Inter-comparison of three AATSR Level 2 (L2) AOD products over China

Abstract. The Advanced Along-Track Scanning Radiometer (AATSR) aboard on ENVISAT is used to observe the Earth by dual-view. The AATSR data can be used to retrieve aerosol optical depth (AOD) over both land and ocean, which is an important merit in the characterization of aerosol properties. In recent years, aerosol retrieval algorithms both over land and ocean have been developed, taking advantages of the feature of dual-view which can help eliminate contribution of Earth's surface to top of atmosphere (TOA) reflectance. Aerosol_cci project as a part of Climate Change Initiative (CCI) provides users three AOD retrieval algorithms for AATSR data, including the Swansea algorithm (SU), the ATSR-2ATSR dual view aerosol retrieval algorithm (ADV) and the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC). The Validation team of Aerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and AE (Level 2 product only) against the AERONET data in a round robin evaluation using validation tool of AeroCOM (Aerosol Comparison between Observations and Models) project. For the purpose of evaluating different performances of these three algorithms on calculating AODs over mainland China, we introduce ground-based data from the CARSNET (the China Aerosol Remote Sensing Network) which is designed for aerosol observation in China. Because China is vast in territory and of great differences in surface, the combination of the AEROENT and the CATRNET data can validate L2 AOD products more comprehensively. The validation results show different performances of these products in 2007, 2008 and 2010. The SU algorithm has very good performance over sites with different surface conditions in mainland China from March to October, but it underestimates AOD slightly with varying mean bias error (MBE) from 0.05 to 0.10 over surface of barren or sparsely vegetation in western China. The ADV product has same precision with high correlation coefficient (CC) larger than 0.90 over most of sites and same error distribution as the SU product. The main limits of ADV algorithm are underestimation and applicability, especially it occurs obvious underestimation over sites of Datong, Lanzhou and Urmuchi where the dominated land cover is grassland with MBE larger than 0.2 and the main source of aerosol is coal combustion and dust. The ORAC algorithm has the ability of retrieving AOD at different ranges including high AOD (larger than 1.0), however, the stability will decease significantly as AOD grows, especially when AOD > 1.0. In addition, ORAC product get matches successfully collocated with CARSNET in winter (December, January and February), whereas other validation results lack matches during winter.

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Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-9655-2016 ◽

2016 ◽

Vol 16 (15) ◽

pp. 9655-9674 ◽

Cited By ~ 13

Author(s):

Yahui Che ◽

Yong Xue ◽

Linlu Mei ◽

Jie Guang ◽

Lu She ◽

...

Keyword(s):

Mainland China ◽

Technical Note ◽

Western China ◽

Mean Bias Error ◽

Retrieval Algorithm ◽

Bias Error ◽

Aerosol Retrieval ◽

Retrieval Algorithms ◽

Land Surfaces ◽

Level 2

Abstract. One of four main focus areas of the PEEX initiative is to establish and sustain long-term, continuous, and comprehensive ground-based, airborne, and seaborne observation infrastructure together with satellite data. The Advanced Along-Track Scanning Radiometer (AATSR) aboard ENVISAT is used to observe the Earth in dual view. The AATSR data can be used to retrieve aerosol optical depth (AOD) over both land and ocean, which is an important parameter in the characterization of aerosol properties. In recent years, aerosol retrieval algorithms have been developed both over land and ocean, taking advantage of the features of dual view, which can help eliminate the contribution of Earth's surface to top-of-atmosphere (TOA) reflectance. The Aerosol_cci project, as a part of the Climate Change Initiative (CCI), provides users with three AOD retrieval algorithms for AATSR data, including the Swansea algorithm (SU), the ATSR-2ATSR dual-view aerosol retrieval algorithm (ADV), and the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC). The validation team of the Aerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and AE (Ångström Exponent) (Level 2 product only) against the AERONET data in a round-robin evaluation using the validation tool of the AeroCOM (Aerosol Comparison between Observations and Models) project. For the purpose of evaluating different performances of these three algorithms in calculating AODs over mainland China, we introduce ground-based data from CARSNET (China Aerosol Remote Sensing Network), which was designed for aerosol observations in China. Because China is vast in territory and has great differences in terms of land surfaces, the combination of the AERONET and CARSNET data can validate the L2 AOD products more comprehensively. The validation results show different performances of these products in 2007, 2008, and 2010. The SU algorithm performs very well over sites with different surface conditions in mainland China from March to October, but it slightly underestimates AOD over barren or sparsely vegetated surfaces in western China, with mean bias error (MBE) ranging from 0.05 to 0.10. The ADV product has the same precision with a low root mean square error (RMSE) smaller than 0.2 over most sites and the same error distribution as the SU product. The main limits of the ADV algorithm are underestimation and applicability; underestimation is particularly obvious over the sites of Datong, Lanzhou, and Urumchi, where the dominant land cover is grassland, with an MBE larger than 0.2, and the main aerosol sources are coal combustion and dust. The ORAC algorithm has the ability to retrieve AOD at different ranges, including high AOD (larger than 1.0); however, the stability deceases significantly with increasing AOD, especially when AOD > 1.0. In addition, the ORAC product is consistent with the CARSNET product in winter (December, January, and February), whereas other validation results lack matches during winter.

