d and cloud r e retrievals with aircraft measurements demonstrate that these retrievals align well with in situ measurements for overcast clouds, but they may substantially differ for broken clouds or clouds with low liquid water path (LWP). The retrievals are applied to four years of ground-based remote sensing measurements of overcast marine boundary layer clouds at the ARM ENA observatory to find that N d (r e ) values exhibit seasonal variations, with higher (lower) values during the summer season and lower (higher) values during the winter season. The ensemble of various retrievals using different measurements and retrieval algorithms such as those in this paper can help to quantify N d retrieval uncertainties and identify reliable N d retrieval scenarios. Of the retrieval methods, we recommend using the using the Micropulse lidar-based method given its good agreement with in situ measurements, it has less sensitivity to issues arising from precipitation and low cloud LWP/optical depth, and it has broad applicability by functioning for both day and nighttime conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

NDSC and JPL stratospheric lidars

The Network for the Detection of Stratospheric Change is an international cooperation providing a set of high-quality, remote-sensing instruments at observing stations around the globe. A brief description of the NDSC and its goals is presented. Lidar has been selected as the NDSC instrument for measurements of stratospheric profiles of ozone, temperature, and aerosol. The Jet Propulsion Laboratory has developed and implemented two stratospheric lidar systems for NDSC. These are located at Table Mountain, California, and at Mauna Loa, Hawaii. These systems, which utilize differential absorption lidar, Rayleigh lidar, raman lidar, and backscatter lidar, to measure ozone, temperature, and aerosol profiles in the stratosphere are briefly described. Examples of results obtained for both long-term and individual profiles are presented.

McDermid, I. Stuart↗
NASA NTRS1994

NDSC and the JPL Stratospheric Lidars

The Network for the Detection of Stratospheric Change (NDSC) is an international cooperation providing a set of high-quality, remote-sensing instruments at observing stations around the globe. A brief description of the NDSC and its goals is presented. Lidar has been selected as the NDSC instrument for measurements of stratospheric profiles of ozone, temperature, and aerosol. The Jet Propulsion Laboratory has developed and implemented two stratospheric lidar systems for NDSC. These are located at Table Mountain, California and at Mauna Loa, Hawaii. These systems, which utilize different absorption lidar, Rayleigh lidar, Raman lidar, and backscatter lidar, to measure ozone, temperature, and aerosol profiles in the stratosphere are briefly described. Examples of results obtained for both long-term and individual profiles are presented.

lidar↗
NASA NTRS2000

Performance Modeling of an Airborne Raman Water Vapor Lidar

A sophisticated Raman lidar numerical model had been developed. The model has been used to simulate the performance of two ground-based Raman water vapor lidar systems. After tuning the model using these ground-based measurements, the model is used to simulate the water vapor measurement capability of an airborne Raman lidar under both day-and night-time conditions for a wide range of water vapor conditions. The results indicate that, under many circumstances, the daytime measurements possess comparable resolution to an existing airborne differential absorption water vapor lidar while the nighttime measurement have higher resolution. In addition, a Raman lidar is capable of measurements not possible using a differential absorption system.

Whiteman, D. N.↗
NASA NTRS2002

Characterization of Upper Troposphere Water Vapor Measurements during AFWEX using LASE

Water vapor profiles from NASA's Lidar Atmospheric Sensing Experiment (LASE) system acquired during the ARM/FIRE Water Vapor Experiment (AFWEX) are used to characterize upper troposphere water vapor (UTWV) measured by ground-based Raman lidars, radiosondes, and in situ aircraft sensors. Initial comparisons showed the average Vaisala radiosonde measurements to be 5-15% drier than the average LASE, Raman lidar, and DC-8 in situ diode laser hygrometer measurements. We show that corrections to the Raman lidar and Vaisala measurements significantly reduce these differences. Precipitable water vapor (PWV) derived from the LASE water vapor profiles agrees within 3% on average with PWV derived from the ARM ground-based microwave radiometer (MWR). The agreement among the LASE, Raman lidar, and MWR measurements demonstrates how the LASE measurements can be used to characterize both profile and column water vapor measurements and that ARM Raman lidar, when calibrated using the MWR PWV, can provide accurate UTWV measurements.

