• ADVANCES IN ATMOSPHERIC SCIENCES, 2018, 35(2): 135-145
    doi: 10.1007/s00376-017-7017-2
    Can MODIS Detect Trends in Aerosol Optical Depth over Land?
    Xuehua FAN1,, Xiang'ao XIA1,2,3, Hongbin CHEN1,2,3

    Abstract:

    The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Aqua satellite has been collecting valuable data about the Earth system for more than 14 years, and one of the benefits of this is that it has made it possible to detect the long-term variation in aerosol loading across the globe. However, the long-term aerosol optical depth (AOD) trends derived from MODIS need careful validation and assessment, especially over land. Using AOD products with at least 70 months' worth of measurements collected during 2002-15 at 53 Aerosol Robotic Network (AERONET) sites over land, Mann-Kendall (MK) trends in AOD were derived and taken as the ground truth data for evaluating the corresponding results from MODIS onboard Aqua. The results showed that the AERONET AOD trends over all sites in Europe and North America, as well as most sites in Africa and Asia, can be reproduced by MODIS/Aqua. However, disagreement in AOD trends between MODIS and AERONET was found at a few sites in Australia and South America. The AOD trends calculated from AERONET instantaneous data at the MODIS overpass times were consistent with those from AERONET daily data, which suggests that the AOD trends derived from satellite measurements of 1-2 overpasses may be representative of those from daily measurements.

    Key words: MODIS; AERONET; Aerosol Optical Depth; Mann-Kendall trend test;
    摘要: MODIS (Moderate Resolution Imaging)气溶胶光学厚度(Aerosol Optical Depth, AOD)产品为研究全球范围的AOD变化趋势提供了很好的数据平台. 然而, 由于陆地下垫面的复杂和陆地上空气溶胶时空分布的多变, MODIS陆地气溶胶产品获得的AOD变化趋势亟需广泛的验证和评估. 本文选取了全球53个(准连续观测70个月以上)AERONET(Aerosol Robotic Network)测站, 用Mann–Kendall(MK)非参数检验方法验证评估了MODIS/Aqua陆地气溶胶光学厚度2002~2015年的变化趋势. 以AERONET日平均AOD计算得到的月中值AODRE趋势作为参考, 将其与MODIS/Terra, MODIS/Aqua过境前后半小时的AERONET瞬时资料计算得到的AODAM, AODPM月中值MK趋势进行比较, AODAM, AODPM与AODRE的变化趋势一致. 这说明, 上, 下午卫星过境前后半小时的AERONET AOD观测可以代表日平均AOD的变化趋势. 此外, 将地基AERONET AODRE MK趋势和MODIS/Aqua气溶胶产品得到的AODMOD变化趋势做了比较, 结果显示: 欧洲和北美的所有测站, 亚洲和非洲的大多数测站, MODIS气溶胶产品得到的AODMOD变化趋势与AERONET AODRE变化趋势相同. 但是, 澳大利亚和南美的测站, 由于MODIS气溶胶产品反演精度低, MODIS AODMOD变化趋势与地基AODRE的变化趋势不一致.
    关键词: MODIS ; AERONET ; 气溶胶光学厚度 ; Mann–Kendall趋势检验
    1. Introduction

    Aerosols remain one of the largest uncertainties in estimates and interpretation of the changes in Earth's energy budget (Boucher et al., 2013). Aerosol optical depth (AOD), which is related to the column-integrated aerosol amount, is a basic optical parameter used to estimate the aerosol radiative forcing. There is compelling reason to study long-term trends in AOD to help us ascertain the effects of aerosols on climate change. The Aerosol Robotic Network (AERONET) is a network of automated ground-based sunphotometers, which has been providing globally distributed, quality-assured observations of AOD since the 1990s (Holben et al., 1998). Observations were made over more than 7 years at 77 stations that are distributed around the world, which make the analysis of long-term trends in AOD possible. Linear trends in AOD and Ångström exponent values using AERONET level 2.0 aerosol products were reported by (Xia, 2011) and (Yoon et al., 2012). Uncertainties in trends due to cloud disturbances were estimated using the weighted trends, depending on the monthly standard deviation (SD) and number of observations. Decreasing trends in AOD were observed in Western Europe, with the maximum trend of -0.12 (10 yr)-1 at Belsk and North America, and with trends ranging from -0.04 (10 yr)-1 at Egbert to -0.07 (10 yr)-1 at MD_Science_Center (Xia, 2011). (Li et al., 2014) presented trends in 440 nm AOD from 90 AERONET stations using two statistical methods——the Mann-Kendall (MK) test with Sen's slope estimator and linear least-squares fitting. Negative trends in AOD were also found at most sites in North America and Europe. In addition, there were downward trends of -0.08, -0.06 and -0.03 (10 yr)-1 at CUIABA-MIRANDA in South America and at Osaka and Shirahma in Japan. Significant increasing trends were found at Kanpur [0.08 (10 yr)-1] in India and at Solar_Village [0.13 (10 yr)-1] on the Arabian Peninsula. The authors noted that the trends derived from AERONET AOD measurements are more robust compared with those from AERONET inversion products.

    AOD from satellite measurements can provide global AOD trends. A number of studies have assessed tendencies in AOD from several satellite products, such as those from the Advanced Very High Resolution Radiometer (Mishchenko et al., 2007; Zhao et al., 2008), the Sea-viewing Wide Field-of-view Sensor (SeaWiFS; Yoon et al., 2011; Hsu et al., 2012), the Multi-angle Imaging SpectroRadiometer (MISR; Kahn et al., 2007), the Moderate Resolution Imaging Spectroradiometer (MODIS; Yu et al., 2009; Zhang and Reid, 2010; Zhang et al., 2017), and the Along Track Scanning Radiometer (Thomas et al., 2010). The AOD retrieval algorithms, underlying assumptions and uncertainties involved in creating the products differ between the satellite sensors (Petrenko et al., 2012). These AOD products have substantial discrepancies (Kokhanovsky et al., 2007; Mishchenko et al., 2007; Thomas et al., 2010). The discrepancies remain so large that they can hardly be utilized to obtain a consistent and unified AOD trend (Li et al., 2009). Especially on regional scales, the differences can be much larger and more complex. Note that most of the studies mentioned above focused on the AOD trends from satellites over the ocean. Few studies have been carried out on AOD trends using satellite aerosol products over land (Yoon et al., 2011; Hsu et al., 2012), where the aerosol and surface characteristics are more complicated than those over the ocean. (Yoon et al., 2011) and (Hsu et al., 2012) evaluated the linear trends of SeaWiFS AOD using AERONET measurements over a limited number of land and coastal sites. Significant positive trends derived from SeaWiFS AOD were found over the Arabian Peninsula, India, southern China and eastern China, with linear slopes of 0.0092 0.0013, 0.0063 0.0020, 0.0049 0.0018 and 0.0032 0.0018 yr-1, respectively. However, (Li et al., 2014) reported declines of -0.10 and -0.18 (10 yr)-1 in AERONET AOD at Beijing (2002-13) and Xianghe (2005-12) on the North China Plain. Negative AOD trends over the east coastal region of China since 2008 were also revealed by MODIS and MISR satellite measurements (Zhang et al., 2017).