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Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

Remote Sensing ◽

10.3390/rs11091052 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1052 ◽

Cited By ~ 3

Author(s):

Reto Stöckli ◽

Jędrzej S. Bojanowski ◽

Viju O. John ◽

Anke Duguay-Tetzlaff ◽

Quentin Bourgeois ◽

...

Keyword(s):

Mean Squared Error ◽

Sensor Data ◽

Mean Bias Error ◽

Retrieval Algorithm ◽

Bias Error ◽

Cloud Detection ◽

Climate Data ◽

Detection Algorithms ◽

Fractional Cover ◽

Spatio Temporal

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

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AerGOM, an improved algorithm for stratospheric aerosol extinction retrieval from GOMOS observations. Part 1: Algorithm development

10.5194/amt-2016-28 ◽

2016 ◽

Cited By ~ 1

Author(s):

Filip Vanhellemont ◽

Nina Mateshvili ◽

Laurent Blanot ◽

Charles E. Robert ◽

Christine Bingen ◽

...

Keyword(s):

Stratospheric Aerosol ◽

Quality Data ◽

Retrieval Algorithm ◽

Aerosol Extinction ◽

Data Inversion ◽

Aerosol Retrieval ◽

Stellar Occultation ◽

Algorithmic Problems ◽

The Subject ◽

Level 2

Abstract. The GOMOS instrument on EnviSat has succesfully demonstrated that a UV/Vis/NIR spaceborne stellar occultation instrument is capable of delivering quality data on the gaseous and particulate composition of Earth's atmosphere. Still, some problems related to data inversion remained to be treated. In the past, it was found that the aerosol extinction proﬁle retrievals in the upper troposphere and stratosphere are of good quality at a reference wavelength of 500 nm, but suffer from anomalous, retrieval-related perturbations at other wavelengths. Identification of algorithmic problems and subsequent improvement was therefore necessary. This work has been carried out; the resulting AerGOM Level 2 retrieval algorithm together with the ﬁrst data version AerGOMv1.0 forms the subject of this paper. First, a brief overview of the operational IPFv6.01 GOMOS algorithm is given, since the AerGOM algorithm is to a certain extent similar. Then, the discussion on the AerGOM algorithm specifically focuses on the new aspects that were implemented to tackle the aerosol retrieval problems. Finally, a ﬁrst assess- ment of the obtained aerosol extinction data quality is presented, clearly showing significant improvement of aerosol proﬁle shape, spectral behaviour and similarity to SAGE II data.

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The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2

10.5194/amt-2019-474 ◽

2020 ◽

Cited By ~ 3

Author(s):

Alexander Sinyuk ◽

Brent N. Holben ◽

Thomas F. Eck ◽

David M. Giles ◽

Ilya Slutsker ◽

...