Ferrare, R. A.↗
NASA NTRS2005

Active Raman sounding of the earth's water vapor field

The typically weak cross-sections characteristic of Raman processes has historically limited their use in atmospheric remote sensing to nighttime application. However, with advances in instrumentation and techniques, it is now possible to apply Raman lidar to the monitoring of atmospheric water vapor, aerosols and clouds throughout the diurnal cycle. Upper tropospheric and lower stratospheric measurements of water vapor using Raman lidar are also possible but are limited to nighttime and require long integration times. However, boundary layer studies of water vapor variability can now be performed with high temporal and spatial resolution. This paper will review the current state-of-the-art of Raman lidar for high-resolution measurements of the atmospheric water vapor, aerosol and cloud fields. In particular, we describe the use of Raman lidar for mapping the vertical distribution and variability of atmospheric water vapor, aerosols and clouds throughout the evolution of dynamic meteorological events. The ability of Raman lidar to detect and characterize water in the region of the tropopause and the importance of high-altitude water vapor for climate-related studies and meteorological satellite performance are discussed.

Earth (Planet)↗
DOE OSTI2024

Deep-learning-derived planetary boundary layer height from conventional meteorological measurements

Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Deep-Learning-derived Boundary Layer Height from Meteorological Data over the SGP, GOAMAZON, CACTI

The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

How Well do State-of-the-Art Techniques Measuring the Vertical Profile of Tropospheric Aerosol Extinction Compare?

The recent Department of Energy Atmospheric Radiation Measurement (ARM) Aerosol Intensive Operations Period (AIOP, May 2003) yielded one of the best measurement sets obtained to date to assess our ability to measure the vertical profile of ambient aerosol extinction sigma(ep)(lambda) in the lower troposphere. During one month, a heavily instrumented aircraft with well-characterized aerosol sampling ability carrying well-proven and new aerosol instrumentation devoted most of the 60 available flight hours to flying vertical profiles over the heavily instrumented ARM Southern Great Plains (SGP) Climate Research Facility (CRF). This allowed us to compare vertical extinction profiles obtained from six different instruments: airborne Sun photometer (AATS-14), airborne nephelometer/absorption photometer, airborne cavity ring-down system, groundbased Raman lidar, and two ground-based elastic backscatter lidars. We find the in situ measured sigma(ep)(lambda) to be lower than the AATS-14 derived values. Bias differences are 0.002-0.004 Km!1 equivalent to 13-17% in the visible, or 45% in the near-infrared. On the other hand, we find that with respect to AATS-14, the lidar sigma(ep)(lambda) are higher: Bias differences are 0.004 Km(-1) (13%) and 0.007 Km(-1) (24%) for the two elastic backscatter lidars (MPLNET and MPLARM, lambda = 523 nm) and 0.029 Km(-1) (54%) for the Raman lidar (lambda = 355 nm). An unnoticed loss of sensitivity of the Raman lidar had occurred leading up to AIOP, and we expect better agreement from the recently restored system. Looking at the collective results from six field campaigns conducted since 1996, airborne in situ measurements of sigma(ep)(lambda) tend to be biased slightly low (17% at visible wavelengths) when compared to airborne Sun photometer sigma(ep)(lambda). On the other hand, sigma(ep)(lambda) values derived from lidars tend to have no or positive biases. From the bias differences we conclude that the typical systematic error associated with measuring the tropospheric vertical profile of the ambient aerosol extinction with current state-of-the-art instrumentation is 15-20% at visible wavelengths and potentially larger in the UV and near-infrared.

Schmid, B.↗
DOE OSTI2025

High-Spectral Resolution Lidar (HSRL) Instrument Handbook

High-spectral-resolution lidar (HSRL) systems provide vertical profiles of optical depth, backscatter cross-section, depolarization, and backscatter phase function. All HSRL measurements are calibrated by reference to molecular scattering, which is measured at each point in the lidar profile. Like the Raman lidar (RL), but unlike simple backscatter lidars such as the micropulse lidar (MPL), this enables the HSRL to measure backscatter cross-sections and optical depths without prior assumptions about the scattering properties of the atmosphere. The depolarization observations allow robust discrimination between ice and water clouds. Rigorous error estimates can be computed for all measurements. A very narrow angular field of view reduces multiple scattering contributions. The small field of view, coupled with a narrow optical bandwidth, nearly eliminates noise due to scattered sunlight. The laser transmitter is a diode-pumped, frequency-doubled Nd:YAG laser. Narrow-band, single-frequency operation is provided by injection seeding with a single-frequency, cw-diode-pumped diode laser. The main laser cavity is maintained in resonance with the seed laser by adjusting the cavity length to minimize the time between the Q-switch trigger and the emission of the laser pulse. The emission wavelength is tuned via temperature control of the seed laser crystal and is locked to line #1109 of the iodine absorption spectra. Locking is accomplished by minimizing the transmission through a 2-cm-long iodine absorption cell. Use of a high-repetition-rate laser and expansion of the transmitted beam through a 400-mm telescope reduces the transmitted energy density to eye-safe levels. It is possible to look directly into the output beam without hazard. The receiver and transmitter use the same afocal telescope, simplifying the maintenance of stable alignment of the transmitter and receiver although the angular FOV is only 100 μrad. The small FOV and the 4-kHz repetition rate also limit the near-field signal strength, making it possible to record continuous profiles that start at an altitude of ~100 m and extend to 30 km using photon counting detectors. The small FOV also suppresses multiple scattering contributions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Improving the Confidence in Retrievals of Vertical Distributions of Cloud Condensation Nuclei Number Concentration from ARM Supported by Aircraft In Situ Observations