    Several recent studies have analyzed aerosol variations and trends using different models and data from multiple platforms. (Chin et al., 2014) investigated aerosol tendencies and variations over a period of 30 years (1980-2009) using the Goddard Chemistry Aerosol Radiation and Transport model and multiple satellite sensors and ground-based networks. They found that the model-calculated global annually averaged AOD values showed little trend——a result that highlights the necessity of studying regional-scale AOD trends. (Pozzer et al., 2015) compared the AOD trends from MODIS, SeaWiFS and MISR satellite measurements with simulated values from the ECHAM/Modular Earth Submodel System Atmospheric Chemistry (EMAC) general circulation model covering the decade (2001-10) on a global scale. The simulation results showed that the model could qualitatively reproduce the MODIS AOD trends over North America, Eastern Europe, North Africa and the Middle East, while some discrepancies were found for other regions. The application of a newly developed satellite AOD dataset, such as the MODIS collection 6, will be helpful to improve the reliability of these datasets and reduce artificial trends (Lyapustin et al., 2014; Pozzer et al., 2015).

    The MODIS sensor onboard NASA's Aqua satellite has been collecting valuable data about the Earth system for more than 14 years, which has made it possible to detect the long-term variation in aerosol loading across the globe. However, the long-term AOD trends derived from MODIS need careful validation and assessment, especially over land. It is necessary to compare the AOD trends from the MODIS sensor with those from ground-based observations, which was the primary objective of the present study. Furthermore, most satellite aerosol products are derived from polar-orbiting satellites that pass overhead at a fixed time of day; for example, Terra passes overhead at 1010-1130 LST and Aqua passes overhead at 1230-1400 LST. Therefore, only 1-2 overpasses with aerosol retrievals collected at a fixed time of the day are available from these polar-orbiting satellites. When we use these satellite aerosol products to study long-term trends, one question that arises is whether the long-term AOD trend derived from satellite measurements of 1-2 overpasses can be representative of that determined from daily measurements. This was the second objective of the present work, which is somewhat similar to the analysis performed by (Kaufman et al., 2000), who investigated whether daily aerosol abundances and properties could be represented by the aerosol products from the Terra and Aqua polar orbiting satellites. They showed that the annual average AOD could be calculated from Terra and Aqua aerosol products within a 2% error level, and this conclusion was independent of the particle size or range of the AOD.

    2. Data and method
    2.1. Multi-sensor Aerosol Products Sampling System AOD

    The Multi-sensor Aerosol Products Sampling System (MAPSS) is a framework that provides statistics on spatial and temporal subsets of level 2.0 aerosol scientific datasets from spaceborne sensors as MODIS and MISR. The data system is described in detail in (Petrenko et al., 2012). The AERONET sites were identified as focal points for spatial statistics. The process of generating the statistics for each spatial spaceborne aerosol product involved extracting values of the pixels that fell within a diameter of approximately 50 km centered on the chosen AERONET site. Statistics for ground-based temporal observations collected at a particular station were derived from measurements taken within 30 min of each satellite overpass of this location. The MODIS dataset obtained from MAPSS used in this paper is a merged dataset comprising the dark target (DT) and deep blue (DB) AOD products filtered by quality assurance (QA) processes. Unless otherwise specified, all the analyses in this paper are based on AOD at 550 nm. The highest quality (QA = 3) AODs retrieved by the DB algorithm have been shown to have an absolute uncertainty of approximately 0.03+0.20τ M for a typical Atmosphere Mass Factor of 2.8 (Sayer et al., 2013), where τ M is MODIS retrieved AOD. The uncertainty of MODIS DT AOD has been estimated to be 0.05+0.15τ A over land (Levy et al., 2010, 2013), where τ A is collocated AERONET AOD. Data at 53 AERONET stations were chosen for this analysis because their measurements are available for a relatively longer period compared with other stations. Figure 1 shows the spatial distribution of these stations, with their data lengths represented by the colors.

    Fig.1. Spatial distribution of stations with at least 70 months of monthly median AOD during 2002-15. The data length is represented by the colors.

    2.2. Data analysis

    We used quality-assured and cloud-screened level 2.0 AERONET AOD data, with a low uncertainty of 0.01-0.02 (Holben et al., 1998). Three daily series were calculated from instantaneous AOD products as follows: AERONET daily mean AOD was calculated from all instantaneous AOD measurements each day; AERONET morning and afternoon mean AOD was calculated from instantaneous AOD measurements within 30 min of the Terra and Aqua overpass times; and monthly median AOD was calculated from the daily mean AERONET AOD if there were more than five daily measurements each month [AOD reference value (AOD RE)], which was taken as a benchmark for comparison with other estimates. The monthly median AOD in the morning (AOD AM) and in the afternoon (AOD PM) were also calculated if at least five daily measurements were available each month. The monthly median was used in the trend test instead of the monthly mean because AOD does not follow a normal distribution. The AOD trends derived from AOD RE, AOD AM and AOD PM were compared to determine whether the trends derived from AERONET AOD measured at the Terra and Aqua overpass times were representative of those derived from daily mean AERONET AOD. The monthly medians of AOD MOD were calculated from all collocated AOD from MODIS/Aqua provided by the MAPSS system if there were more than five daily measurements each month. The AOD trends from AOD MOD were compared with those of AOD RE to determine whether AERONET AOD trends could be reproduced by MODIS. The trends from AERONET and MODIS/Aqua AOD at each site were calculated based on the measurements during the same measurement period. Although the comparison of the AOD RE and AOD MOD trends mentioned above was based on measurements during the same study period, AOD measurements were not necessarily available simultaneously from AERONET and MODIS each day. Therefore, the monthly median AOD from AERONET and MODIS were probably calculated from different daily measurements. In other words, it was possible that daily AOD was available from AERONET, but MODIS/Aqua had no AOD product, or vice versa. This situation arose because of insufficient temporal coverage by MODIS or insufficient spatial coverage by AERONET. Since the difference in temporal sampling was not considered in calculating the monthly median AOD values, the trends in the MODIS and AERONET data may not have been consistent. The AOD measurements from AERONET and MODIS/Aqua were matched day-by-day, and the monthly medians were then recalculated for all the sites to assess the potential effects of sampling issues on the comparison of AOD trends.

    2.3. Trend detection

    The MK test was used to detect monotonic trends. The MK test is a rank-based non-parametric test for assessing the significance of a trend, which requires that the data be independent. The null hypothesis (H0) is that a sample of data, {X1,X2…,Xn}, is independent and identically distributed. The alternative hypothesis (H1) is that a monotonic trend exists in X. The slope b of the trend is computed using the method proposed by (Sen, 1968) as follows: \begin{equation} b={\rm Median}\left(\dfrac{X_i-X_j}{i-j}\right),\quad \forall i>j , \ \ (1)\end{equation} where b is the slope of the trend, Xi and Xj are the ith and the jth observations respectively. The slope is robust for estimating the magnitude of a trend (Yue and Wang, 2002) and much less sensitive to outliers compared with linear regression coefficient (Li et al., 2014).

