Comparison of hourly aerosol retrievals from JAXA Himawari/AHI in version 3.0 and a simple customized method

Advanced Himawari imager (AHI) carried on the new-generation geostationary meteorological Himawari-8 satellite of Japan has been generating aerosol observations with a high temporal resolution since 7 July 2015. However, the previous studies lack a comprehensive quality assessment and spatial coverage analysis of AHI hourly aerosol products (level 3 version 3.0) across the full disk scan. The monitoring accuracy of different AHI aerosol products (AODpure and AODmerged) and a simple customized product (AODmean) was evaluated against Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN) observations from May 2016 to February 2019 in this study. Results showed that AHI AODmean demonstrates a better agreement to AERONET AOD measurements than AODpure and AODmerged over land (R = 0.81, bias =  − 0.011) and all the AHI land retrievals present a significant regional performance differences, while the relatively better performance is observed in AODmerged over the coastal regions (R = 0.89, bias = 0.053). Over ocean, AHI exhibited overall overestimation in retrieving AOD against MAN observations and the relatively lower uncertainties were found in AODpure retrievals (R = 0.96, bias = 0.057). The hourly comparisons in different AHI products demonstrated a robust performance in the late afternoon (16:00–17:00 LT) over land and around the noon (10:00–13:00 LT) over coast. AHI AOD products indicated an obvious underestimation when compared to MODIS AOD retrievals over both land and ocean. Furthermore, the performance differences of AHI AOD products have also affected by the vegetation cover, pollution levels and relative humidity. For spatiotemporal coverage, the results of different AHI products demonstrated that AODmean can achieve relatively higher coverage than AODpure and AODmerged, and AHI retrievals present significant regional differences in coverage capability.


Materials and methods
Materials. High-accuracy AERONET and MAN AOD observations are selected for validation and comparison to assess the newly released AHI L3 aerosol products over the full disk scans. The AHI aerosol products (level 2 version 2.1 and level 3 version 3.0), AERONET cloud-screened and quality-assured AOD (level 2.0 version 3) and all points MAN AOD (level 2) were collected from May 2016 to February 2019. Meanwhile, the MODIS C6.1 AOD products (Level 2) which are extensively adopted for the characterization of aerosol distribution and temporal variation were downloaded for comparison within the same period. To evaluate AHI AOD products under different meteorological factors, the relevant meteorological datasets were also collected in this study. The fine particulate matter (PM 2.5 ) observations of major cities in China were downloaded from the China Environmental Monitoring Center (CNEMC) (http://www.cnemc .cn/), and the relative humidity (RH) and planetary boundary layer height (PBLH) were obtained from the ERA5 reanalysis dataset of European Centre for Medium-Range Weather Forecasts (ECMWF) (https ://www.ecmwf .int/en/forec asts/datas ets/reana lysis -datas ets/era5). The distribution of 86 AERONET sites in the full scene of AHI measurement (80°E-160°W and 60°N-60°S) is shown in Fig. 1.
AHI hourly aerosol product. Two primary datasets known as AOD pure and AOD merged are included in the newly updated AHI hourly aerosol products provided by the Japan Aerospace Exploration Agency (JAXA). The strict cloud-screening quality control of L2 retrievals is applied to reduce cloud contamination in AOD pure and AOD merged is obtained from the spatiotemporal optimum interpolation of AOD pure within one hour to compensate for the missing values due to the elimination of sensor noise and insufficient cloud-screening in the AOD pure product 29,30 . Four confidence levels, namely, "very good", "good", "marginal" and "no confidence" (or "no retrieval"), are included in the quality assurance (QA) flag of AOD pure and AOD merged , and only the "very good" level of pure and merged AOD observations were used. More details of the AHI L3 aerosol product scheme is available in the official documentation instruction 40 . Moreover, we have used a customized method to derive the average AOD and AE over an hour (denoted by AOD mean and AE mean ) based on L2 retrievals using the same QA level ("very good") for quality control to perform a more extensive evaluation in this study.
