scholarly journals Retrieving Properties of Thin Clouds from Solar Aureole Measurements

2009 ◽  
Vol 26 (12) ◽  
pp. 2531-2548 ◽  
Author(s):  
J. G. DeVore ◽  
A. T. Stair ◽  
A. LePage ◽  
D. Rall ◽  
J. Atkinson ◽  
...  
Keyword(s):  
Particle Size ◽  
Size Distribution ◽  
Power Law ◽  
Field Experiments ◽  
Cumulus Clouds ◽  
Cloud Properties ◽  
First Results ◽  
Spherical Cloud ◽  
Cloud Particles ◽  
Central Facility

Abstract This paper describes a newly designed Sun and Aureole Measurement (SAM) aureolegraph and the first results obtained with this instrument. SAM measurements of solar aureoles produced by cirrus and cumulus clouds were taken at the Atmospheric Radiation Measurement Program (ARM) Central Facility in Oklahoma during field experiments conducted in June 2007 and compared with simultaneous measurements from a variety of other ground-based instruments. A theoretical relationship between the slope of the aureole profile and the size distribution of spherical cloud particles is based on approximating scattering as due solely to diffraction, which in turn is approximated using a rectangle function. When the particle size distribution is expressed as a power-law function of radius, the aureole radiance as a function of angle from the center of the solar disk also follows a power law, with the sum of the two powers being −5. This result also holds if diffraction is modeled with an Airy function. The diffraction approximation is applied to SAM measurements with optical depths ≲2 to derive the effective radii of cloud particles and particle size distributions between ∼2.5 and ∼25 μm. The SAM results yielded information on cloud properties complementary to that obtained with ARM Central Facility instrumentation. A network of automated SAM units [similar to the Aerosol Robotic Network (AERONET) system] would provide a practical means to gain fundamental new information on the global statistical properties of thin (optical depth ≲ 10) clouds, thereby providing unique information on the effects of such clouds upon the earth’s energy budget.

scholarly journals Mineral snowflakes on exoplanets and brown dwarfs

2020 ◽  
Vol 639 ◽  
pp. A107 ◽  
Author(s):  
D. Samra ◽  
Ch. Helling ◽  
M. Min
Keyword(s):  
Particle Size ◽  
High Resolution ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Brown Dwarfs ◽  
Spherical Particles ◽  
Cloud Formation ◽  
Spectral Features ◽  
Spherical Cloud ◽  
Cloud Particles

Context. Exoplanet atmosphere characterisation has become an important tool in understanding exoplanet formation, evolution, and it also is a window into potential habitability. However, clouds remain a key challenge for characterisation: upcoming space telescopes (e.g. the James Webb Space Telescope, JWST, and the Atmospheric Remote-sensing Infrared Exoplanet Large-survey) and ground-based high-resolution spectrographs (e.g. the next-generation CRyogenic high-resolution InfraRed Echelle Spectrograph) will produce data requiring detailed understanding of cloud formation and cloud effects for a variety of exoplanets and brown dwarfs. Aims. We aim to understand how the micro-porosity of cloud particles affects the cloud structure, particle size, and material composition on exoplanets and brown dwarfs. We further examine the spectroscopic effects of micro-porous particles, the particle size distribution, and non-spherical cloud particles. Methods. We expanded our kinetic non-equilibrium cloud formation model to study the effect of micro-porosity on the cloud structure using prescribed 1D (Tgas–pgas) profiles from the DRIFT-PHOENIX model atmosphere grid. We applied the effective medium theory and the Mie theory to model the spectroscopic properties of cloud particles with micro-porosity and a derived particle size distribution. In addition, we used a statistical distribution of hollow spheres to represent the effects of non-spherical cloud particles. Results. Highly micro-porous cloud particles (90% vacuum) have a larger surface area, enabling efficient bulk growth higher in the atmosphere than for compact particles. Increases in single scattering albedo and cross-sectional area for these mineral snowflakes cause the cloud deck to become optically thin only at a wavelength of ~100 μm instead of at the ~20 μm for compact cloud particles. A significant enhancement in albedo is also seen when cloud particles occur with a locally changing Gaussian size distribution. Non-spherical particles increase the opacity of silicate spectral features, which further increases the wavelength at which the clouds become optically thin. Conclusions. Retrievals of cloud properties, particularly particle size and mass of clouds, are biased by the assumption of compact spherical particles. The JWST mid-infrared instrument will be sensitive to signatures of micro-porous and non-spherical cloud particles based on the wavelength at which clouds are optically thin. Details of spectral features are also dependent on particle shape, and greater care must be taken in modelling clouds as observational data improves.