    The non-parametric test is more suitable for non-normally distributed, censored, and missing data (Yue and Wang, 2002), such as the AERONET AOD data (Li et al., 2014). However, the AOD time series, which have distinct seasonal variability, frequently display statistically significant serial correlation. The existence of positive serial correlation in a time series increases the probability of the MK test detecting a significant trend (Hirsch and Slack, 1984; Yue and Wang, 2002). Therefore, a pre-whitening scheme proposed by (Yue et al., 2002) was used to eliminate the influence of serial correlation on the MK trend detections. First, the slope (b) of the trend in an AOD time series was estimated using Eq. (1). If b was almost equal to zero, then it was not necessary to continue performing the trend analysis. If it differed from zero, the AOD time series were detrended using \begin{equation} X'_t=X_t-T_t=X_t-bt ,\ \ (2) \end{equation} where X't is the `trend-removed' residual series, Xt denotes the raw AOD time series, and Tt is the identified trend with the slope b of the trend at time t. Second, the lag-1 autocorrelation component was removed from the detrended time series (X't) using \begin{equation} Y'_t=X'_t-r_1X'_{t-1} , \ \ (3)\end{equation} where r1 is the lag-1 autocorrelation coefficient, X't-1 is the detrended series at time t-1. Third, the identified trend was added back to the residual Y't according to the following equation: \begin{equation} Y_t=Y'_t+T_t . \ \ (4)\end{equation} It was evident that the blended time series (Yt) preserved the true trend and was no longer influenced by the effects of autocorrelation. The MK test and Sen's slope estimator were applied to the blended series to estimate the trend in AOD time series. (Hirsch and Slack, 1984) developed a seasonal MK test and estimated the annual trend as the median of the seasonal trends——an approach that is resistant to data seasonality and serial dependence. Thus, the pre-whitening scheme described by (Yue et al., 2002) was adopted first. Then, the seasonal MK test of (Hirsch and Slack, 1984) and Sen's slope estimator were applied to the pre-whitened time series of AOD.

    3. Results and discussion

    Table 1 shows that relatively low monthly mean AOD (<0.15) was found in Australia, Europe, North America and South America, except at CUIABA-MIRANDA, where the aerosols were influenced by biomass burning in the Amazon region. The sites located in the semi-arid Sahel region (such as Banizoumbou, Dakar, and IER_Cinzana), and in East Asia (Beijing and Chen-Kung_Univ), as well as northern India (Kanpur), showed higher monthly mean AOD (>0.35).

    Table 1. Site locations, study periods and 550-nm monthly mean AOD SD from the four data records at the 53 selected stations. AOD RE is monthly mean AOD calculated from the daily AERONET AOD if there were more than five daily measurements each month. AOD AM and AOD PM is monthly mean AOD calculated from AERONET instantaneous AOD measurements within 30 min of the Terra and Aqua overpass times. AOD MOD were calculated from all collocated AOD from MODIS/Aqua provided by the MAPSS system if there were more than five daily measurements each month.
    Averaged AOD
    Site Latitude, longitude Study period AOD RE AOD AM AOD PM AOD MOD
    Africa
    Banizoumbou 13.5°N, 2.7°E 2002.7-2015.4 0.44 0.23 0.44 0.23 0.44 0.23 0.42 0.22
    Blida 36.5°N, 2.9°E 2003.11-2010.10 0.17 0.09 0.18 0.09 0.20 0.09 0.14 0.07
    Capo_Verde 16.7°N, 22.9°W 2002.7-2015.4 0.25 0.13 0.26 0.14 0.25 0.13 0.30 0.13
    Dakar 14.4°N, 17.0°W 2003.6-2015.4 0.39 0.17 0.38 0.16 0.39 0.18 0.32 0.16
    IER_Cinzana 13.3°N, 5.9°W 2004.6-2015.4 0.40 0.18 0.39 0.18 0.40 0.18 0.34 0.18
    La_Laguna 28.5°N, 16.3°W 2006.7-2014.8 0.15 0.10 0.15 0.10 0.15 0.10 0.18 0.09
    Saada 31.6°N, 8.2°W 2004.7-2015.4 0.17 0.10 0.17 0.09 0.16 0.09 0.12 0.04
    Santa_Cruz_Tenerife 28.5°N, 16.3°W 2005.7-2013.12 0.14 0.08 0.14 0.08 0.14 0.08 0.18 0.08
    Skukuza 25.0°S, 31.6°E 2002.7-2011.7 0.16 0.07 0.17 0.08 0.16 0.07 0.11 0.07
    Asia
    Beijing 40.0°N, 116.4°E 2002.7-2015.4 0.35 0.22 0.32 0.24 0.37 0.25 0.46 0.25
    Chen-Kung_Univ 23.0°N, 120.2°E 2002.7-2015.4 0.49 0.20 0.49 0.21 0.46 0.18 0.59 0.18
    IMS-METU-ERDEMLI 36.6°N, 34.3°E 2003.2-2014.12 0.19 0.08 0.19 0.08 0.18 0.07 0.18 0.07
    Kanpur 26.5°N, 80.2°E 2002.7-2014.6 0.58 0.18 0.58 0.19 0.58 0.19 0.58 0.19
    Nes_Ziona 31.9°N, 34.8°E 2002.7-2014.10 0.17 0.04 0.19 0.05 0.17 0.04 0.21 0.05
    SEDE_BOKER 30.9°N, 34.8°E 2002.7-2015.4 0.13 0.04 0.13 0.04 0.13 0.05 0.27 0.07
    Shirahama 33.7°N, 135.4°E 2002.7-2015.4 0.19 0.08 0.17 0.09 0.19 0.08 0.23 0.08
    Solar_Village 24.9°N, 46.4°E 2002.7-2013.4 0.31 0.14 0.29 0.14 0.31 0.14 0.27 0.12
    Xianghe 39.8°N, 117.0°E 2004.9-2015.4 0.34 0.21 0.33 0.23 0.34 0.20 0.38 0.29
    Australia
    Birdsville 25.9°S, 139.4°E 2005.8-2013.9 0.03 0.02 0.03 0.02 0.03 0.02 0.05 0.03
    Canberra 35.3°S, 149.1°E 2003.11-2015.4 0.05 0.02 0.05 0.02 0.05 0.02 0.04 0.02
    Jabiru 12.7°S, 132. 9°E 2002.7-2014.12 0.13 0.08 0.13 0.082 0.13 0.08 0.08 0.06
    Lake_Argyle 16.1°S, 128.8°E 2002.7-2015.4 0.11 0.08 0.10 0.08 0.11 0.08 0.07 0.05
    Europe
    Avignon 43.9°N, 4.9°E 2002.7-2012.10 0.14 0.06 0.14 0.06 0.13 0.05 0.15 0.06
    Barcelona 41.4°N, 2.1°E 2004.12-2015.1 0.13 0.06 0.12 0.06 0.13 0.06 0.15 0.07
    Burjassot 39.5°N, 0.4°W 2007.5-2015.4 0.12 0.05 0.12 0.06 0.11 0.04 0.10 0.05
    Cabo_da_Roca 38.8°N, 9.5°W 2003.11-2015.4 0.10 0.05 0.10 0.06 0.11 0.05 0.14 0.08
    Carpentras 44.1°N, 5.1°E 2003.2-2015.4 0.11 0.05 0.11 0.05 0.11 0.05 0.14 0.06
    Evora 38.6°N, 7.9°W 2003.7-2014.10 0.12 0.06 0.12 0.07 0.13 0.07 0.09 0.06
    FORTH_CRETE 35.3°N, 25.3°E 2003.3-2013.9 0.14 0.04 0.14 0.04 0.14 0.04 0.18 0.05
    Granada 37.2°N, 3.6°W 2005.1-2014.12 0.12 0.04 0.12 0.04 0.12 0.05 0.13 0.05
    Lecce_University 40.3°N, 18.1°E 2003.3-2013.11 0.18 0.06 0.19 0.06 0.18 0.06 0.12 0.05
    OHP_OBSERVATOIRE 43.9°N, 5.7°E 2005.6-2014.10 0.08 0.04 0.08 0.04 0.09 0.04 0.09 0.05
    Palencia 42.0°N, 4.5°W 2003.4-2015.4 0.08 0.03 0.08 0.03 0.07 0.03 0.09 0.05
    Rome_Tor_Vergata 41.8°N, 12.7°E 2002.9-2015.4 0.14 0.05 0.14 0.05 0.16 0.06 0.17 0.06
    Sevastopol 44.6°N, 33.5°E 2006.6-2013.8 0.15 0.04 0.15 0.05 0.15 0.05 0.14 0.04
    Thessaloniki 40.6°N, 23.0°E 2005.10-2015.4 0.18 0.07 0.19 0.08 0.17 0.07 0.20 0.10
    Toulon 43.1°N, 6.0°E 2004.11-2015.4 0.10 0.05 0.10 0.05 0.11 0.05 0.12 0.06
    Venise 45.3°N, 12.5°E 2002.7-2015.4 0.21 0.06 0.22 0.07 0.20 0.07 0.25 0.08
    Villefranche 43.7°N, 7.3°E 2004.1-2014.5 0.13 0.06 0.12 0.06 0.13 0.06 0.15 0.07
    North America
    BONDVILLE 40.1°N, 88.4°W 2002.7-2014.9 0.11 0.06 0.11 0.08 0.11 0.07 0.14 0.08
    CCNY 40.8°N, 74.0°W 2002.7-2015.4 0.08 0.04 0.08 0.04 0.09 0.06 0.21 0.13
    Frenchman_Flat 36.8°N, 15.9°W 2006.11-2014.7 0.05 0.02 0.05 0.02 0.05 0.02 0.06 0.03
    Fresno 36.8°N, 19.8°W 2002.7-2011.12 0.13 0.05 0.14 0.06 0.12 0.05 0.13 0.05
    GSFC 39.0°N, 76.8°W 2002.7-2014.6 0.12 0.10 0.12 0.09 0.12 0.11 0.19 0.15
    KONZA_EDC 39.1°N, 96.6°W 2002.7-2014.9 0.11 0.08 0.10 0.08 0.11 0.08 0.07 0.05
    La_Jolla 32.9°N, 17.3°W 2003.2-2013.10 0.08 0.03 0.09 0.03 0.08 0.03 0.09 0.05
    MD_Science_Center 39.3°N, 76.6°W 2002.7-2014.10 0.10 0.68 0.10 0.07 0.11 0.08 0.15 0.09
    Sioux_Falls 43.7°N, 96.6°W 2002.7-2015.4 0.10 0.05 0.10 0.05 0.10 0.05 0.13 0.07
    UCSB 34.4°N, 19.8°W 2003.2-2014.12 0.08 0.05 0.08 0.05 0.08 0.04 0.06 0.04
    South America
    CEILAP-BA 34.6°S, 58.5°W 2002.8-2013.7 0.07 0.02 0.07 0.02 0.08 0.02 0.14 0.05
    CUIABA-MIRANDA 15.7°S, 56.0°W 2003.4-2014.10 0.23 0.23 0.24 0.24 0.23 0.23 0.19 0.28
    La_Parguera 18.0°N, 67.1°W 2002.7-2015.4 0.11 0.07 0.11 0.07 0.12 0.07 0.17 0.08
    Trelew 43.2°S, 65.3°W 2005.11-2014.8 0.03 0.02 0.03 0.01 0.03 0.02 0.05 0.03

