G. E. Thomas

Calculation of Mie derivatives

Analytical expressions are found for the derivatives of commonly used Mie scattering parameters, in particular the absorption and the scattering efficiencies, and for the angular intensity functions. These expressions are based on the analytical derivatives of the Mie scattering amplitudes a(n) and b(n) with respect to the particle size parameter and complex refractive index. In addition, analytical derivatives are found for the volume absorption and scattering coefficients, as well as for the intensity functions of a population of particles with log normal size distribution. These derivatives are given with respect to the total number density, to the median radius and spread of the distribution, and to the refractive index. Comparison between analytically and numerically computed derivatives showed the analytical version to be 2.5 to 6.5 times as fast for the single-particle and particle-distribution cases, respectively.

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Clouds play an important role in balancing the Earth’s radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud) which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase. The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick, greater than 50 optical depths, where the cloud begins to saturate. The cost proved a good indicator of multi-layer scenarios. Both the retrieval cost and the error need to be considered together in order to evaluate the quality of the retrieval. This algorithm in the configuration described here has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation) project to produce a 14 yr consistent record for climate research.

Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci)

Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e., dust aerosol, aerosol absorption).

Measuring volcanic plume and ash properties from space

Abstract The remote sensing of volcanic ash plumes from space can provide a warning of an aviation hazard and knowledge on eruption processes and radiative effects. In this paper new algorithms are presented to provide volcanic plume properties from measurements by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), the Advanced Along Track Scanning Radiometer (AATSR) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI). A challenge of remote sensing is to provide near-real-time methods to identify, and so warn of, the presence of volcanic ash. To achieve this, a singular vector decomposition method has been developed for the MIPAS instrument on board the Environmental Satellite. This method was applied to observations of the ash clouds from the eruptions of Nabro and the Puyehue–Cordón Caulle in 2011 and led to a sensitive volcanic signal flag which was capable of tracking changes in the volcanic signal spectra as the plume evolved. A second challenge for remote sensing is to identify the ash plume height. This is a critical parameter for the initialization of algorithms that numerically model the evolution and transport of a volcanic plume. As MIPAS is a limb sounder, the identification of ash also provides an estimate of height provided the plume is above about 6 km. This is complemented by a new algorithm, Stereo Ash Plume Height Retrieval Algorithm, that identifies plume height using the parallax between images provided by Along Track Scanning Radiometer-type instruments. The algorithm was tested on an image taken at 14:01 GMT on 6 June 2011 of the Puyehue–Cordón Caulle eruption plume and gave a height of 11.9±1.4 km, which agreed with the value derived from the location of the plume shadow (12.7±1.8 km). This plume height was similar to the height observed by MIPAS (12 ± 1.5 km) at 02:56 GMT on 6 June. The quantitative use of satellite imagery and the full exploitation of high-resolution spectral measurements of ash depends upon knowing the optical properties of the observed ash. Laboratory measurements of ash from the 1993 eruption of Mt Aso, Japan have been used to determine the refractive indices from 1 to 20 µm. These preliminary measurements have spectral features similar to ash values that have been used to date, albeit with slightly different positions and strengths of the absorption bands. The refractive indices have been used to retrieve ash properties (plume height, optical depth and ash effective radius) from AATSR and SEVIRI instruments using two versions of Oxford-RAL Retrieval of Aerosol and Cloud (ORAC) algorithm. For AATSR a new ash cloud type was used in ORAC for the analysis of the plume from the 2011 Eyjafjallajökull eruption. For the first c. 500 km of the plume ORAC gave values for plume height of 2.5–6.5 km, optical depth 1–2.5 and effective radius 3–7 µm, which are in agreement with other observations. A weakness of the algorithm occurs when underlying cloud invalidates the assumption of a single cloud layer. This is rectified in a modified version of ORAC applied to SEVIRI measurements. In this case an extra model of a cloud underlying the ash plume was included in the range of applied models. In cases where the plume overlay cloud, this new model worked well, showing good agreement with correlative Cloud–Aerosol Lidar with Orthogonal Polarization observations.

Oxford-RAL Aerosol and Cloud (ORAC): aerosol retrievals from satellite radiometers

This chapter describes an optimal estimation retrieval scheme for the derivation of the properties of atmospheric aerosol from top-of-atmosphere (TOA) radiances measured by satellite-borne visible-IR radiometers. The algorithm makes up part of the Oxford-RAL Aerosol and Cloud (ORAC) retrieval scheme (the other part of the algorithm performs cloud retrievals and is described in detail elsewhere [by Watts et al.] [37]).

Some implications of sampling choices on comparisons between satellite and model aerosol optical depth fields

Abstract. The comparison of satellite and model aerosol optical depth (AOD) fields provides useful information on the strengths and weaknesses of both. However, the sampling of satellite and models is very different and some subjective decisions about data selection and aggregation must be made in order to perform such comparisons. This work examines some implications of these decisions, using GlobAerosol AOD retrievals at 550 nm from Advanced Along-Track Scanning Radiometer (AATSR) measurements, and aerosol fields from the GEOS-Chem chemistry transport model. It is recommended to sample the model only where the satellite flies over on a particular day; neglecting this can cause regional differences in model AOD of up to 0.1 on monthly and annual timescales. The comparison is observed to depend strongly upon thresholds for sparsity of satellite retrievals in the model grid cells. Requiring at least 25% coverage of the model grid cell by satellite data decreases the observed difference between the two by approximately half over land. The impact over ocean is smaller. In both model and satellite datasets, there is an anticorrelation between the proportion p of a model grid cell covered by satellite retrievals and the AOD. This is attributed to small p typically occuring due to high cloud cover and lower AODs being found in large clear-sky regions. Daily median AATSR AODs were found to be closer to GEOS-Chem AODs than daily means (with the root mean squared difference being approximately 0.05 smaller). This is due to the decreased sensitivity of medians to outliers such as cloud-contaminated retrievals, or aerosol point sources not included in the model.