scholarly journals Global variability of phytoplankton functional types from space: assessment via the particle size distribution

2010 ◽  
Vol 7 (3) ◽  
pp. 4295-4340 ◽  
Author(s):  
T. S. Kostadinov ◽  
D. A. Siegel ◽  
S. Maritorena
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Power Law ◽  
Seasonal Succession ◽  
Remote Sensing Data ◽  
Good Correspondence ◽  
Size Classes ◽  
Functional Types ◽  
Phytoplankton Size

Abstract. A new method of retrieving the parameters of a power-law particle size distribution (PSD) from ocean color remote sensing data was used to assess the global distribution and dynamics of phytoplankton functional types (PFT's). The method retrieves the power-law slope, ξ, and the abundance at a reference diameter, N0, based upon the shape and magnitude of the particulate backscattering coefficient spectrum. Relating the PSD to PFT's on global scales assumes that the open ocean particulate assemblage is biogenic. The retrieved PSD's can be integrated to define three size-based PFT's by the percent volume concentration contribution of three phytoplankton size classes – picoplankton (0.5–2 μm in equivalent spherical diameter), nanoplankton (2–20 μm) and microplankton (20–50 μm). Validation with in-situ HPLC diagnostic pigments results in satisfactory match-ups for the pico- and micro-phytoplankton size classes. Global climatologies derived from SeaWiFS monthly data reveal PFT and particle abundance spatial patterns that are consistent with current understanding. Oligotrophic gyres are characterized by lower particle abundance and higher contribution by picoplankton-sized particles than transitional or eutrophic regions. Seasonal succession patterns for size-based PFT's reveal good correspondence between increasing chl and percent contribution by microplankton, as well as increasing particle abundance. Long-term trends in particle abundances are generally inconclusive yet are well correlated with the MEI index indicating increased oligotrophy (i.e. lower particle abundance and increased contribution of picoplankton-sized particles) during the warm phase of an El Niño event. This work demonstrates the utility and future potential of assessing phytoplankton functional types using remote characterization of the particle size distribution.

scholarly journals Bayesian Retrievals of Vertically Resolved Cloud Particle Size Distribution Properties

2017 ◽  
Vol 56 (3) ◽  
pp. 745-765 ◽  
Author(s):  
Derek J. Posselt ◽  
James Kessler ◽  
Gerald G. Mace
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Prior Knowledge ◽  
Unique Solution ◽  
Joint Probability Distribution ◽  
Sufficient Information ◽  
Near Infrared Reflectance ◽  
Cloud Properties

AbstractRetrievals of liquid cloud properties from remote sensing observations by necessity assume sufficient information is contained in the measurements, and in the prior knowledge of the cloudy state, to uniquely determine a solution. Bayesian algorithms produce a retrieval that consists of the joint probability distribution function (PDF) of cloud properties given the measurements and prior knowledge. The Bayesian posterior PDF provides the maximum likelihood estimate, the information content in specific measurements, the effect of observation and forward model uncertainties, and quantitative error estimates. It also provides a test of whether, and in which contexts, a set of observations is able to provide a unique solution. In this work, a Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to sample the joint posterior PDF for retrieved cloud properties in shallow liquid clouds over the remote Southern Ocean. Combined active and passive observations from spaceborne W-band cloud radar and visible and near-infrared reflectance are used to retrieve the parameters of a gamma particle size distribution (PSD) for cloud droplets and drizzle. Combined active and passive measurements are able to distinguish between clouds with and without precipitation; however, unique retrieval of PSD properties requires specification of a scene-appropriate prior estimate. While much of the uncertainty in an unconstrained retrieval can be mitigated by use of information from 94-GHz passive brightness temperature measurements, simply increasing measurement accuracy does not render a unique solution. The results demonstrate the robustness of a Bayesian retrieval methodology and highlight the importance of an appropriately scene-consistent prior constraint in underdetermined remote sensing retrievals.

