Errors In Aerosol Optical Depth Research Articles

Addressing observational gaps in aerosol parameters using machine learning: Implications to aerosol radiative forcing

The role of atmospheric aerosols has remained hugely uncertain due to lack of long-term systematic observations. This is particularly the case in the global emission hotspot the Indo-Gangetic Plain. In this paper, we apply a machine learning (ML) approach to fill gaps in aerosol observations by combining meteorological reanalysis, and satellite-observations. Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Asymmetry Parameter (AP) are critical parameters for understanding the atmospheric aerosol properties, radiative forcing and climate patterns. We considered AOD, SSA, and AP over an urban site, Kanpur, in the Indo-Gangetic Plain, which is a global hot-spot of high aerosol loading. We used ML technique to fill the observational gaps in AOD, SSA, and AP at 440 nm. Accurate ground-based daily observations of AOD, SSA, and AP from AERONET (Aerosol Robotic Network) over a period of ∼2 decades (2001–2022) are used in the analysis, which have ∼ 37%, ∼62%, and ∼58% data gap, respectively. To reduce observational gaps, an ML model was trained with reanalysis (meteorological parameters) and satellite (angstrom exponent and absorbing aerosol index) datasets by optimally tuning the hyperparameters. The model tested on unseen data showed that it could simulate the AOD (coefficient of determination, R2 = 0.51 and refined index of agreement, RIA = 0.66), SSA (R2 = 0.60 and RIA = 0.70), and AP (R2 = 0.64 and RIA = 0.71), successfully with moderate accuracies: root mean square error of 0.25, 0.018, and 0.018, respectively. This magnitude of error in AOD is comparable to the errors, 0.26 and 0.25, in satellite (MODIS-Moderate Resolution Imaging Spectroradiometer) derived and reanalysis (MERRA-2- Modern-Era Retrospective analysis for Research and Applications, Version 2) AODs, respectively for the study region. When MODIS AOD was included as a predictor in the ML model, it could reproduce AOD with much smaller errors of 0.14, reproducing 84% of total variability. Thus, based on available input data, we reduced observational gaps of daily AOD, SSA, and AP by ∼10% (∼2 years), ∼23% (∼5 years), and ∼21% (∼4 years), respectively, at the study region. Aerosol Radiative Forcing (ARF) at the top-of-atmosphere was estimated using AOD, SSA, and AP both with and without gap-filling. We found that data gaps can significantly impact the estimations of aerosol climate forcing. A considerable change in ARF, ∼ ± 3 Wm−2 indicating that the observational gaps can mislead our understanding of aerosols' impact on climate. This points toward the need for more comprehensive measurements with minimal observational gaps. Our study highlights the possibility of compensating observational gaps by an optimised ML model and that such innovative approach can potentially lead to better understanding of impact of aerosols on climate. Further, the present study could be of immense interest for the community as our validated approach can be applied for other pollutants and for other data-scarce regions in the South and Southeast Asia.

Accuracy and error cause analysis, and recommendations for usage of Himawari-8 aerosol products over Asia and Oceania

The Himawari-8 aerosol algorithm was updated to version 3 (V30). However, no study has evaluated its performance. The purpose of this study is to verify and to compare version 2.1 (V21) and V30 aerosol products, to explain which factor dominates the aerosol optical depth (AOD) error, and to provide recommendations for aerosol product usage. The AOD accuracy of V30 was better than that of V21, with a higher correlation coefficient (R) and a higher expected error (EE_DT). The V30 AOD metrics (including R, EE_DT, and the root mean square error) exceeded those of V21 on more than 69% of the AERONET sites and its bias from MODIS AOD was smaller than that of V21 AOD. However, the V30 AOD does not meet the metric of EE_DT > 0.66. The analysis results suggest that aerosol type parameters (primarily the Ångström exponent (AE)) may be the dominant factor determining the AOD error. This reveals the direction of H8 algorithm improvement. More than 59% of the H8 AE value meets the expected error but they do not capture the variety (R < 0.3). The FMF and SSA retrieved by H8 performed poorly. The V30 AOD performs best in Japan and South Korea (83.3% of AERONET sites meet the EE_DT > 0.66 requirement) and has better data accuracy in the morning. Therefore, we recommend V30 AOD morning data to users in Japan and South Korea regions.

Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations

This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records.

Evaluation and possible uncertainty source analysis of JAXA Himawari-8 aerosol optical depth product over China

The latest version of JAXA aerosol optical depth (AOD) product over China in 2018 from Himawari-8 Advanced Himawari Imager (AHI) was evaluated, including validation with global AErosol RObotic NETwork (AERONET) and China Aerosol Research Network (CARSNET) ground-based observations, and comparison against the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD product. Considering both the quality and quantity of the AHI AOD product, the Quality Assurance Flags (QA_Flags) with “good” and “very good” are chosen for evaluation and analysis. AHI and MODIS AOD products are well correlated and the spatial distributions are similar, even though underestimation from AHI AOD in the morning and overestimation in the afternoon are found when compared with MODIS AOD. Validation with ground-based observations also indicates a good correlation (R > 0.75) when sparsely vegetated surfaces are excluded. It is found that 52.74% fall below the expected error (EE) at 10:30 BJT when compared with CARSNET which has more vegetation sites and 56.91% fall above the EE at 13:30 BJT when compared with AERONET which has more urban sites. The slope of the fit line is around 1.0 for all matched sites, while the scatter plots have divided into two parts as the performance of AHI AOD depends on the observation times and surface types. The temporal variations of AHI and AERONET AODs show that the overestimation from AHI AOD occurs in the afternoon for barren surface while the underestimation occurs in the morning for well-vegetation surface. For monthly variation, the overestimation of AHI AOD is found over Northern China in winter and those seasons when the plants are growing or disappearing rapidly.The assumption that the surface reflectance remains unchanged and the solar geometries are nearly identical within a month causes the underestimation of surface reflectance and thus contributes to the AHI AOD overestimation for low vegetation and bare surfaces in autumn and winter. While over the vegetated surfaces, neglecting the background aerosols causes overestimation of the surface reflectance, and thus leads to underestimation of AHI AOD. The difference between backward and forward scattering contributes to the larger JAXA AHI AOD error in the afternoon, which aggravates positive deviation for barren land sites and alleviates the underestimation for vegetation surface types in the afternoon. In addition, aerosol model selection and cloud contamination also affect the accuracy of aerosol products, especially when AOD is large.Overall, the JAXA AHI AOD provides a high temporal resolution and relative reliable aerosol product for environmental research. The analysis on characteristics and uncertainty sources in this study are to further enhance the understanding of the geostationary satellite aerosol products for applications, and provide useful information for improving the aerosol retrieval algorithm in future.

Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA.

Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30-210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling.

Cross-calibration of S-NPP VIIRS moderate resolution reflective solar bands against MODIS Aqua over dark water scenes.

The Visible Infrared Imaging Radiometer Suite (VIIRS) is being used to continue the record of Earth Science observations and data products produced routinely from National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. However, the absolute calibration of VIIRS's reflected solar bands is thought to be biased, leading to offsets in derived data products such as aerosol optical depth (AOD) as compared to when similar algorithms are applied to different sensors. This study presents a cross-calibration of these VIIRS bands against MODIS Aqua over dark water scenes, finding corrections to the NASA VIIRS Level 1 (version 2) reflectances between approximately +1 % and -7 % (dependent on band) are needed to bring the two into alignment (after accounting for expected differences resulting from different band spectral response functions), and indications of relative trending of up to ^0.35 % per year in some bands. The derived calibration gain corrections are also applied to the VIIRS reflectance and then used in an AOD retrieval, and are shown to decrease the bias and total error in AOD across the midvisible spectral region compared to the standard VIIRS NASA reflectance calibration. The resulting AOD bias characteristics are similar to those of NASA MODIS AOD data products, which is encouraging in terms of multisensor data continuity.

