Parametrization of Surface Albedo for Nadir Aerosol Retrieval SYNAER

Aerosol retrieval working in the visible channels has to deal with an ill-posed problem. AOD and surface albedo are both unknown parameters within a nadir-only aerosol retrieval as the synergistic aerosol retrieval SYNAER. SYNAER is a retrieval algorithm using a combination of radiometer and spectrometer measurements onboard the same satellite platform. In a first step the aerosol amount can only be estimated with the knowledge of the surface albedo within the visible channel. Kaufman, et al.1 introduced a linear parametrization of the albedo, which is dependent on the shortwave infrared radiometer measurement. This parametrization is a common way in the so called dark field method for aerosol retrievals2. For a general understanding of the dependences between red, near and shortwave infrared channels ASRVN datasets were analyzed. ASRVN is a MODIS based dataset of surface albedo, atmospheric corrected with adjacent AERONET aerosol measurements. Additionally measured surface spectra were investigated on synthetic radiometer channels. A linear relationship between shortwave infrared (1.6 um) and red (670 nm) can be determined as Kaufman suggested, but the relationship is not constant. As suggested by Holzer-Popp2, et al. for AATSR and by Mei et al.3 for AVHRR these linear relationship can be related to the NDVI (normalized differential vegetation index). By analyzing the ASRVN and spectrometer measurements it turned out that a simple linear relationship only by using the NDVI is not sufficient for a larger variety of surface types. In order to describe the SWIR to RED dependence an additional vegetation index has to be introduced. This index accounts not only for the vegetation amount of the surface as the NDVI does, but also allows a measure for the water amount of the surface, which affects the NIR and SWIR channels of a radiometer. Introducing the NDII (normalized differential infrared index) promises a more accurate determination of the RED surface reflectance based on the SWIR channel reflectance. Additionally, an analytical equation for the SWIR to RED reflectance can be derived including both vegetation indices NDVI and NDII. Nevertheless it has to be considered that the vegetation indices themselves are affected by the aerosol amount, so the approach needs their iterative correction for the aerosol impact. The extended parametrization of surface albedo is used in the synergistic aerosol retrieval. Theoretical calculations and application to satellite datasets will be discussed in the presentation

Classifying direct normal irradiance 1-minute temporal variability from spatial characteristics of geostationary satellite-based cloud observations

t Variability of solar surface irradiances in the 1-minute range is of interest especially for solar energy applications. Eight variability classes were previously defined for the 1 min resolved direct normal irradiance (DNI) variability inside an hour. In this study spatial structural parameters derived fromsatellite-based cloud observations are used as classifiers in order to detect the associated direct normal irradiance (DNI) variability class in a supervised classification scheme. A neighbourhood of 3×3 to 29×29 satellite pixels is evaluated to derive classifiers describing the actual cloud field better than just using a single satellite pixel at the location of the irradiance observation. These classifiers include cloud fraction in a window around the location of interest, number of cloud/cloud free changes in a binary cloud mask in this window, number of clouds, and a fractal box dimension of the cloud mask within the window. Furthermore, cloud physical parameters as cloud phase, cloud optical depth, and cloud top temperature are used as pixel-wise classifiers. A classification scheme is set up to search for the DNI variability class with a best agreement between these classifiers and the pre-existing knowledge on the characteristics of the cloud field within each variability class from the reference data base. Up to 55 % of all DNI variability class members are identified in the same class as in the reference data base. And up to 92 % cases are identified correctly if the neighbouring class is counted as success as well – the latter is a common approach in classifying natural structures showing no clear distinction between classes as in our case of temporal variability. Such a DNI variability classification method allows comparisons of different project sites in a statistical and automatic manner e.g. to quantify short-term variability impacts on solar power production. This approach is based on satellite-based cloud observations only and does not require any ground observations of the location of interest

Merging regional and global aerosol optical depth records from major available satellite products

