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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

DIAL with heterodyne detection including speckle noise: Aircraft/shuttle measurements of O3, H2O, and NH3 with pulsed tunable CO2 lasers

Atmospheric trace constituent measurements with higher vertical resolution than attainable with passive radiometers are discussed. Infrared differential absorption lidar (DIAL), which depends on Mie scattering from aerosols, has special advantages for tropospheric and lower stratospheric applications and has great potential importance for measurements from shuttle and aircraft. Differential absorption lidar data reduction involves comparing large amplitude signals which have small differences. The accuracy of the trace constituent concentration inferred from DIAL measurements depends strongly on the errors in determining the amplitude of the signals. Thus, the commonly used SNR expression (signal divided by noise in the absence of signal) is not adequate to describe DIAL measurement accuracy and must be replaced by an expression which includes the random coherent (speckle) noise within the signal. A comprehensive DIAL computer algorithm is modified to include heterodyne detection and speckle noise. Examples for monitoring vertical distributions of O3, H2O, and NH3 using a ground-, aircraft-, or shuttle-based pulsed tunable CO2 laser DIAL system are given.

Brockman, P.↗
NASA NTRS2018

Performance Analysis of Airborne Photon-Counting Lidar Data in Preparation for the ICESat-2 Mission

Two airborne photon-counting laser altimeters have been deployed in direct support of National Aeronautics and Space Adminsitration (NASA's) upcoming Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission. Multiple Altimeter Beam Experimental Lidar (MABEL) was developed specifically for ICESat-2 testing and development. MABEL data are used to simulate key aspects of the ICESat-2 measurement strategy and are critical to the development of the algorithms for geophysical data-product generation. Slope Imaging Multipolarization Photon-counting Lidar (SIMPL) is a NASA Goddard Space Flight Center instrument that has also been deployed in support of ICESat-2 performance discovery. Both instruments are photon-counting, small footprint laser altimeters that sample in both the 532- and 1064-nm wavelengths. And both instruments serve as a proxy for ICESat-2 operational performance and error assessment and a basis for the development of potential validation strategies. This paper provides an overview of how data from MABEL and SIMPL overflights have specifically provided the foundation for understanding the quality of ICESat-2 data and how we can plan to evaluate ICESat-2 products through comparison with other modalities of lidar data and/or groundtruth locations using ground fiducials.

MABEL↗
NASA NTRS1992

Observation of Stratospheric Ozone with NIES Lidar System in Tsukuba, Japan

Lidars are expected to play important roles in an international monitoring network of the stratosphere such as the Network for the Detection of Stratospheric Change (NDSC). The National Institute for Environmental Studies (NIES) in Tsukuba constructed an ozone lidar system in March 1988 and started observation in August 1988. The lidar system has a 2-m telescope and injection locked XeCl and XeF excimer lasers which can measure ozone profiles (15-45 km) and temperature profiles (30-80 km). From December 1991, lidar observations have been carried out in which the second Stokes line of the stimulated Raman scattering of a KrF laser has been used. Ozone profiles obtained with the NIES lidar system are compared with the data provided by the SAGE II satellite sensor. Results showed good agreement for the individual and the zonal mean profiles. Variations of ozone with various time scales at each altitude can be studied using the data obtained with the NIES ozone lidar system. Seasonal variations are easily found at 20 km, 30 km, and 35 km, which are qualitatively understood as a result of dynamical and photochemical effects. Systematic errors of ozone profiles due to the Pinatubo stratospheric aerosols have been detected using multi-wavelength observation.

Nakane, H.↗
NASA NTRS1998

Vertical Distribution of Saharan Dust from the Goddard Three Dimensional Transport Model: Comparison with LITE Data