Matching AERONET observations with AHI retrievals in the same spatiotemporal scale is the first step to evaluate the performance of hourly AOD products over land and ocean. Considering the AHI AOD retrievals estimated with different temporal definitions, the corresponding matching methods were formulated: (1) The median observation time of L2 retrievals is adopted in AHI AOD mean and its estimates within ± 30 min of AERONET AOD observations were collected in this study; (2) AHI AOD pure is the available values after threshold filtering in L2 retrievals, thereby the temporal window of AOD pure was set as ± 10 min; (3) AHI AOD merged is derived from 6 slots of AOD pure (ie., past 1 h) with the weighted-average interpolation. Consequently, the MODIS. The new MODIS (Terra/Aqua) C6.1 level 2 atmospheric aerosol products (MOD04_L2 for Terra and MYD04_L2 for Aqua) with a spatial resolution of 10 km were used in this study to gather the DT AOD retrievals from May 2016 to February 2019. The scientific data set (SDS) of MODIS C6.1 DT AOD retrievals named "Optical Depth Land And Ocean" were collected and such data were filtered with the recommended high-quality flag over land (QF = 3) and ocean (QF > 0) 25 . In addition, the MODIS AOD was transformed from 550 to 500 nm using the ground-measured AERONET AE. The ground-based network data were matched with MODIS aerosol product in time and location by using the following criteria: (1) a 30-min interval limitation of the AERONET and MODIS AOD; (2) a total of 15-km geographical distance between the MODIS AOD and ground site; (3) no fewer than 10 satellite AOD retrievals in the matching range. The variance check was also carried out in MODIS AOD data as AHI collocations. In addition, Normalized difference vegetation index (NDVI) is the commonly used measurements of surface vegetation dynamics in most studies, and the data was also acquired from MODIS 16-day composite NDVI products (MOD13C1/MYD13C1) in this study.
AERONET. The AERONET is a worldwide ground-based sunphotometer monitoring network that provides prolonged, continuous and high-accuracy measurements of the atmospheric aerosol parameters 41 . Available AERONET AOD data online have the following three quality levels: Level 1.0 (unscreened), Level 1.5 (cloud cleared), and Level 2.0 (cloud cleared and quality controlled) 42,43 . The AHI AOD performance was evaluated using the recommended AERONET version 3 observations (the estimated uncertainty from ~ 0.01 to 0.02) with a fully automatic cloud screening and quality control in this work 44 . The AERONET AOD (version 3 level 2.0) has a high temporal frequency of approximately 3-15 min for the following bands: 340, 380, 440, 500, 675, 870, 1020, and 1640 nm 44 , and the measurements at 500 nm channel was employed for the inter-comparison of satellite-based AOD observations. Additionally, the characteristics of sand-water mixture surface and complex aerosol types in the coastal regions are widely different from the inland aerosol properties 45,46 . Therefore, the AERONET sites in this study were divided into inland and coastal measurements.
MAN. The MAN is a ship-based network established as a complement to AERONET, and has been collecting AOD observations across oceans worldwide by using Microtops II handheld Sun photometers 47 . MAN has been providing high-quality aerosol observations with an estimated uncertainty of < ± 0.02, which is sufficient for the evaluation of satellite observations over oceans 47,48 . Marine aerosols are measured by the Microtops II instruments in 5 bands within the scope of 340 nm to 1020 nm 47 . In particular, MAN AOD measurements at 500 nm were selected for the collocation and evaluation in this study. The average of AHI AOD within the various spatiotemporal window centered on a MAN observation site was also used as a matching value in this work.  of the matchups on a scatterplot was used in the subsequent validation with AERONET to assess the quality and uncertainty of satellite retrievals quantitatively. Specifically, the expected accuracy (EA) and expected precision (EP) were separated to describe the EE envelope of AHI more accurately, which represent the average deviations from truth and the fluctuation of deviations, respectively 32,49 . Thus, the EE can be expressed as EA ± EP. Moreover, the accuracy and performance of AHI AOD were analyzed by using the following statistical parameters: (1) bias, representing the average error between the different data sets (AHI-AERONET); (2) root mean square error (RMSE), the standard deviation of the prediction errors; (3) correlation coefficient (R), the statistical standard to evaluate the correlation between retrievals and ground-based measurements; (4) global climate observing system fraction (GCOSF), the fraction of satellite retrievals that meet the Global Climate Observing System requirement for AOD accuracy (smaller than the maximum of 0.03 or 10%) 32,50 ; (5) mean percentage error, the bias of matched satellite retrievals from AERONET sites, which can be calculated by mean( AHI AOD −AERONET AOD AERONET AOD ) × 100%.

