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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Coherent CO2 lidar: A superior system for observing clouds?

Lidars have long been used to study various parameters of clouds. NOAA's Wave Propagation Laboratory has operated a coherent CO2 lidar for over a decade, using Doppler to study wind fields and turbulence, atmospheric absorption for Differential Absorption Lidar (DIAL) applications, backscatter to investigate aerosol distributions, and backscatter and extinction measurements on clouds. A system under development for our laboratory promises to overcome the older system's problems of large size, frequency instability, and need for continual operator attention. We are also thoroughly evaluating the capabilities of CO2 lidar for observing clouds, including development of some new techniques. CO2 lidar provides a view of clouds different in many ways from that of lidars at other extinction measurements from clouds. This discussion argues in support of the proposition: coherent CO2 lidar can be a superior lidar system for measuring most cloud properties.

Eberhard, Wynn L.↗
NASA NTRS2023

Earth-System-Model Evaluation of Cloud and Precipitation Occurrence for Supercooled and Warm Clouds Over the Southern Ocean's Macquarie Island

Over the remote Southern Ocean (SO), cloud feedbacks contribute substantially to Earth system model (ESM) radiative biases. The evolution of low Southern Ocean clouds (cloud-top heights < ∼ 3 km) is strongly modulated by precipitation and/or evaporation, which act as the primary sink of cloud condensate. Constraining precipitation processes in ESMs requires robust observations suitable for process-level evaluations. A year-long subset (April 2016–March 2017) of ground-based profiling instrumentation deployed during the Macquarie Island Cloud and Radiation Experiment (MICRE) field campaign (54.5∘ S, 158.9∘ E) combines a 95 GHz (W-band) Doppler cloud radar, two lidar ceilometers, and balloon-borne soundings to quantify the occurrence frequency of precipitation from the liquid-phase cloud base. Liquid-based clouds at Macquarie Island precipitate ∼ 70 % of the time, with deeper and colder clouds precipitating more frequently and at a higher intensity compared to thinner and warmer clouds. Supercooled cloud layers precipitate more readily than layers with cloud-top temperatures > 0 ∘C, regardless of the geometric thickness of the layer, and also evaporate more frequently. We further demonstrate an approach to employ these observational constraints for evaluation of a 9-year GISS-ModelE3 ESM simulation. Model output is processed through the Earth Model Column Collaboratory (EMC2) radar and lidar instrument simulator with the same instrument specifications as those deployed during MICRE, therefore accounting for instrument sensitivities and ensuring a coherent comparison. Relative to MICRE observations, the ESM produces a smaller cloud occurrence frequency, smaller precipitation occurrence frequency, and greater sub-cloud evaporation. The lower precipitation occurrence frequency by the ESM relative to MICRE contrasts with numerous studies that suggest a ubiquitous bias by ESMs to precipitate too frequently over the SO when compared with satellite-based observations, likely owing to sensitivity limitations of spaceborne instrumentation and different sampling methodologies for ground- versus space-based observations. Despite these deficiencies, the ESM reproduces the observed tendency for deeper and colder clouds to precipitate more frequently and at a higher intensity. The ESM also reproduces specific cloud regimes, including near-surface clouds that account for ∼ 25 % of liquid-based clouds during MICRE and optically thin, non-precipitating clouds that account for ∼ 27 % of clouds with bases higher than 250 m. We suggest that the demonstrated framework, which merges observations with appropriately constrained model output, is a valuable approach to evaluate processes responsible for cloud radiative feedbacks in ESMs.

cloud feedbacks↗
NASA NTRS1992

Error Analysis of Wind Measurements for the University of Illinois Sodium Doppler Temperature System

Four-frequency lidar measurements of temperature and wind velocity require accurate frequency tuning to an absolute reference and long term frequency stability. We quantify frequency tuning errors for the Illinois sodium system, to measure absolute frequencies and a reference interferometer to measure relative frequencies. To determine laser tuning errors, we monitor the vapor cell and interferometer during lidar data acquisition and analyze the two signals for variations as functions of time. Both sodium cell and interferometer are the same as those used to frequency tune the laser. By quantifying the frequency variations of the laser during data acquisition, an error analysis of temperature and wind measurements can be calculated. These error bounds determine the confidence in the calculated temperatures and wind velocities.