Spatial distributions of Saharan dust are simulated by the Goddard 3D chemistry transport model driven by meteorological fields from the Goddard Earth Observing System Data Assimilation System, GEOS-2 DAS. An aerosol module accounts for gravitational settling and the aerosol source function is based on surface moisture content and wind speed. The simulated aerosol fields are compared with Total Ozone Mapping Spectrometer (TOMS) by P. Ginoux in this session. The main focus is how ventilation of the assimilation winds will impact the simulated aerosol vertical distribution. Excessive ventilation would transport too much aerosol in the upper troposphere and lead to errors in the long range transport as well as errors in any radiative forcing calculations derived from the simulated aerosol fields. Simulated aerosol fields using GEOS-1 DAS wind fields for September 1994 will be compared with lidar observations during the Lidar In-space Technology Experiment (LITE). The lidar return signal clearly delineates the vertical extent and horizontal variability of the aerosol layer allowing rigorous comparison with the simulated fields. A second focus is the simulation of aerosol scavenging due to moist processes. Three-dimensional precipitation fields can be derived from two possible sources: Assimilated precipitation fields from GEOS-DAS or fields obtained from the Special Sensor Microwave/imager (SSM/1). Of interest is the sensitivity of the modeled aerosol fields to the different precipitation fields driving the moist scavenging module.

Weaver, Clark J.↗
NASA NTRSDate not supplied

Improvements to GNSS Positioning in Challenging Environments by 3DMA Lidar Informed Selective Satellites Usage

The use of global navigation satellite systems (GNSS) for position estimation tends to yield poor results when operating inside of an urban canyon due to large obstructions (e.g., buildings) that disrupt signals as they travel from a satellite to a receiver resulting in a position estimate that may significantly fluctuate in magnitude and direction. Identifying and removing signals that are non-line-of-sight (NLOS) to the receiver and only using signals that are line-of-sight (LOS) can improve the estimated position. However, quickly and accurately determining the LOS status of each measurement can be challenging without additional information about the operating environment. Use of publicly available lidar data can be used to incorporate techniques, such as 3D-mapping-aided (3DMA), to estimate the LOS status of satellites and augment the position solution accordingly. To complicate the issue, the error on the GNSS position estimate in an urban canyon is often so large that is it not sufficient to use as an approximate location for LOS prediction. That is, at times the calculated GNSS solution is not representative of the true location and cannot be used to accurately predict which satellites are within LOS due to the difference in the physical geometry associated with the two locations. This paper explores the use of a GNSS/inertial fused position solution as the initial position estimate for predicting which satellites are within LOS in an urban environment and the impact that removal of predicted NLOS satellites has on the GNSS position solution.

GPS↗
NASA NTRS2021

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Elias Ayrey↗
NASA NTRS1990

Launch flexibility using NLP guidance and remote wind sensing

This paper examines the use of lidar wind measurements in the implementation of a guidance strategy for a nonlinear programming (NLP) launch guidance algorithm. The NLP algorithm uses B-spline command function representation for flexibility in the design of the guidance steering commands. Using this algorithm, the guidance system solves a two-point boundary value problem at each guidance update. The specification of different boundary value problems at each guidance update provides flexibility that can be used in the design of the guidance strategy. The algorithm can use lidar wind measurements for on pad guidance retargeting and for load limiting guidance steering commands. Examples presented in the paper use simulated wind updates to correct wind induced final orbit errors and to adjust the guidance steering commands to limit the product of the dynamic pressure and angle-of-attack for launch vehicle load alleviation.

Cramer, Evin J.↗
NASA NTRS2019

Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS’s Bathymetric Mapping Performance

NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) K(d)(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and K(d)(490) in the range of 0.05–0.12 per m. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.

ICESat-2↗
NASA NTRS1996

High Resolution Doppler Lidar

This Grant supported the development of an incoherent lidar system to measure winds and aerosols in the lower atmosphere. During this period the following activities occurred: (1) an active feedback system was developed to improve the laser frequency stability; (2) a detailed forward model of the instrument was developed to take into account many subtle effects, such as detector non-linearity; (3) a non-linear least squares inversion method was developed to recover the Doppler shift and aerosol backscatter without requiring assumptions about the molecular component of the signal; (4) a study was done of the effects of systematic errors due to multiple etalon misalignment. It was discovered that even for small offsets and high aerosol loadings, the wind determination can be biased by as much as 1 m/s. The forward model and inversion process were modified to account for this effect; and (5) the lidar measurements were validated using rawinsonde balloon measurements. The measurements were found to be in agreement within 1-2 m/s.

Source record↗
NASA NTRS1988

An inertial velocity reference for the NASA airborne Doppler lidar

The following four tasks were studied: (1) modification of the calibration routines to calibrate the Inertial Measurement Unit gyroscope drifts with fixed platform heading; (2) modification of the calibration routines to calibrate the Inertial Measurement Unit accelerometers; (3) checking overall software again for errors; and (4) providing documentation on the above work describing changes to the present software, results of these changes and future operating procedures.