Investigation of Simultaneous Effects of Aerosol Properties and Aerosol Peak Height on the Air Mass Factors for Space-Borne NO2 Retrievals

We investigate the simultaneous effects of aerosol peak height (APH), aerosol properties, measurement geometry, and other factors on the air mass factor for NO2 retrieval at sites with high NO2 concentration. A comparison of the effects of high and low surface reflectance reveals that NO2 air mass factor (AMF) values over a snowy surface (surface reflectance 0.8) are generally higher than those over a deciduous forest surface (surface reflectance 0.05). Under high aerosol optical depth (AOD) conditions, the aerosol shielding effect over a high-albedo surface is revealed to reduce the path-length of light at the surface, whereas high single scattering albedo (SSA) conditions (e.g., SSA = 0.95) lead to an increase in the aerosol albedo effect, which results in an increased AMF over areas with low surface reflectance. We also conducted an in-depth study of the APH effect on AMF. For an AOD of 0.1 and half width (HW) of 5 km, NO2 AMF decreases by 29% from 1.36 to 0.96 as APH changes from 0 to 2 km. In the case of high-AOD conditions (0.9) and HW of 5 km, the NO2 AMF decreases by 240% from 1.85 to 0.54 as APH changes from 0 to 2 km. The AMF variation due to error in the model input parameters (e.g., AOD, SSA, aerosol shape, and APH) is also examined. When APH is 0 km with an AOD of 0.4, SSA of 0.88, and surface reflectance of 0.05, a 30% error in AOD induces an AMF error of between 4.85% and −3.67%, an SSA error of 0.04 leads to NO2 VCD errors of between 4.46% and −4.77%, and a 30% error in AOD induces an AMF error of between −9.53% and 8.35% with an APH of 3 km. In addition to AOD and SSA, APH is an important factor in calculating AMF, due to the 2 km error in APH under high-SZA conditions, which leads to an NO2 VCD error of over 60%. Aerosol shape is also found to have a measureable effect on AMF under high-AOD and small relative azimuth angle (RAA) conditions. The diurnal effect of the NO2 profile is also examined and discussed.

Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS)

Abstract. An aerosol model optimized for northeast Asia is updated with the inversion data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-northeast (NE) Asia campaign which was conducted during spring from March to May 2012. This updated aerosol model was then applied to a single visible channel algorithm to retrieve aerosol optical depth (AOD) from a Meteorological Imager (MI) on-board the geostationary meteorological satellite, Communication, Ocean, and Meteorological Satellite (COMS). This model plays an important role in retrieving accurate AOD from a single visible channel measurement. For the single-channel retrieval, sensitivity tests showed that perturbations by 4 % (0.926 ± 0.04) in the assumed single scattering albedo (SSA) can result in the retrieval error in AOD by over 20 %. Since the measured reflectance at the top of the atmosphere depends on both AOD and SSA, the overestimation of assumed SSA in the aerosol model leads to an underestimation of AOD. Based on the AErosol RObotic NETwork (AERONET) inversion data sets obtained over East Asia before 2011, seasonally analyzed aerosol optical properties (AOPs) were categorized by SSAs at 675 nm of 0.92 ± 0.035 for spring (March, April, and May). After the DRAGON-NE Asia campaign in 2012, the SSA during spring showed a slight increase to 0.93 ± 0.035. In terms of the volume size distribution, the mode radius of coarse particles was increased from 2.08 ± 0.40 to 2.14 ± 0.40. While the original aerosol model consists of volume size distribution and refractive indices obtained before 2011, the new model is constructed by using a total data set after the DRAGON-NE Asia campaign. The large volume of data in high spatial resolution from this intensive campaign can be used to improve the representative aerosol model for East Asia. Accordingly, the new AOD data sets retrieved from a single-channel algorithm, which uses a precalculated look-up table (LUT) with the new aerosol model, show an improved correlation with the measured AOD during the DRAGON-NE Asia campaign. The correlation between the new AOD and AERONET value shows a regression slope of 1.00, while the comparison of the original AOD data retrieved using the original aerosol model shows a slope of 1.08. The change of y-offset is not significant, and the correlation coefficients for the comparisons of the original and new AOD are 0.87 and 0.85, respectively. The tendency of the original aerosol model to overestimate the retrieved AOD is significantly improved by using the SSA values in addition to size distribution and refractive index obtained using the new model.

