Professor Roy Grainger

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 clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and 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 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear 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 up to 20 %.

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:

New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude–longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.