Results and discussion
Regional evaluation with AERONET. As shown in the scatter plots of Fig. 2a-c (first row), the performance of AHI AOD pure and AOD merged retrievals is similar over land, with the commensurate R (0.80 vs. 0.81) and GCOSF (21% vs. 22%), suggesting the original retrieval accuracy of AHI AOD pure is nearly maintained in the spatiotemporal interpolation of AOD merged . Almost the same performance of AOD pure and AOD merged is also mentioned in Li et al. 36 . Moreover, AOD mean shows a slightly better agreement with AERONET retrievals when compare it to that of AOD pure and AODmerged, with an increased R of 0.81, a larger GCOSF of 24%, a smaller bias of − 0.011, and a lower RMSE of 0.177. It is equally remarkable that a larger number of collocations are observed in AOD mean than it of AOD merged with a spatiotemporal window of the same size (43,242 vs. 27,205). Figure 2d-f (second row) show the boxplots of AHI AOD biases with the variation of AERONET AOD. According to the statistical results, the mean AHI AOD biases change from positive to negative with the increase of ground-truth AOD, and the minimum deviations can be found when the AERONET AOD is between 0.2 and 0.3. The AHI AOD biases as a function of AERONET AOD are described by EA: EA mean = − 0.25 × τ AERONET + 0.06, EA pure = − 0.28 × τ AERONET + 0.08 and EA merged = − 0.31 × τ AERONET + 0.06. The systematic underestimation trend of AHI AOD can be found in other studies [36][37][38] and it could be attributed to the overestimation of surface reflectance estimation and limited representation of aerosol model 30,32 . The EEs of different AHI AOD retrievals are shown in Fig The validation results in Fig. 2g-i (third row) present the AOD mean percentage error of three AHI datasets over different land regions. Significant positive biases and negative biases of AHI AOD are still noticed over Australia and Southeast Asia in most of the AERONET stations respectively, which have been found in the previous AHI L2 aerosol products [31][32][33] and the validation results of AHI L3 aerosol products 37 . Comparatively, the AHI AOD performs better over eastern China, Korean Peninsula and Japan with a relatively lower uncertainty. These regional differences of AHI aerosol retrievals over land may be related to the surface types (e.g. dark or bright surface) and aerosol models (e.g. sphericity or size distribution), which suggests that the AHI retrieval algorithm and performance need to be further improved and perfected for such areas. As for the performance of different AHI AOD products, AOD pure and AOD merged tend to be slightly better consistent with AERONET AOD than AOD mean over eastern China, Korean Peninsula and Japan, while the AOD mean retrievals exhibit smaller biases in many Southeast Asia sites.
The comparison for the AHI L3 AOD products against coastal AERONET sites is shown in Fig. 3. The first row presents the scatter plots for AHI AOD mean , AOD pure and AOD merged products versus AERONET AOD over coastal regions from May 2016 to February 2019, respectively. In comparison to the land aerosol retrieval analysis presented above, the coastal AOD retrievals from AHI referenced to AERONET have a relatively better performance with a higher R of 0.84-0.90, and a smaller RMSE of 0.115-0.152 than that of land. The comparison results show that the AOD merged has a higher AOD retrieval accuracy than AOD mean and AOD pure over coastal areas, with an increased R (0.89 vs. 0.84 and 0.90), a larger GCOSF (27% vs. 24% and 24%), a smaller bias (0.053 vs. 0.077 and 0.66), and a lower RMSE (0.115 vs. 0.152 and 0.117). Moreover, smaller estimation biases are also found in AOD pure compared to AOD mean retrievals (bias = 0.066 vs. 0.077; RMSE = 0.117 vs. 0.152).
