Retrieving Volcanic Ash Top Height through Combined Polar Orbit Active and Geostationary Passive Remote Sensing Data

Weiren Zhu,Lin Zhu,Hongfu Sun,Jun Li

doi:10.3390/rs12060953

Abstract

Taking advantage of both the polar orbit active remote sensing data (from the Cloud-Aerosol Lidar with Orthogonal Polarization—CALIOP) and vertical information and the geostationary passive remote sensing measurements (from the Spinning Enhanced Visible and Infrared Imager) with large coverage, a methodology is developed for retrieving the volcanic ash cloud top height (VTH) from combined CALIOP and Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. This methodology is a deep-learning-based algorithm through hybrid use of Stacked Denoising AutoEncoder (SDA), the Genetic Algorithm (GA), and the Least Squares Support Vector Regression (LSSVR). A series of eruptions over Iceland’s Eyjafjallajökull volcano from April to May 2010 and the Puyehue-Cordón Caulle volcanic complex eruptions in Chilean Andes in June 2011 were selected as typical cases for independent validation of the VTH retrievals under various meteorological backgrounds. It is demonstrated that using the hybrid deep learning algorithm, the nonlinear relationship between satellite-based infrared (IR) radiance measurements and the VTH can be well established. The hybrid deep learning algorithm not only performs well under a relatively simple meteorological background but also is robust under more complex meteorological conditions. Adding atmospheric temperature vertical profile as additional information further improves the accuracy of VTH retrievals. The methodology and approaches can be applied to the measurements from the advanced imagers onboard the new generation of international geostationary (GEO) weather satellites for retrieving the VTH science product.

Highlights

Volcanic eruptions are natural disasters that can strongly affect climate and aviation safety [1,2]
The uniqueness of this study consists in the following: (1) combining active remote sensing data from LEO with vertical information and passive remote sensing measurements from GEO with large coverage and high spatial–temporal resolution, and (2) using machine learning techniques to handle the nonlinearity between satellite-based IR radiances and Volcanic ash cloud top height (VTH) and avoid the radiative transfer model (RTM) uncertainties in ash cloudy situations, especially in multilayer cloudy skies
Brightness temperature (BT) from Spinning Enhanced Visible and Infrared Imager (SEVIRI) IR channels, the channel 4 (3.9 μm), 5 (6.25 μm), 7 (8.7 μm), 9 (10.8 μm), (12.0 μm), and (13.4 μm), were used to build a volcanic ash matchup dataset for training and testing the VTH retrieval model

Summary

Introduction

Volcanic eruptions are natural disasters that can strongly affect climate and aviation safety [1,2]. Based on the combined use of forward radiative transfer model (RTM) and inverse theory, many algorithms have been developed for retrieving volcanic ash and SO2 parameters (such as height and mass loading), those include the look-up table method [4,6,23,28], a one-dimensional variational (1DVAR) method [5,29,30,31], linear statistical regression methods [7], and neural network method [15,32]. The uniqueness of this study consists in the following: (1) combining active remote sensing data from LEO with vertical information and passive remote sensing measurements from GEO with large coverage and high spatial–temporal resolution, and (2) using machine learning techniques to handle the nonlinearity between satellite-based IR radiances and VTH and avoid the RTM uncertainties in ash cloudy situations, especially in multilayer cloudy skies. The detailed descriptions of the machine learning algorithms used in this study are given in Appendix A

SEVIRI L1 Data

CALIOP Data

Atmospheric Profile Data

VTH Product From 1DVAR Approach

Data Preprocess and Quality Control

Results and Validation