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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Allometric and Mobile Terrestrial LiDAR Modeling of Aboveground Woody Biomass of Populus in Coppice Production

Poplars ( Populus spp.) and their hybrids are increasingly being grown in coppice production to generate bioenergy feedstocks at frequent intervals. Allometric equations are re-quired to predict aboveground biomass (AGB) of coppiced individuals with minimal field measurements. Likewise, remote sensing tools like LiDAR (light detection and ranging) can be used if models are available to predict AGB from point cloud data. Therefore, this study sought to develop equations to predict dry woody AGB from field measurements and LiDAR data from coppiced poplar field trials containing eastern cottonwood ( P. del-toides ) and hybrid poplar taxa. We found that taxa-specific allometric models containing the summed basal area of the three largest stems in the coppice provided the best predictive model, with stem height and stem count failing to provide additional explanatory power. The best predictive LiDAR-based model was independent of taxa but had slightly lower adjusted R 2 and higher RMSE than the allometric model. It contained four parameters including crown volume, leaf area index, variance of height returns, and the top point density (i.e., density metric 9 or the proportion of points in the highest point interval when the point cloud is evenly divided into ten vertical intervals). In total, these models can be used to quickly and efficiently estimate dry woody AGB of Populus coppice systems for bioenergy feedstock production.

AGB↗
DOE OSTI2023

Southern Ocean Low Cloud and Precipitation Phase Observed During the Macquarie Island Cloud and Radiation Experiment (MICRE)

Shallow cloud decks residing in or near the boundary layer cover a large fraction of the Southern Ocean (SO) and play a major role in determining the amount of shortwave radiation reflected back to space from this region. In this article, we examine the macrophysical characteristics and thermodynamic phase of low clouds (tops <3 km) and precipitation using ground-based ceilometer, depolarization lidar and vertically-pointing W-band radar measurements collected during the Macquarie Island Cloud and Radiation Experiment (MICRE) from April 2016 to March 2017. During MICRE, low clouds occurred ~65% of the time on average (slightly more often in austral winter than summer). About 2/3 of low clouds were cold-topped (temperatures ≤0°C). These were thicker and had higher bases on average than warm-topped clouds. 83%–88% of cold-topped low clouds were liquid phase at cloud base (depending on the season). The majority of low clouds had precipitation in the vertical range 150–250 m below cloud base, a significant fraction of which did not reach the surface. Phase characterization is limited to the period between April 2016 and November 2016. Small-particle (low-radar-reflectivity) precipitation (which dominates precipitation occurrence) was mostly liquid below-cloud, while large-particle precipitation (which dominates total accumulation) was predominantly mixed/ambiguous or ice phase. Approximately 40% of cold-topped clouds had mixed/ambiguous or ice phase precipitation below (with predominantly liquid phase cloud droplets at cloud base). Below-cloud precipitation with radar reflectivity factors below about -10 dBZ were predominantly liquid, while reflectivity factors above about 0 dBZ were predominantly ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Time-Gated, Time-Correlated Single-Photon-Counting Lidar to Observe Atmospheric Clouds at Submeter Resolution

Most lidars used for cloud observations have the range resolution of about 10 m, so they are incapable of resolving submeter-scale processes that are crucial to cloud evolution. This article describes a prototype of a ground-based, vertically pointing, time-gated, time-correlated single-photon-counting lidar (referred to as the T2 lidar) developed to explore atmospheric clouds at range resolution two orders of magnitude finer than traditional atmospheric lidars. The T2 lidar emits green-light pulses (532 nm) at a repetition rate of 20.6 kHz and a pulse width of ∼650 ps, which enables the observation of aerosol and cloud layers at heights from a few hundred meters to 7.28 km above the ground level at range resolution down to 10 cm. In addition, a digital delay pulse generator controls the detector to only receive photons for a short period after each laser pulse. This time-gated technique blocks photons arriving from regions outside the target zone, thus significantly reducing the noise level and allowing observation even inside clouds. Initial observations show that the T2 lidar can detect sharp cloud boundaries and fine structures near the cloud base. Such refined measurements of cloud structure could lead to an improved understanding of microphysical processes such as droplet activation, entrainment and mixing, and precipitation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Cirrus properties deduced from CO2 lidar observations of zenith-enhanced backscatter from oriented crystals

