Abstract

Abstract. A new optimal estimation algorithm for the retrieval of volcanic ash properties has been developed for use with the Infrared Atmospheric Sounding Interferometer (IASI). The retrieval method uses the wave number range 680–1200 cm−1, which contains window channels, the CO2 ν2 band (used for the height retrieval), and the O3 ν3 band.Assuming a single infinitely (geometrically) thin ash plume and combining this with the output from the radiative transfer model RTTOV, the retrieval algorithm produces the most probable values for the ash optical depth (AOD), particle effective radius, plume top height, and effective radiating temperature. A comprehensive uncertainty budget is obtained for each pixel. Improvements to the algorithm through the use of different measurement error covariance matrices are explored, comparing the results from a sensitivity study of the retrieval process using covariance matrices trained on either clear-sky or cloudy scenes. The result showed that, due to the smaller variance contained within it, the clear-sky covariance matrix is preferable. However, if the retrieval fails to pass the quality control tests, the cloudy covariance matrix is implemented.The retrieval algorithm is applied to scenes from the Eyjafjallajökull eruption in 2010, and the retrieved parameters are compared to ancillary data sources. The ash optical depth gives a root mean square error (RMSE) difference of 0.46 when compared to retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) instrument for all pixels and an improved RMSE of 0.2 for low optical depths (AOD < 0.1). Measurements from the Facility for Airborne Atmospheric Measurements (FAAM) and Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) flight campaigns are used to verify the retrieved particle effective radius, with the retrieved distribution of sizes for the scene showing excellent consistency. Further, the plume top altitudes are compared to derived cloud-top altitudes from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and show agreement with RMSE values of less than 1 km.

Highlights

  • The detection of volcanic ash and the retrieval of its properties have become a topic of increasing interest following the eruption of Eyjafjallajökull in 2010

  • A further criterion was imposed upon the MODerate-resolution Imaging Spectroradiometer (MODIS) data that all of the data points averaged onto the Infrared Atmospheric Sounding Interferometer (IASI) pixel resolution must be flagged as ash by the MODIS algorithm for the aggregated pixel to be used in the comparison

  • The altitude retrieved by IASI does not match the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) effective cloud top and the resultant altitude is closer to the bottom of the plume, the latitudinal location of the plume is correct

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Summary

Introduction

The detection of volcanic ash and the retrieval of its properties have become a topic of increasing interest following the eruption of Eyjafjallajökull in 2010. 2012; Pavolonis et al, 2013a), singular value decomposition (Klüser et al, 2013), and a split window (Wen and Rose, 1994a; Prata and Grant, 2001) These methods have been applied to both hyperspectral and broadband satellite instruments, each of which has advantages and disadvantages. Presented here is a new optimal estimation algorithm for the retrieval of volcanic ash properties that has been developed for IASI to take advantage of its spectral information, which could be further adapted for use with other hyperspectral satellite instruments. The method uses an optimal estimation retrieval algorithm to obtain probable values for the ash optical depth (AOD), particle effective radius, plume top height, and effective radiating temperature. The work shown here uses IASI level 1c radiance data obtained from the British Atmospheric Data Centre (BADC) archive

Optimal estimation algorithm
Assembling the error covariance matrix
Forward model description
Validation of retrieved parameters
MODIS retrieval method
Co-locating the data
Results
Aircraft description
CALIOP
Conclusions
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