    Table 1. Site locations, study periods and 550-nm monthly mean AOD SD from the four data records at the 53 selected stations. AOD RE is monthly mean AOD calculated from the daily AERONET AOD if there were more than five daily measurements each month. AOD AM and AOD PM is monthly mean AOD calculated from AERONET instantaneous AOD measurements within 30 min of the Terra and Aqua overpass times. AOD MOD were calculated from all collocated AOD from MODIS/Aqua provided by the MAPSS system if there were more than five daily measurements each month.

    Table 2. Site locations, study periods and decadal trends in 550-nm AOD from the four data records at the 53 selected stations. Bold font indicates trends that are statistically significant at the 95% confidence level.
    MK trend of AOD (10 yr-1)
    Site Latitude, longitude Study period AOD RE AOD AM AOD PM AOD MOD
    Africa
    Banizoumbou 13.5°N, 2.7°E 2002.7-2015.4 -0.01 -0.01 -0.00 0.02
    Blida 36.5°N, 2.9°E 2003.11-2010.10 -0.07 -0.06 -0.05 -0.03
    Capo_Verde 16.7°N, 22.9°W 2002.7-2015.4 -0.05 -0.03 -0.02 -0.06
    Dakar 14.4°N, 17.0°W 2003.6-2015.4 -0.08 -0.09 -0.09 -0.04
    IER_Cinzana 13.3°N, 5.9°W 2004.6-2015.4 -0.05 -0.06 -0.06 -0.04
    La_Laguna 28.5°N, 16.3°W 2006.7-2014.8 -0.01 -0.01 -0.01 -0.01
    Saada 31.6°N, 8.2°W 2004.7-2015.4 -0.04 -0.04 -0.04 -0.01
    Santa_Cruz_Tenerife 28.5°N, 16.3°W 2005.7-2013.12 -0.07 -0.06 -0.07 -0.04
    Skukuza 25.0°S, 31.6°E 2002.7-2011.7 0.02 0.02 0.01 0.05
    Asia
    Beijing 40.0°N, 116.4°E 2002.7-2015.4 -0.08 -0.07 -0.10 0.01
    Chen-Kung_Univ 23.0°N, 120.2°E 2002.7-2015.4 -0.07 -0.08 -0.06 -0.10
    IMS-METU-ERDEMLI 36.6°N, 34.3°E 2003.2-2014.12 -0.02 -0.01 -0.01 -0.00
    Kanpur 26.5°N, 80.2°E 2002.7-2014.6 0.03 0.07 0.03 0.05
    Nes_Ziona 31.9°N, 34.8°E 2002.7-2014.10 -0.02 -0.02 -0.03 0.01
    SEDE_BOKER 30.9°N, 34.8°E 2002.7-2015.4 -0.01 -0.01 -0.01 0.04
    Shirahama 33.7°N, 135.4°E 2002.7-2015.4 -0.05 -0.03 -0.01 -0.01
    Solar_Village 24.9°N, 46.4°E 2002.7-2013.4 0.13 0.14 0.12 0.09
    Xianghe 39.8°N, 117.0°E 2004.9-2015.4 -0.01 -0.01 0.00 0.04
    Australia
    Birdsville 25.9°S, 139.4°E 2005.8-2013.9 0.01 0.01 0.01 -0.07
    Canberra 35.3°S, 149.1°E 2003.11-2015.4 -0.00 -0.00 -0.00 0.01
    Jabiru 12.7°S, 132. 9°E 2002.7-2014.12 -0.02 -0.01 -0.01 0.01
    Lake_Argyle 16.1°S, 128.8°E 2002.7-2015.4 0.00 0.00 0.00 0.00
    Europe
    Avignon 43.9°N, 4.9°E 2002.7-2012.10 -0.04 -0.04 -0.05 -0.02
    Barcelona 41.4°N, 2.1°E 2004.12-2015.1 -0.06 -0.05 -0.07 -0.05
    Burjassot 39.5°N, 0.4°W 2007.5-2015.4 -0.01 -0.00 -0.00 -0.02
    Cabo_da_Roca 38.8°N, 9.5°W 2003.11-2015.4 -0.03 -0.02 -0.02 -0.03
    Carpentras 44.1°N, 5.1°E 2003.2-2015.4 -0.05 -0.04 -0.05 -0.04
    Evora 38.6°N, 7.9°W 2003.7-2014.10 -0.03 -0.03 -0.03 -0.01
    FORTH_CRETE 35.3°N, 25.3°E 2003.3-2013.9 -0.03 -0.03 -0.03 -0.01
    Granada 37.2°N, 3.6°W 2005.1-2014.12 -0.02 -0.01 -0.03 -0.02
    Lecce_University 40.3°N, 18.1°E 2003.3-2013.11 -0.03 -0.03 -0.01 -0.01
    OHP_OBSERVATOIRE 43.9°N, 5.7°E 2005.6-2014.10 -0.01 -0.00 -0.01 -0.00
    Palencia 42.0°N, 4.5°W 2003.4-2015.4 -0.03 -0.06 -0.07 -0.03
    Rome_Tor_Vergata 41.8°N, 12.7°E 2002.9-2015.4 -0.03 -0.03 -0.05 -0.05
    Sevastopol 44.6°N, 33.5°E 2006.6-2013.8 -0.03 -0.03 -0.02 -0.05
    Thessaloniki 40.6°N, 23.0°E 2005.10-2015.4 -0.04 -0.09 -0.06 -0.03
    Toulon 43.1°N, 6.0°E 2004.11-2015.4 -0.03 -0.03 -0.03 -0.02
    Venise 45.3°N, 12.5°E 2002.7-2015.4 -0.06 -0.07 -0.06 -0.06
    Villefranche 43.7°N, 7.3°E 2004.1-2014.5 -0.03 -0.05 -0.04 -0.02
    North America
    BONDVILLE 40.1°N, 88.4°W 2002.7-2014.9 -0.01 -0.00 -0.01 -0.02
    CCNY 40.8°N, 74.0°W 2002.7-2015.4 -0.02 -0.02 -0.03 -0.03
    Frenchman_Flat 36.8°N, 15.9°W 2006.11-2014.7 -0.02 -0.00 -0.00 -0.00
    Fresno 36.8°N, 19.8°W 2002.7-2011.12 -0.01 -0.03 -0.01 -0.01
    GSFC 39.0°N, 76.8°W 2002.7-2014.6 -0.01 -0.00 -0.00 -0.05
    KONZA_EDC 39.1°N, 96.6°W 2002.7-2014.9 -0.00 -0.00 -0.01 -0.01
    La_Jolla 32.9°N, 17.3°W 2003.2-2013.10 -0.02 -0.01 -0.01 -0.02
    MD_Science_Center 39.3°N, 76.6°W 2002.7-2014.10 -0.03 -0.02 -0.01 -0.03
    Sioux_Falls 43.7°N, 96.6°W 2002.7-2015.4 -0.00 -0.00 -0.01 -0.01
    UCSB 34.4°N, 19.8°W 2003.2-2014.12 -0.02 -0.03 -0.04 -0.02
    South America
    CEILAP-BA 34.6°S, 58.5°W 2002.8-2013.7 -0.01 -0.02 -0.00 0.01
    CUIABA-MIRANDA 15.7°S, 56.0°W 2003.4-2014.10 -0.08 -0.08 -0.07 -0.06
    La_Parguera 18.0°N, 67.1°W 2002.7-2015.4 -0.01 -0.01 -0.00 0.01
    Trelew 43.2°S, 65.3°W 2005.11-2014.8 -0.01 -0.00 -0.00 0.03