Accurate quantification of the vertical distribution of cloud condensation nuclei (CCN) number concentrations is critical for improving our understanding of aerosol–cloud interactions. Ground-based Raman lidars operated by the Atmospheric Radiation Measurement (ARM) program, together with surface CCN measurements, are used to retrieve vertically resolved CCN number concentrations (Retrieved Number concentration of CCN, RNCCN). These retrievals rely on several assumptions, including that aerosol composition is vertically homogeneous. To assess this assumption, we developed and tested a framework to infer the dominant aerosol classes/types at different altitudes. This was done by applying a k-Nearest-Neighbors (kNN) algorithm to lidar ratio and linear depolarization ratio measurements from Raman lidar. We evaluated the framework using aircraft aerosol and CCN measurements from the ARM Holistic Interactions of Shallow Clouds, Aerosols, and Land Ecosystems (HI-SCALE) field campaign. The results show that RNCCN performance degrades as vertical aerosol complexity increases, i.e., RNCCN agrees with the aircraft CCN in vertically homogeneous conditions, but closure decreases in layered aerosol structures. To generalize beyond individual examples, we introduce a metric (heterogeneity index) that quantifies the vertical complexity by assessing the variation of inferred aerosol classes/types. Case-level statistics show a tendency for RNCCN and aircraft differences to increase with this metric. By detecting retrievals that are likely compromised by aerosol vertical heterogeneity, the proposed framework improves the interpretability and effective use of RNCCN used for long-term evaluation of models and aerosol–cloud interactions.

Tian, Jingjing↗
NASA NTRS2012

Correction Technique for Raman Water Vapor Lidar Signal-Dependent Bias and Suitability for Water Wapor Trend Monitoring in the Upper Troposphere

The MOHAVE-2009 campaign brought together diverse instrumentation for measuring atmospheric water vapor. We report on the participation of the ALVICE (Atmospheric Laboratory for Validation, Interagency Collaboration and Education) mobile laboratory in the MOHAVE-2009 campaign. In appendices we also report on the performance of the corrected Vaisala RS92 radiosonde measurements during the campaign, on a new radiosonde based calibration algorithm that reduces the influence of atmospheric variability on the derived calibration constant, and on other results of the ALVICE deployment. The MOHAVE-2009 campaign permitted the Raman lidar systems participating to discover and address measurement biases in the upper troposphere and lower stratosphere. The ALVICE lidar system was found to possess a wet bias which was attributed to fluorescence of insect material that was deposited on the telescope early in the mission. Other sources of wet biases are discussed and data from other Raman lidar systems are investigated, revealing that wet biases in upper tropospheric (UT) and lower stratospheric (LS) water vapor measurements appear to be quite common in Raman lidar systems. Lower stratospheric climatology of water vapor is investigated both as a means to check for the existence of these wet biases in Raman lidar data and as a source of correction for the bias. A correction technique is derived and applied to the ALVICE lidar water vapor profiles. Good agreement is found between corrected ALVICE lidar measurments and those of RS92, frost point hygrometer and total column water. The correction is offered as a general method to both quality control Raman water vapor lidar data and to correct those data that have signal-dependent bias. The influence of the correction is shown to be small at regions in the upper troposphere where recent work indicates detection of trends in atmospheric water vapor may be most robust. The correction shown here holds promise for permitting useful upper tropospheric water vapor profiles to be consistently measured by Raman lidar within NDACC (Network for the Detection of Atmospheric Composition Change) and elsewhere, despite the prevalence of instrumental and atmospheric effects that can contaminate the very low signal to noise measurements in the UT.