Many lidar researchers have occasionally observed zenith-enhanced backscatter (ZEB) from middle and high clouds. The ZEB signature consists of strong backscatter when the lidar is pointed directly at zenith and a dramatic decline in backscatter as the zenith angle dips slightly off zenith. Mirror-like reflection from horizontal facets of oriented crystals (especially plates) is generally accepted as the cause. It was found during a 3-year observation program that approximately 50 percent of ice clouds had ZEB, regardless of cloud height. The orientation of crystals and the ZEB they cause are important to study and understand for several reasons. First, radiative transfer in clouds with oriented crystals is different than if the same particles were randomly oriented. Second, crystal growth depends partly on the orientation of the particles. Third, ZEB measurements may provide useful information about cirrus microphysical and radiative properties. Finally, the remarkable effect of ZEB on lidar signals should be understood in order to properly interpret lidar data.

Eberhard, Wynn L.↗
NASA NTRS2014

Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation for the ICESat-2 Mission

NASA's Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission is a decadal survey mission (2016 launch). The mission objectives are to measure land ice elevation, sea ice freeboard, and changes in these variables, as well as to collect measurements over vegetation to facilitate canopy height determination. Two innovative components will characterize the ICESat-2 lidar: 1) collection of elevation data by a multibeam system and 2) application of micropulse lidar (photon-counting) technology. A photon-counting altimeter yields clouds of discrete points, resulting from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of the returned points to reflectors of interest. The objective of this paper is to derive an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2 data, based on airborne observations with a Sigma Space micropulse lidar. The mathematical algorithm uses spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors, and geostatistical classification parameters and hyperparameters. Validation shows that ground and canopy elevation, and hence canopy height, can be expected to be observable with high accuracy by ICESat-2 for all expected beam energies considered for instrument design (93.01%-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp), and 72.85%-98.68% for 0.48 msp). The algorithm derived here is generally applicable for elevation determination from photoncounting lidar altimeter data collected over forested areas, land ice, sea ice, and land surfaces, as well as for cloud detection.

Algorithms↗
NASA NTRS2021

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

The frequency and severity of spruce bark beetle outbreaks are increasing in boreal forests leading to widespread tree mortality and fuel conditions promoting extreme wildfire. Detection of beetle infestation is a forest health monitoring (FHM) priority but is hampered by the challenges of detecting early stage (“green”) attack from the air. There is indication that green stage might be detected from vertical gradients of spectral data or from shortwave infrared information distributed within a single crown. To evaluate the efficacy of discriminating “non-infested”, “green”, and “dead” health statuses at the landscape scale in Alaska, USA, this study conducted spectral and structural fusion of data from: (1) Unoccupied aerial vehicle (UAV) multispectral (6 cm) + structure from motion point clouds (~700 pts per sq. m); and (2) Goddard Lidar Hyperspectral Thermal (G-LiHT) hyperspectral (400 to 1000 nm, 0.5 m) + SWIR-band lidar (~32 pts per sq.m). We achieved 78% accuracy for all three health statuses using spectral + structural fusion from either UAV or G-LiHT and 97% accuracy for non-infested/dead using G-LiHT. We confirm that UAV 3D spectral (e.g., greenness above versus below median height in crown) and lidar apparent reflectance metrics (e.g., mean reflectance at 99th percentile height in crown), are of high value, perhaps capturing the vertical gradient of needle degradation. In most classification exercises, UAV accuracy was lower than G-LiHT indicating that collecting ultra-high spatial resolution data might be less important than high spectral resolution information. While the value of passive optical spectral information was largely confined to the discrimination of non-infested versus dead crowns, G-LiHT hyperspectral band selection (~400, 675, 755, and 940 nm) could inform future FHM mission planning regarding optimal wavelengths for this task. Interestingly, the selected regions mostly did not align with the band designations for our UAV multispectral data but do correspond to, e.g., Sentinel-2 red edge bands, suggesting a path forward for moderate scale bark beetle detection when paired with suitable structural data.

