Quantifying the response of the ORAC aerosol optical depth retrieval for MSG SEVIRI to aerosol model assumptions

The fine-mode aerosol optical depth (AODf) is an important parameter for the environment and climate change study, which mainly represents the anthropogenic aerosols component. The Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) instrument can detect polarized signal from multi-angle observation and the polarized signal mainly comes from the radiation contribution of the fine-mode aerosols, which provides an opportunity to obtain AODf directly. However, the currently operational algorithm of Laboratoire d’Optique Atmosphérique (LOA) has a poor AODf retrieval accuracy over East China on high aerosol loading days. This study focused on solving this issue and proposed a grouped residual error sorting (GRES) method to determine the optimal aerosol model in AODf retrieval using the traditional look-up table (LUT) approach and then the AODf retrieval accuracy over East China was improved. The comparisons between the GRES retrieved and the Aerosol Robotic Network (AERONET) ground-based AODf at Beijing, Xianghe, Taihu and Hong_Kong_PolyU sites produced high correlation coefficients (r) of 0.900, 0.933, 0.957 and 0.968, respectively. The comparisons of the GRES retrieved AODf and PARASOL AODf product with those of the AERONET observations produced a mean absolute error (MAE) of 0.054 versus 0.104 on high aerosol loading days (AERONET mean AODf at 865 nm = 0.283). An application using the GRES method for total AOD (AODt) retrieval also showed a good expandability for multi-angle aerosol retrieval of this method.

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Effect of aerosol sub-grid variability on aerosol optical depth and cloud condensation nuclei: Implications for global aerosol modelling

10.5194/acp-2016-360 ◽

2016 ◽

Cited By ~ 1

Author(s):

N. Weigum ◽

N. Schutgens ◽

P. Stier

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Spatial Scales ◽

Cloud Condensation Nuclei ◽

Convective Transport ◽

Condensation Nuclei ◽

Gaseous Species ◽

Climate Effects ◽

Aerosol Model ◽

The Impact

Abstract. A fundamental limitation of grid-based models is their inability to resolve variability on scales smaller than a grid box. Past research has shown that significant aerosol variability exists on scales smaller than these grid-boxes, which can lead to discrepancies in simulated aerosol climate effects between high and low resolution models. This study investigates the impact of neglecting sub-grid variability in present-day global microphysical aerosol models on aerosol optical depth (AOD) and cloud condensation nuclei (CCN). We introduce a novel technique to isolate the effect of aerosol variability from other sources of model variability by varying the resolution of aerosol and trace gas fields while maintaining a constant resolution in the rest of the model. We compare WRF-Chem runs in which aerosol and gases are simulated at 80 km and again at 10 km resolutions; in both simulations the other model components, such as meteorology and dynamics, are kept at the 10 km baseline resolution. We find that AOD is underestimated by 13 % and CCN is overestimated by 27 % when aerosol and gases are simulated at 80 km resolution compared to 10 km. Processes most affected by neglecting aerosol sub-grid variability are gas-phase chemistry and aerosol uptake of water through aerosol/gas equilibrium reactions. The inherent non-linearities in these processes result in large changes in aerosol parameters when aerosol and gaseous species are artificially mixed over large spatial scales. These changes in aerosol and gas concentrations are exaggerated by convective transport, which transports these altered concentrations to altitudes where their effect is more pronounced. These results demonstrate that aerosol variability can have a large impact on simulating aerosol climate effects, even when meteorology and dynamics are held constant. Future aerosol model development should focus on accounting for the effect of sub-grid variability on these processes at global scales in order to improve model predictions of the aerosol effect on climate.

