Laser Disdrometer / Daily Standardized Files
The Parsivel2 Laser Disdrometers provide accurate measurements of precipitation, hail and snow. They also provide droplet size distributions.
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Laser Disdrometer / Daily Standardized Files
This dataset contains standardized data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
Laser Disdrometer / Daily Standardized Files
This dataset contains raw data from the laser disdrometer at Nantucket. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
Laser Disdrometer / Daily Standardized Files
This dataset contains standardized data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
Laser Disdrometer / Standardized Data
The Parsivel2 Laser Disdrometers provide accurate measurements of precipitation, hail and snow. They also provide droplet size distributions.
17 WIND ENERGY↗
Laser Disdrometer / Standardized Data
The Parsivel2 Laser Disdrometers provide accurate measurements of precipitation, hail and snow. They also provide droplet size distributions.
17 WIND ENERGY↗
Laser Disdrometer / Standardized Data
The Parsivel2 Laser Disdrometers provide accurate measurements of precipitation, hail and snow. They also provide droplet size distributions.
17 WIND ENERGY↗
Laser Disdrometer / Standardized Data
The Parsivel2 Laser Disdrometers provide accurate measurements of precipitation, hail and snow. They also provide droplet size distributions.
17 WIND ENERGY↗
Laser Disdrometer / Standardized Data
The Parsivel2 Laser Disdrometers provide accurate measurements of precipitation, hail and snow. They also provide droplet size distributions.
17 WIND ENERGY↗
ASIT Laser Disdrometer / Raw Data
This dataset contains raw data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
ASIT Laser Disdrometer 2 / Raw Data
This dataset contains raw data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
Rapid growth of anthropogenic organic nanoparticles greatly alters cloud lifecycle in the Amazon rainforest
Atmospheric nanoparticles can significantly influence Earth’s climate if they grow to sizes large enough to nucleate cloud droplets. While the contribution of extremely low volatility vapors to nanoparticle growth has been discussed extensively, the role of semivolatile organics has been largely overlooked. Here we examine the growth and impacts of air pollution nanoparticles from an isolated metropolis amidst the Amazon rainforest. Although extremely low volatility organics are necessary for the initial growth of nanoparticles, model analysis suggests that dynamic gas-particle partitioning of semivolatile oxidation products of anthropogenic and natural hydrocarbons is predominantly responsible for the observed rapid growth to 50 nm. Furthermore, cloud-resolving simulations demonstrate that the grown particles appreciably modify shallow cloud droplet size distribution, suppress precipitation, and enhance the transition to deep clouds. With condensable semivolatile organics typically formed in greater proportion, similar nanoparticle growth and impacts on clouds could likely occur in heavily urbanized forested regions globally.
Zaveri, Rahul A↗
DATASET: Rapid growth of anthropogenic organic nanoparticles greatly alters cloud lifecycle in the Amazon rainforest
Atmospheric nanoparticles can significantly influence Earth’s climate if they grow to sizes large enough to nucleate cloud droplets. While the contribution of extremely low volatility vapors to nanoparticle growth has been discussed extensively, the role of semivolatile organics has been largely overlooked. Here we examine the growth and impacts of air pollution nanoparticles from an isolated metropolis amidst the Amazon rainforest. Although extremely low volatility organics are necessary for the initial growth of nanoparticles, model analysis suggests that dynamic gas-particle partitioning of semivolatile oxidation products of anthropogenic and natural hydrocarbons is predominantly responsible for the observed rapid growth to 50 nm. Furthermore, cloud-resolving simulations demonstrate that the grown particles appreciably modify shallow cloud droplet size distribution, suppress precipitation, and enhance the transition to deep clouds. With condensable semivolatile organics typically formed in greater proportion, similar nanoparticle growth and impacts on clouds could likely occur in heavily urbanized forested regions globally.
Zaveri, Rahul A↗
The development of rainfall retrievals from radar at Darwin
Abstract. The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data. The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dual-polarization quantities was wavelength dependent for 1 10mmh-1. Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 1 10mmh-1 (deeper convective rain).
