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This paper presents an innovative method for obtaining a daily estimate of a quality-controlled aerosol optical thickness (AOT) of a vertical column of the atmosphere over the continents. Because properties of land surface are more stationary than the atmosphere, the temporal dimension is exploited for simultaneous retrieval of the surface and aero...
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... this period, the method provides a daily estimate of AOT at 550 nm to be compared with the initial AOT used to calculate the synthetic reflectance. Results of the com- parison are reported in Figure 3, which reveals a fairly good statistical agreement since no bias is observed and relative error is low whatever the AOT value. The use of a priori information and the accumulation of observations during the day are intended to reduce the effects of the outliers coming, for instance, from residual cloud contamination since 17% of noise has a poor impact on the quality of the results. ...
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... Retrieval algorithms have been developed in the past years to monitor aerosols from GEO satellites, including MSG (Govaerts et al., 2010(Govaerts et al., , 2018Luffarelli and Govaerts, 2019;Thieuleux et al., 2005), GOES (Knapp, 2002;Kondragunta et al., 2020), Himawari (Gupta et al., 2019;Lim et al., 2018;Yoshida et al., 2018), and other missions such as the Korean Geostationary Ocean Color Imager (GOCI; Choi et al., 2016). One example is the AERUS-GEO (Aerosol and surface albEdo Retrieval Using a directional Splitting method-application to GEOstationary data) method, which provides AOD at 635 nm from the Spinning Enhanced Visible InfraRed Imager (SEVIRI) on MSG (Carrer et al., 2010 and more recently from a constellation of GEO imagers providing quasi-global coverage (Ceamanos et al., 2021). One of the main strengths of this algorithm is the use of a Kalman filter to estimate the surface bidirectional reflectance distribution function (BRDF) -a key parameter for a successful aerosol retrieval -and to propagate it with time such that it can be used as prior information in future days. ...
... BRDF of the current day. The use of past estimates relies on the assumption of invariability in the directionality of surface reflectance for a time offset of a few days (Carrer et al., 2010;Lyapustin et al., 2018). ...
... Surface BRDF is estimated at the end of the day for each SEVIRI pixel by the use of all the available observations of ρ TOL . This is done following a strategy that is similar to the one used in the original AERUS-GEO algorithm (Carrer et al., 2010) to retrieve daily average AOD (τ daily ) and surface BRDF (ρ s ) simultaneously. Modifications were made in the instance of iAERUS-GEO to provide the best surface BRDF possible -contrary to AERUS-GEO, which focuses on τ daily , being the main output -as a reliable estimate of surface reflectance is key to achieve the instantaneous estimation of AOD during the day. ...
Geostationary meteorological satellites are unique tools to monitor atmospheric aerosols from space. The observation of the Earth several times per hour allows these types of imaging systems to provide high-temporal-resolution observations of these suspended particles, which are of interest for research and operational topics, including climate, air quality, numerical weather prediction, and volcanic risk management. However, some challenges need to be addressed to achieve the sub-daily retrieval of aerosol properties mainly due to the varying sensitivity of geostationary imagers to aerosols during the day. In this article we propose a new algorithm named iAERUS-GEO (instantaneous Aerosol and surfacE Retrieval Using Satellites in GEOstationary orbit) that estimates the diurnal evolution of aerosol optical depth (AOD) over land and ocean from the Meteosat Second Generation (MSG) satellite. This is achieved by the use of an optimal-estimation method combined with several aerosol models and other features, including the daily retrieval of the surface reflectance directionality using Kalman filtering. AOD estimates provided by iAERUS-GEO every 15 min – the acquisition frequency of the Spinning Enhanced Visible InfraRed Imager (SEVIRI) on MSG – are assessed with collocated reference aerosol observations. First, comparison to AERONET ground-based data proves the overall satisfactory accuracy of iAERUS-GEO over land, with the exception of some higher biases found over bright surfaces and for high scattering angles. The confidence measure provided by iAERUS-GEO is proved useful to filter these less satisfactory retrievals that generally arise due to a low information content on aerosols provided by SEVIRI. Second, comparison to the GRASP/POLDER satellite product shows similar scores for the two aerosol data sets, with a significantly larger number of retrievals for iAERUS-GEO. This added value – which we illustrate here by inspecting the sub-daily variation in AOD over selected regions – allows geostationary satellites to break the temporal barrier set by traditional aerosol remote sensing from the low Earth orbit. Furthermore, the aerosol retrievals presented in this work are expected to be improved in the near future thanks to the enhanced sensing capabilities of the upcoming Meteosat Third Generation Imager mission.
