scholarly journals Normalized particle size distribution for remote sensing application

2014 ◽  
Vol 119 (7) ◽  
pp. 4204-4227 ◽  
Author(s):  
J. M. E. Delanoë ◽  
A. J. Heymsfield ◽  
A. Protat ◽  
A. Bansemer ◽  
R. J. Hogan
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Remote Sensing Application ◽  
Sensing Application

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.

Particle size distribution of river-suspended sediments determined by in situ measured remote-sensing reflectance

2015 ◽  
Vol 54 (20) ◽  
pp. 6367 ◽  
Author(s):  
Yuanzhi Zhang ◽  
Zhaojun Huang ◽  
Chuqun Chen ◽  
Yijun He ◽  
Tingchen Jiang
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Suspended Sediments ◽  
Remote Sensing Reflectance

Improved algorithm of aerosol particle size distribution based on remote sensing data

2019 ◽  
Vol 58 (29) ◽  
pp. 8075 ◽  
Author(s):  
Qing Yan ◽  
Huige Di ◽  
Jing Zhao ◽  
Xiaonan Wen ◽  
Yufeng Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Aerosol Particle ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Aerosol Particle Size ◽  
Improved Algorithm

The Occurrence of Particle Size Distribution Bimodality in Midlatitude Cirrus as Inferred from Ground-Based Remote Sensing Data

2011 ◽  
Vol 68 (6) ◽  
pp. 1162-1177 ◽  
Author(s):  
Yang Zhao ◽  
Gerald G. Mace ◽  
Jennifer M. Comstock
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Size Range ◽  
Remote Sensing Data ◽  
General Nature ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Sensing Data

Abstract Data collected in midlatitude cirrus clouds by instruments on jet aircraft typically show particle size distributions that have distinct distribution modes in both the 10–30-μm maximum dimension (D) size range and the 200–300-μm D size range or larger. A literal interpretation of the small D mode in these datasets suggests that total concentrations Nt in midlatitude cirrus are, on average, well in excess of 1 cm−3 whereas more conventional analyses of in situ data and cloud process model results suggest Nt values a factor of 10 less. Given this wide discrepancy, questions have been raised regarding the influence of data artifacts caused by the shattering of large crystals on aircraft and probe surfaces. This inconsistency and the general nature of the cirrus particle size distribution are examined using a ground-based remote sensing dataset. An algorithm using millimeter-wavelength radar Doppler moments and Raman lidar-derived extinction is developed to retrieve a bimodal particle size distribution and its uncertainty. This algorithm is applied to case studies as well as to 313 h of cirrus measurements collected at the Atmospheric Radiation Measurement site near Lamont, Oklahoma, in 2000. It is shown that particle size distributions in cirrus can often be described as bimodal, and that this bimodality is a function of temperature and location within cirrus layers. However, the existence of Nt > 1 cm−3 in cirrus is rare (<1% of the time) and the Nt implied by the remote sensing data tends to be on the order of 100 cm−3.

Effects of Particle Size Distribution on Corrosion Rate of Carbon Steel in Soil

2020 ◽  
Vol 69 (4) ◽  
pp. 102-106
Author(s):  
Shota Ohki ◽  
Shingo Mineta ◽  
Mamoru Mizunuma ◽  
Soichi Oka ◽  
Masayuki Tsuda
Keyword(s):  
Particle Size ◽  
Carbon Steel ◽  
Corrosion Rate ◽  
Particle Size Distribution ◽  
Size Distribution

DENSE SPRAY CORRECTIONS FOR DIFFRACTION-BASED PARTICLE SIZE DISTRIBUTION MEASUREMENTS

1995 ◽  
Vol 5 (1) ◽  
pp. 75-87 ◽  
Author(s):  
Christine M. Woodall ◽  
James E. Peters ◽  
Richard O. Buckius
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution
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