    Table 2. Site locations, study periods and decadal trends in 550-nm AOD from the four data records at the 53 selected stations. Bold font indicates trends that are statistically significant at the 95% confidence level.

    Table 2 shows that the majority of sites exhibited negative trends of AOD RE, including all stations in Europe, North America, South America and most sites in Africa (eight of nine) and Asia (seven of nine), although the trends were not statistically significant at some sites. A significant increasing trend in AOD RE [0.12 (10 yr)-1] was found only at Solar_Village in Saudi Arabia. The strong positive trend there has also been noted in AOD measurements collected by SeaWiFS (Hsu et al., 2012), MISR (de Meij et al., 2012a) and MODIS/Aqua (this study), as well as EMAC model simulations (de Meij et al., 2012b; Pozzer et al., 2015). AOD RE at Skukuza in South Africa, Kanpur in India, and Birdsville in Australia showed trends that were increasing but were not statistically significant.

    Comparisons of the trends between AOD RE and AOD AM, between AOD RE and AOD PM, and between AOD RE and AOD MOD are illustrated in Figs. 2-4, respectively. The tendencies in AOD RE were reproduced by AOD AM and AOD PM at nearly all stations. The trend magnitudes of AOD AM and AOD PM were also close to those from AOD RE at most stations. The results suggest that AOD measured at Terra or Aqua overpass times could represent the trends derived from daily measurements.

    Fig.2. Comparison of decadal trends between AOD RE and AOD AM in different regions. The trends estimated from AOD RE and AOD AM are shown on the x-axes and the y-axes, respectively, in the scatterplots. Blue solid diamonds indicate that the trends from both AOD RE and AOD AM were statistically significant. Red solid squares indicate that the AOD RE trend was statistically significant but the AOD AM trend was statistically non-significant. Green asterisks indicate that the AOD RE trend was statistically non-significant but the AOD AM trend was statistically significant. Cyan crosses indicate that the trends from AOD RE and AOD AM were statistically non-significant. The black dots in the map represented the locations of sites, and the numbers of sites in the different regions are given in parentheses in the scatter-plots.

    Fig.3. As Fig. 2, except that the trend comparisons are between AOD RE and AOD PM.

    Fig.4. As Fig. 2, except that the trend comparisons are between AOD RE and AOD MOD.

    Fig.5. Comparison of AERONET AOD (AOD RE) and MODIS/Aqua AOD (AOD MOD) at (a) Birdsville in Australia, (b) Trelew in Argentina, and (c) SEDE_BOKER in Israel. The AOD anomalies are shown as blue solid lines, which are matched to the right-hand ordinate axis, and the blue dashed lines indicate that the anomaly is 0.

    The trends in AOD RE and AOD MOD were consistent over 44 of the sites. All sites in Europe and North America showed the same tendencies. Opposite MK trends between AOD RE and AOD MOD were found at one of nine sites in Africa (Banizoumbou), four of nine sites in Asia (Beijing, Xianghe, Nes_Ziona and SEDE_BOKER), three of four sites in Oceania (Birdsville, Canberra and Jabiru), and three of four sites in South America (CEILAP-BA, La_Parguera, and Trelew).

    The tendencies of the strictly matched monthly medians of AOD RE and AOD MOD (i.e., monthly medians calculated from simultaneous measurements from both products) remained unchanged except at Beijing in China, which suggests that the temporal differences in sampling between AOD RE and AOD MOD has little effect on the detection of AOD trends in most cases. If the temporal sampling difference was not accounted for, the AOD RE at Beijing showed a significant decreasing trend [-0.08 (10 yr)-1] but the AOD MOD exhibited a weak increasing trend [0.01 (10 yr)-1]. The trend in AOD MOD changed to -0.03 (10 yr)-1 if the daily measurements from AERONET and MODIS/Aqua were strictly matched. Additionally, the seasonal MK tests of MODIS and AERONET AOD exhibited the same trend signs at Xianghe as those reported by (Zhang et al., 2017). Increasing trends of 0.001 yr-1 (MODIS) and 0.004 yr-1 (AERONET) were found from 2002 to 2007, but there were downward trends of -0.006 yr-1 (MODIS) and -0.004 yr-1 (AERONET) from 2008 to 2015. However, consistent declines in AERONET AOD and MODIS/Aqua AOD were found at Beijing during the two periods (2002-07 and 2008-15).