Figure 3d-f (second row) show the differences between AHI aerosol products and ground-based AOD from May 2016 to February 2019. For coastal areas, the linear regression for these positive differences of AHI AOD mean in Fig. 3d is represented by the EA = 0.01 × τ AERONET + 0.08, which demonstrates a very weak dependence between the AHI-AERONET AOD and AERONET AOD than that of land. Figure 3e shows that the trend of AHI AOD pure biases relative to AERONET is analogous to that of AOD mean in Fig  www.nature.com/scientificreports/ 2019. The overall mean biases of L3 AHI-derived aerosol retrievals against AERONET present positive over coastal regions and extremely high biases can be found in the AERONET sites located on the Australian coast and near the equator. Notably, the AERONET AOD of Hong Kong (114.180°E, 22.303°N) in southern China with a smallest mean positive biases in AOD mean (0.005) and a special mean negative bias (− 0.120) in AOD merged , which may be influenced by the dominate urban fine aerosols (moderately absorbing) from the industrialized regions of Pearl River Delta 32,51 . Overall, the three datasets perform a similar distribution of mean biases against the coastal AERONET sites. The different AHI AE retrievals versus ground measurements over both land and coast are compared in the scatter plots of Fig. 4. The comparative results reveal that the AHI AE performance over land is poorer than the retrievals over coast. The land AE retrievals in first row show the worse performance of AE merged with larger estimation uncertainty (bias = − 0.865, RMSE = 0.962) compared to that of AE mean and AE pure (bias = − 0.215 and − 0.300, RMSE = 0.553 and 0.583). By contrast, there is a relatively better AE retrieval performance over coast in Fig  www.nature.com/scientificreports/ is adopted from the L2 AE retrievals when AOD pure is a valid value 29 , and it shows a similar agreement against AERONET AE with AE mean retrievals over both land and coast in Fig. 4. In addition, the coast AE merged retrievals present a relatively smaller bias than the land AE merged (bias = − 0.253 vs. bias = − 0.865). Overall, the poor performance of AHI L2 AE retrievals [31][32][33] continues to be noted in the L3 hourly products.
In AHI L3 hourly combined algorithm, the AE merged product was obtained from AE pure retrievals by the same optimal interpolation method as AOD merged and can be calculated as follows 29,30 : www.nature.com/scientificreports/ where AE pure (x i , y i , t i ) is the AE pure retrievals at a given time t i and location (x i , y i ) within a radius of 12.5 km and past 1 h from (x 0 , y 0 , t 0 ). N is the total number of valid pixels in the calculation domain. σ pure (x i , y i , t i ) 2 and σ merged (x 0 , y 0 , t 0 ) 2 are the error variance. As shown in Eq. (1), the calculation of AE merged depends on AE pure retrievals and weight coefficient W i , but AE pure has been proved to have reliable accuracy in Fig. 4b,e. Further, σ pure (x i , y i , t i ) 2 is fundamentally derived from a lookup table that is calculated from the AOD spatiotemporal variability. Thus, the same weight as AOD merged retrievals may result in the poor performance of the AE merged . Moreover, the aerosol spatiotemporal variability is larger over land than over ocean, which may lead to the AE merged performance differences between land and coast in Fig. 4c,f. In order to describe the performance differences of AHI products more intuitively, a display of regional AE retrievals is created in Fig. 5, and four regions (i.e., southeastern China, Northeast Asia, Southeast Asia and eastern Australia) within the AHI full disk coverage, including land and ocean, were mainly selected for this study. These samples in Fig. 5 were randomly selected when sufficient observations were available in the target region. Overall, the spatial coverage of AE mean is substantially higher compared to AE pure and AE merged retrievals over both land and ocean. It should be pointed out that the AE merged retrievals are universally lower than AE mean and AE pure in all regions over land, which is consistent with the results shown in Fig. 4a-c. Furthermore, AHI AE merged retrievals present a relatively reliable performance over coast and ocean in the subplot (iii) of Fig. 5, although many low value anomalies can be found at the edge of retrieval areas. These findings confirm the defects in the AE merged retrieval algorithm, but further investigation on underlying causes and improvement methods is required in future research.