Analysis of the Error in Retrievals of Aerosol Optical Properties from Sunphotometer Measurements of CARSNET Due to a Variety of Objective Factors

In situ observation of the aerosol optical properties is important to the validations of satellite and modeling results; however, the operational measurements can be affected by some objective factors. An experiment study has been performed in order to analyze the error in retrievals of aerosol optical properties from sunphotometer measurements caused by a variety of in situ objective factors. The standard instrument relative error analysis method was used to determine the relative error of aerosol optical depth (AOD) and Ångström exponent (AE) under the effects of five factors: spider web inside the collimator (F1); collimator bending (F2); dust inside the optical head (F3); incrustation scale inside the optical head (F4); and dust and incrustation scale inside the optical head (F5). The results showed that the five factors caused error for AOD retrieved at 1020, 870, 670 and 440 nm, with the maximum error occurring at 870 nm due to the more sensitive measurement signals. The error ranges of AOD derived from the direct solar measurements in the four bands were −0.34%–8.77%, −6.22%–9.68%, −0.05%–2.52%, −0.96%–3.48% and 5.42%–13.38% for F1, F2, F3, F4 and F5, respectively. The maximum error occurred under the influence of F5 with an average error value of 10%, while the minimum occurred owing to F3 with an average error value of 1%. All of the AEs retrieved from the experimental instruments were smaller than that from the reference instrument. The AE error values were 15.19%, 25.57%, 4.56%, 4.41% and 8.83% for F1, F2, F3, F4 and F5, respectively. The average AE retrieval error value was 11.7%.

Characterizing the Aerosol and Surface Reflectance Over Australia Using AATSR

We present a new algorithm that simultaneously retrieves aerosol properties and land surface bidirectional reflectance distribution function (BRDF) over Australian from Advanced Along-Track Scanning Radiometer images. Three key components are addressed: 1) an analytical radiative transfer formulation, based on Green's function, linking top-of-atmosphere (TOA) reflectance to the surface BRDF; 2) a novel approach to modeling BRDF using an extended compositing period; and 3) a set of representative aerosol models based on a published typology of Australian aerosols. Due to the generally low aerosol loadings and widespread bright surfaces over Australia, BRDF modeling is crucial. By using a 9-month compositing period, 90% of the Australian continent can be modeled with an error in the forward-to-nadir reflectance ratio of 2.5% or less. Comparison with suitably processed Moderate Resolution Imaging Spectroradiometer BRDF/albedo products demonstrates satisfactory agreement. For the studying period from 2002 to 2008, validation of aerosol optical depth (AOD) against eight sun photometers across Australia encompassing widely different atmospheric and surface regimes shows high accuracy, with a mean absolute error in AOD at 550 nm of 0.03 and a bias of 0.007. About 60% of the matchup points are within an absolute error of 0.03, 80% are within 0.05, and 96% are within 0.1. The algorithm selects for each cell an optimal aerosol type from a set of four predefined candidate models. Continental aerosol maps derived from the new method indicate broadscale agreement with known seasonal aerosol sources, while providing new insights into the spatial and temporal distributions of aerosol over the Australian continent.