Janice Cessna↗
NASA NTRS2020

Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar

Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R^2 = 0.81, RMSE = 1.09 kg/sq. m) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R^2 = 0.74, RMSE = 1.26 kg/sq. m), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts./sq. m) to the G-LiHT SfM point cloud (500–2000 pts./sq. m) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.

Michael Alonzo↗
DOE OSTI2026

A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications

Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information

Silber, Israel↗
DOE OSTI2026

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS2012

An Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation of the ICESat-2 Mission

The Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission has been selected by NASA as a Decadal Survey mission, to be launched in 2016. Mission objectives are to measure land ice elevation, sea ice freeboard/ thickness and changes in these variables and to collect measurements over vegetation that will facilitate determination of canopy height, with an accuracy that will allow prediction of future environmental changes and estimation of sea-level rise. The importance of the ICESat-2 project in estimation of biomass and carbon levels has increased substantially, following the recent cancellation of all other planned NASA missions with vegetation-surveying lidars. Two innovative components will characterize the ICESat-2 lidar: (1) Collection of elevation data by a multi-beam system and (2) application of micropulse lidar (photon counting) technology. A micropulse photon-counting altimeter yields clouds of discrete points, which result from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of returned points to reflectors of interest including canopy and ground in forested areas. The objective of this paper is to derive and validate an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2-type data. Data are based on airborne observations with a Sigma Space micropulse lidar and vary with respect to signal strength, noise levels, photon sampling options and other properties. A mathematical algorithm is developed, using spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors and geostatistical classification parameters and hyperparameters. Validation shows that the algorithm works very well and that ground and canopy elevation, and hence canopy height, can be expected to be observable with a high accuracy during the ICESat-2 mission. A result relevant for instrument design is that even the two weaker beam classes considered can be expected to yield useful results for vegetation measurements (93.01-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp9) and 72.85% - 98.68% for 0.48 msp (msp4)). Resampling options affect results more than noise levels. The algorithm derived here is generally applicable for analysis of micropulse lidar altimeter data collected over forested areas as well as other surfaces, including land ice, sea ice and land surfaces.

Herzfeld, Ute C.↗
DOE OSTI2023

Boundary Layer Controls on the Shallow-to-Deep Cumulus Transition (Final Technical Report)

This project advanced our understanding of the processes governing cumulus cloud formation and provides an improved observational basis for validating earth system models. To be specific, the project used laser- and radar-remote sensors to examine the physical properties of updrafts that rise from earth’s surface and initiate clouds deeper in the atmosphere. These updrafts comprise “thermals” and “plumes” and occur at small spatial and temporal scales (e.g., 10s of minutes, 100s of meters). These small scales preclude explicit representation in most earth system and climate models, and thus necessitate “sub-grid-scale” parameterization of updraft processes. The innovation of this project was to directly measure the size, shape, strength and water vapor content of these updrafts with Doppler and Raman lidars, respectively, and to link these updraft properties to cloud processes using vertically pointed weather radars. The resulting data sets comprise 100s of thousands of updrafts and thousands of clouds, which far exceeds previous efforts, and thereby provides a robust statistical and physical representation of these processes. From these large datasets the project produced a sequence of scientific analyses that: (1) Elucidate how variations in the turbulent structure of the convective boundary layer control shallow cumulus convection, (2) Quantify the upward transport of water vapor to cloud base via thermals and plumes, (3) Validate large-eddy simulations of updrafts and shallow convective clouds, (4) Demonstrate a size-to-strength relationship between updraft width and updraft speed, and (5) Demonstrate how updrafts interact with the stability at the top of the convective boundary layer to modulate the depth and vigor of convective clouds. These results have been disseminated via several published journal articles, academic theses, and conference presentations. Collectively these results contribute to the Atmospheric System Research (ASR) program’s goal to “improve understanding of the key cloud, aerosol, precipitation, and radiation processes that affect the Earth’s radiative balance and hydrological cycle, particularly processes that limit the predictive ability of regional and global earth system models”.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Retrieval of Aerosol Optical Depth Under Thin Cirrus from MODIS: Application to an Ocean Algorithm