Keck, Thomas S.↗
NASA NTRS1975

On the lidar determination of atmospheric transmittance

A solution of the lidar equation is given with the aim of determining the attenuation coefficient of the atmosphere under conditions of the existence of a relation between it and the effective backscatter area. The errors of determining the attenuation coefficient from measurements of the echo signal power are analyzed. A proposed method of determining that coefficient by sounding with variable pulse lengths is examined.

Stepanenko, V. D.↗
NASA NTRS2000

Hurricane Wind Field Measurements with Scanning Airborne Doppler Lidar During CAMEX-3

During the 1998 Convection and Moisture Experiment (CAMEX-3), the first hurricane wind field measurements with Doppler lidar were achieved. Wind fields were mapped within the eye, along the eyewall, in the central dense overcast, and in the marine boundary layer encompassing the inflow region. Spatial coverage was determined primarily by cloud distribution and opacity. Within optically-thin cirrus slant range of 20- 25 km was achieved, whereas no propagation was obtained during penetration of dense cloud. Measurements were obtained with the Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) on the NASA DC-8 research aircraft. MACAWS was developed and operated cooperatively by the atmospheric lidar remote sensing groups of NOAA Environmental Technology Laboratory, NASA Marshall Space Flight Center, and Jet Propulsion Laboratory. A pseudo-dual Doppler technique ("co-planar scanning") is used to map the horizontal component of the wind at several vertical levels. Pulses from the laser are directed out the left side of the aircraft in the desired directions using computer-controlled rotating prisms. Upon exiting the aircraft, the beam is completely eyesafe. Aircraft attitude and speed are taken into account during real-time signal processing, resulting in determination of the ground-relative wind to an accuracy of about 1 m/s magnitude and about 10 deg direction. Beam pointing angle errors are about 0.1 deg, equivalent to about 17 m at 10 km. Horizontal resolution is about 1 km (along-track) for typical signal processor and scanner settings; vertical resolution varies with range. Results from CAMEX-3 suggest that scanning Doppler wind lidar can complement airborne Doppler radar by providing wind field measurements in regions that are devoid of hydrometeors. At present MACAWS observations are being assimilated into experimental forecast models and satellite Doppler wind lidar simulations to evaluate the relative impact.

Rothermel, Jeffry↗
NASA NTRSDate not supplied

Investigation of the Potential for Lidar & Polarimeter Combined Aerosol Retrievals Using Various Instrument Combinations

As a scientific community, we are motivated to achieve better observational capabilities for aerosols, to reduce uncertainties about aerosol’s role in climate. Encouraged by the National Academies Decadal Survey, many are looking to the synergy of lidar and polarimeter as a means of achieving improved measurements. Polarimetric observations of total-column radiances have excellent sensitivity to aerosol size distribution and absorption properties. Active lidar measurements have fewer wavelengths and view angles and therefore more limited sensitivity to aerosol microphysical properties such as complex refractive index. Yet the advantage of vertically resolved profiles is game-changing, especially given that models contain significant uncertainty about the vertical distribution of aerosol and that polarimeter and other passive retrievals must make assumptions about vertical distribution which can impact performance. To optimize the synergy, a joint polarimeter + lidar mission requires a joint retrieval. However, the exact instruments that will be launched on a future satellite are unknown, and the capabilities of polarimeters and lidars cover a large range. In this study, we perform information content analysis for a range of nominal instrument combinations. The methodology is ideal for assessing the impact of measurements and a priori information (assumptions and constraints) with respect to the desired retrieval products, and for comparing measurement systems. It has advantages that make it useful in advance of or in parallel with retrieval development, such as producing quantitatively useful comparisons even for retrieval scenarios that are significantly underdetermined and therefore impossible to retrieve without significant assumptions, and it is not impacted by retrieval-specific sources of error such as incomplete or incorrect convergence which can complicate the interpretation of a retrieval performance analysis. Here, we will derive the expected uncertainty in retrieved aerosol concentration, effective radius, fine mode fraction and single scattering albedo, for a combination of a polarimeter with various grades of lidar, from an elastic backscatter lidar, similar to the pathfinder CALIOP on the CALIPSO satellite, to a multi-wavelength high spectral resolution lidar.