The Size Distribution of Particles Released by Garments During Helmke Drum Tests

2001 ◽  
Vol 44 (4) ◽  
pp. 24-27 ◽  
Author(s):  
David Ensor ◽  
Jenni Elion ◽  
Jan Eudy
Keyword(s):  
Particle Size ◽  
Size Distribution ◽  
Power Law ◽  
High Performance ◽  
Fine Particles ◽  
Particle Diameter ◽  
Test Method ◽  
Power Law Distribution ◽  
Controlled Environments ◽  
Size Distribution Of Particles

The Helmke Drum test method to measure particles shed from garments was developed twenty years ago. It consists of a tumbling drum containing the garment under test. A probe connected to an optical particle counter is used to transport the sample from the drum. Dilution air is drawn into the drum from the surrounding cleanroom. The optical particle counters at the time of development were limited in resolution to 0.5 μm diameter. This particle size requirement is still in the current version of IEST-RP-CC003.2, Garment Systems Considerations for Cleanrooms and Other Controlled Environments. A question was raised in the current IEST Contamination Control Working Group 003, "Garment System Considerations for Cleanrooms and Other Controlled Environments," as to whether the method could be extended to smaller particle diameters. The method would benefit by including measurements of smaller particle diameters for two reasons: the higher particle counts expected for sub-0.5 μm particles might improve the statistics of the method; and there is a growing need to consider contamination by ultra-fine particles during the manufacture of high performance products. We hypothesized that the size distribution of particles released by garments follows a power law similar to that for cleanroom classes. The form of the power law distribution is N(d) = Ad(-B), where N(d) is the cumulative concentration greater to or equal to d, d is the particle diameter, and A and B are statistically determined coefficients. The size distributions from a number of Helmke Drum tests were analyzed and were found to be highly correlated to the power law equation. However, the slopes appeared to vary depending on the type of garment tested. These results support including guidance with respect to particle size in the Helmke Drum test section in the upcoming revision of IEST-RP-CC003.2.

Particle size distribution and wastewater filter performance

1997 ◽  
Vol 36 (4) ◽  
pp. 217-224 ◽  
Author(s):  
Iris Kaminski ◽  
Nicolae Vescan ◽  
Avner Adin
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Power Law ◽  
Filter Performance ◽  
Natural Lakes ◽  
Size Groups ◽  
Filter Operation ◽  
Power Law Function ◽  
And Function

Particle size distribution (PSD) allows more accurate simulations of filtration models and better understanding of filter performance. PSD in municipal activated sludge effluent filtration is determined, varying filtration rate, grain size, flocculant type and dosage and function parameters are examined in this work. Results show, that removal efficiency varies for different size groups: small particles in the range of 5-10 μm in initialization stage, with no chemical aids, are poorly removed. Higher rate filters were more sensitive to the particle size than lower rate filters. Filtration with chemical aids is more sensitive to filtration conditions than filtration with no chemical additions. Particle size distribution in filtrate generally fits power law function behavior better than in raw effluent. The treatment smoothens the function somewhat. In a similar manner to the effect of settling in tanks or in natural lakes. Degree of correlation to power law function may indicate the mode of filter operation: high - working stage, low - breakthrough stage. β may also reflect on filters performance: high values - initial filtration stages. Decrease in β values - cycle progress towards breakthrough. Low β values, with low PSD correlation to power law function, may indicate low filtration efficiency or breakthrough.

scholarly journals 1.1.17 A Search for Forward Scattering of Sunlight from the Lunar Libration Clouds

1976 ◽  
Vol 31 ◽  
pp. 73-73
Author(s):  
C.L. Ross
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Power Law ◽  
White Light ◽  
Index Of Refraction ◽  
Particle Type ◽  
Upper Limit ◽  
The Mean ◽  
Complex Index Of Refraction

Observations to determine the radiance of forward scattered sunlight from particles in lunar libration regions have been attempted with the white light coronagraph on Skylab. The libration regions could not be distinguished against the solar K + F coronal background; the upper limit to the libration cloud radiance is determined to be 2.5 × 10−11 Bo, where Bo is the radiance of the mean solar disk. Employing models of the particle type and size distribution in the libration clouds, density enhancements have been calculated on the basis of the upper limit of the forward scattered radiance presented herein, and on the basis of earlier observations of the libration region backscattered radiance. The cases where the power law particle size distribution exponent K and complex index of refraction m are 2.5, 1.33-0.051 and 2.5, 1.50-0.051, respectively, are inconsistent with the forward and backscatter observations. Finally, the brightness contrast of remaining possible models of the libration clouds with respect to the K- and F-coronal background is calculated, and is shown to be a maximum in the vicinity of elongation angle ~30°.

scholarly journals On the treatment of particulate organic matter sinking in large-scale models of marine biogeochemical cycles

2008 ◽  
Vol 5 (1) ◽  
pp. 55-72 ◽  
Author(s):  
I. Kriest ◽  
A. Oschlies
Keyword(s):  
Particle Size ◽  
Organic Matter ◽  
Water Column ◽  
Size Distribution ◽  
Power Law ◽  
Particulate Organic Matter ◽  
Large Scale ◽  
Regional Patterns ◽  
Size Dependent ◽  
Biogeochemical Models