Keyword(s):

Standard Deviation ◽

Optical Depth ◽

Solar Flux ◽

Gas Absorption ◽

Retrieval Algorithm ◽

Dust Aerosols ◽

Aerosol Retrieval ◽

Absorbing Aerosols ◽

Level 2 ◽

Good Agreement

Abstract. The Aerosol Robotic Network (AERONET) version 3 (V3) aerosol retrieval algorithm is described, which is based on the version 2 (V2) algorithm with numerous updates. Comparisons of V3 aerosol retrievals to those of V2 are presented, along with a new approach to estimate uncertainties in many of the retrieved aerosol parameters. Changes in V3 aerosol retrieval algorithm include: 1) a new polarized radiative transfer code (RTC), which replaced the scalar RTC of V2, 2) detailed characterization of gas absorption by adding NO2 and H2O to specify total gas absorption in the atmospheric column, specification of vertical profiles of all the atmospheric species, 3) new Bidirectional Reflectance Distribution Function (BRDF) parameters for land sites adopted from the MODIS BRDF/Albedo product, 4) a new version of the extraterrestrial solar flux spectrum, and 5) new temperature correction procedure of both direct sun and sky radiance measurements. The potential effect of each change in V3 on single scattering albedo (SSA) retrievals was analyzed. The operational almucantar retrievals of V2 versus V3 were compared for four AERONET sites: GSFC, Mezaira, Mongu, and Kanpur. Analysis showed very good agreement in retrieved parameters of the size distributions. Comparisons of SSA retrievals for dust aerosols (Mezaira) showed a good agreement in 440 nm SSA while for longer wavelengths V3 SSAs are systematically higher than those of V2 with the largest mean difference at 675 nm due to cumulative effects of both extraterrestrial solar flux and BRDF changes. For non-dust aerosols, the largest SSA deviation is at 675 nm due to differences in extraterrestrial solar flux spectrums used in each version. Further, the SSA 675 nm mean differences are very different for weakly (GSFC) and strongly (Mongu) absorbing aerosols which is explained by the lower sensitivity to a bias in aerosol scattering optical depth by less absorbing aerosols. A new hybrid (HYB) sky radiance measurements scan is introduced and discussed. The HYB combines features of scans in two different planes to maximize the range of scattering angles and achieve scan symmetry, thereby allowing for cloud screening and spatial averaging which is an advantage over the principal plane scan that lacks robust symmetry. We show that due to extended range of scattering angles HYB SSA retrievals for dust aerosols exhibit smaller variability with SZA than those of almucantar (ALM) which allows extending HYB SSA retrievals to solar zenith angles (SZA) less than 50° to as small as 25°. The comparison of SSA retrievals from closely time matched HYB and ALM scans in the 50° to 75° SZA range showed good agreement with the differences below ~0.005. We also present an approach to estimate retrieval uncertainties which utilizes the variability in retrieved parameters generated by perturbing both measurements and auxiliary input parameters as a proxy for retrievals uncertainty. The perturbations in measurements and auxiliary inputs are assumed as estimated biases in aerosol optical depth (AOD), radiometric calibration of sky radiances combined with solar spectral irradiance, and surface reflectance. For each set of Level 2 Sun/sky radiometer observations, 27 inputs corresponding to 27 combinations of biases were produced and separately inverted and to generate the following statistics of the inversion results: average, standard deviation, minimum and maximum values. From these statistics standard deviation (labeled as U27) is used as a proxy for estimated uncertainty and a lookup table (LUT) approach was implemented to reduce the computational time. The U27 climatological LUT was generated from the entire AERONET almucantar (1993–2018) and hybrid (2014–2018) scan database by binning U27s in AOD (440 nm), Angstrom Exponent (AE, 440–870nm), and SSA (440, 675, 870, 1020 nm). Using this LUT approach, the uncertainty estimates U27 for each individual V3 Level 2 retrieval can be obtained by interpolation using the corresponding measured and inverted combination of AOD, AE, and SSA.

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The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2

Atmospheric Measurement Techniques ◽

10.5194/amt-13-3375-2020 ◽

2020 ◽

Vol 13 (6) ◽

pp. 3375-3411 ◽

Cited By ~ 11

Author(s):

Alexander Sinyuk ◽

Brent N. Holben ◽

Thomas F. Eck ◽

David M. Giles ◽

Ilya Slutsker ◽

...