MOHAVE-2009↗
NASA NTRS2008

Measurements of Humidity in the Atmosphere and Validation Experiments (Mohave, Mohave II): Results Overview

The Measurements of Humidity in the Atmosphere and Validation Experiments (MOHAVE, MOHAVE-II) inter-comparison campaigns took place at the Jet Propulsion Laboratory (JPL) Table Mountain Facility (TMF, 34.5(sup o)N) in October 2006 and 2007 respectively. Both campaigns aimed at evaluating the capability of three Raman lidars for the measurement of water vapor in the upper troposphere and lower stratosphere (UT/LS). During each campaign, more than 200 hours of lidar measurements were compared to balloon borne measurements obtained from 10 Cryogenic Frost-point Hygrometer (CFH) flights and over 50 Vaisala RS92 radiosonde flights. During MOHAVE, fluorescence in all three lidar receivers was identified, causing a significant wet bias above 10-12 km in the lidar profiles as compared to the CFH. All three lidars were reconfigured after MOHAVE, and no such bias was observed during the MOHAVE-II campaign. The lidar profiles agreed very well with the CFH up to 13-17 km altitude, where the lidar measurements become noise limited. The results from MOHAVE-II have shown that the water vapor Raman lidar will be an appropriate technique for the long-term monitoring of water vapor in the UT/LS given a slight increase in its power-aperture, as well as careful calibration.

lidar↗
NASA NTRS2008

Demonstration of Aerosol Property Profiling by Multi-wavelength Lidar Under Varying Relative Humidity Conditions

The feasibility of using a multi-wavelength Mie-Raman lidar based on a tripled Nd:YAG laser for profiling aerosol physical parameters in the planetary boundary layer (PBL) under varying conditions of relative humidity (RH) is studied. The lidar quantifies three aerosol backscattering and two extinction coefficients and from these optical data the particle parameters such as concentration, size and complex refractive index are retrieved through inversion with regularization. The column-integrated, lidar-derived parameters are compared with results from the AERONET sun photometer. The lidar and sun photometer agree well in the characterization of the fine mode parameters, however the lidar shows less sensitivity to coarse mode. The lidar results reveal a strong dependence of particle properties on RH. The height regions with enhanced RH are characterized by an increase of backscattering and extinction coefficient and a decrease in the Angstrom exponent coinciding with an increase in the particle size. We present data selection techniques useful for selecting cases that can support the calculation of hygroscopic growth parameters using lidar. Hygroscopic growth factors calculated using these techniques agree with expectations despite the lack of co-located radiosonde data. Despite this limitation, the results demonstrate the potential of multi-wavelength Raman lidar technique for study of aerosol humidification process.

Whiteman, D.N.↗
NASA NTRS2005

Direct Detection Doppler Lidar Wind Measurements Obtained During the 2002 International H2O Project (IHOP)

The Goddard Lidar Observatory for Winds (GLOW) is a mobile Doppler lidar system that uses direct detection techniques for profiling winds in the troposphere and lower stratosphere. In May and June of 2002 GLOW was deployed to the Southern Great Plains of the US to participate in the International H2O Project (IHOP). GLOW was located at the Homestead profiling site in the Oklahoma panhandle about 15 km east of the SPOL radar. Several other Goddard lidars, the Scanning Raman Lidar (SRL) and HARLIE, as well as radars and passive instruments were permanently operated from the Homestead site during the IHOP campaign providing a unique cluster of observations. During the IHOP observation period (May 14, 2002 to June 25, 2002) over 240 hours of wind profile measurements were obtained with GLOW. In this paper we will describe the GLOW instrument as it was configured for the IHOP campaign and we will present examples of wind profiles obtained.

Gentry, Bruce↗
NASA NTRS1973

Tropospheric Transmissivity Measurements Using the Raman Nitrogen Lidar Technique

LIDAR measurements in Azusa, California, during October 1972, were made in which the backscattered Raman-shifted nitrogen return was ratioed at different altitudes in order to obtain transmissivity. Rawinsonde data from nearby El Monte were used to determine the temperature and nitrogen number density altitude profiles. These data and other meteorological data are compared to the vertical aerosol and transmissivity structure determined by LIDAR. Also data analysis techniques are shown for obtaining q2 (transmissivity) and beta (attenuation coefficient) as a function of altitude.

McCormick, M. P.↗