ScholarLens Back to search
2008•Unpublished venueRequires access

Aerosol size distribution using sun-photometer and artificial neural network

Hamed Parsiani, Andres Bonilla

Open publisher page 5 citations

Abstract

Abstract:-Aerosol size distribution (ASD) is an integral parameter in regional atmospheric models [1]. The LIDAR laboratory at UPRM will provide Puerto Rico a means of measuring ASD, and therefore improve these models. This project intends to develop a method of obtaining ASD with the use of a sun-photometer, local CIMEL data obtained from AERONET (Aerosol Robotic Network) [2], and an artificial neural network (ANN). The sun-photometer used is an instrument that measures Aerosol Optical Depth (AOD) on five wavelengths. A feed-forward, back-propagation artificial neural network was used to map the underlying pattern between AOD and ASD in southwestern Puerto Rico. All of the data points available from the La Parguera AERONET station was used for the training of the network, and the ASD predictions by the ANN were very close with small errors in the order of 10-4.

About this research paper

What this paper is about

Abstract:-Aerosol size distribution (ASD) is an integral parameter in regional atmospheric models [1]. The LIDAR laboratory at UPRM will provide Puerto Rico a means of measuring ASD, and therefore improve these models. This project intends to develop a method of obtaining ASD with the use of a sun-photometer, local CIMEL data obtained from AERONET (Aerosol Robotic Network) [2], and an artificial neural network (ANN). The sun-photometer used is an instrument that measures Aerosol Optical Depth (AOD) on five wavelengths. A feed-forward, back-propagation artificial neural network was used to map the underlying pattern between AOD and ASD in southwestern Puerto Rico. All of the data points available from the La Parguera AERONET station was used for the training of the network, and the ASD predictions by the ANN were very close with small errors in the order of 10-4.

Why it matters

OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Abstract:-Aerosol size distribution (ASD) is an integral parameter in regional atmospheric models [1]. The LIDAR laboratory at UPRM will provide Puerto Rico a means of measuring ASD, and therefore improve these models. This project intends to develop a method of obtaining ASD with the use of a sun-photometer, local CIMEL data obtained from AERONET (Aerosol Robotic Network) [2], and an artificial neural network (ANN). The sun-photometer used is an instrument that measures Aerosol Optical Depth (AOD) on five wavelengths. A feed-forward, back-propagation artificial neural network was used to map the underlying pattern between AOD and ASD in southwestern Puerto Rico. All of the data points available from the La Parguera AERONET station was used for the training of the network, and the ASD predictions by the ANN were very close with small errors in the order of 10-4.

Key concepts: AERONET, Sun photometer, Photometer, Aerosol, Artificial neural network, Remote sensing, Environmental science, Lidar

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
Aerosol size distribution using sun-photometer and artificial neural network — Research Paper | ScholarLens