A strategy for retrieving aerosol optical depth (AOD) under conditions of thin cirrus coverage from the Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. We adopt an empirical method that derives the cirrus contribution to measured reflectance in seven bands from the visible to shortwave infrared (0.47, 0.55, 0.65, 0.86, 1.24, 1.63, and 2.12 μm, commonly used for AOD retrievals) by using the correlations between the top-of-atmosphere (TOA) reflectance at 1.38 micron and these bands. The 1.38 micron band is used due to its strong absorption by water vapor and allows us to extract the contribution of cirrus clouds to TOA reflectance and create cirrus-corrected TOA reflectances in the seven bands of interest. These cirrus-corrected TOA reflectances are then used in the aerosol retrieval algorithm to determine cirrus-corrected AOD. The cirrus correction algorithm reduces the cirrus contamination in the AOD data as shown by a decrease in both magnitude and spatial variability of AOD over areas contaminated by thin cirrus. Comparisons of retrieved AOD against Aerosol Robotic Network observations at Nauru in the equatorial Pacific reveal that the cirrus correction procedure improves the data quality: the percentage of data within the expected error +/-(0.03 + 0.05 ×AOD) increases from 40% to 80% for cirrus-corrected points only and from 80% to 86% for all points (i.e., both corrected and uncorrected retrievals). Statistical comparisons with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals are also carried out. A high correlation (R = 0.89) between the CALIOP cirrus optical depth and AOD correction magnitude suggests potential applicability of the cirrus correction procedure to other MODIS-like sensors.

cirrus correction↗
NASA NTRS2002

Cloud Thickness from Offbeam Returns (THOR) Validation Campaign on NASA's P3B Over the ARM/SGP

Physical thickness of a cloud layer, sometimes multiple cloud layers, is a crucial controller of solar heating of the Earth- atmosphere system, which drives the convective processes that produce storm systems. Yet clouds of average optical thickness are opaque to conventional lidar, so their thickness is well estimated only by combining a lidar above and another below cloud, or a radar and lidar on the same side, dual facilities not widely available. Here we report initial observations of a new airborne multiple field of view lidar, capable of determining physical thickness of cloud layers from time signatures of off-beam returns from a I kHz micropulse lidar at 540 rim. For a single layer, the time delay of light returning from the outer diffuse halo of light surrounding the beam entry point, relative to the time delay at beam center, determines the cloud physical thickness. The delay combined with the pulse stretch gives the optical thickness. This halo method requires cloud optical thickness exceeding 2, and improves with cloud thickness, thus complimenting conventional lidar, which cannot penetrate thick clouds. Results are presented from March 25, 2002, when THOR flew a butterfly pattern over the ARM site at 8.3 km, above a thin ice cloud at 5 km, and a thick boundary-layer stratus deck with top at 1.3 km, as shown by THOR channel 1, and a base at about 0.3 km as shown by the ground-based MPL. Additional information is included in the original extended abstract.

Cahalan, R. F.↗
DOE OSTI2022

microbasew.c1

The continuous baseline microphysical retrieval, Wacr-based (MICROBASEW) VAP is a baseline retrieval of cloud microphysical properties. MICROBASEW uses a combination of observations from the W-band, zenith pointing radar (WACR), the ceilometer, the micropulse lidar (MPL), the microwave radiometer (MWR) and a merged thermodynamic profile (MERGED SOUNDING) VAP in order to determine the profiles of liquid/ice water content (L/IWC), liquid/ice cloud particle effective radius (re) and cloud fraction.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Deep Convective Cloud Top Heights and Their Thermodynamic Control During CRYSTAL-FACE