Sharon P Burton↗
NASA NTRS1992

Tropospheric and stratospheric wind profiling with a direct detection Doppler lidar

The Space Physics Research Laboratory at the University of Michigan has been operating a direct detection, high resolution Doppler Lidar (HRDL) to measure winds in the boundary layer, free troposphere and lower stratosphere. A direct detection Doppler lidar measures the Doppler shift of the aerosol or Rayleigh backscattered signal, from which the wind velocity vector can be retrieved (Benedetti-Michelangeli et al, 1972, 1974; Chanin et al., 1989; Abreu et al., 1992). The system components are shown. The transmitting system is a Continuum NY-60 Nd:YAG laser frequency doubled to a wavelength of 532 nm. The laser is injection seeded for single line mode operation yielding a linewidth of 0.0045 cm(exp -1) (135 MHz) with excellent shot-to-shot frequency stability. The laser produces 60 mJ pulses and operates at a 50 Hz repetition rate for an effective output power of 3.0 W. A description of the University of Michigan's Doppler lidar is given with examples of wind profiles for the boundary layer, free troposphere, and for the lower stratosphere. The system provides a reliable method of remotely measuring the wind. The wind error is smallest in regions of high aerosols. The system also produces aerosol extinction profiles versus altitude which can be determined by the shape of the spectra. The system has been installed in a trailor so that measurements can be made for field campaigns. Winds and aerosol data are available immediately at the site for use in forecasting.

Abreu, Vincent J.↗
NASA NTRS2022

SO2 Plumes Observation with LMOL: Theory, Modeling, and Validation

LMOL, the NASA Langley Mobile Ozone Lidar, is locatednear NASA’s LaRC steam plant when not deployed in campaigns. Theplant produces steam through the incineration of local trash, and itsexhaust plume occasionally contains SO2.SO2is a known, regulated, pollutant that affects O3observations in theUV, as is the case with LMOL. In this work, we show how we modifiedLMOL to detect the plant SO2plumes and compute its density when theO3background is stable; we observed densities compatible with what isexpected from the typical plume exhaust. The selection of laser lines sothat an O3variation would not get confused with a SO2detection isexplained in details, and a model capable of simulating the Lidar signalwith (notably) O3and SO2absorption for the validation of the retrievalresolution and uncertainty is presented. Finally, the comparison betweenthe modeled and observed performances of the system is shown: themaximum altitude, resolution, and error in the modeled signal and theobserved signals are the same within reasonable margins.This work demonstrates that LMOL is fully capable of working withSO2. The next step being the addition of an additional laser channel,which is simplified by the tunable aspect of the LMOL laser, to addressO3and SO2simultaneously, allowing the assessment of SO2when O3has variations.

Guillaume Gronoff↗
NASA NTRS1981

Airborne differential absorption lidar system for water vapor investigations

Range-resolved water vapor measurements using the differential-absorption lidar (DIAL) technique is described in detail. The system uses two independently tunable optically pumped lasers operating in the near infrared with laser pulses of less than 100 microseconds separation, to minimize concentration errors caused by atmospheric scattering. Water vapor concentration profiles are calculated for each measurement by a minicomputer, in real time. The work is needed in the study of atmospheric motion and thermodynamics as well as in forestry and agriculture problems.

Browell, E. V.↗
NASA NTRS1985

Observations of Oklahoma severe thunderstorms using airborne Doppler lidar

The NASA airborne Doppler lidar was successfully employed in obtaining detailed views of the horizontal wind fields near a complex of severe multicell thunderstorms in central Oklahoma on June 30, 1981. Despite uncertainties caused by inertial navigation errors, clear pictures of the relative reflectivity distributions, horizontal wind velocity, and velocity spectral width near the cloud base were obtained. The presence of numerous gust front vortices along the leading edge of the advancing storm outflow were noted which correspond to inflections in the shape of the gust front arcus cloud formation. Explanations for the observed vortical circulations and calculated vorticities are given.

Mccaul, E. W., Jr.↗
NASA NTRS2016

Reducing Multisensor Satellite Monthly Mean Aerosol Optical Depth Uncertainty: 1. Objective Assessment of Current AERONET Locations

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)- based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by approximately 27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.

aerosol optical depth↗