Keyword(s):

Standard Deviation ◽

Optical Depth ◽

Solar Flux ◽

Gas Absorption ◽

Retrieval Algorithm ◽

Dust Aerosols ◽

Aerosol Retrieval ◽

Absorbing Aerosols ◽

Level 2 ◽

Good Agreement

Abstract. The Aerosol Robotic Network (AERONET) Version 3 (V3) aerosol retrieval algorithm is described, which is based on the Version 2 (V2) algorithm with numerous updates. Comparisons of V3 aerosol retrievals to those of V2 are presented, along with a new approach to estimate uncertainties in many of the retrieved aerosol parameters. Changes in the V3 aerosol retrieval algorithm include (1) a new polarized radiative transfer code (RTC), which replaced the scalar RTC of V2, (2) detailed characterization of gas absorption by adding NO2 and H2O to specify total gas absorption in the atmospheric column, specification of vertical profiles of all the atmospheric species, (3) new bidirectional reflectance distribution function (BRDF) parameters for land sites adopted from the MODIS BRDF/Albedo product, (4) a new version of the extraterrestrial solar flux spectrum, and (5) a new temperature correction procedure of both direct Sun and sky radiance measurements. The potential effect of each change in V3 on single scattering albedo (SSA) retrievals was analyzed. The operational almucantar retrievals of V2 versus V3 were compared for four AERONET sites: GSFC, Mezaira, Mongu, and Kanpur. Analysis showed very good agreement in retrieved parameters of the size distributions. Comparisons of SSA retrievals for dust aerosols (Mezaira) showed a good agreement in 440 nm SSA, while for longer wavelengths V3 SSAs are systematically higher than those of V2, with the largest mean difference at 675 nm due to cumulative effects of both extraterrestrial solar flux and BRDF changes. For non-dust aerosols, the largest SSA deviation is at 675 nm due to differences in extraterrestrial solar flux spectrums used in each version. Further, the SSA 675 nm mean differences are very different for weakly (GSFC) and strongly (Mongu) absorbing aerosols, which is explained by the lower sensitivity to a bias in aerosol scattering optical depth by less absorbing aerosols. A new hybrid (HYB) sky radiance measurement scan is introduced and discussed. The HYB combines features of scans in two different planes to maximize the range of scattering angles and achieve scan symmetry, thereby allowing for cloud screening and spatial averaging, which is an advantage over the principal plane scan that lacks robust symmetry. We show that due to an extended range of scattering angles, HYB SSA retrievals for dust aerosols exhibit smaller variability with solar zenith angles (SZAs) than those of almucantar (ALM), which allows extension of HYB SSA retrievals to SZAs less than 50∘ to as small as 25∘. The comparison of SSA retrievals from closely time-matched HYB and ALM scans in the 50 to 75∘ SZA range showed good agreement with the differences below ∼0.005. We also present an approach to estimate retrieval uncertainties which utilizes the variability in retrieved parameters generated by perturbing both measurements and auxiliary input parameters as a proxy for retrieval uncertainty. The perturbations in measurements and auxiliary inputs are assumed as estimated biases in aerosol optical depth (AOD), radiometric calibration of sky radiances combined with solar spectral irradiance, and surface reflectance. For each set of Level 2 Sun/sky radiometer observations, 27 inputs corresponding to 27 combinations of biases were produced and separately inverted to generate the following statistics of the inversion results: average, standard deviation, minimum and maximum values. From these statistics, standard deviation (labeled U27) is used as a proxy for estimated uncertainty, and a lookup table (LUT) approach was implemented to reduce the computational time. The U27 climatological LUT was generated from the entire AERONET almucantar (1993–2018) and hybrid (2014–2018) scan databases by binning U27s in AOD (440 nm), Angström exponent (AE, 440–870 nm), and SSA (440, 675, 870, 1020 nm). Using this LUT approach, the uncertainty estimates U27 for each individual V3 Level 2 retrieval can be obtained by interpolation using the corresponding measured and inverted combination of AOD, AE, and SSA.

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Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

Geoscientific Model Development ◽

10.5194/gmd-9-2377-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 2377-2389 ◽

Cited By ~ 4

Author(s):

Galina Wind ◽

Arlindo M. da Silva ◽

Peter M. Norris ◽

Steven Platnick ◽

Shana Mattoo ◽

...