Pfenninger, W. Matthew↗
NASA NTRS1986

Correction of Doppler-broadened Rayleigh backscattering effects in H2O dial measurements

A general method of solutions for treating effects of Doppler-broadened Rayleigh backscattering in H2O Differential Absorption Lidar (DIAL) measurements are described and discussed. Errors in vertical DIAL measuremtns caused by this laser line broadening effect can be very large and, therfore, this effect has to be accounted for accurately. To analyze and correct effects of Doppler-broadened Rayleigh backscattering in DIAL experiments, a generalized DIAL approximation was derived starting from a lidar equation, which includes Doppler broadening. To evaluate the accuracy of H2O DIAL measurements, computer simulations were performed. It was concluded that correction of Doppler broadened Rayleigh backscattering is possible with good accuracy in most cases of tropospheric H2O DIAL measurements, but great care has to be taken when layers with steep gradients of Mie backscattering like clouds or inversion layers are present.

Ansmann, A.↗
NASA NTRS1984

The Joint Airport Weather Studies Project - Current analysis highlights in the aviation safety context

The principal objective of the Joint Airport Weather Studies Project was to obtain high-resolution velocity, turbulence, and thermodynamic data on a convective outflow called a microburst, an intense downdraft and resulting horizontal outflow near the surface. Data collection occurred during the summer of 1982 near Denver, CO. Data sensors included three pulsed-microwave Doppler and two pulsed CO2 lidar radars, along with 27 Portable Automated Mesonet surface weather stations, the FAA's low-level-wind-shear alert system (LLWSAS), and five instrumented research aircraft. Convective storms occurred on 75 of 91 operational days, with Doppler data being collected on at least 70 microbursts. Analyses reported included a thorough examination of microburst-climatology statistics, the capability of the LLWSAS to detect adequately and accurately the presence of low-altitude wind shear danger to aircraft, the capability of a terminal Doppler radar system development to provide improved wind-shear detection and warning, and progress toward improved wind-shear training for pilots.

Mccarthy, J.↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
NASA NTRS1984

Lidar Studies of the Alkali Metals

The development of the lidar technique in the early sixties, and the subsequent introduction of tunable lasers, made accurate measurements of the vertical distribution of the alkali metals in the atmosphere possible for the first time. Over the last decade a great deal of information was obtained on the spatial and temporal variations of sodium, and rather less information was obtained about potassium and lithium. The possibility of making continuous observations of the vertical distribution of sodium, coupled with temperature measurements via the determination of the Doppler spectrum of the returned lidar signal, offers a potentially useful technique for studying the dynamics of the 80 to 100 km region of the atmosphere.

Clemesha, B. R.↗
NASA NTRS2005

CRYSTAL-FACE Polarization Lidar Research

The University of Alaska Fairbanks (UAF) Polarization Diversity Lidar (PDL, Sassen 1994) participated in the July 2002 CRYSTAL-FACE field campaign, shortly after the PI moved from the University of Utah to UAF. The truck-mounted PDL is an advanced dual wavelength (1.06 and 0.532 micron), high resolution (0.1-s by 1.5-m), scanning lidar system designed as a testbed for evaluating laser backscatter depolarization techniques for the study of clouds and aerosols in the atmosphere. The main goals identified in our proposal for the CRYSTAL-FACE experiment were, i) the characterization of Florida thunderstorm anvil macrophysical and microphysical properties from lidar backscattering and depolarization, ii) the study of thin to subvisual tropopause-topped subtropical cirrus, iii) the search for indirect cloud effects of trans- Atlantic advected Saharan dust storm aerosols on clouds, and iv) the investigation of melting layer effects on lidar and multi-wavelength Doppler radar measurements in precipitation. Although we experienced adversity in the field during the campaign, sufficient data was collected to begin addressing these topics, and several conference presentations, three journal articles, and one book chapter have resulted from the data analysis effort supported by this grant. (PDL operations were delayed by FAA concerns over the initial sighting at the Kendall-Tamiami Airport, and a brief but major laser breakdown was experienced during the re- setup at the remote Ochopee Everglades site that also supported the N-POL radar.) All lidar data collected by the PDL system were processed and quality checked, and submitted to the CRYSTAL-FACE data archive in a timely manner.