Satellite instruments provide a vantage point for studying aerosol loading consistently over different regions of the world. However, the typical lifetime of a single satellite platform is on the order of 5-15 years; thus, for climate studies, the use of multiple satellite sensors should be considered. Discrepancies exist between aerosol optical depth (AOD) products due to differences in their information content, spatial and temporal sampling, calibration, cloud masking, and algorithmic assumptions. Users of satellite-based AOD time-series are confronted with the challenge of choosing an appropriate dataset for the intended application. In this study, 16 monthly AOD products obtained from different satellite sensors and with different algorithms were inter-compared and evaluated against Aerosol Robotic Network (AERONET) monthly AOD. Global and regional analyses indicate that products tend to agree qualitatively on the annual, seasonal and monthly timescales but may be offset in magnitude. Several approaches were then investigated to merge the AOD records from different satellites and create an optimised AOD dataset. With few exceptions, all merging approaches lead to similar results, indicating the robustness and stability of the merged AOD products. We introduce a gridded monthly AOD merged product for the period 1995-2017. We show that the quality of the merged product is as least as good as that of individual products. Optimal agreement of the AOD merged product with AERONET further demonstrates the advantage of merging multiple products. This merged dataset provides a long-term perspective on AOD changes over different regions of the world, and users are encouraged to use this dataset

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).Peer reviewe

Estimating surface reflectance for aerosol retrieval SYNAER Envisat and MetOp, based on analysis of ASRVN and spectrometer data

A new suggestion for the surface treatment within SYNAER, with a vegetation index dependent regression from infrared to red channel, is presented and described. The work is based on analysis of measured vegetation spectra and the satellite based ASRVN surface database

Ensembles of satellite aerosol retrievals based on three AATSR algorithms within aerosol_cci

Ensemble techniques are widely used in the modelling community, combining different modelling results in order to reduce uncertainties. This approach could be also adapted to satellite measurements. Aerosol_cci is an ESA funded project, where most of the European aerosol retrieval groups work together. The different algorithms are homogenized as far as it makes sense, but remain essentially different. Datasets are compared with ground based measurements and between each other. Three AATSR algorithms (Swansea university aerosol retrieval, ADV aerosol retrieval by FMI and Oxford aerosol retrieval ORAC) provide within this project 17 year global aerosol records. Each of these algorithms provides also uncertainty information on pixel level. Within the presented work, an ensembles of the three AATSR algorithms is performed. The advantage over each single algorithm is the higher spatial coverage due to more measurement pixels per gridbox. A validation to ground based AERONET measurements shows still a good correlation of the ensemble, compared to the single algorithms. Annual mean maps show the global aerosol distribution, based on a combination of the three aerosol algorithms. In addition, pixel level uncertainties of each algorithm are used for weighting the contributions, in order to reduce the uncertainty of the ensemble. Results of different versions of the ensembles for aerosol optical depth will be presented and discussed. The results are validated against ground based AERONET measurements. A higher spatial coverage on daily basis allows better results in annual mean maps. The benefit of using pixel level uncertainties is analysed

Aerosol retrieval algorithm for MERIS

The Medium Resolution Imaging Spectrometer (MERIS) has been launched in 2002 onboard the ESA Envisat satellite and was providing data until Envisat’s end in 2012. The MERIS spectrometer was designed primarily for surface and land scientific applications and measured reflected solar radiation in 15 channels from 400-900nm. Based on the work of Sayer and Hsu for SeaWiFS, an aerosol retrieval for MERIS was developed as part of the ESA Aerosol_cci project. The high spectral resolution of the instrument provides capability for retrieving aerosols. The algorithm can be transferred also to the OLCI measurements operating onboard the Sentinel-3 satellites since 2015. The presentation will describe the general concept of the retrieval algorithm and show results of aerosol properties retrieved from the MERIS measurements. An information content analysis is used to assess the general capability of retrieving aerosol optical depth (AOD) from the MERIS channels. Challenges concerning the retrieval algorithm and its input data will be discussed

Satellite based mapping of Particulate Matter

Satellite based mapping of Particulate Matter was presented. Satellite retrieved aerosol optical depth products from SYNAER and MODIS were used to derive air quality over Germany and Europe. This work was done within the EU-Project myAir Pasodoble

Classifying 1 Minute Temporal Variability in Global and Direct Normal Irradiances within Each Hour from Ground-Based Measurements

This study aims at classifying the 1 min resolved DNI and GHI variability in an hour in generic variability classes. It couples existing variability indices and adds new indicators to quantify aspects of the time series not being quantified so far. Additionally, the focus is laid on an automatic detection scheme. Eight classes are differentiated and described with respect to several aspects of variability