Professor Roy Grainger

Abstract:

Satellite remote sensing is a valuable method for detecting and quantifying sulfur dioxide (SO2) emissions at volcanoes. The use of ultra‐violet satellite instruments for monitoring purposes has been assessed in numerous studies, but there are advantages to using infrared measurements, including that they can operate at night and during high latitude winters. This study focuses on the Infrared Atmospheric Sounding Interferometer (IASI). Retrievals developed for this instrument have been shown to be successful when applied to large eruptions, but little has been done to explore their potential for detecting and quantifying emissions from smaller and lower altitude emissions or for the assessment of ongoing activity. Here, a ‘fast’ linear retrieval has been applied across the globe to detect volcanic sources of SO2. The results are dominated by emissions from explosive eruptions, but signals are also evident from weak eruptions, passive degassing, and anthropogenic activity. Ecuador and Kamchatka were selected for further study with a more processing intensive iterative retrieval which can quantify the SO2 amount. At Tungurahua in Ecuador, good agreement was seen between IASI, the Ozone Monitoring Instrument (OMI) and ground based flux data, demonstrating that the retrieval is capable of capturing relative changes in activity. Similarly, good agreement was found between IASI and OMI in Kamchatka. In this high latitude region, OMI is unable to operate for three or four months in each year. It is therefore suggested that IASI could be used alongside other instruments for evaluating changes in volcanic activity.

Abstract:

The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model which, includes the 5 clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), the “fast” radiative transfer solution (which includes a multiple scattering treatment) All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modelling errors become more significant. The retrieval method is then presented describing 10 optimal estimation in general, the non-linear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10% for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors ranging up to 20%.