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Impact of the Dust Aerosol Model on the VIIRS Aerosol Optical Depth (AOD) Product across China

Remote Sensing ◽

10.3390/rs12060991 ◽

2020 ◽

Vol 12 (6) ◽

pp. 991 ◽

Cited By ~ 1

Author(s):

Yang Wang ◽

Liangfu Chen ◽

Jinyuan Xin ◽

Xinhui Wang

Keyword(s):

Model Selection ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Southern China ◽

Selection Process ◽

Atmospheric Condition ◽

Regression Equation ◽

Research Network ◽

Unexpected Result ◽

Aerosol Model

The Visible Infrared Imaging Radiometer Suite (VIIRS) has been observing aerosol optical depth (AOD), which is a critical parameter in air pollution and climate change, for more than 7 years since 2012. Due to limited and uneven distribution of the Aerosol Robotic Network (AERONET) station in China, the independent data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) was used to evaluate the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD products in six typical sites and analyze the influence of the aerosol model selection process in five subregions, particularly for dust. Compared with ground-based observations, the performance of all retrievals (except the Shapotou (SPT) site) is similar to other previous studies on a global scale. However, the results illustrate that the AOD retrievals with the dust model showed poor consistency with a regression equation as y = 0.312x + 0.086, while the retrievals obtained from the other models perform much better with a regression equation as y = 0.783x + 0.119. The poor AOD retrieval with the dust model was also verified by a comparison with the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product. The results show they have a lower correlation coefficient (R) and a higher mean relative error (MRE) when the aerosol model used in the retrieval is identified as dust. According to the Ultraviolet Aerosol Index (UVAI), the frequency of dust type over southern China is inconsistent with the actual atmospheric condition. In addition, a comparison of ground-based Ångström exponent (α) values yields an unexpected result that the dust model percentage exceed 40% when α < 1.0, and the mean α shows a high value of ~0.75. Meanwhile, the α peak value (~1.1) of the “dust” model determined by a satellite retravel algorithm indicate there is some problem in the dust model selection process. This mismatching of the aerosol model may partly explain the low accuracy at the SPT and the systemic biases in regional and global validations.

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Aerosol water parameterization: long-term evaluation and importance

10.5194/acp-2018-450 ◽

2018 ◽

Cited By ~ 1

Author(s):

Swen Metzger ◽

Mohamed Abdelkader ◽

Benedikt Steil ◽

Klaus Klingmüller

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Water Uptake ◽

Climate Model ◽

Climate Modeling ◽

Mixed Solution ◽

Hygroscopic Growth ◽

Aerosol Model ◽

Term Evaluation

Abstract. We scrutinize the importance of aerosol water for the aerosol optical depth (AOD) calculations by a long-term evaluation of the EQuilibrium Simplified Aerosol Model V4 for climate modeling, which was introduced by Metzger et al. (2016a). EQSAM4clim is based on a sin-gle solute coefficient approach that efficiently parameterizes hygroscopic growth, account- ing for aerosol water uptake from the deliquescence relative humidity up to supersaturation. EQSAM4clim extends the single solute coefficient approach to treat water uptake of multi- component mixtures. The gas-aerosol partitioning and the mixed solution water uptake can be solved analytically, preventing the need for iterations, which is computationally efficient. EQSAM4clim has been implemented in the global chemistry climate model EMAC and com- pared to ISORROPIA II (Fountoukis and Nenes, 2007) at climate time-scales. Our global modeling results show that (I) our EMAC results of the aerosol optical depth (AOD) are comparable to independent results of Pozzer et al. (2015) for the period 2000–2010, (II) the results of various aerosol properties of EQSAM4clim and ISORROPIA II are similar and in agreement with AERONET and EMEP observations for the period 2000–2013, and (III) that the underlying assumptions on the aerosol water uptake limitations are important for derived AOD calculations. Sensitivity studies of different levels of chemical aging and associated water uptake show larger effects on AOD calculations for the year 2005 compared to the differences associated with the application of the two gas-liquid-solid partitioning schemes. Altogether, our study reveals the importance of the aerosol water for climate applications.

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Aerosol optical depth assimilation for a modal aerosol model: Implementation and application in AOD forecasts over East Asia

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.137430 ◽

2020 ◽

Vol 719 ◽

pp. 137430 ◽

Cited By ~ 1

Author(s):

Jiongming Pang ◽

Xuemei Wang ◽

Min Shao ◽

Weihua Chen ◽

Ming Chang

Keyword(s):

East Asia ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Aerosol Model

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Bayesian uncertainty quantification in aerosol optical depth retrieval applied to TROPOMI measurements

10.5194/amt-2021-328 ◽

2021 ◽

Author(s):

Anu Kauppi ◽

Antti Kukkurainen ◽

Antti Lipponen ◽

Marko Laine ◽

Antti Arola ◽

...