... Second, it allows iAERUS-GEO to satisfy the constraints of near real time processing that make impossible the use of the surface BRDF of the current day. The use of past estimates relies on the assumption of invariability of the directionality of surface reflectance for a time offset of a few days (Carrer et al., 2010;Lyapustin et al., 2018). 140 Step 3 (every 15-min) Step 2 (every day) CM https://doi.org/10.5194/amt-2023-1 ...
... Surface BRDF is estimated at the end of the day for each SEVIRI pixel by the use of all the available observations of )*-. This is done following a strategy that is similar to the one used in the original AERUS-GEO algorithm (Carrer et al., 2010) to retrieve daily-average AOD ( daily ) and surface BRDF ( % ) simultaneously. Modifications were made in the instance of iAERUS-GEO to provide the best surface BRDF possible -contrary to AERUS-GEO which focuses on daily , being the main output-as a reliable estimate of surface reflectance is key to achieve the instantaneous estimation of AOD during the day. ...
... This approximation was found to provide a precision of 99,7% for zenith angles up to 70° in Carrer et al., (2010). 715 ...
Geostationary meteorological satellites are unique tools to monitor atmospheric aerosols from space. The observation of the Earth several times per hour allows this type of imaging systems to provide high temporal resolution observations of these suspended particles which are of interest for research topics including air quality, numerical weather prediction, and volcanic risk management. However, some challenges need to be addressed to achieve the sub-daily retrieval of aerosol properties mainly due to the varying sensitivity of geostationary imagers to aerosols during the day. In this article we propose a new algorithm named iAERUS-GEO (instantaneous Aerosol and surfacE Retrieval Using Satellites in GEOstationary orbit) that estimates the diurnal evolution of aerosol optical depth (AOD) from the Meteosat Second Generation (MSG) satellite. This is achieved by the use of an optimal estimation method combined with several aerosol models and other features including the daily retrieval of the surface reflectance directionality using Kalman filtering. AOD estimates provided by iAERUS-GEO every 15 minutes —the acquisition frequency of the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on MSG— are assessed with collocated reference aerosol observations. First, comparison to AERONET ground-based data proves the overall satisfactory accuracy of iAERUS-GEO with the exception of some higher biases found over bright surfaces and for high scattering angles. The confidence measure provided by iAERUS-GEO is proved useful to filter these less satisfactory retrievals that generally arise due to a low information content on aerosols provided by SEVIRI. Second, comparison to the GRASP/POLDER satellite product shows similar scores for the two aerosol data sets, with a significantly larger number of retrievals for iAERUS-GEO. This added value —which we illustrate here by inspecting the sub-daily variation of AOD over selected regions— allows geostationary satellites to break the temporal barrier set by traditional aerosol remote sensing from the low Earth orbit. Furthermore, the aerosol retrievals presented in this work are expected to be improved in the near future thanks to the enhanced sensing capabilities of the upcoming Meteosat Third Generation-Imager mission.
... The chemical processes that affect the removal are dependent on the ability of atmospheric chemistry to oxidize the carbonaceous component of the smoke, via reactions with oxidative species such as ozone and nitrogen oxides, both of which are found at all levels of the atmosphere, and which also occur at greater concentrations when air is heated to high temperatures. Historical data on residence times of aerosols, albeit a different mixture of aerosols, in this case stratospheric sulfur aerosols and volcanic ash from megavolcano eruptions, appear to be in the one-to-two-year time scale, however aerosol-atmosphere interactions are still poorly understood (Carrer et al., 2010). ...
Nuclear geochemistry is the sub-field of isotope geochemistry dealing with radioactivity, nuclear processes, and transformations in the nuclei of atoms, such as nuclear transmutation and nuclear properties. The goal of this book is to clarify the basics and implementations of nuclear geochemistry. Topics covered include radioactive elements, nuclear reaction, radiometric dating, nuclear fuel cycle, nuclear pollution, and nuclear forensics.
... Recent atmospheric correction algorithms have emerged that explicitly account for the SR anisotropy. One example is the Aerosol and surface albedo Retrieval Using a directional Slitting method-application to GEOstationary data (AERUS-GEO) algorithm originally developed in [36,37] to process observations from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on board the Meteosat Second Generation (MSG) geostationary satellite. The algorithm adopts the semi-analytical Ross-Thick-Li-Sparse (RTLS) model to represent surface BRDF and evaluates its regulation on the TOA reflectance in a linearized semi-physical model at low computational cost [36]. ...
