scholarly journals The Spectral Ice Habit Prediction System (SHIPS). Part IV: Box Model Simulations of the Habit-Dependent Aggregation Process

2011 ◽  
Vol 68 (6) ◽  
pp. 1142-1161 ◽  
Author(s):  
Tempei Hashino ◽  
Gregory J. Tripoli
Keyword(s):  
Particle Size ◽  
Simple Model ◽  
Terminal Velocity ◽  
Crystal Habit ◽  
Size Distributions ◽  
Prediction System ◽  
Aggregation Process ◽  
Particle Size Distributions ◽  
Particle Properties ◽  
Aggregation Processes

Abstract The purpose of this paper is to assess the prediction of particle properties of aggregates and particle size distributions with the Spectral Ice Habit Prediction System (SHIPS) and to investigate the effects of crystal habits on aggregation process. Aggregation processes of ice particles are critical to the understanding of precipitation and the radiative signatures of cloud systems. Conventional approaches taken in cloud-resolving models (CRMs) are not ideal to study the effects of crystal habits on aggregation processes because the properties of aggregates have to be assumed beforehand. As described in Part III, SHIPS solves the stochastic collection equation along with particle property variables that contain information about crystal habits and maximum dimensions of aggregates. This approach makes it possible to simulate properties of aggregates explicitly and continuously in CRMs according to the crystal habits. The aggregation simulations were implemented in a simple model setup, assuming seven crystal habits and several initial particle size distributions (PSDs). The predicted PSDs showed good agreement with observations after rescaling except for the large-size end. The ice particle properties predicted by the model, such as the mass–dimensional (m-D) relationship and the relationship between diameter of aggregates and number of component crystals in an aggregate, were found to be quantitatively similar to those observed. Furthermore, these predictions were dependent on the initial PSDs and habits. A simple model for the growth of a particle’s maximum dimension was able to simulate the typically observed fractal dimension of aggregates when an observed value of the separation ratio of two particles was used. A detailed analysis of the collection kernel indicates that the m-D relationship unique to each crystal habit has a large impact on the growth rate of aggregates through the cross-sectional area or terminal velocity difference, depending on the initial equivalent particle distribution. A significant decrease in terminal velocity differences was found in the inertial flow regime for all the habits but the constant-density sphere. It led to formation of a local maximum in the collection kernel and, in turn, formed an identifiable mode in the PSDs. Remaining issues that must be addressed in order to improve the aggregation simulation with the quasi-stochastic model are discussed.

scholarly journals The Spectral Ice Habit Prediction System (SHIPS). Part III: Description of the Ice Particle Model and the Habit-Dependent Aggregation Model

2011 ◽  
Vol 68 (6) ◽  
pp. 1125-1141 ◽  
Author(s):  
Tempei Hashino ◽  
Gregory J. Tripoli
Keyword(s):  
Terminal Velocity ◽  
Crystal Habit ◽  
Particle Model ◽  
Prediction System ◽  
Aggregation Process ◽  
Cross Sectional ◽  
Growth History ◽  
History Of ◽  
Model Setup ◽  
Simple Growth

Abstract The purpose of this paper is to describe a numerical scheme of the Spectral Ice Habit Prediction System (SHIPS) that simulates the dependency of aggregation process explicitly on crystal habit and size in cloud-resolving models (CRMs). The sizes and shapes of ice crystals are known to modulate the aggregation process, which is a critical part of physical processes leading to precipitation in addition to vapor deposition and riming processes. A problem with conventional formulation of aggregation process in CRMs is that it is not designed to predict the aggregates’ properties based on the information on crystals. To simulate such dependency, SHIPS solves a quasi-stochastic model that describes growth tendency for a group of particles, together with particle property variables (PPVs) that carry information on habit and types of ice particles. SHIPS diagnoses the ice particle properties based on the PPVs for each mass bin at given a time and space, which are used to calculate the collision cross-sectional area and terminal velocity differences based on crystal habits explicitly. To achieve prediction of properties of aggregates and rimed particles, SHIPS introduces 1) the use of a conceptually based, circumscribing shape, called the ice particle model, and 2) the explicit prediction of the circumscribing sphere volume based on a simple growth model of the maximum dimension. Based on these properties, SHIPS is able to predict the mass–dimensional relationships of aggregates so that they are physically consistent with the growth history of the particles. In addition, the information about the crystals making up the aggregates is predicted. Part IV of this series will present the results and evaluation of the application of this formulation to idealized tests in a Lagrangian “box model” setup.

