Aerosol and Clouds

In this paper we summarize the main achievements of the Aerosol_cci project within the ESA Climate Change Initiative (CCI) and discuss the future needs for
algorithm development (i. a. in the ESA CCI+ program). We also highlight the perspectives for routine dataset processing within the Copernicus Climate Change Service (C3S).

The main outcome of Aerosol_cci is as follows:

-       substantial improvement of dataset quality

-       consistent long-term records over 1-3 decades

-       complementary parameters (Dust AOD, Fine Mode AOD, Total AOD, Absorbing AOD, stratospheric extinction) from different sensors AATSR, IASI, POLDER, MERIS, GOMOS

-       demonstration of the information content for layer height (IASI), diurnal cycles (SEVIRI)

-       establishment of a concept for pixel-level uncertainties

-       establishment of AEROSAT as international forum

-       integration and visibility of the European aerosol retrieval community

-       evaluation of the usefulness of the datasets in 8 user case studies

-       transfer of the routine tasks to C3S

-       qualitative understanding of the reasons for differences between datasets processed with different algorithms (cloud masking, quality filtering – trade-off between accuracy and coverage)

-       derivation of robust trends from different algorithms (with remaining biases)

Future algorithm work is needed for record extension (i. a. with Sentinel sensors), consolidation (e. g. propagation to gridded product uncertainties) and consistent integration of the various complementary datasets (e. g. ensembles, integrated products).

Stratospheric aerosols play an important role in the Earth system and in the climate. Through their scattering of solar radiation back to the space and by heating the stratosphere through the absorption of thermal infrared radiation upwelling from the troposphere for the case of strong aerosol loading, stratospheric aerosol directly impacts the radiative forcing and thus the energy balance of the Earth’s atmosphere. In addition, an indirect impact of stratospheric aerosols arises from providing a surface for heterogeneous chemical reactions, which release halogens and lead to the catalytic depletion of ozone. As the presence of the aerosol alters scattering properties of the atmosphere, a good knowledge of aerosol characteristics is also essential to achieve a high accuracy in the remote sensing of the atmospheric trace gases (e.g. ozone). In spite of the progress made since the discovery of the Junge layer, our current understanding of the sources and sinks of the stratospheric aerosols and our ability to predict how the stratospheric aerosol layer may be affected by future climate change, or how it impacts on climate change, are limited and not sufficient to meet the need of policymakers.

Valuable information to fill the gap in our knowledge is provided by the retrieval of global data sets of stratospheric aerosol particle size distribution parameters, gained from space borne passive remote sensing observations in the visible-NIR-SWIR spectral ranges. Recently, a data set of two parameters of the aerosol particle distribution (mode radius and distribution width) has been created at the University of Bremen using the measurements of the scattered solar light from Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) instrument operated on board the European ENVISAT Satellite from August 2002 to April 2012. This data set has been used to investigate the seasonal and inter-annual variations in the stratospheric aerosol distributions and their response to the volcanic eruptions. The results of this study are summarized in this presentation.

As ENVISAT ceased its operation unexpected in 2012 and there is no European replacement, it is extremely important to look for alternative sources of information about stratospheric aerosols.  Besides SAGE III/ISS instrument of NASA launched in 2017, only the ALTIUS instrument planned for launch in 2021 in the framework of the European Earth Watch Programme has a capability of providing information sufficient for a retrieval of two or more parameters of the stratospheric aerosol particle size distribution.  As the measurements in the limb viewing geometry to be performed by ALTIUS and its wide spectral coverage (in particular NIR and SWIR ranges) are quite similar to those of SCIAMACHY, there is a high potential for a successful adaptation of the SCIAMACHY retrieval to ALTIUS. In the second part of this presentation we discuss the information potential of the measurements of the scattered solar light from ALTIUS with respect to the retrieval of the particle size distribution parameters of stratospheric aerosols.

The GRASP (Generalized Retrieval of Aerosol and Surface Properties, Dubovik et al. 2011, 2014) algorithm is capable of deriving an extended set of aerosol and surface parameters including detailed particle size distribution, spectral refractive index, single scattering albedo and the fraction of non-spherical particles. Specifically, it uses the new multi-pixel concept - a simultaneous fitting of a large group of pixels with additional constraints limiting the time variability of surface properties and spatial variability of aerosol properties. This principle provides a possibility to improve the retrieval from multiple observations even if the observations are not exactly co-incident or co-located. All calculations are done on-line without using traditional look-up tables, with detailed aerosol and surface models and fully accounting for all multiple interactions of scattered solar light with aerosol, gases and the underlying surface.

