A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO₂ using MODIS data

• Main Authors: A. Piscini, M. Picchiani, M. Chini, S. Corradini, L. Merucci, F. Del Frate, S. Stramondo
• Format: Article
• Language: English
• Published: Copernicus Publications 2014-12-01
• Series: Atmospheric Measurement Techniques
• Online Access: http://www.atmos-meas-tech.net/7/4023/2014/amt-7-4023-2014.pdf

In this work neural networks (NNs) have been used for the retrieval of volcanic ash and sulfur dioxide (SO₂) parameters based on Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral measurements. Different neural networks were built in order for each parameter to be retrieved, for experimenting with different topologies and evaluating their performances. The neural networks' capabilities to process a large amount of new data in a very fast way have been exploited to propose a novel applicative scheme aimed at providing a complete characterization of eruptive products. <br><br> As a test case, the May 2010 Eyjafjallajókull eruption has been considered. A set of seven MODIS images have been used for the training and validation phases. <br><br> In order to estimate the parameters associated to the volcanic eruption, such as ash mass, effective radius, aerosol optical depth and SO₂ columnar abundance, the neural networks have been trained using the retrievals from well-known algorithms. These are based on simulated radiances at the top of the atmosphere and are estimated by radiative transfer models. <br><br> Three neural network topologies with a different number of inputs have been compared: (a) three thermal infrared MODIS channels, (b) all multispectral MODIS channels and (c) the channels selected by a pruning procedure applied to all MODIS channels. <br><br> Results show that the neural network approach is able to estimate the volcanic eruption parameters very well, showing a root mean square error (RMSE) below the target data standard deviation (SD). The network built considering all the MODIS channels gives a better performance in terms of specialization, mainly on images close in time to the training ones, while the networks with less inputs reveal a better generalization performance when applied to independent data sets. In order to increase the network's generalization capability and to select the most significant MODIS channels, a pruning algorithm has been implemented. The pruning outcomes revealed that channel sensitive to ash parameters correspond to the thermal infrared, visible and mid-infrared spectral ranges. <br><br> The neural network approach has been proven to be effective when addressing the inversion problem for the estimation of volcanic ash and SO₂ cloud parameters, providing fast and reliable retrievals, important requirements during volcanic crises.