scholarly journals Microphysical particle properties derived from inversion algorithms developed in the framework of EARLINET

2015 ◽  
Vol 8 (12) ◽  
pp. 12823-12885 ◽  
Author(s):  
D. Müller ◽  
C. Böckmann ◽  
A. Kolgotin ◽  
L. Schneidenbach ◽  
E. Chemyakin ◽  
...  
Keyword(s):  
Particle Size ◽  
Optical Data ◽  
Current Status ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Ongoing Research ◽  
Particle Properties ◽  
Data Products ◽  
Future Work ◽  
Aerosol Types

Abstract. We present a summary on the current status of two inversion algorithms that are used in EARLINET for the inversion of data collected with EARLINET multiwavelength Raman lidars. These instruments measure backscatter coefficients at 355, 532, and 1064 nm, and extinction coefficients at 355 and 532 nm. Development of these two algorithms started in 2000 when EARLINET was founded. The algorithms are based on manually controlled inversion of optical data which allows for detailed sensitivity studies and thus provides us with comparably high quality of the derived data products. The algorithms allow us to derive particle effective radius, and volume and surface-area concentration with comparably high confidence. The retrieval of the real and imaginary parts of the complex refractive index still is a challenge in view of the accuracy required for these parameters in climate change studies in which light-absorption needs to be known with high accuracy. Single-scattering albedo can be computed from the retrieved microphysical parameters and allows us to categorize aerosols into high and low absorbing aerosols. We discuss the current status of these manually operated algorithms, the potentially achievable accuracy of data products, and the goals for future work on the basis of a few exemplary simulations with synthetic optical data. The optical data used in our study cover a range of Ångström exponents and extinction-to-backscatter (lidar) ratios that are found from lidar measurements of various aerosol types. We also tested aerosol scenarios that are considered highly unlikely, e.g., the lidar ratios fall outside the commonly accepted range of values measured with Raman lidar, even though the underlying microphysical particle properties are not uncommon. The goal of this part of the study is to test robustness of the algorithms toward their ability to identify aerosol types that have not been measured so far, but cannot be ruled out based on our current knowledge of aerosol physics. We computed the optical data from monomodal logarithmic particle size distributions, i.e., we explicitly excluded the more complicated case of bimodal particle size distributions which is a topic of ongoing research work. Another constraint is that we only considered particles of spherical shape in our simulations. We considered particle radii as large as 7–10 μm in our simulations. That particle size does not only cover the size range of particles in the fine-mode fraction of naturally occurring particle size distributions but also covers a considerable part of the coarse-mode fraction of particle size distributions. We considered optical-data errors of 15 % in the simulation studies. We target 50 % uncertainty as a reasonable threshold for our data products, though we attempt to obtain data products with less uncertainty in future work.

254. A Comparison of the Particle Size Distributions for Aerosols Generated During Wet Grinding and Dry Deburring of Beryllium

1999 ◽  
Author(s):  
K.K. Ellis ◽  
R. Buchan ◽  
M. Hoover ◽  
J. Martyny ◽  
B. Bucher-Bartleson ◽  
...  
Keyword(s):  
Particle Size ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Wet Grinding

Measurements of Mineral Particle Size Distributions by a Buoyancy Weighing Method

2010 ◽  
Vol 126 (10/11) ◽  
pp. 577-582 ◽  
Author(s):  
Katsuhiko FURUKAWA ◽  
Yuichi OHIRA ◽  
Eiji OBATA ◽  
Yutaka YOSHIDA
Keyword(s):  
Particle Size ◽  
Mineral Particle ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Weighing Method

Certain Problems with the Application of Stochastic Diffusion Processes for the Description of Chemical Engineering Phenomena. Diffusional Change of Solid Particle Size

1996 ◽  
Vol 61 (4) ◽  
pp. 536-563
Author(s):  
Vladimír Kudrna ◽  
Pavel Hasal
Keyword(s):  
Particle Size ◽  
Solid Particle ◽  
Chemical Engineering ◽  
Diffusion Processes ◽  
Granular Systems ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Diffusion Term ◽  
Population Balances ◽  
And Diffusion

To the description of changes of solid particle size in population, the application was proposed of stochastic differential equations and diffusion equations adequate to them making it possible to express the development of these populations in time. Particular relations were derived for some particle size distributions in flow and batch equipments. It was shown that it is expedient to complement the population balances often used for the description of granular systems by a "diffusion" term making it possible to express the effects of random influences in the growth process and/or particle diminution.

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