54 ENVIRONMENTAL SCIENCES↗
Particle inertial effects on radar Doppler spectra simulation
Abstract. Radar Doppler spectra observations provide a wealth of information about cloud and precipitation microphysics and dynamics. The interpretation of these measurements depends on our ability to simulate these observations accurately using a forward model. The effect of small-scale turbulence on the radar Doppler spectra shape has been traditionally treated by implementing the convolution process on the hydrometeor reflectivity spectrum and environmental turbulence. This approach assumes that all the particles in the radar sampling volume respond the same to turbulent-scale velocity fluctuations and neglects the particle inertial effect. Here, we investigate the inertial effects of liquid-phase particles on the forward modeled radar Doppler spectra. A physics-based simulation (PBS) is developed to demonstrate that big droplets, with large inertia, are unable to follow the rapid change of the velocity field in a turbulent environment. These findings are incorporated into a new radar Doppler spectra simulator. Comparison between the traditional and newly formulated radar Doppler spectra simulators indicates that the conventional simulator leads to an unrealistic broadening of the spectrum, especially in a strong turbulent environment. This study provides clear evidence to illustrate the droplet inertial effect on radar Doppler spectrum and develops a physics-based simulator framework to accurately emulate the Doppler spectrum for a given droplet size distribution (DSD) in a turbulence field. The proposed simulator has various potential applications for the cloud and precipitation studies, and it provides a valuable tool to decode the cloud microphysical and dynamical properties from Doppler radar observation.
54 ENVIRONMENTAL SCIENCES↗
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
Abstract. Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments that are at least 2 orders of magnitude greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require the extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar-attenuated backscatter (Rmax) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of Rmax to within 5 m can constrain Nd to within several tens of percent. However, operational lidar networks provide vertical resolutions of > 15 m, making a direct calculation of Nd from Rmax very uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion such as lidar-derived extinction and radar reflectivity near the cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the MARCUS and CAPRICORN II campaigns, we demonstrate the utility of the methodology.
54 ENVIRONMENTAL SCIENCES↗
Evaluating spectral cloud effective radius retrievals from the Enhanced MODIS Airborne Simulator (eMAS) during ORACLES
Satellite remote sensing retrievals of cloud effective radius (CER) are widely used for studies of aerosol–cloud interactions. Such retrievals, however, rely on forward radiative transfer (RT) calculations using simplified assumptions that can lead to retrieval errors when the real atmosphere deviates from the forward model. Here, coincident airborne remote sensing and in situ observations obtained during NASA's ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign are used to evaluate retrievals of CER for marine boundary layer stratocumulus clouds and to explore impacts of forward RT model assumptions and other confounding factors. Specifically, spectral CER retrievals from the Enhanced MODIS Airborne Simulator (eMAS) and the Research Scanning Polarimeter (RSP) are compared with polarimetric retrievals from RSP and with CER derived from droplet size distributions (DSDs) observed by the Phase Doppler Interferometer (PDI) and a combination of the Cloud and Aerosol Spectrometer (CAS) and the Two-Dimensional Stereo Probe (2D-S). The sensitivities of the eMAS and RSP spectral retrievals to assumptions about the DSD effective variance (CEV) and liquid water complex index of refraction are explored. CER and CEV inferred from eMAS spectral reflectance observations of the backscatter glory provide additional context for the spectral CER retrievals. The spectral and polarimetric CER retrieval agreement is case dependent, and updating the retrieval RT assumptions, including using RSP polarimetric CEV retrievals as a constraint, yields mixed results that are tied to differing sensitivities to vertical heterogeneity. Moreover, the in situ cloud probes, often used as the benchmark for remote sensing CER retrieval assessments, themselves do not agree, with PDI DSDs yielding CER values 1.3–1.6 µm larger than CAS and with CEV roughly 50 %–60 % smaller than CAS. Implications for the interpretation of spectral and polarimetric CER retrievals and their agreement are discussed.
Meyer, Kerry [NASA Goddard Space Flight Center (GS↗
ARM Aerial Facility (AAF) - Unmanned Aircraft Systems, Cloud Droplet Probe with QC and lat/lon/alt
The Cloud Droplet Probe (CDP) is designed to measure cloud droplet size distribution from 2 µm to 50 µm. The CDP and an appropriate data system can also calculate various other parameters including particle concentrations, effective diameter (ED), Median Volume Diameter (MVD), and Liquid Water Content (LWC). The b1 level adds standard quality control flags, and merges in flight navigation data.
54 ENVIRONMENTAL SCIENCES↗