Keyword(s):

Model Selection ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Bayesian Model Averaging ◽

Forward Model ◽

Selection Problem ◽

Aerosol Model ◽

Model Discrepancy ◽

Aerosol Types ◽

Model Selection Problem

Abstract. We present here an aerosol model selection based statistical method in Bayesian framework for retrieving atmospheric aerosol optical depth (AOD) and pixel-level uncertainty. Especially, we focus on to provide more realistic uncertainty estimate by taking into account a model selection problem when searching for the solution by fitting look-up table (LUT) models to a satellite measured top-of-atmosphere reflectance. By means of Bayesian model averaging over the best-fitting aerosol models we take into account an aerosol model selection uncertainty and get also a shared inference about AOD. Moreover, we acknowledge model discrepancy, i.e. forward model error, arising from approximations and assumptions done in forward model simulations. We have estimated the model discrepancy empirically by a statistical approach utilizing residuals of model fits. We use the measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor in ultraviolet and visible bands, and in one wavelength band 675 nm in near-infrared, in order to study the functioning of the retrieval in a broad wavelength range. We exploit a fundamental classification of the aerosol models as weakly absorbing, biomass burning and desert dust aerosols. For experimental purpose we have included some dust type of aerosols having non-spherical particle shapes. For this study we have created the aerosol model LUTs with radiative transfer simulations using the libRadtran software package. It is reasonably straightforward to experiment with different aerosol types and evaluate the most probable aerosol type by the model selection method. We demonstrate the method in wildfire and dust events in a pixel level. In addition, we have evaluated in detail the results against ground-based remote sensing data from the AErosol RObotic NETwork (AERONET). Based on the case studies the method has ability to identify the appropriate aerosol types, but in some wildfire cases the AOD is overestimated compared to the AERONET result. The resulting uncertainty when accounting for the model selection problem and the imperfect forward modelling is higher compared to uncertainty when only measurement error is included in an observation model, as can be expected.

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Recovery of the first ever multi-year lidar dataset of the stratospheric aerosol layer, from Lexington, MA, and Fairbanks, AK, January 1964 to July 1965

Earth System Science Data ◽

10.5194/essd-13-4407-2021 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4407-4423

Author(s):

Juan-Carlos Antuña-Marrero ◽

Graham W. Mann ◽

John Barnes ◽

Albeht Rodríguez-Vega ◽

Sarah Shallcross ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Coupled Model ◽