Sassen, Kenneth↗
DOE OSTI2021

Clarifying remotely-retrieved precipitation of shallow marine clouds from the NSF/NCAR Gulfstream V

Precipitation is a key process within the shallow cloud lifecycle. The Cloud System Evolution in the Trades (CSET) campaign included the first deployment of a 94 GHz Doppler radar and 532 nm lidar. Despite a larger sampling volume, initial mean radar/lidar retrieved rain rates (Schwartz et al. 2019) based on the upward-pointing remote sensor datasets are systematically less than those measured by in-situ precipitation probes in the cumulus regime. Subsequent retrieval improvements produce rainrates that compare better to in-situ values, but still underestimate. Retrieved shallow cumulus drop sizes can remain too small and too few, with an overestimated shape parameter narrowing the raindrop size distribution too much. Three potential causes for the discrepancy are explored: the gamma functional fit to the dropsize distribution, attenuation by rain and cloud water, and an underaccounting of Mie dampening of the reflectivity. A truncated exponential fit may represent the dropsizes below a showering cumulus cloud more realistically, although further work would be needed to fully evaluate the impact of a different dropsize representation upon the retrieval. The rain attenuation is within the measurement uncertainty of the radar. Mie dampening of the reflectivity is shown to be significant, in contrast to previous stratocumulus campaigns with lighter rain rates, and may be difficult to constrain well with the remote measurements. An alternative approach combines an a priori determination of the dropsize distribution width based on the in-situ data with the mean radar Doppler velocity and reflectivity. This can produce realistic retrievals, although a more comprehensive assessment is needed to better characterize the retrieval errors.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Ultraviolet Lidar Would Measure Wind Velocity

Incoherent Doppler-shifted scattering from aerosols reveals important weather data. Remote-sensing system in early stage of development makes global measurements of wind velocities along probing beam of ultraviolet light. In aviation system promotes safety through detection of wind shears and clear-air turbulence. System includes range-gated ultraviolet-excimer laser operating at 308 or 353 nm.

Mcdermid, Iain Stuart↗
DOE OSTI2020

Drizzle, Turbulence, and Density Currents Below Post Cold Frontal Open Cellular Marine Stratocumulus Clouds

Nine cases of postcold frontal marine stratocumulus clouds exhibiting open cellular mesoscale organization are analyzed to characterize the drizzle, turbulence, and density currents below them. Data collected by the vertically pointing Doppler cloud radar and multiple lidars part of the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site are used in these analyses. A total of 251 drizzle shafts passed over the site with 76 shafts sampled for more than 15 min by the vertically pointing instruments. On average the drizzle shafts were ~18 km wide with below-cloud drizzle water path of ~40 g m −2 , cloud base rain rate of 7.78 mm day −1 , and cloud base drizzle modal diameter of 393 μm. The widths of the drizzle shafts did not exhibit any relationship with the cloud base rain rate, below-cloud drizzle water path, and the total water removed from the cloud. The downdrafts in the lowest 500 m within the drizzle cells strengthened with increasing cloud base rain rates, while the updrafts within drizzle cells did not exhibit this behavior. On average, the surface air density, pressure, water vapor mixing ratio, and wind speed along the background wind during the drizzle shaft were higher by 1.88 g m −3 , 8.60 Pa, 0.24 g kg −1 , and 1.22 m s −1 , respectively, from their background values. On the other hand, the surface air temperature was lower by 0.43 K during the drizzle shaft than its background value. The coincident measurements highlight the drizzle-turbulence-surface coupling in these cloud systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Site E - Virtual Tower High-Frequency Wind Velocity / Derived Data

This dataset contains Dual-Doppler wind reconstruction from stare lidar scans two rotor diameters south of the King Plains G02 turbine. The line-of-sight velocity data can be found in sa5.lidar.z03.b0 and sa1.lidar.z03.b0. The reconstruction is done neglecting the contribution from vertical velocity.

17 WIND ENERGY↗
NASA NTRS1978

The characteristics of evolving mesoscale systems over mountainous terrain as revealed by radar and PAM

The paper deals with measurements made within the South Park area (which is located near Denver, Colorado, and is a natural genesis area for cumulus clouds) in July and August 1977, using powered aircraft, sailplanes, rawinsondes, boundary layer profilers, acoustic sounders, micrometeorological towers, lidar, research radar, triple-Doppler radar, and a square grid of surface meteorological stations. Extensive analysis revealed a very complex picture of thunderstorm-scale, large-mesoscale, and synoptic-scale interactions.