... One example is the Aerosol and surface albedo Retrieval Using a directional Slitting method-application to GEOstationary data (AERUS-GEO) algorithm originally developed in [36,37] to process observations from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on board the Meteosat Second Generation (MSG) geostationary satellite. The algorithm adopts the semi-analytical Ross-Thick-Li-Sparse (RTLS) model to represent surface BRDF and evaluates its regulation on the TOA reflectance in a linearized semi-physical model at low computational cost [36]. The AERUS-GEO algorithm has been applied to process GOES ABI and Himawari AHI data [38]. ...
This study developed a new atmospheric correction algorithm, GeoNEX-AC, that is independent from the traditional use of spectral band ratios but dedicated to exploiting information from the diurnal variability in the hypertemporal geostationary observations. The algorithm starts by evaluating smooth segments of the diurnal time series of the top-of-atmosphere (TOA) reflectance to identify clear-sky and snow-free observations. It then attempts to retrieve the Ross-Thick–Li-Sparse (RTLS) surface bi-directional reflectance distribution function (BRDF) parameters and the daily mean atmospheric optical depth (AOD) with an atmospheric radiative transfer model (RTM) to optimally simulate the observed diurnal variability in the clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they serve as the prior information to estimate the AOD levels for the following days and update the surface BRDF information with the new clear-sky observations. This process is iterated through the full time span of the observations, skipping only totally cloudy days or when surface snow is detected. We tested the algorithm over various Aerosol Robotic Network (AERONET) sites and the retrieved results well agree with the ground-based measurements. This study demonstrates that the high-frequency diurnal geostationary observations contain unique information that can help to address the atmospheric correction problem from new directions.
... In addition to the operational aerosol estimation techniques, there have been various attempts to use different approximate methods for AOD estimation, where a few of them are computationally cost-effective but sometimes trade off the accuracy of modeling (Seidel et al., 2010) (Carrer et al., 2010). Such models are more efficient for remote sensing applications as they enable faster parameter retrieval based on a simple physics based approach. ...
The decoupling of aerosol signals from the at-sensor reflectance measured through a space-borne sensor is a complex task due to the involved coupling mechanism of the interaction of light with the Earth's surface and the atmosphere. Specifically, the retrieval becomes more challenging over the land surface due to appreciable reflectance from the target. This paper presents a simplistic physics-based approach to retrieve Aerosol Optical Depth (AOD) (at a spatial resolution of 0.01°) over the land surface from the top-of-atmosphere (TOA) signals measured from an Indian space-borne sensor, Ocean Color Monitor-2 (onboard OCEANSAT-2 satellite). The estimated AOD from OCM-2 has been compared with that from Moderate Resolution Spectroradiometer (MODIS) onboard AQUA satellite (Collection 6.1 Deep Blue/Dark Target product). The comparison is illustrated for two different conditions prevalent over the Indian region, i.e., smoke-dominated (post-monsoonal) and dust-laden (pre-monsoonal) periods. A case study is also illustrated for the pre- and post-COVID-19 lockdown time period for two different zones of the Indian region. The validation (comparison) of OCM-2 AOD is performed with respect to the data from AERONET stations (MODIS), and we found a correlation coefficient (R²) of 0.84 (0.83) with root mean square error (RMSE) of 0.14 (0.20). Also, ∼87% (70%) of the retrieved OCM-2 (MODIS) AOD values were observed within the expected error (EE) envelope. The sensitivity of the retrieval algorithm with respect to the input parameters is also presented.
... The advantages of geostationary satellites for aerosol remote sensing were demonstrated by Xu et al. (2014) who found a higher number of satisfactory AOD estimates with respect to MODIS aerosol products for the AERUS-GEO (Aerosol and surface albEdo Retrieval Using a directional Splitting method-application to GEOstationary data) aerosol product. AERUS-GEO provides daily-averaged AOD and surface albedo from the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) aboard the geostationary satellite Meteosat Second Generation (MSG; Carrer et al., 2010Carrer et al., , 2014. Many other aerosol products exist for geostationary satellites around the globe such as MSG (Govaerts et al., 2010;Thieuleux et al., 2005), the series of Geostationary Operational Environmental Satellites (GOES; Knapp, 2002;Kondragunta et al., 2020), Himawari (Gupta et al., 2019;Yoshida et al., 2018), and the Geostationary Ocean Color Imager (GOCI; Choi et al., 2016). ...