Stratospheric Aerosol ◽

Aerosol Layer ◽

Large Variability ◽

Aerosol Model ◽

Intercomparison Project ◽

Relative Errors ◽

The Tropics

Abstract. We report the recovery and processing methodology of the first ever multi-year lidar dataset of the stratospheric aerosol layer. A Q-switched ruby lidar measured 66 vertical profiles of 694 nm attenuated backscatter at Lexington, Massachusetts, between January 1964 and August 1965, with an additional nine profile measurements conducted from College, Alaska, during July and August 1964. We describe the processing of the recovered lidar backscattering ratio profiles to produce mid-visible (532 nm) stratospheric aerosol extinction profiles (sAEP532) and stratospheric aerosol optical depth (sAOD532) measurements, utilizing a number of contemporary measurements of several different atmospheric variables. Stratospheric soundings of temperature and pressure generate an accurate local molecular backscattering profile, with nearby ozone soundings determining the ozone absorption, which are used to correct for two-way ozone transmittance. Two-way aerosol transmittance corrections are also applied based on nearby observations of total aerosol optical depth (across the troposphere and stratosphere) from sun photometer measurements. We show that accounting for these two-way transmittance effects substantially increases the magnitude of the 1964/1965 stratospheric aerosol layer's optical thickness in the Northern Hemisphere mid-latitudes, then ∼ 50 % larger than represented in the Coupled Model Intercomparison Project 6 (CMIP6) volcanic forcing dataset. Compared to the uncorrected dataset, the combined transmittance correction increases the sAOD532 by up to 66 % for Lexington and up to 27 % for Fairbanks, as well as individual sAEP532 adjustments of similar magnitude. Comparisons with the few contemporary measurements available show better agreement with the corrected two-way transmittance values. Within the January 1964 to August 1965 measurement time span, the corrected Lexington sAOD532 time series is substantially above 0.05 in three distinct periods, October 1964, March 1965, and May–June 1965, whereas the 6 nights the lidar measured in December 1964 and January 1965 had sAOD values of at most ∼ 0.03. The comparison with interactive stratospheric aerosol model simulations of the Agung aerosol cloud shows that, although substantial variation in mid-latitude sAOD532 are expected from the seasonal cycle in the stratospheric circulation, the Agung cloud's dispersion from the tropics would have been at its strongest in winter and weakest in summer. The increasing trend in sAOD from January to July 1965, also considering the large variability, suggests that the observed variations are from a different source than Agung, possibly from one or both of the two eruptions that occurred in 1964/1965 with a Volcanic Explosivity Index (VEI) of 3: Trident, Alaska, and Vestmannaeyjar, Heimaey, south of Iceland. A detailed error analysis of the uncertainties in each of the variables involved in the processing chain was conducted. Relative errors for the uncorrected sAEP532 were 54 % for Fairbanks and 44 % Lexington. For the corrected sAEP532 the errors were 61 % and 64 %, respectively. The analysis of the uncertainties identified variables that with additional data recovery and reprocessing could reduce these relative error levels. Data described in this work are available at https://doi.org/10.1594/PANGAEA.922105 (Antuña-Marrero et al., 2020a).

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Error Analysis for the Himawari-8 Aerosol Optical Depth Basing on Parts of Aerosol Model and Sun Position over Wuhan, Central China

10.5194/egusphere-egu2020-14410 ◽

2020 ◽

Author(s):

Yingying Ma ◽

Ming Zhang ◽

Yifan Shi ◽

Wei Gong ◽

Shikuan Jin

Keyword(s):

Error Analysis ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Central China ◽

Scattering Angle ◽

Aerosol Model ◽

Transfer Path ◽

Geostationary Satellites ◽

Sun Photometer ◽

The Difference

<p>Aerosols attract great attention as having critical influence on the Earth&#8217;s energy budget and human health. Geostationary satellites like Himawari-8 process advantages on temporal resolution that allows rapidly changing weather phenomena tracking and aerosol monitoring. This work aims at providing a novel error analysis for the Advanced Himawari Imager (AHI) aerosol optical depth (AOD) retrieval from the aspect of aerosol model and sun position combing with the high quality ground-based observation in Wuhan, central China. Three-year co-located AOD dataset from AHI and sun-photometer are used. AHI underestimates AOD in all the seasons. Aerosol size distributions and phase functions are discussed as parts of aerosol model to explain the underestimation of AOD. AHI sets a low fine-mode particle median radius comparing with the in-site measurement in Wuhan that increases backscattering, and finally leads to the underestimation of AOD. Sun position also affects AHI AOD retrieval, and we use solar zenith angle (SZA) and scattering angle to represent sun position. Geostationary satellites get fixed satellite position for one site that provides convenience to the discussion. SZA influences AOD retrieval mainly through the length of transfer path and higher percent of samples within expected error often appears at low SZAs. Scattering angle also has obvious influence on AOD retrieval through the simulation of phase function and causes the difference of correlation performance between AHI and sun-photometer in aspect of SZA in morning and afternoon. Finally, we applied the dark target method to retrieve AHI AOD. The comparison of AODs reveals that the retrieval method of AHI performs better in Wuhan. The better performance of AHI AOD may be due to high aerosol loading and lack of enough prior information of aerosol properties in Wuhan. Our work could also be performed on other areas or other geostationary satellites, and help us to further understand the controlling factors that affect AOD retrieval accuracy, then contribute to better AOD retrieval.</p>

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Validation of a modified AVHRR aerosol optical depth retrieval algorithm over Central Europe