We will present the latest results of the applications to Sentinel-5P/TROPOMI, Sentinel-3/OLCI, PARASOL/POLDER, ENVISAT/MERIS, TERRA/MISR, AQUA/MODIS, and the ongoing preparations for the Sentinel-4 mission. The lessons learned with each new mission has been consolidated into the GRASP Cloud. The wide range of applicability, from multi-angular polarimetric to single-view intensity only observations, from sun-synchronous to geostationary platforms, reflects the versatility of GRASP.

1. Dubovik, O., M. Herman, A. Holdak, T. Lapyonok, D. Tanré, J. L. Deuzé, F. Ducos, A. Sinyuk, and A. Lopatin, “Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations”, Atmos. Meas. Tech., 4, 975-1018, 2011.

2. Dubovik, O., T. Lapyonok, P. Litvinov, M. Herman, D. Fuertes, F. Ducos, A. Lopatin, A. Chaikovsky, B. Torres, Y. Derimian, X. Huang, M. Aspetsberger, and C. Federspiel “GRASP: a versatile algorithm for characterizing the atmosphere”, SPIE: Newsroom, DOI:10.1117/2.1201408.005558, Published Online: September 19, 2014. http://spie.org/x109993.xml.

Abstract — The Advanced Himawari Imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this study, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the AErosol RObotic NETwork (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14,713 colocation points in 2016 over East Asia shows a high correlation coefficient: R=0.88, RMSE=0.17, and approximately 69.9 % AOD retrieval results within the expected error of . A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency (JMA) is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land.

The aim of the ESA study "Characterisation of particulates in the UTLS" was to investigate the cloud and aerosol measurement capabilities of a potential infrared limb sounding (IRLS) instrument. Therefore, we examined advanced available particle measurement techniques for MIPAS/Envisat and transferred them to the IRLS instrument specifications.
The investigated IRLS instrument has a coarser spectral resolution, but a higher spatial resolution than MIPAS. Despite the reduced spectral resolution we could show that the MIPAS detection methods are transferable to IRLS with a comparable sensitivity. For the IRLS the spatial information, e.g. cloud top height, was improved. Moreover, for cirrus clouds we demonstrated that the better spatial resolution of IRLS measurements allows for 3D retrievals of clouds. The comparison of the detection sensitivities and coverage with those of established instruments (e.g. SAGE II, CALIOP, OSIRIS, GOMOS) showed that infrared limb emission instruments can provide global information on a daily basis and hence, can fill measurement gaps in the horizontal and vertical coverage e.g. at high latitudes and in the UTLS, and in detection sensitivity e.g for sulfate aerosol.
The wealth of spectral information provided by infrared limb emission measurements allows for the retrieval of microphysical properties, e.g cloud/aerosol type, particle size, and extinction. For small ice cloud particles, a particle size distribution retrieval was demonstrated. For polar stratospheric clouds (PSC) the PSC type classification algorithm was demonstrated and the microphysical retrieval was advanced. For the aerosol detection the performance of the ice cloud filter was assessed, it was demonstrated that a reliable classification between volcanic ash and sulfate is feasible, and a retrieval scheme (radius, number, extinction) was developed.
For ice clouds, PSCs, and volcanic aerosol we found all MIPAS aerosol and cloud retrieval methods to be transferable to the IRLS and achieving a comparable or better sensitivity. Moreover, the spatial detection capabilities were significantly improved for the IRLS. From the transferability of the methods to the IRLS we conclude that they also will be applicable to existing infrared limb emission instruments, such as GLORIA, past instruments, such as CRISTA, and future instruments, such as ATMOSAT.

The retrieval of the particle size distribution of stratospheric aerosols, along with their derived microphysical and radiative properties remains an important challenge, as size information is of particular importance to characterize stratospheric aerosols and to understand their impact on climate.

In this work, we retrieve particle size information using data from GOMOS. The retrieval of the particle size distribution is based on the aerosol extinction coefficients at a set of wavelengths between 350 and 750nm provided by the AerGOM algorithm, which is optimized for the retrieval of aerosol properties.

While it is particularly efficient to provide a high measurement rate, the use of stars as light sources however implies a limited signal-to-noise ratio. Therefore, strategies have to be implemented to alleviate the impact of the varying and star-dependent measurement quality on the retrieval of extinction, and beyond that, on the additional inversion of extinction needed to retrieve the aerosol size parameters.

In this presentation, we’ll give the latest results of our work on the particle size distribution retrieval, and present the methodology used to derive size information. We’ll show our first time series (2002-2012) obtained for several size parameters and derived quantities, and a first evaluation of our dataset against results of simulations by the chemistry-climate model EMAC. In this respect, we will analyse the size information provided by GOMOS using EMAC time series of the contribution of the different modes (Aitken mode versus accumulation mode; simulation of volcanic and dust events) as diagnostic tools.

presentation