... AERUS-GEO is a retrieval algorithm that provides estimates of daily-averaged total AOD and surface albedo from VNIR geostationary observations (Carrer et al., 2010. It uses as input data: ...
... More details on the retrieval method can be found in Carrer et al. (2010Carrer et al. ( , 2014. ...
Geostationary meteorological satellites are recently receiving a great deal of attention from the aerosol remote sensing community due to their increasingly advanced performances including their ability to acquire multiple views of the Earth per day. However, this type of observing systems are limited by their partial coverage of the globe, which makes them inappropriate for monitoring long-range-transported particles or assimilating the derived aerosol products into global atmospheric models. This shortcoming is overcome in this pioneering investigation thanks to the simultaneous use of the geostationary satellites GOES-17, GOES-16, Meteosat-11, Meteosat-8, and Himawari-8. The combination of these five spacecrafts (herein referred to as GEO-ring) covers the full globe except for polar regions, which are not seen from geostationary orbit. Data acquired by the GEO-ring in 2020 are processed by the algorithm AERUS-GEO to provide quasi-global maps of daily aerosol optical depth (AOD). These data are proved to be accurate with respect to AERONET ground measurements. The potential of the GEO-ring for aerosol science is then demonstrated. First, retrieved AOD maps are proved to be potentially useful for global aerosol forecasting due to the improved Earth coverage of the GEO-ring with respect to the polar orbiting satellites Terra/Aqua (51.4% vs. 26.6%). Second, retrieved aerosol data are used to track long-range-transported aerosols emitted during the 2020 Western US wildfires. This article is a prelude to the aerosol remote sensing that will be possible when a GEO-ring exclusively consisting of next generation imagers will be available with the launch of Meteosat Third Generation-Imager.
... AOD retrievals from the AERUS-GEO have been widely evaluated. Carrer et al. (2010) validated the original method by comparing the AOD estimates to Aerosol Robotic Network (AERONET) ground observations and MODIS Collection 5 products. A mean bias lower than 20% was determined in comparison with AER-ONET measurements. ...
... In contrast, the AERUS-GEO product ( Figure 5c) does not capture AOD much higher than this background value. Carrer et al. (2010) note a potential 20% underestimation with respect to AERONET measurements. ...
... 12 of 23 average about 0.15 smaller. This value exceeds the biases documented in Carrer et al. (2010) with respect to AERONET measurements. However, for all products and both types of HW, the distribution is wider and shows significant departure from the climatology in case of HW. ...
This paper makes use of spaceborne observational data sets in order to characterize radiative processes involved in spring time heat waves in the Sahel. Spring corresponds to the hottest period of the year, with a high aerosol load, a gradual moistening, and the presence of clouds contributing to greenhouse effect. Heat waves are defined as synoptic events that have a large spatial extent and a duration longer than 3 days. Two types of heat waves are studied: daytime heat waves, detected with the daily maximum temperature and nighttime heat waves, detected with the daily minimum temperature. Daytime heat waves correspond to situations where cloud optical thickness is lower than the climatology and a large number of these situations are also associated with a lower aerosol load and a drier atmosphere. Nighttime heat waves correspond to a moister atmosphere compared to the climatology. In a large fraction of them, an increase in aerosol loading is also observed. This study, only based on observational data sets, highlights the subtle but different radiative balance at play in both types of events.
... It is much more challenging to estimate AOD from geostationary satellite retrievals than it is to do so based on data acquired from low earth orbit satellites [76]. However, advances in retrieval algorithms for geostationary satellites are found in scientific literature, with direct applications of SEVIRI data [29,56,57,59,[77][78][79][80][81][82]. The main advantage in these approaches is the higher temporal resolution that the geostationary satellites provide, e.g., [58]. ...
This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (Poland–AOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (June–September 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48–0.83). The mean bias is 0.10–0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with Poland–AOD correlation values are 0.55 to 0.71. The mean bias is 0.04–0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33–0.39). Biases of −0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the ground–stations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the Poland–AOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts.
... It affords better temporal coherences and spatial completeness of the estimates in time. The advantage to add constraints on the parameters themselves in the inversion of the linear model was already shown by Li et al. [39], Hagolle et al. [40], Geiger et al. [29], Carrer et al. [23,30,41] and a related approach was also adopted by Pokrovsky et al. [42]. In the following, the important steps of this recursive method are summarised. ...