Operational Retrieval of aerosol optical depth over Indian subcontinent and Indian Ocean using INSAT-3D/Imager product validation

Abstract. Aerosol optical depth (AOD) over Indian subcontinent and Indian Ocean region is derived operationally for the first time from the geostationary earth orbit (GEO) satellite INSAT-3D Imager data at 0.65 μm wavelength. Single visible channel algorithm based on clear sky composites gives larger retrieval error in AOD than other multiple channel algorithms due to errors in estimating surface reflectance and atmospheric property. However, since MIR channel signal is insensitive to the presence of most aerosols, therefore in present study, AOD retrieval algorithm employs both visible (centred at 0.65 μm) and mid-infrared (MIR) band (centred at 3.9 μm) measurements, and allows us to monitor transport of aerosols at higher temporal resolution. Comparisons made between INSAT-3D derived AOD (τI) and MODIS derived AOD (τM) co-located in space (at 1° resolution) and time during January, February and March (JFM) 2014 encompasses 1165, 1052 and 900 pixels, respectively. Good agreement found between τI and τM during JFM 2014 with linear correlation coefficients (R) of 0.87, 0.81 and 0.76, respectively. The extensive validation made during JFM 2014 encompasses 215 co-located AOD in space and time derived by INSAT 3D (τI) and 10 sun-photometers (τA) that includes 9 AERONET (Aerosol Robotic Network) and 1 handheld sun-photometer site. INSAT-3D derived AOD i.e. τI, is found within the retrieval errors of τI = ±0.07 ±0.15τA with linear correlation coefficient (R) of 0.90 and root mean square error equal (RMSE) to 0.06. Present work shows that INSAT-3D aerosol products can be used quantitatively in many applications with caution for possible residual clouds, snow/ice, and water contamination.

Accuracy assessment of Terra-MODIS aerosol optical depth retrievals

Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products have been widely used to address environment and climate change subjects with daily global coverage. Aerosol optical depth (AOD) is retrieved by different algorithms based on the pixel surface, determining between land and ocean. MODIS-Terra and Global Aerosol Robotic Network (AERONET) products can be obtained from the Multi-sensor Aerosol Products Sampling System (MAPSS) for coastal regions during 2000-2010. Using data collected from 83 coastal stations worldwide from AERONET from 2000-2010, accuracy assessments are made for coastal aerosol optical depth (AOD) retrieved from MODIS aboard the Terra satellite. AOD retrieved from MODIS at 0.55μm wavelength has been compared With the AERONET derived AOD, because it is reliable with the major wavelength used by many chemistry transport and climate models as well as previous MODIS validation studies. After removing retrievals with quality flags below1 for Ocean algorithm and below 3 for Land algorithm, The accuracy of AOD retrieved from MODIS Dark Target Ocean algorithms (correlation coefficient R2 is 0.844 and a regression equation of τM = 0.91·τA + 0.02 (where subscripts M and A represent MODIS and AERONET respectively), is the greater than the MODIS Dark Target Land algorithms (correlation coefficient R2 is 0.764 and τM = 0.95·τA + 0.03) and the Deep Blue algorithm (correlation coefficient R2 is 0.652 and τM = 0.81·τA + 0.04). The reasons of the retrieval error in AOD are found to be the various underlying surface reflectance. Therefore, the aerosol models and underlying surface reflectance are the dominant factors which influence the accuracy of MODIS retrieval performance. Generally the MODIS Land algorithm implements better than the Ocean algorithm for coastal sites.

Does variation in mineral composition alter the short-wave light scattering properties of desert dust aerosol?

Mineral dust aerosol is a major component of natural airborne particulates. Using satellite measurements from the visible and near-infrared, there is insufficient information to retrieve a full microphysical and chemical description of an aerosol distribution. As such, refractive index is one of many parameters that must be implicitly assumed in order to obtain an optical depth retrieval. This is essentially a proxy for the dust mineralogy.Using a global soil map, it is shown that as long as a reasonable refractive index for dust is assumed, global dust variability is unlikely to cause significant variation in the optical properties of a dust aerosol distribution in the short-wave, and so should not greatly affect retrievals of mineral dust aerosol from space by visible and near-infrared radiometers. Errors in aerosol optical depth due to this variation are expected to be ≲1%. The work is framed around the ORAC AATSR aerosol retrieval, but is equally applicable to similar satellite retrievals. In this case, variations in the top-of-atmosphere reflectance caused by mineral variation are within the noise limits of the instrument.