Keyword(s):

Remote Sensing ◽

Weather Prediction ◽

Retrieval Algorithm ◽

Model Parameters ◽

Aerosol Retrieval ◽

Model Aerosol ◽

Numerical Experimentation ◽

Retrieval Algorithms ◽

Sensor Cloud ◽

Aerosol Properties

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land–ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

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On Scatterometer Ocean Stress

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2008jtecho578.1 ◽

2009 ◽

Vol 26 (2) ◽

pp. 368-382 ◽

Cited By ~ 58

Author(s):

M. Portabella ◽

A. Stoffelen

Keyword(s):

Wind Stress ◽

Roughness Length ◽

Spatial Scales ◽

Weather Prediction ◽

Neutral Winds ◽

Level 3 ◽

Stress Estimation ◽

The Stability ◽

Ocean Wind ◽

Level 2

Abstract Scatterometers estimate the relative atmosphere–ocean motion at spatially high resolution and provide accurate inertial-scale ocean wind forcing information, which is crucial for many ocean, atmosphere, and climate applications. An empirical scatterometer ocean stress (SOS) product is estimated and validated using available statistical information. A triple collocation dataset of scatterometer, and moored buoy and numerical weather prediction (NWP) observations together with two commonly used surface layer (SL) models are used to characterize the SOS. First, a comparison between the two SL models is performed. Although their roughness length and the stability parameterizations differ somewhat, the two models show little differences in terms of stress estimation. Second, a triple collocation exercise is conducted to assess the true and error variances explained by the observations and the SL models. The results show that the uncertainty in the NWP dataset is generally larger than in the buoy and scatterometer wind/stress datasets, but it depends on the spatial scales of interest. The triple collocation analysis also shows that scatterometer winds are as close to real winds as to equivalent neutral winds, provided that the appropriate scaling is used. An explanation for this duality is that the small stability effects found in the analysis are masked by the uncertainty in SL models and their inputs. The triple collocation analysis shows that scatterometer winds can be straightforwardly and reliably transformed to wind stress. This opens the door for the development of wind stress swath (level 2) and gridded (level 3) products for the Advanced Scatterometer (ASCAT) on board Meterological Operation (MetOp) and for further geophysical development.

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Surface solar irradiance from SCIAMACHY measurements: algorithm and validation

Atmospheric Measurement Techniques ◽

10.5194/amt-4-875-2011 ◽

2011 ◽

Vol 4 (5) ◽

pp. 875-891 ◽

Cited By ~ 16

Author(s):

P. Wang ◽

P. Stammes ◽

R. Mueller

Keyword(s):

Standard Deviation ◽

Mean Difference ◽

Surface Radiation ◽

Mean Bias Error ◽

Retrieval Algorithm ◽

Bias Error ◽

Retrieval Scheme ◽

The Mean ◽

Scanning Imaging ◽

One Year

Abstract. Broadband surface solar irradiances (SSI) are, for the first time, derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) satellite measurements. The retrieval algorithm, called FRESCO (Fast REtrieval Scheme for Clouds from the Oxygen A band) SSI, is similar to the Heliosat method. In contrast to the standard Heliosat method, the cloud index is replaced by the effective cloud fraction derived from the FRESCO cloud algorithm. The MAGIC (Mesoscale Atmospheric Global Irradiance Code) algorithm is used to calculate clear-sky SSI. The SCIAMACHY SSI product is validated against globally distributed BSRN (Baseline Surface Radiation Network) measurements and compared with ISCCP-FD (International Satellite Cloud Climatology Project Flux Dataset) surface shortwave downwelling fluxes (SDF). For one year of data in 2008, the mean difference between the instantaneous SCIAMACHY SSI and the hourly mean BSRN global irradiances is −4 W m−2 (−1 %) with a standard deviation of 101 W m−2 (20 %). The mean difference between the globally monthly mean SCIAMACHY SSI and ISCCP-FD SDF is less than −12 W m−2 (−2 %) for every month in 2006 and the standard deviation is 62 W m−2 (12 %). The correlation coefficient is 0.93 between SCIAMACHY SSI and BSRN global irradiances and is greater than 0.96 between SCIAMACHY SSI and ISCCP-FD SDF. The evaluation results suggest that the SCIAMACHY SSI product achieves similar mean bias error and root mean square error as the surface solar irradiances derived from polar orbiting satellites with higher spatial resolution.