Land surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. This article presents the algorithm concepts for the remote sensing of this variable based on the heritage of several developments which were performed at Méteo France over the last decade and described in several papers by Carrer et al. The scientific algorithm comprises four steps: an atmospheric correction, a sensor harmonisation (optional), a BRDF (Bidirectional Reflectance Distribution Function) inversion, and the albedo calculation. At the time being, the method has been applied to 11 sensors in the framework of two European initiatives (Satellite Application Facility on Land Surface Analysis—LSA SAF, and Copernicus Climate Change Service—C3S): NOAA-7-9-11-14-16-17/AVHRR2-3, SPOT/VGT1-2, Metop/AVHRR-3, PROBA-V, and MSG/SEVIRI. This work leads to a consistent archive of almost 40 years of satellite-derived albedo data (available in 2020). From a single sensor, up to three different albedo products with different characteristics have been developed to address the requirements of both, near real-time (NRT) (weather prediction with a demand of timeliness of 1 h) and climate communities. The evaluation of the algorithm applied to different platforms was recently made by Lellouch et al. and Sánchez Zapero et al. in 2020 which can be considered as companion papers. After a summary of the method for the retrieval of these surface albedos, this article describes the specificities of each retrieval, lists the differences, and discusses the limitations. The plan of continuity with the next European satellite missions and perspectives of improvements are introduced. For example, Metop/AVHRR-3 albedo will soon become the medium resolution sensor product with the longest NRT data record, since MODIS is approaching the end of its life-cycle. Additionally, Metop-SG/METimage will ensure its continuity thanks to consistent production of data sets guaranteed till 2050 by the member states of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). In the end, the common strategy which we proposed through the different programmes may offer an unprecedented opportunity to study the temporal trends affecting surface properties and to analyse human-induced climate change. Finally, the access to the source code (called PYALUS) is provided through an open access platform in order to share with the community the expertise on the satellite retrieval of this variable.
... The retrieval algorithm [6,7,9] was developed based on the previous developments of the SA products [6][7][8][9] based on MSG/SEVIRI and Metop/AVHRR in the framework of the EUMETSAT LSA SAF project [4], and it was later adapted here to these other sensors in CGLS and C3S [43]. The input data were Collection 3 of SPOT/VGT, which corrects the bug previously detected regarding the incorrect calculation of the Sun-Earth distance of Collection 2, includes improved cloud and snow/ice detection, and revises the absolute radiometric calibration for the entire archive [44]. ...
The Copernicus Climate Change Service (C3S) includes estimates of Essential Climate Variables (ECVs) as a series of Climate Data Records (CDRs) derived from satellite data. The C3S Surface Albedo (SA) v1.0 CDR is composed of observations from National Oceanic and Atmospheric Administration (NOAA) Very High Resolution Radiometers (AVHRR) (1981–2005), and VEGETATION sensors onboard Satellites for the Observation of the Earth (SPOT/VGT) (1998–2014) and Project for Onboard Autonomy satellite (PROBA-V) (2014–2020), and will continue with Sentinel-3 (from 2020 onwards). The goal of this study is to assess the uncertainties associated with the C3S PROBA-V SA v1.0 product, with a focus on the transition from SPOT/VGT to PROBA-V. The methodology followed the good practices recommended by the Land Product Validation sub-group (LPV) of the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observing Satellites (CEOS) for the validation of satellite-derived global albedo products. Several performance criteria were evaluated, including an intercomparison with National Aeronautics and Space Agency (NASA) MCD43A3 C6 products. C3S PROBA-V SA v1.0 and MCD43A3 C6 showed similar completeness but had higher fractions of missing data than C3S SPOT/VGT SA v1.0. C3S PROBA-V SA v1.0 showed similar precision (~1%) to MCD43A3 C6, improving the results of SPOT/VGT SA v1.0 (2–3%), but C3S PROBA-V SA v1.0 provided residual noise in the near-infrared (NIR). Good spatio-temporal continuity between C3S PROBA-V and SPOT/VGT SA v1.0 products was found with a mean bias between ±2%. The comparison with MCD43A3 C6 showed positive mean biases (5%, 8%, and 12% for visible, NIR and total shortwave, respectively). The accuracy assessment with ground measurements showed a median error of 18.4% with systematic overestimation (positive bias of 11.5%). The percentage of PROBA-V retrievals complying with the C3S target requirements was 28.6%.
