scholarly journals Measurement of aerosol size distribution by using a multi-wavelength laser radar. A proposal of an application of a neural network for inverse problems.

1989 ◽  
Vol 17 (2) ◽  
pp. 148-157 ◽  
Author(s):  
Pu QING ◽  
Shinzo KITAMURA
Keyword(s):  
Neural Network ◽  
Inverse Problems ◽  
Size Distribution ◽  
Laser Radar ◽  
Aerosol Size Distribution ◽  
Multi Wavelength

scholarly journals Neural network modelling to estimate particle size distribution based on other particle sections and meteorological parameters

2021 ◽  
Author(s):  
Pak Lun Fung ◽  
Martha Arbayani Zaidan ◽  
Ola Surakhi ◽  
Sasu Tarkoma ◽  
Tuukka Petäjä ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Meteorological Parameters ◽  
Aerosol Size Distribution ◽  
Sufficient Information ◽  
Electrical Mobility ◽  
Particle Sizer ◽  
Mass Concentrations

Abstract. In air quality research, often only particle mass concentrations as indicators of aerosol particles are considered. However, the mass concentrations do not provide sufficient information to convey the full story of fractionated size distribution, which are able to deposit differently on respiratory system and cause various harm. Aerosol size distribution measurements rely on a variety of techniques to classify the aerosol size and measure the size distribution. From the raw data the ambient size distribution is determined utilising a suite of inversion algorithms. However, the inversion problem is quite often ill-posed and challenging to invert. Due to the instrumental insufficiency and inversion limitations, models for fractionated particle size distribution are of great significance to fill the missing gaps or negative values. The study at hand involves a merged particle size distribution, from a scanning mobility particle sizer (NanoSMPS) and an optical particle sizer (OPS) covering the aerosol size distributions from 0.01 to 0.42 μm (electrical mobility equivalent size) and 0.3 μm to 10 μm (optical equivalent size) and meteorological parameters collected at an urban background region in Amman, Jordan in the period of 1st Aug 2016–31st July 2017. We develop and evaluate feed-forward neural network (FFNN) models to estimate number concentrations at particular size bin with (1) meteorological parameters, (2) number concentration at other size bins, and (3) both of the above as input variables. Two layers with 10–15 neurons are found to be the optimal option. Lower model performance is observed at the lower edge (0.01 

Numerical simulation of the retrieval of aerosol size distribution from multiwavelength laser radar measurements

1989 ◽  
Vol 28 (24) ◽  
pp. 5259 ◽  
Author(s):  
Pu Qing ◽  
Hideaki Nakane ◽  
Yasuhiro Sasano ◽  
Shinzo Kitamura
Keyword(s):  
Numerical Simulation ◽  
Size Distribution ◽  
Laser Radar ◽  
Aerosol Size Distribution ◽  
Radar Measurements ◽  
Multiwavelength Laser

scholarly journals The approximation neural-network method for solving nonlinear multi-criteria inverse problems of geophysics

2021 ◽  
Vol 1715 ◽  
pp. 012045
Author(s):  
M I Shimelevich ◽  
E A Obornev ◽  
I E Obornev ◽  
E A Rodionov
Keyword(s):  
Neural Network ◽  
Inverse Problems ◽  
Neural Network Method ◽  
Network Method

scholarly journals Evaluating model parameterizations of submicron aerosol scattering and absorption with in situ data from ARCTAS 2008

2016 ◽  
Vol 16 (14) ◽  
pp. 9435-9455 ◽  
Author(s):  
Matthew J. Alvarado ◽  
Chantelle R. Lonsdale ◽  
Helen L. Macintyre ◽  
Huisheng Bian ◽  
Mian Chin ◽  
...  
Keyword(s):  
Optical Properties ◽  
Size Distribution ◽  
Aerosol Size Distribution ◽  
Visible Radiation ◽  
Submicron Aerosol ◽  
In Situ Data ◽  
Aerosol Absorption ◽  
Aerosol Scattering ◽  
The Impact

Abstract. Accurate modeling of the scattering and absorption of ultraviolet and visible radiation by aerosols is essential for accurate simulations of atmospheric chemistry and climate. Closure studies using in situ measurements of aerosol scattering and absorption can be used to evaluate and improve models of aerosol optical properties without interference from model errors in aerosol emissions, transport, chemistry, or deposition rates. Here we evaluate the ability of four externally mixed, fixed size distribution parameterizations used in global models to simulate submicron aerosol scattering and absorption at three wavelengths using in situ data gathered during the 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) campaign. The four models are the NASA Global Modeling Initiative (GMI) Combo model, GEOS-Chem v9-02, the baseline configuration of a version of GEOS-Chem with online radiative transfer calculations (called GC-RT), and the Optical Properties of Aerosol and Clouds (OPAC v3.1) package. We also use the ARCTAS data to perform the first evaluation of the ability of the Aerosol Simulation Program (ASP v2.1) to simulate submicron aerosol scattering and absorption when in situ data on the aerosol size distribution are used, and examine the impact of different mixing rules for black carbon (BC) on the results. We find that the GMI model tends to overestimate submicron scattering and absorption at shorter wavelengths by 10–23 %, and that GMI has smaller absolute mean biases for submicron absorption than OPAC v3.1, GEOS-Chem v9-02, or GC-RT. However, the changes to the density and refractive index of BC in GC-RT improve the simulation of submicron aerosol absorption at all wavelengths relative to GEOS-Chem v9-02. Adding a variable size distribution, as in ASP v2.1, improves model performance for scattering but not for absorption, likely due to the assumption in ASP v2.1 that BC is present at a constant mass fraction throughout the aerosol size distribution. Using a core-shell mixing rule in ASP overestimates aerosol absorption, especially for the fresh biomass burning aerosol measured in ARCTAS-B, suggesting the need for modeling the time-varying mixing states of aerosols in future versions of ASP.

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