Hourly evaluation with AERONET. It is reported in previous studies that the performance of AHI aerosol products exhibits an obvious variation at different daytime hours [31][32][33]37,38 . Accordingly, the evaluations of AHI temporal variation are also executed in this study. The local time (LT) of AHI measurements are translated from universal time coordinated (UTC) through the longitude of AERONET sites. The validation results of AHI L3 AOD datasets for different daytime hours (8:00-17:00 LT) over land are displayed in Fig. 6. The analy- www.nature.com/scientificreports/ sis indicates that the AOD merged performs better than AOD mean in the late afternoon (16:00-17:00 LT) but with more underestimations before 13:00 LT (bias = − 0.115 to − 0.045 vs. bias = − 0.060-0.000). These discrepancies may be result from the frequent occurrence of cloud-contaminated pixels in the afternoon 52 . By comparison with AOD pure , AHI AOD merged retrievals are more consistent with ground-based measurements after 11:00 LT, but poorer performance can be found in the morning (8:00-11:00 LT). Overall, Himawari-8/AHI can more accurately obtain AOD retrievals at 16:00-17:00 LT than in other periods, which is probably related to the scattering angle. Furthermore, an obvious tendency of underestimating the aerosol loadings can be noticed in AHI AOD relative to AERONET AOD at 10:00-11:00 LT, with a large negative bias of − 0.115 to − 0.059 in different products. www.nature.com/scientificreports/ By comparison with the land assessments, the hourly validation results of the AHI AOD retrievals against the ground-based AOD measurements over the coast are shown in Fig. 7. As compared to the AOD mean , AOD merged collocations are generally fit better against AERONET AOD in each subplot, with a smaller bias (0.012-0.100 vs. 0.034-0.124), a lower RMSE (0.089-0.138 vs. 0.110-0.196) and a larger R (0.88-0.95 vs. 0.79-0.93), although slightly fewer retrievals fall within GCOSF at 8:00-9:00 LT and 16:00 LT. In Fig. 7b,c, the AHI AOD merged retrievals perform a little bit better than AOD pure during most of the day, except for a large bias in the period of 13:00-14:00 LT. Overall, all the AHI products tend to overestimate the coastal AOD during the daytime, especially for the retrievals before 9:00 LT and after 13:00 LT. Moreover, AHI costal retrievals show the better performance at 10:00-13:00 LT than other times, with the relatively smaller bias (0.012-0.070), lower RMSE (0.087-0.170) and higher GCOSF (27-43%), which indicates that a smaller solar zenith angle or a large scattering angle may help to the high-quality coastal AOD retrieval.