    AOD over Australia is typically low, with occasional high AOD events mostly resulting from locally derived or transported smoke from wildfires and mineral dust (Qin and Mitchell, 2009). Another interesting feature seen in this region is that the surface reflectance is relatively high, because of the abundance of bare soil and desert. Both factors indicate that it is very hard to retrieve AOD with high quality. Validation has shown that the correlation between AERONET and MODIS AOD is lower over Australia than in other regions (Sayer et al., 2013). Figure 5a shows the time series of the monthly medians of AOD RE and AOD MOD (shown as a dependent variable on the left-hand y-axis) and the AOD anomaly (shown as a dependent variable on the right-hand y-axis) defined as the difference between the AOD MOD and the monthly averaged AOD RE over all years at Birdsville, Australia. An interesting feature is that all the AOD anomalies were positive before September 2008, i.e., MODIS/Aqua overestimated AOD, but AOD was underestimated by MODIS afterwards. The shift in the AOD anomalies resulted in the artificially negative trend in AOD MOD seen at Birdsville. This same scenario also occurred at Canberra. Trelew is in the middle of the Argentinean Patagonia, where the monthly median AOD is less than 0.10. A shift in AOD anomalies after March 2008 (Fig. 5b) resulted in an artificially positive tendency in AOD MOD. The reason for these abrupt changes in MODIS performance compared with AERONET at these stations needs further study.

    Banizoumbou, in the semi-arid Sahel region and influenced by dust emissions and/or biomass burning (Levy et al., 2010), showed high AOD. Studies have shown that, from the mid-1980s to the mid-2000s in the Sahara and Sahel regions, near-surface wind speed decreased, precipitation increased, and therefore dust emissions decreased (Chin et al., 2014). This finding of a reduction in AOD is also supported by in-situ aerosol measurements (Li et al., 2014) and model simulations (Chin et al., 2014). The AOD RE and AOD MOD at other stations in the Sahel showed a declining trend. Therefore, the increasing trend at Banizoumbou (although non-significant) seen in the MODIS data seems likely to be spurious.

    The sites of SEDE_BOKER and Nes_Ziona are in Israel, where aerosols are influenced both by mineral dust transported from the Saharan and Arabian deserts and fine-mode pollution emitted by the petroleum industry in this region (Yoon et al., 2012). The AOD at these two sites showed a decreasing tendency based on AERONET measurements, which is consistent with the results reported by (Yoon et al., 2012) and (Li et al., 2014). (Yoon et al., 2012) noted that the decreased AERONET AOD at SEDE_BOKER was due to a decrease in coarse-mode particles [approximately -0.29 (10 yr)-1 for coarse-mode AOD]. MODIS/Aqua AOD generally produced overestimates at SEDE_BOKER, where 98% of the monthly median anomalies were positive (Fig. 5c). The retrieval uncertainty of MODIS AOD under conditions with heavy aerosol loading is mainly related to the aerosol mode assumptions. The MODIS retrieval algorithm assumes a constant single-scattering albedo value; however, the AERONET single-scattering albedo at SEDE_BOKER shows an increasing trend [0.004 (10 yr)-1] (Li et al., 2014). Therefore, the temporal change in single-scattering albedo may produce a spurious tendency in the AOD retrieved by MODIS (Mishchenko et al., 2007, 2012; Li et al., 2014).

    In addition, we found a few high-altitude sites (>1.2 km) in North America (BSRN_BAO_Boulder, Railroad_Valley, Sevilleta, TABLE_MOUNTAIN_CA) and Asia (Dalanzadgad) where AERONET AOD was not suitable for representing the regional AOD trend due to a lack of spatial representation. The retrieval accuracy of MODIS AOD at such sites is impacted by the weak aerosol signal due to the very low aerosol loading (Levy et al., 2010; Sayer et al., 2013). Thus, high-altitude sites (>1.2 km) were excluded in the AOD trend analyses.

    4. Conclusions

    AOD trends derived from MODIS/Aqua and AERONET at 53 stations in land areas in various locations across the globe were compared and analyzed. The major findings can be summarized as follows:

    AOD trends calculated from daily measurements can be accurately reproduced by AOD measurements collected at the overpass time of MODIS, which indicates that MODIS has the potential to capture AOD trends.

    Consistent AOD tendencies calculated from the monthly medians of the AERONET and MODIS/Aqua AOD products were derived at 44 of 53 sites. However, this was not true for sites in Australia, where the retrieval accuracy of MODIS/Aqua AOD data was found to be poor due to the very low aerosol loading (monthly median AOD <0.15) and the relatively higher surface reflectance of bare soil and/or desert. Disagreement in the AOD trends between MODIS and AERONET was also found at a few sites in South America. This was due to the low retrieval quality in MODIS/Aqua AOD and likely resulted from improper surface reflectance parameterization and uncertainties in the aerosol model used in the algorithm (Levy et al., 2010). Furthermore, sampling issues should be evaluated carefully when deriving long-term trends in AOD in regions where AOD shows substantial day-to-day variation.

    Merged data based on the MODIS AOD products derived from both the DT and DB algorithms are used in many studies to investigate the long-term AOD trends over land areas in various locations throughout the world. In some regions, the typical AOD and its variability are low, close to the magnitude of the retrieval uncertainty, which affects the estimation of the AOD trends. However, the current MODIS AOD products do provide a continuous aerosol data record for monitoring long-term climate change.

    Acknowledgements. This work was supported by the National Natural Science Foundation of China (Grant Nos. 41475027,41475138 and 41675033). We are grateful to the AERONET team and MAPSS team, especially the PIs of the selected AERONET sites, for providing the data used in this study. We are also thankful for the suggestions and comments from the two anonymous reviewers and the editor, which helped to improve the manuscript.