Infrared (11 micron) radiances from GOES-8 and local radiosonde profiles, collected during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE) in July 2002, are used to assess the vertical distribution of Florida-area deep convective cloud top height and test predictions as to its variation based on parcel theory. The highest infrared tops (Z(sub 11)) reached approximately to the cold point, though there is at least a 1-km uncertainty due to unknown cloud-environment temperature differences. Since lidar shows that visible 'tops' are 1 km or more above Z(sub 11), visible cloud tops frequently penetrated the lapse-rate tropopause (approx. 15 km). Further, since lofted ice content may be present up to approx. 1 km above the visible tops, lofting of moisture through the mean cold point (15.4 km) was probably common. Morning clouds, and those near Key West, rarely penetrated the tropopause. Non-entraining parcel theory (i.e., CAPE) does not successfully explain either of these results, but can explain some of the day-to-day variations in cloud top height over the peninsula. Further, moisture variations above the boundary layer account for most of the day-today variability not explained by CAPE, especially over the oceans. In all locations, a 20% increase in mean mixing ratio between 750 and 500 hPa was associated with about 1 km deeper maximum cloud penetration relative to the neutral level. These results suggest that parcel theory may be useful for predicting changes in cumulus cloud height over time, but that parcel entrainment must be taken into account even for the tallest clouds. Accordingly, relative humidity above the boundary layer may exert some control on the height of the tropical troposphere.

Sherwood, Steven C.↗
NASA NTRS1992

Evidence for liquid-phase cirrus cloud formation from volcanic aerosols - Climatic implications

Supercooled droplets in cirrus uncinus cell heads between -40 and -50 C are identified from the First International Satellite Cloud Climatology Project Regional Experiment polarization lidar measurements. Although short-lived, complexes of these small liquid cells seem to have contributed importantly to the formation of the cirrus. Freezing-point depression effects in solution droplets, apparently resulting from relatively large cloud condensation nuclei of volcanic origin, can be used to explain this rare phenomenon. An unrecognized volcano-cirrus cloud climate feedback mechanism is implied by these findings.

Sassen, Kenneth↗
DOE OSTI2020

The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory

Ground-based observatories use multi-sensor observations to characterize cloud and precipitation properties. One of the challenges is how to design strategies to best use these observations to understand these properties and evaluate weather and climate models. This paper introduces the Cloud resolving model Radar SIMulator (CR-SIM), which uses output from high-resolution cloud resolving models (CRMs) to emulate multi-wavelength, zenith-pointing, and scanning radar observables and multi-sensor (radar and lidar) products. CR-SIM allows direct comparison between an atmospheric model simulation and remote-sensing products using a forward-modeling framework consistent with the microphysical assumptions used in the atmospheric model. CR-SIM has the flexibility to easily incorporate additional microphysical modules, such as microphysical schemes and scattering calculations, and expand the applications to simulate multi-sensor retrieval products. In this paper, we present several applications of CR-SIM for evaluating the representativeness of cloud microphysics and dynamics in a CRM, quantifying uncertainties in radar-lidar integrated cloud products and multi-Doppler wind retrievals, and optimizing radar sampling strategy using observing system simulation experiments. These applications demonstrate the application of CR-SIM as a virtual observatory operator on high-resolution model output for a consistent comparison between model results and observations to aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements. CR-SIM is licensed under the GNU GPL package and both the software and the user guide are publicly available to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Very High-Resolution Satellite-Derived Bathymetry and Habitat Mapping Using Pleiades-1 and ICESat-2

Accurate and reliable bathymetric data are needed for a wide diversity of marine research and management applications. Satellite-derived bathymetry represents a time saving method to map large shallow waters of remote regions compared to the current costly in situ measurement techniques. This study aims to create very high-resolution (VHR) bathymetry and habitat mapping in Mayotte island waters (Indian Ocean) by fusing 0.5 m Pleiades-1 passive multispectral imagery and active ICESat-2 LiDAR bathymetry. ICESat-2 georeferenced photons were filtered to remove noise and corrected for water column refraction. The bathymetric point clouds were validated using the French naval hydrographic and oceanographic service Litto3D® dataset and then used to calibrate the multispectral image to produce a digital depth model (DDM). The latter enabled the creation of a digital albedo model used to classify benthic habitats. ICESat-2 provided bathymetry down to 15 m depth with a vertical accuracy of bathymetry estimates reaching 0.89 m. The benthic habitats map produced using the maximum likelihood supervised classification provided an overall accuracy of 96.62%. This study successfully produced a VHR DDM solely from satellite data. Digital models of higher accuracy were further discussed in the light of the recent and near-future launch of higher spectral and spatial resolution satellites.

Bathymetry↗