To explore the temporal performance difference of AHI and MODIS aerosol retrievals, the hourly variations of MODIS AOD over both land and coast are presented in Fig. 8. Unlike the geostationary satellite, Terra-MODIS (ascending orbit) and Aqua-MODIS (ascending orbit) can only provide global observations at about 10:00-12:00 LT and 12:00-14:00 LT in the daytime, respectively. Compare to the AHI retrievals in Fig. 6, the performance of MODIS observations is comparatively better over land during the same period with 30-37% of retrievals falling within the GCOSF, larger R (0.90-0.92) and smaller RMSE (0.141-0.166). As for the coastal retrievals, the accuracy of MODIS AOD is similar to that of AHI AOD merged before 12:00 LT (R = 0.80-0.92, within GCOSF = 36-45% Evaluation for angular dependence. To further examine the geometry dependency of different AHI aerosol products, the AOD retrieval bias in AHI products with solar zenith angle, satellite zenith angle and scattering angle is shown in Fig. 9. As shown, the accuracy of AHI AOD retrievals are less dependent on solar zenith angle in the geometry dependency analysis. In Fig. 9c, the AOD bias changes from positive (~ 0.05) to negative (~ − 0.15) with the increase of satellite zenith angle and a relatively large bias of AOD pure in the range of 20° to 30° is due to the less matchups in this case. Over coast, there is an obvious missing of AHI AOD matchups when the satellite zenith angle is larger than 50° and a relatively high retrieval error in AHI AOD retrievals can be found in the small satellite zenith angle (10°-20°). Figure 9e,f show that the relation between AHI AOD bias and scattering angle follows an inverted U-shaped curve. The AHI AOD retrievals are severely underestimated over land when scattering angle is larger than 160° or smaller than 80°, but with the optimum performance in the range of 100° to 140°. Furthermore, the coastal AOD can be retrieved more accurately at a larger (> 140°) or smaller (< 80°) scattering angle. The scattering angle which affects the scattering phase function in radiative transfer model varied with time is considered to be one of the important factors that results in AHI temporal variations. Overall, the AHI AOD mean , AOD pure , and AOD merged retrievals exhibit a similar bias trend with the variation of solar zenith angle, satellite zenith angle and scattering angle. www.nature.com/scientificreports/ Temporal variation evaluation with AERONET. Figure 10 presents the time series of hourly and monthly averaged AOD of AHI and AERONET over land and coast between May 2016 to February 2019. Over land, the relatively large biases of AHI AOD observations appear in December to March, which may be related to the frequent haze events and in northern hemisphere during this period 32 . Meanwhile, the coastal AHI AOD observations are generally overestimated throughout the period of the study, as shown in the right panel of Fig. 10. Besides, the relatively large biases of AHI AOD relative to ground-based AOD can be found from November 2018 to February 2019 in Fig. 10b Evaluation for land surface cover. Different land cover types affect satellite-derived aerosol product by influencing surface reflectance estimation. Figure 11 displays the scatterplots of AHI aerosol products against AERONET measurements under different NDVI ranges. Overall, AHI AOD retrievals show a better performance over dense vegetation areas (NDVI ≥ 0.7) than bright surfaces (NDVI < 0.3), with a higher R (0.85-0.93 vs. 0.74-0.81) and GCOSF percentage (25-38% vs. 22-28%), and all the AHI retrieval biases change from positive to negative with the increase of NDVI values. Moreover, when NDVI ≥ 0.6, AHI AOD mean retrievals perform better against AERONET AOD than AOD pure and AOD merged products, with a lower RMSE (0.105-0.157 vs. 0.107-0.180 and 0.110-0.198) and a smaller bias (− 0.020 to − 0.046 vs. − 0.037 to − 0.090 and − 0.050 to − 0.098). This suggests that the AHI hourly combined algorithm may erroneously eliminate many normal observations and cause a serious underestimation over these regions.
Evaluation for meteorological conditions. To evaluate the performance of AHI aerosol products under different air pollution levels, Fig. 12a demonstrates the relation of AHI retrieval biases (AHI AOD-AERONET AOD) and ground PM 2.5 concentration. It is worth noting that AHI can retrieve aerosol with a stable accuracy at low pollution levels. However, AHI AOD pure and AOD merged retrievals are significantly overestimated when PM 2.5 concentration is higher than 110 μg/m 3 , while AOD mean still presents a relatively smaller bias within the PM 2.5 concentration range of 110-150 μg/m 3 . For the heavy pollution level (> 150 μg/m 3 ), AOD pure and AOD merged retrievals achieve a better agreement with AERONET than AOD mean . Therefore, the hourly combined algorithm is more susceptible to haze events under the moderate pollution level (115-150 μg/m 3 ). Relative humidity (RH) is an important proxy to describe the water vapor content in atmosphere, and the water vapor content play a role in aerosol components and properties. As shown in Fig. 12b, AHI AOD is overestimated at a low relative humidity (RH < 20%) and underestimated when relative humidity is higher than 40%. The optimum humidity condition for AHI AOD retrieval is within the range of 20-40%. Moreover, more severely underestimated retrievals can be found in AOD merged under moist atmosphere (RH > 60%), while AHI AOD pure exhibits a relatively high positive bias for dry atmosphere (RH < 20%). The PBLH is another important meteorological parameter to characterize the lowest part of atmosphere. Thus, Fig. 12c displays the effect of PBLH on different AHI AOD retrievals. Obviously, all the AHI AOD retrievals are weakly dependent on the PBLH when it is lower than 3 km. But for a higher PBL, the biases of AHI AOD retrievals increased quickly with the height. In addition, there are no significant differences in retrieval accuracy among the AHI AOD mean , AOD pure www.nature.com/scientificreports/ and AOD merged products in Fig. 12c, which suggests that the PBLH has little contribution to the performance discrepancies of the three AOD retrievals.