George, R. L.↗
NASA NTRS2013

Contemplating Synergistic Algorithms for the NASA ACE Mission

ACE is a proposed Tier 2 NASA Decadal Survey mission that will focus on clouds, aerosols, and precipitation as well as ocean ecosystems. The primary objective of the clouds component of this mission is to advance our ability to predict changes to the Earth's hydrological cycle and energy balance in response to climate forcings by generating observational constraints on future science questions, especially those associated with the effects of aerosol on clouds and precipitation. ACE will continue and extend the measurement heritage that began with the A-Train and that will continue through Earthcare. ACE planning efforts have identified several data streams that can contribute significantly to characterizing the properties of clouds and precipitation and the physical processes that force these properties. These include dual frequency Doppler radar, high spectral resolution lidar, polarimetric visible imagers, passive microwave and submillimeter wave radiometry. While all these data streams are technologically feasible, their total cost is substantial and likely prohibitive. It is, therefore, necessary to critically evaluate their contributions to the ACE science goals. We have begun developing algorithms to explore this trade space. Specifically, we will describe our early exploratory algorithms that take as input the set of potential ACE-like data streams and evaluate critically to what extent each data stream influences the error in a specific cloud quantity retrieval.

precipitation↗
NASA NTRS2018

(GO)2-SIM: a GCM-Oriented Ground-Observation Forward-Simulator Framework for Objective Evaluation of Cloud and Precipitation Phase

General circulation model (GCM) evaluation using ground-based observations is complicated by inconsistencies in hydrometeor and phase definitions. Here we describe (GO)2-SIM, a forward simulator designed for objective hydrometeor-phase evaluation, and assess its performance over the North Slope of Alaska using a 1-year GCM simulation. For uncertainty assessment, 18 empirical relationships are used to convert model grid-average hydrometeor (liquid and ice, cloud, and precipitation) water contents to zenith polarimetric micropulse lidar and Ka-band Doppler radar measurements, producing an ensemble of 576 forward-simulation realizations. Sensor limitations are represented in forward space to objectively remove from consideration model grid cells with undetectable hydrometeor mixing ratios, some of which may correspond to numerical noise.Phase classification in forward space is complicated by the inability of sensors to measure ice and liquid signals distinctly. However, signatures exist in lidar–radar space such that thresholds on observables can be objectively estimated and related to hydrometeor phase. The proposed phase-classification technique leads to misclassification in fewer than 8% of hydrometeor-containing grid cells. Such misclassifications arise because, while the radar is capable of detecting mixed-phase conditions, it can mistake water- for ice-dominated layers. However, applying the same classification algorithm to forward-simulated and observed fields should generate hydrometeor-phase statistics with similar uncertainty. Alternatively, choosing to disregard how sensors define hydrometeor phase leads to frequency of occurrence discrepancies of up to 40%. So, while hydrometeor-phase maps determined in forward space are very different from model "reality" they capture the information sensors can provide and thereby enable objective model evaluation.

Lamer, K.↗
NASA NTRS1990

Cockpit display of hazardous wind shear information

Information on cockpit display of wind shear information is given in viewgraph form. Based on the current status of windshear sensors and candidate data dissemination systems, the near-term capabilities for windshear avoidance will most likely include: (1) Ground-based detection: TDWR (Terminal Doppler Weather Radar), LLWAS (Low-Level Windshear Alert System), Automated PIREPS; (2) Ground-Air datalinks: Air traffic control voice channels, Mode-S digital datalink, ACARS alphanumeric datalink. The possible datapaths for integration of these systems are illustrated in a diagram. In the future, airborne windshear detection systems such as lidars, passive IR detectors, or airborne Doppler radars may also become available. Possible future datalinks include satellite downlink and specialized en route weather channels.