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-3-785-2010 ◽

2010 ◽

Vol 3 (1) ◽

pp. 785-819 ◽

Cited By ~ 1

Author(s):

M. Riffler ◽

C. Popp ◽

A. Hauser ◽

F. Fontana ◽

S. Wunderle

Keyword(s):

Aerosol Optical Depth ◽

Central Europe ◽

Optical Depth ◽

Satellite Data ◽

Large Data ◽

Retrieval Algorithm ◽

Small Scale ◽

Error Statistics ◽

Aerosol Model ◽

Resolution Imaging

Abstract. The Advanced Very High Resolution Radiometer (AVHRR) carried on board the National Oceanic and Atmospheric Administration (NOAA) and the Meteorological Operational Satellite (MetOp) polar orbiting satellites is the only instrument offering more than 25 years of satellite data to analyse aerosols on a daily basis. The present study assessed a modified AVHRR aerosol optical depth τa retrieval over land. The initial approach has used a relationship between Sun photometer measurements from the Aerosol Robotic Network (AERONET) and the satellite data to post-process the retrieved τa. Herein a stand-alone procedure, which is more suitable for the pre-AERONET era, is presented. In addition, the estimation of surface reflectance, threshold values, and the aerosol model are adapted. The method's cross-platform applicability was tested by validating τa from NOAA-17 and NOAA-18 AVHRR at 15 AERONET sites in Central Europe (40.5° N–50° N, 0° E–17° E) from August 2005 to December 2007. Furthermore, the accuracy of the AVHRR retrieval was related to products from two newer instruments, the Medium Resolution Imaging Spectrometer (MERIS) on board the Environmental Satellite (ENVISAT) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua/Terra. Considering the linear correlation coefficient R, the AVHRR results were similar to those of MERIS with even lower root mean square error RMSE. Not surprisingly, MODIS, with its high spectral coverage gave the highest R and lowest RMSE. Regarding monthly averaged τa, the results were ambiguous. Focusing on small-scale structures, R was reduced for all sensors, whereas the RMSE solely for MERIS substantially increased. Regarding larger areas like Central Europe, the error statistics were similar to the individual match-ups. This was mainly explained with sampling issues. With the successful validation of AVHRR we are now able to concentrate on our large data archive dating back to 1985. This is a unique opportunity for both climate and air pollution studies over land surfaces.

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Estimating aerosol emissions by assimilating observed aerosol optical depth in a global aerosol model

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-12-3075-2012 ◽

2012 ◽

Vol 12 (1) ◽

pp. 3075-3130 ◽

Cited By ~ 3

Author(s):

N. Huneeus ◽

F. Chevallier ◽

O. Boucher

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

A Priori ◽

Global Scale ◽

Aerosol Model ◽

Initial Errors ◽

Emission Fluxes ◽

Independent Observations ◽

Fine Mode ◽

The Impact

Abstract. This study estimates the emission fluxes of a range of aerosol species and aerosol precursor at the global scale. These fluxes are estimated by assimilating daily total and fine mode aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) into a global aerosol model of intermediate complexity. Monthly emissions are fitted homogenously for each species over a set of predefined regions. The performance of the assimilation is evaluated by comparing the AOD after assimilation against the MODIS observations and against independent observations. The system is effective in forcing the model towards the observations, for both total and fine mode AOD. Significant improvements for the root mean square error and correlation coefficient against both the assimilated and independent datasets are observed as well as a significant decrease in the mean bias against the assimilated observations. The assimilation is more efficient over land than over ocean. The impact of the assimilation of fine mode AOD over ocean demonstrates potential for further improvement by including fine mode AOD observations over continents. The Angström exponent is also improved in African, European and dusty stations. The estimated emission flux for black carbon is 14.5 Tg yr−1, 119 Tg yr−1 for organic matter, 17 Pg yr−1 for sea salt, 82.7 TgS yr−1 for SO2 and 1383 Tg yr−1 for desert dust. They represent a difference of +45%, +40%, +26%, +13% and −39% respectively, with respect to the a priori values. The initial errors attributed to the emission fluxes are reduced for all estimated species.

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