Regional evaluation with MAN. The comparison results of all AHI L3 AOD retrievals against ship-borne AOD measurements for the open ocean regions are depicted in Fig. 13 to supplement the results for AERONET coastal sites. The scatter plots of the AHI AOD against MAN AOD show that the retrieval accuracy in AOD merged is better compared with AOD mean , with a lower bias (0.068 vs. 0.081), a smaller RMSE (0.100 vs. 0.111), and a larger GCOSF (35% vs. 27%). Besides, the AOD pure shows slightly better agreement with MAN observations in comparison to AOD merged , with a lower bias of 0.057 and a smaller RMSE of 0.082. As for the performance of AHI aerosol products at regional scales, the retrievals are generally overestimated in most ocean areas but relatively slightly overestimated in the oceans around South Korea and Japan. Contrary to the AHI land retrievals in Fig. 2, AOD mean retrievals without strict cloud screening show poorer performance than AOD pure and AOD merged , exhibiting high positive biases (~ 100%) in most of the sites over oceanic regions. The spatial variability of AOD is considered to be greater over land than over ocean 30 , but the same spatial window is used for hourly combined retrievals (AOD pure and AOD merged ) over both land and ocean. This may lead to the performance discrepancies of the AOD pure and AOD merged retrievals between land and ocean when compared to the AOD mean retrievals, and it is probably better to use a smaller spatial window for the land retrievals in the hourly combined algorithm.

Comparison with MODIS.
To provide an additional independent measurement other than AERONET, the evaluations of AHI against MODIS are also included in this study. The scatter plots of AHI AOD against MODIS AOD over the land and ocean against MODIS AOD are presented in Fig. 14. The comparison results demonstrate that there is a similar linear relationship between different AHI products and MODIS retrievals over land, with an approximate slope (0.81-0.86) and intercept (0-0.02). The AHI products tend to underestimate AOD compared with MODIS retrievals over land, which suggests that the AOD retrieval performance of Himawari-8/AHI remains to be further improved and needs to narrow the gap with MODIS collections. Over ocean, the linear fitting slope of AOD mean product against MODIS product is closer to the 1:1 line than that of AOD pure and AOD merged (0.82 vs. 0.73 and 0.74). The performance differences between AHI AOD and MODIS AOD may depend on their own unique algorithms, observation geometries and sensor characteristics 36,38 . However, there may be larger positive deviations observed in MODIS retrievals over ocean, because the slopes of AHI AOD against MODIS AOD are lower than 1 in Fig. 14d-f and AHI retrievals present a high positive bias over ocean in Fig. 13. For the configuration of spectral bands, AHI lacks a 1.38 µm channel for the cirrus cloud detection compared to MODIS, which may cause the differences of cloud recognition before AOD retrieve.
Evaluation of spatio-temporal coverage. The spatial coverage ratio from May 2016 to February 2019 is evaluated between different AHI hourly aerosol products in Fig. 15. As illustrated in Fig. 15a-c without QA screening, the AOD pure and AOD merged products have much lower spatial coverage than the AOD mean products over most land and ocean regions due to the removal of cloud-contaminated observations, especially in the Qinghai-Tibet Plateau, New Zealand, and tropical islands such as Borneo and New Guinea. These missing values are mainly for the equatorial regions with more clouds and the high-altitude areas covered by snow and ice, www.nature.com/scientificreports/ where most of the AHI retrieval algorithms cannot be implemented. Furthermore, AOD merged can be obtained by interpolating if there are sufficient AOD pure observations around 29,30 . Therefore, it can be found in Fig. 15b,c that the coverage ratio of AOD pure has been recovered to some extent in AOD merged .