Retrieving vertical profile of aerosol extinction by multi-axis differential optical absorption spectroscopy

Using the oxygen dimer (O4 information measured by multi-axis differential optical absorption spectroscopy (MAX-DOAS)), an inversion method of vertical profile of aerosol extinction based on nonlinear optimal estimation algorithm is developed. At first we study how to calculate some parameters (weighting function, the covariance matrices of measurement and a priori) of the algorithm and design nonlinear iteration strategy suited to Chinese region where aerosol usually shows rapid variation and high load. Then this inversion method is verified by computer simulation combined with discussion about error source in three typical cases of low, high and elevated aerosol. After that a continuous observation is reported in the city of Hefei. The aerosol optical depth (AOD) derived from MAX-DOAS is compared with that of CE318 sun photometer and the correlation coefficient is 0.94. The total relative error of AOD is about 20%. In addition the aerosol extinction in the lowest altitude (0-0.3 km) is compared with that of visibility meter and the correlation coefficient is about 0.65. The total relative error of surface-near aerosol extinction is about 25%. Both of simulation verification and comparison experiment indicate that the inversion method can well rebuild the vertical profile of aerosol extinction in the troposphere.

Assessment of error in aerosol optical depth measured by AERONET due to aerosol forward scattering

We present an analysis of the effect of aerosol forward scattering on the accuracy of aerosol optical depth (AOD) measured by CIMEL Sun photometers. The effect is quantified in terms of AOD and solar zenith angle using radiative transfer modeling. The analysis is based on aerosol size distributions derived from multi‐year climatologies of AERONET aerosol retrievals. The study shows that the modeled error is lower than AOD calibration uncertainty (0.01) for the vast majority of AERONET level 2 observations, ∼99.53%. Only ∼0.47% of the AERONET database corresponding mostly to dust aerosol with high AOD and low solar elevations has larger biases. We also show that observations with extreme reductions in direct solar irradiance do not contribute to level 2 AOD due to low Sun photometer digital counts below a quality control cutoff threshold.

The effect and correction of aerosol forward scattering on retrieval of aerosol optical depth from Sun photometer measurements

The Sun photometer is widely used to retrieve aerosol optical depth (AOD). In this study, the effect of atmospheric forward scattering on the measurement of direct solar irradiance is analyzed. In most cases, this effect can be safely neglected. However, in the case of heavy dust loading, errors in AOD derived from Sun photometer measurements can be significant which warrants a correction of the forward scattering to derive accurate values of AOD. An algorithm is presented for such a correction.

An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals

Abstract. MODIS Collection 5 retrieved aerosol optical depth (AOD) over land (MOD04/MYD04) was evaluated using 4 years of matching AERONET observations, to assess its suitability for aerosol data assimilation in numerical weather prediction models. Examination of errors revealed important sources of variation in random errors (e.g., atmospheric path length, scattering angle "hot spot"), and systematic biases (e.g., snow and cloud contamination, surface albedo bias). A set of quality assurance (QA) filters was developed to avoid conditions with potential for significant AOD error. An empirical correction for surface boundary condition using the MODIS 16-day albedo product captured 25% of the variability in the site mean bias at low AOD. A correction for regional microphysical bias using the AERONET fine/coarse partitioning information increased the global correlation between MODIS and AERONET from r2 = 0.62–0.65 to r2 = 0.71–0.73. Application of these filters and corrections improved the global fraction of MODIS AOD within (0.05 ± 20%) of AERONET to 77%, up from 67% using only built-in MODIS QA. The compliant fraction in individual regions was improved by as much as 20% (South America). An aggregated Level 3 product for use in a data assimilation system is described, along with a prognostic error model to estimate uncertainties on a per-observation basis. The new filtered and corrected Level 3 product has improved performance over built-in MODIS QA with less than a 15% reduction in overall data available for data assimilation.