    References

    [1] Boucher, O., Coauthors, 2013: Clouds and Aerosols. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker et al., Eds., Cambridge University Press.
    [Cited within:1]
    [2] Chin, M., Coauthor, 2014: Multi-decadal aerosol variations from 1980 to 2009: A perspective from observations and a global model.Atmospheric Chemistry and Physics14,3657-3690,https://doi.org/10.5194/acp-14-3657-2014.
    Aerosol variations and trends over different land and ocean regions from 1980 to 2009 are analyzed with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and observations from multiple satellite sensors and available ground-based networks. Excluding time periods with large volcanic influence, aerosol optical depth (AOD) and surface concentration over polluted land regions generally vary with anthropogenic emissions, but the magnitude of this association can be dampened by the presence of natural aerosols, especially dust. Over the 30-year period in this study, the largest reduction in aerosol levels occurs over Europe, where AOD has decreased by 4060% on average and surface sulfate concentrations have declined by a factor of up to 34. In contrast, East Asia and South Asia show AOD increases, but the relatively high level of dust aerosols in Asia reduces the correlation between AOD and pollutant emission trends. Over major dust source regions, model analysis indicates that the change of dust emissions over the Sahara and Sahel has been predominantly driven by the change of near-surface wind speed, but over Central Asia it has been largely influenced by the change of the surface wetness. The decreasing dust trend in the North African dust outflow region of the tropical North Atlantic and the receptor sites of Barbados and Miami is closely associated with an increase of the sea surface temperature in the North Atlantic. This temperature increase may drive the decrease of the wind velocity over North Africa, which reduces the dust emission, and the increase of precipitation over the tropical North Atlantic, which enhances dust removal during transport. Despite significant trends over some major continental source regions, the model-calculated global annual average AOD shows little change over land and ocean in the past three decades, because opposite trends in different land regions cancel each other out in the global average, and changes over large open oceans are negligible. This highlights the necessity for regional-scale assessment of aerosols and their climate impacts, as global-scale average values can obscure important regional changes.
    DOI:10.5194/acp-14-3657-2014      URL     [Cited within:3]
    [3] de Meij, A., A. Pozzer, J. Lelieveld, 2012a: Trend analysis in aerosol optical depths and pollutant emission estimates between 2000 and 2009.Atmos. Environ.,51,75-85,https://doi.org/10.1016/j.atmosenv.2012.01.059..
    We evaluated global and regional aerosol optical depth (AOD) trends in view of aerosol (precursor) emission changes between 2000 and 2009. We used AOD data products from MODIS, MISR and AERONET and emission estimates from the EMEP, REAS and IPCC inventories. The trends in global monthly AOD of MODIS (L3), MISR (L3) and AERONET (L2) are significantly negative over Europe and North America, whereas over South and East Asia they are mostly positive. The calculated 20002009 trends from the monthly L3 products correspond well with the more detailed daily MODIS L2 AODs for three selected regions (Central Mediterranean, North-East America and East Asia). Furthermore, daily and monthly AERONET L2 AOD trends agree well. The trends in AOD are compared to estimated emission changes of SO2, NOx, NH3 and black carbon. We associate the downward trends in AOD over Europe and North America with decreasing emissions of SO2, NOx and other pollutants. Over East Asia the MODIS L2 trends are generally positive, consistent with increasing pollutant emissions by fossil energy use and growing industrial and urban activities. It appears that SO2 emission changes dominate the AOD trends, although especially in Asia NOx emissions may become increasingly important. Our results suggest that solar brightening due to decreasing SO2 emissions and the resulting downward AOD trends over Europe may have weakened in the 2000s compared to the 1990s.
    DOI:10.1016/j.atmosenv.2012.01.059      URL     [Cited within:1]
    [4] de Meij A., A. Pozzer A., K. Pringle H. Tost, and J. Lelieveld, 2012b: EMAC model evaluation and analysis of atmospheric aerosol properties and distribution with a focus on the Mediterranean region.Atmos. Res.,114-115,38-69,https://doi.org/10.1016/j.atmosres.2012.05.014.
    The skill of the EMAC atmospheric chemistry-climate model to predict the aerosol optical depth (AOD) is evaluated by comparing with remote sensing data products from AERONET, MODIS, MISR and CALIOP with a focus on the Mediterranean region. In addition, calculated aerosol concentrations are compared with measurements from the CASTNET, IMPROVE, EMEP, EANET and CAWNET networks. Calculated sulphate concentrations are in good agreement with the measurements, whilst the agreement is less satisfactory for ammonium and nitrate, possibly because of measurement artefacts. The model reproduces the main spatial atmospheric distribution of the sulphate and ammonium aerosols. For nitrate some differences are found when compared to observations. The analysis of black and organic carbon (BC and OC) over Europe shows that the model typically overestimates observed BC concentrations by a factor of 1.6 and underestimates OC by a factor of 2.6. For the USA BC and OC are in general overestimated and for China BC and OC are in general underestimated by the model. The seasonal distribution of elevated AODs is well represented by the model when compared to MODIS and MISR, though AODs are somewhat low-biased. Calculated annual mean AODs are in general lower than of AERONET and the temporal correlation coefficients vary between 0.11 and 0.68. High temporal correlation coefficients are found for biomass burning regions (South America and West Africa), indicating that the seasonal cycle of this source category is well represented in the model, based on the GFED inventory. High temporal correlation coefficients are obtained for the Mediterranean region during summer, which indicates that the model captures the dust intrusions. Our model calculations show that inorganic particles and associated water are the most abundant aerosol components over Europe, North America and Asia, whilst over the Mediterranean during summer dust dominates the total AOD. An analysis of the meridional vertical distribution of model calculated dust indicates good agreement with CALIOP observations for locations near the Mediterranean and over northern Africa. The modelled underestimation of the AODs over Europe and the USA is larger at low than at high relative humidity, indicating that the concentrations of hygroscopic aerosols are too low.
    DOI:10.1016/j.atmosres.2012.05.014      URL     [Cited within:1]
    [5] Hirsch R. M., J. R. Slack, 1984: A nonparametric trend test for seasonal data with serial dependence.Water Res. Res.,20,727-732,https://doi.org/10.1029/WR020i006p00727.
    Statistical tests for monotonic trend in seasonal (e.g., monthly) hydrologic time series are commonly confounded by some of the following problems: nonnormal data, missing values, seasonality, censoring (detection limits), and serial dependence. An extension of the Mann-Kendall test for trend (designed for such data) is presented here. Because the test is based entirely on ranks, it is robust against nonnormality and censoring. Seasonality and missing values present no theoretical or computational obstacles to its application. Monte Carlo experiments show that, in terms of type I error, it is robust against serial correlation except when the data have strong long-term persistence (e.g., ARMA (1, 1) monthly processes with 蠒 > 0.6) or short records (藴 5 years). When there is no serial correlation, it is less powerful than a related simpler test which is not robust against serial correlation.
    DOI:10.1029/WR020i006p00727      URL     [Cited within:3]
    [6] Holben, B. N., Coauthors, 1998: AERONET federated instrument network and data archive for aerosol characterization.Remote Sens. Environ.66,1-16,https://doi.org/10.1016/S0034-4257( 98) 00031- 5.
    The concept and description of a remote sensing aerosol monitoring network initiated by NASA, developed to support NASA, CNES, and NASDA’s Earth satellite systems under the name AERONET and expanded by national and international collaboration, is described. Recent development of weather-resistant automatic sun and sky scanning spectral radiometers enable frequent measurements of atmospheric aerosol optical properties and precipitable water at remote sites. Transmission of automatic measurements via the geostationary satellites GOES and METEOSATS’ Data Collection Systems allows reception and processing in near real-time from approximately 75% of the Earth’s surface and with the expected addition of GMS, the coverage will increase to 90% in 1998. NASA developed a UNIX-based near real-time processing, display and analysis system providing internet access to the emerging global database. Information on the system is available on the project homepage, http://spamer.gsfc.nasa.gov . The philosophy of an open access database, centralized processing and a user-friendly graphical interface has contributed to the growth of international cooperation for ground-based aerosol monitoring and imposes a standardization for these measurements. The system’s automatic data acquisition, transmission, and processing facilitates aerosol characterization on local, regional, and global scales with applications to transport and radiation budget studies, radiative transfer-modeling and validation of satellite aerosol retrievals. This article discusses the operation and philosophy of the monitoring system, the precision and accuracy of the measuring radiometers, a brief description of the processing system, and access to the database.