Abstract. Various functions have been suggested and applied to represent the sedimentation and remineralisation of particulate organic matter (POM) in numerical ocean models. Here we investigate some representations commonly used in large-scale biogeochemical models: a constant sinking speed, a sinking speed increasing with depth, a spectrum of particles with different size and different size-dependent sinking velocities, and a model that assumes a power law particle size distribution everywhere in the water column. The analysis is carried out for an idealised one-dimensional water column, under stationary boundary conditions for surface POM. It focuses on the intrinsic assumptions of the respective sedimentation function and their effect on POM mass, mass flux, and remineralisation profiles. A constant and uniform sinking speed does not appear appropriate for simulations exceeding a few decades, as the sedimentation profile is not consistent with observed profiles. A spectrum of size classes, together with size-dependent sinking and constant remineralisation, causes the sinking speed of total POM to increase with depth. This increase is not strictly linear with depth. Its particular form will further depend on the size distribution of the POM ensemble at the surface. Assuming a power law particle size spectrum at the surface, this model results in unimodal size distributions in the ocean interior. For the size-dependent sinking model, we present an analytic integral over depth and size that can explain regional variations of remineralisation length scales in response to regional patterns in trophodynamic state.

scholarly journals The Truncated Geometric Approximation and the Size Distribution of Small Atmospheric Particles

2011 ◽  
Vol 28 (6) ◽  
pp. 779-786 ◽  
Author(s):  
J. G. DeVore
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Optical Depth ◽  
Power Law ◽  
Spherical Particles ◽  
Simple Relationship ◽  
Nasa Goddard Space ◽  
Geometric Approximation ◽  
Power Law Function

Abstract This paper describes a simple relationship between the slope of particulate optical depth as a function of wavelength and the size distribution of spherical particles. It is based on approximating extinction using a truncated geometric optics relationship and is applicable when optical depth decreases with wavelength. The new relationship suggests that extinction versus wavelength measurements are most sensitive to particles that are comparable in size to the wavelength. When optical depth is expressed as a power-law function of wavelength, the resulting particle size distribution is also a power-law function of size, with the two exponents reproducing the well-known relationship between the Ångström and Junge exponents. Examples of applying the new relationship are shown using both numerical calculations based on Mie theory and measurements from the Aerosol Robotic Network (AERONET) sun photometer at NASA Goddard Space Flight Center (GSFC). Since the truncated geometric approximation makes no assumptions per se concerning the form of the particle size distribution, it may find application in supplementing solar aureole profile measurements in retrieving the size distributions of particles in thin clouds—for example, cirrus—or when they are present.

scholarly journals On the treatment of particulate organic matter sinking in large-scale models of marine biogeochemical cycles

2007 ◽  
Vol 4 (4) ◽  
pp. 3005-3040
Author(s):  
I. Kriest ◽  
A. Oschlies
Keyword(s):  
Particle Size ◽  
Organic Matter ◽  
Water Column ◽  
Size Distribution ◽  
Power Law ◽  
Particulate Organic Matter ◽  
Large Scale ◽  
Regional Patterns ◽  
Size Dependent ◽  
Biogeochemical Models

Abstract. Various functions have been suggested and applied to represent the sedimentation and remineralisation of particulate organic matter (POM) in numerical ocean models. Here we investigate some representations commonly used in large-scale biogeochemical models: a constant sinking speed, a sinking speed increasing with depth, a spectrum of particles with different size and different size-dependent sinking velocities, and a model that assumes a power-law particle size distribution everywhere in the water column. The analysis is carried out for an idealised one-dimensional water column, under stationary boundary conditions for surface POM. It focuses on the intrinsic assumptions of the respective sedimentation function and their effect on POM mass, mass flux, and remineralisation profiles. A constant and uniform sinking speed does not appear appropriate for simulations exceeding a few decades, as the sedimentation profile is not consistent with observed profiles. A spectrum of size classes, together with size-dependent sinking and constant remineralisation, causes the sinking speed of total POM to increase with depth. This increase is not strictly linear with depth. Its particular form will further depend on the size distribution of the POM ensemble at the surface. Assuming a power-law particle size spectrum at the surface, this model results in unimodal size distributions in the ocean interior. For the size-dependent sinking model, we present an analytic integral over depth and size that can explain regional variations of remineralisation length scales in response to regional patterns in trophodynamic state.

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