Wanke, Craig↗
NASA NTRS1994

Characterizing the wake vortex signature for an active line of sight remote sensor

A recurring phenomenon, described as a wake vortex, develops as an aircraft approaches the runway to land. As the aircraft moves along the runway, each of the wing tips generates a spiraling and expanding cone of air. During the lifetime of this turbulent event, conditions exist over the runway which can be hazardous to following aircraft, particularly when a small aircraft is following a large aircraft. Left to themselves, these twin vortex patterns will converge toward each other near the center of the runway, harmlessly dissipating through interaction with each other or by contact with the ground. Unfortunately, the time necessary to disperse the vortex is often not predictable, and at busy airports can severely impact terminal area productivity. Rudimentary methods of avoidance are in place. Generally, time delays between landing aircraft are based on what is required to protect a small aircraft. Existing ambient wind conditions can complicate the situation. Reliable detection and tracking of a wake vortex hazard is a major technical problem which can significantly impact runway productivity. Landing minimums could be determined on the basis of the actual hazard rather than imposed on the basis of a worst case scenario. This work focuses on using a windfield description of a wake vortex to generate line-of-sight Doppler velocity truth data appropriate to an arbitrarily located active sensor such as a high resolution radar or lidar. The goal is to isolate a range Doppler signature of the vortex phenomenon that can be used to improve detection. Results are presented based on use of a simplified model of a wake vortex pattern. However, it is important to note that the method of analysis can easily be applied to any vortex model used to generate a windfield snapshot. Results involving several scan strategies are shown for a point sensor with a range resolution of 1 to 4 meters. Vortex signatures presented appear to offer potential for detection and tracking.

Heil, Robert Milton↗
DOE OSTI2022

Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles

The space-borne active sounders have been contributing invaluable vertically resolved information of atmospheric optical properties since the launch of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in 2006. To build long-term records from space-borne lidars useful for climate studies, one has to understand the differences between successive space lidars operating at different wavelengths, flying on different orbits, and using different viewing geometries, receiving paths, and detectors. In this article, we compare the results of Atmospheric Laser Doppler INstrument (ALADIN) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidars for the period from 28 June to 31 December 2019. First, we build a dataset of ALADIN–CALIOP collocated profiles (Δdist<1°; Δtime<6 h). Then we convert ALADIN's 355 nm particulate backscatter and extinction profiles into the scattering ratio vertical profiles SR(z) at 532 nm using molecular density profiles from Goddard Earth Observing System Data Assimilation System, version 5 (GEOS-5 DAS). And finally, we build the CALIOP and ALADIN globally gridded cloud fraction profiles CF(z) by applying the same cloud detection threshold to the SR(z) profiles of both lidars at the same spatial resolution. Before comparing the SR(z) and CF(z) profiles retrieved from the two analyzed lidar missions, we performed a numerical experiment to estimate the best achievable cloud detection agreement CDA norm (z) considering the differences between the instruments. We define CDA norm (z) in each latitude–altitude bin as the occurrence frequency of cloud layers detected by both lidars, divided by a cloud fraction value for the same latitude–altitude bin. We simulated the SR(z) and CF(z) profiles that would be observed by these two lidars if they were flying over the same atmosphere predicted by a global model. By analyzing these simulations, we show that the theoretical limit for CDA$_{norm}^{theor}$(z) for a combination of ALADIN and CALIOP instruments is equal to 0.81±0.07 at all altitudes. In other words, 19 % of the clouds cannot be detected simultaneously by two instruments due to said differences. The analyses of the actual observed CALIOP–ALADIN collocated dataset containing ~78 000 pairs of nighttime SR(z) profiles revealed the following points: (a) the values of SR(z) agree well up to ~3 km height. (b) The CF(z) profiles show agreement below ~3 km, where ~80 % of the clouds detected by CALIOP are detected by ALADIN as expected from the numerical experiment. (c) Above this height, the CDA$_{norm}^{obs}$(z) reduces to ~50 %. (d) On average, better sensitivity to lower clouds skews ALADIN's cloud peak height in pairs of ALADIN–CALIOP profiles by ~0.5±0.6 km downwards, but this effect does not alter the heights of polar stratospheric clouds and high tropical clouds thanks to their strong backscatter signals. (e) The temporal evolution of the observed CDA$_{norm}^{obs}$(z) does not reveal any statistically significant change during the considered period. This indicates that the instrument-related issues in ALADIN L0/L1 have been mitigated, at least down to the uncertainties of the following CDA$_{norm}^{obs}$(z) values: 68±12 %, 55±14 %, 34±14 %, 39±13 %, and 42±14 % estimated at 0.75, 2.25, 6.75, 8.75, and 10.25 km, respectively.

54 ENVIRONMENTAL SCIENCES↗