A method to estimate missing AERONET AOD values based on artificial neural networks.

LUIS E. OLCESE; GUSTAVO G. PALANCAR; BEATRIZ M. TOSELLI

PERGAMON-ELSEVIER SCIENCE LTD

Lugar: Amsterdam; Año: 2015 vol. 113 p. 140 - 140

n this work, we present a method to predict missing aerosol optical depth (AOD) values at an AERONET station. The aim of the method is to fill gaps and/or to extrapolate temporal series in the station datasets, i.e. to obtain AOD values under cloudy sky conditions and in other situations where there is a temporary or permanent lack of data. To accomplish that, we used historical AOD values at two stations, air mass trajectories passing through both of them (calculated by using the HYSPLIT model) and ANN calculations to process all the information. The variables included in the neural network training were the station numbers, parameters representing the annual average trend of meteorological conditions, the number of hours and the distance traveled by the air mass between the stations, and the arrival height of the air mass.The method was firstly applied to predict AOD at 440 nm in 9 stations located in the East Coast of the US, during the years 1999-2012. The coefficient of determina