To evaluate the impact of QA levels on the coverage of different AHI AOD products, Fig. 15d-i show the influence of different quality controls on the spatial coverage of AHI hourly products in Himawari-8 observation regions. Considering the QA flags of "good" and "marginal" are not used in the AHI datasets, only the AOD retrievals with the QA confidence level of "very good" are discussed in this study. As presented in Fig. 15d-f, there is a comparatively higher coverage in AOD mean than products after the QA screening, and the highest percentage of AHI observations is also caught in Australia than other regions. For the differences in Fig. 15g-i, the variation of AOD mean is found to be much larger than AOD pure and AOD merged , with a percentage of > 20% in most areas. Moreover, there is a lower percentage of < 5% for the coverage changes over all the oceanic and most terrestrial regions in AOD pure and AOD merged retrievals (Fig. 15h,i). Moreover, the relatively large coverage www.nature.com/scientificreports/ variations of AHI products can be observed in northern China, eastern India, Myanmar, Thailand and Australia, which probably mean a larger uncertainty of AHI retrieval algorithm in these areas.

Conclusions
In this study, the latest Himawari-8/AHI hourly atmospheric aerosol product and simple customized product were validated by AERONET and MAN observations over land and ocean in the Himawari-scope region from May 2016 to February 2019. The extended comparisons between different datasets of AHI aerosol products (i.e., AOD mean , AOD pure and AOD merged ) and the difference in performance of satellite-derived retrievals between AHI and MODIS were discussed in detail. Over land, the matched AOD mean retrievals showed a slightly better  www.nature.com/scientificreports/ performance compared with AOD pure and AOD merged products and capture a larger number of coincident observations in the same spatiotemporal window. For the regional comparisons, AHI AOD products achieved a relatively better performance in East Asia, whereas the significant regional underestimations and overestimations can be observed in Southeast Asia and Australia, respectively. Over coast, the AHI AOD merged was generally agree better with the AERONET AOD than AOD mean and AOD pure products and AHI retrievals presented a comparatively smaller uncertainty at the coastal sites of East Asia. Over ocean, it can be found that the AOD pure achieved a better agreement with AERONET AOD as compared to that of AOD mean and AOD merged retrievals. However, overall AHI ocean observations were extensively overestimated by the validation with ship-borne AOD measurements, especially showing a large number of high positive biases (~ 100%) over the South Pacific. For the L3 AE products, AE mean and AE merged provided more accurate retrievals than AE merged , and exhibited similar performance over both land and coast.
Comparing the results under different daytime hours, the best performance of AHI AOD retrievals occurred in the late afternoon (16:00-17:00 LT) over land and around the noon (10:00-13:00 LT) over coast, respectively, which is mainly affected by the scattering angle. AHI AOD retrieval accuracy can be also influenced by many other factors, such as surface NDVI, PM 2.5 concentrations, RH, PBLH, etc. The comparison results indicated a good correlation between AHI and MODIS, but AHI products generally underestimated AOD relative to MODIS retrievals over land and ocean. For the spatial coverage, more observations were available in AOD mean over Himawari-domain region, with a higher percentage of coverage than AOD pure and AOD merged . Moreover, it should be noted that AHI AOD retrievals lack adequate observations in the Qinghai-Tibet Plateau, New Zealand, and tropical islands such as Borneo and New Guinea.
This work has evaluated different Himawari-8/AHI hourly aerosol products in version 3.0, and we suggest that the regional performance differences and data availability should be taken into consideration in the selection of AHI products in related aerosol studies. The underlying mechanisms of Himawari-8/AHI AOD performance differences under various influential factors must be further analyzed in future research, and AHI retrieval algorithm requires further improvement to highlight its advantage of high temporal resolution.