Theoretical evaluation of errors in aerosol optical depth retrievals from ground-based direct-sun measurements due to circumsolar and related effects

Abstract Aerosol optical depth (AOD) has a crucial importance for estimating the optical properties of the atmosphere, and is constantly present in optical models of aerosol systems. Any error in aerosol optical depth (∂AOD) has direct and indirect consequences. On the one hand, such errors affect the accuracy of radiative transfer models (thus implying, e.g., potential errors in the evaluation of radiative forcing by aerosols). Additionally, any error in determining AOD is reflected in the retrieved microphysical properties of aerosol particles, which might therefore be inaccurate. Three distinct effects (circumsolar radiation, optical mass, and solar disk’s brightness distribution) affecting ∂AOD are qualified and quantified in the present study. The contribution of circumsolar (CS) radiation to the measured flux density of direct solar radiation has received more attention than the two other effects in the literature. It varies rapidly with meteorological conditions and size distribution of the aerosol particles, but also with instrument field of view. Numerical simulations of the three effects just mentioned were conducted, assuming otherwise “perfect” experimental conditions. The results show that CS is responsible for the largest error in AOD, while the effect of brightness distribution (BD) has only a negligible impact. The optical mass (OM) effect yields negligible errors in AOD generally, but noticeable errors for low sun (within 10° of the horizon). In general, the OM and BD effects result in negative errors in AOD (i.e. the true AOD is smaller than that of the experimental determination), conversely to CS. Although the rapid increase in optical mass at large zenith angles can change the sign of ∂AOD, the CS contribution frequently plays the leading role in ∂AOD. To maximize the accuracy in AOD retrievals, the CS effect should not be ignored. In practice, however, this effect can be difficult to evaluate correctly unless the instantaneous aerosols size distribution is known from, e.g., inversion techniques.

Preliminary retrieval of aerosol optical depth from all-sky images

The relationship between the radiance ratio (radiance at wavelength 450 nm to 650 nm) and aerosol optical depth (AOD) is analyzed in this paper by numerical simulation and a “LUT” (look-up table) approach is then presented for the retrieval of AOD from the radiance ratio. In this LUT approach, the AOD retrieval error depends mainly on the assumption of aerosol types. From the preliminary simulation, a typical error of 15%–20% in AOD obtained from the radiance ratio is estimated, due to neglecting changes in the ground albedo and background atmosphere. At its worst, the AOD error reached a maximum of around 50%, which will be refined in the future. In the latter part of the paper, comparisons are made between AOD from the imager and from the CE-318 sun photometer, both located at Xianghe observatory in Hebei Province (39.75°N, 116.96°E). This field experiment shows that AOD from the imager is highly correlated with AOD from the sun photometer, with a correlation coefficient of 0.95 and an average retrieval error of around 7%. A contrast experiment confirms the feasibility of retrieving AOD from all-sky images, but more analysis and future research are required to improve the accuracy.

Retrieval of aerosol optical depth in vicinity of broken clouds from reflectance ratios: Sensitivity study

We conducted a sensitivity study to better understand the potential of a new method for retrieving aerosol optical depth (AOD) under partly cloudy conditions. This method exploits ratios of reflectances in the visible spectral range and provides an effective way to avoid three-dimensional (3D) cloud effects. The sensitivity study is performed for different observational conditions and random errors in input data. The results of the sensitivity study suggest that this ratio method has the ability to detect clear pixels even in close proximity to clouds. Such detection does not require a statistical analysis of the two-dimensional (2D) horizontal distribution of reflected solar radiation, which makes it suitable for operational retrievals. In comparison with previously suggested approaches, the ratio method has the capability to increase the “harvest” of clear pixels. Similar to the traditional independent pixel approximation (IPA), the ratio method has a low computational cost for retrieving AOD. In contrast to the IPA method, the ratio method provides much more accurate estimations of the AOD values under broken cloud conditions: pixel-based and domain-averaged estimations of errors in AOD are about 25% and 10%, respectively. Finally, both the ratio-based cloud screening and the accuracy of domain-averaged ratio-based AOD values do not suffer greatly when 5% random errors are introduced in the reflectances.