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Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land

Remote Sensing ◽

10.3390/rs11091108 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1108 ◽

Cited By ~ 7

Author(s):

Wenhao Zhang ◽

Hui Xu ◽

Lili Zhang

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Atmospheric Aerosol ◽

Regional Variation ◽

Retrieval Algorithm ◽

Aerosol Retrieval ◽

Level 3 ◽

Comprehensive Comparison ◽

Aerosol Monitoring ◽

Good Agreement

This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AERONET) level 2.0 direct sun AOD measurements over the last three years (May 2016–December 2018) at 58 selected AERONET sites. A comprehensive comparison between AHI and AERONET AOD was carried out, which yielded a correlation coefficient (R) of 0.82, a slope of 0.69, and a root mean square error (RMSE) of 0.16. The results indicate a good agreement between AHI and AERONET AOD, while revealing that the AHI aerosol retrieval algorithm tends to underestimate the atmospheric aerosol load. In addition, the expected uncertainty of AHI Level 3 AOD (Version 3.0) is ± (0.1 + 0.3 × AOD). Furthermore, the performance of the AHI aerosol retrieval algorithm exhibits regional variation. The best performance is reported over East Asia (R 0.86), followed by Southeast Asia (R 0.79) and Australia (R 0.35). The monthly and seasonal comparisons between AHI and AERONET show that the best performance is found in summer (R 0.93), followed by autumn (R 0.84), winter (R 0.82), and spring (R 0.76). The worst performance was observed in March (R 0.75), while the best performance appeared in June (R 0.94). The variation in the annual mean AHI AOD on the scale of hours demonstrates that AHI can perform continuous (no less than ten hours) aerosol monitoring.

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Surface solar irradiance from SCIAMACHY measurements: algorithm and validation

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-4-873-2011 ◽

2011 ◽

Vol 4 (1) ◽

pp. 873-912 ◽

Cited By ~ 1

Author(s):

P. Wang ◽

P. Stammes ◽

R. Mueller

Keyword(s):

Standard Deviation ◽

Mean Difference ◽

Surface Radiation ◽

Mean Bias Error ◽

Retrieval Algorithm ◽

Bias Error ◽

Retrieval Scheme ◽

The Mean ◽

Scanning Imaging ◽

One Year

Abstract. Broadband surface solar irradiances (SSI) are, for the first time, derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) satellite measurements. The retrieval algorithm, called FRESCO (Fast REtrieval Scheme for Clouds from Oxygen A band) SSI, is similar to the Heliosat method. In contrast to the standard Heliosat method, the cloud index is replaced by the effective cloud fraction derived from the FRESCO cloud algorithm. The MAGIC (Mesoscale Atmospheric Global Irradiance Code) algorithm is used to calculate clear-sky SSI. The SCIAMACHY SSI product is validated against the globally distributed BSRN (Baseline Surface Radiation Network) measurements and compared with the ISCCP-FD (International Satellite Cloud Climatology Project Flux Dataset) surface shortwave downwelling fluxes (SDF). For one year of data in 2008, the mean difference between the instantaneous SCIAMACHY SSI and the hourly mean BSRN global irradiances is −4 W m−2(−1%) with a standard deviation of 101 W m−2 (20%). The mean difference between the globally monthly mean SCIAMACHY SSI and ISCCP-FD SDF is less than −12 W m−2 (−2%) for every month in 2006 and the standard deviation is 62 W m−2 (12%). The correlation coefficient is 0.93 between SCIAMACHY SSI and BSRN global irradiances and is greater than 0.96 between SCIAMACHY SSI and ISCCP-FD SDF. The evaluation results suggest that the SCIAMACHY SSI product achieves similar mean bias error and root mean square error as the surface solar irradiances derived from polar orbiting satellites with higher spatial resolution.

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