    DOI:10.1016/S0034-4257(98)00031-5      URL     [Cited within:2]
    [7] Hsu N. C., R. Gautam, A. M. Sayer, C. Bettenhausen, C. Li, M. J. Jeong, S. -C. Tsay, and B. N. Holben, 2012: Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010.Atmos. Chem. Phys.12,8037-8053,https://doi.org/10.5194/acp-12-8037-2012.
    Both sensor calibration and satellite retrieval algorithm play an important role in the ability to determine accurately long-term trends from satellite data. Owing to the unprecedented accuracy and long-term stability of its radiometric calibration, SeaWiFS measurements exhibit minimal uncertainty with respect to sensor calibration. In this study, we take advantage of this well-calibrated set of measurements by applying a newly-developed aerosol optical depth (AOD) retrieval algorithm over land and ocean to investigate the distribution of AOD, and to identify emerging patterns and trends in global and regional aerosol loading during its 13-yr mission. Our correlation analysis between climatic indices (such as ENSO) and AOD suggests strong relationships for Saharan dust export as well as biomass-burning activity in the tropics, associated with large-scale feedbacks. The results also indicate that the averaged AOD trend over global ocean is weakly positive from 1998 to 2010 and comparable to that observed by MODIS but opposite in sign to that observed by AVHRR during overlapping years. On regional scales, distinct tendencies are found for different regions associated with natural and anthropogenic aerosol emission and transport. For example, large upward trends are found over the Arabian Peninsula that indicate a strengthening of the seasonal cycle of dust emission and transport processes over the whole region as well as over downwind oceanic regions. In contrast, a negative-neutral tendency is observed over the desert/arid Saharan region as well as in the associated dust outflow over the north Atlantic. Additionally, we found decreasing trends over the eastern US and Europe, and increasing trends over countries such as China and India that are experiencing rapid economic development. In general, these results are consistent with those derived from ground-based AERONET measurements.
    DOI:10.5194/acp-12-8037-2012      URL     [Cited within:4]
    [8] Kahn R. A., M. J. Garay, D. L. Nelson, K. K. Yau, M. A. Bull, B. J. Gaitley, J. V. Martonchik, and R. C. Levy, 2007: Satellite-derived aerosol optical depth over dark water from MISR and MODIS: Comparisons with AERONET and implications for climatological studies.J. Geophys. Res.,112,D18205,https://doi.org/10.1029/2006JD008175.
    [1] Although the current Multiangle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite passive remote sensing midvisible aerosol optical thickness (AOT) products are accurate overall to about 0.05 or 20%, they differ systematically on a global, monthly average basis, by about 0.03 to 0.05. Some key climate change and other applications require accuracies of 0.03 or better. The instruments are sufficiently stable and well characterized, and have adequate signal-to-noise, to realize such precision. However, assumptions made in the current standard aerosol retrieval algorithms produce AOT biases that must be addressed first. We identify the causes of AOT discrepancies over dark water under typical, relatively low AOT conditions and quantify their magnitudes on the basis of detailed analysis. Examples were selected to highlight key issues for which there are coincident MISR, MODIS, and Aerosol Robotic Network (AERONET) observations. Instrument calibration and sampling differences, assumptions made in the MISR and MODIS standard algorithms about ocean surface boundary conditions, missing particle property or mixture options, and the way reflectances used in the retrievals are selected each contribute significantly to the observed differences under some circumstances. Cloud screening is also identified as a factor, though not fully examined here, as are the relatively rare high-AOT cases over ocean. Specific algorithm upgrades and further studies indicated by these findings are discussed, along with recommendations for effectively using the currently available products for regional and global applications.
    DOI:10.1029/2006JD008175      URL     [Cited within:1]
    [9] Kaufman Y. J., B. N. Holben, D. Tanré I. Slutsker, A. Smirnov, and T. F. Eck, 2000: Will aerosol measurements from Terra and Aqua Polar Orbiting satellites represent the daily aerosol abundance and properties? Geophys. Res. Lett., 27( 23), 3861- 3864.
    The Terra and Aqua missions will help quantify aerosol radiative forcing of climate by providing innovative measurements of the aerosol daily spatial distribution and identifying dust, smoke and regional pollution. However, these measurements are acquired at specific times of the day. To what extent can such measurements represent the daily average aerosol forcing of climate? We answer this question using 7 years of data from the Aerosol Robotic Network (AERONET) of 50-70 global ground-based instruments. AERONET measures the aerosol spectral optical thickness and the total precipitable water vapor every 15 minutes throughout the day. With a data set of 1/2 million measurements, AERONET demonstrates that Terra and Aqua aerosol measurements can represent the annual average value within 2% error. This excellent Terra representation of the daily average optical thickness is independent of the particle size or range of the optical thickness. This finding should facilitate ingest of satellite aerosol measurements in models that calculate radiative forcing and predict climate change.
    DOI:10.1029/2000GL011968      URL     [Cited within:1]
    [10] Kokhanovsky, A. A., Coauthors, 2007: Aerosol remote sensing over land: A comparison of satellite retrievals using different algorithms and instruments.Atmos. Res.,85,372-394,https://doi.org/10.1016/j.atmosres.2007.02.008.
    An inter-comparison study of the aerosol optical thickness (AOT) at 0.5502μm retrieved using different satellite instruments and algorithms based on the analysis of backscattered solar light is presented for a single scene over central Europe on October 13th, 2005. For the first time comparisons have been performed for as many as six instruments on multiple satellite platforms. Ten different algorithms are briefly discussed and inter-compared. It was found that on the scale of a single pixel there can be large differences in AOT retrieved over land using different retrieval techniques and instruments. However, these differences are not as pronounced for the average AOT over land. For instance, the average AOT at 0.5502μm for the area 7–12E, 49–53N was equal to 0.14 for MISR, NASA MODIS and POLDER algorithms. It is smaller by 0.01 for the ESA MERIS aerosol product and larger by 0.04 for the MERIS BAER algorithm. AOT as derived using AATSR gives on average larger values as compared to all other instruments, while SCIAMACHY retrievals underestimate the aerosol loading. These discrepancies are explained by uncertainties in a priori assumptions used in the different algorithms and differences in the sensor characteristics. Validation against AERONET shows that MERIS provides the most accurate AOT retrievals for this scene.
    DOI:10.1016/j.atmosres.2007.02.008      URL     [Cited within:1]
    [11] Levy R. C., L. A. Remer, R. G. Kleidman, S. Mattoo, C. Ichoku, R. Kahn, and T. F. Eck, 2010: Global evaluation of the collection 5 MODIS dark-target aerosol products over land.Atmos. Chem. Phys.10,10 399-10 420,https://doi.org/10.5194/acp-10-10399-2010.
    NASA's MODIS sensors have been observing the Earth from polar orbit, from Terra since early 2000 and from Aqua since mid 2002. We have applied a consistent retrieval and processing algorithm to both sensors to derive the Collection 5 (C005) dark-target aerosol products over land. Here, we validate the MODIS along-orbit Level 2 products by comparing to quality assured Level 2 AERONET sunphotometer measurements at over 300 sites. From 85 463 collocations, representing mutually cloud-free conditions, we find that >66% (one standard deviation) of MODIS-retrieved aerosol optical depth (AOD) values compare to AERONET-observed values within an expected error (EE) envelope of (0.05 + 15%), with high correlation (R = 0.9). Thus, the MODIS AOD product is validated and quantitative. However, even though we can define EEs for MODIS-reported ngstr m exponent and fine AOD over land, these products do not have similar physical validity. Although validated globally, MODIS-retrieved AOD does not fall within the EE envelope everywhere. We characterize some of the residual biases that are related to specific aerosol conditions, observation geometry, and/or surface properties, and relate them to situations where particular MODIS algorithm assumptions are violated. Both Terra's and Aqua's搑etrieved AOD are similarly comparable to AERONET, however, Terra's global AOD bias changes with time, overestimating (by ~0.005) before 2004, and underestimating by similar magnitude after. This suggests how small calibration uncertainties of <2% can lead to spurious conclusions about long-term aerosol trends.
    DOI:10.5194/acp-10-10399-2010      URL     [Cited within:4]
    [12] Levy R. C., S. Mattoo, L. A. Munchak, L. A. Remer, A. M. Sayer, F. Patadia, and N. C. Hsu, 2013: The Collection 6 MODIS aerosol products over land and ocean.Atmospheric Measurement Techniques6,2989-3034,https://doi.org/10.5194/amt-6-2989-2013.
    The twin Moderate resolution Imaging Spectroradiometer (MODIS) sensors have been flying on Terra since 2000 and Aqua since 2002, creating an extensive data set of global Earth observations. Here, we introduce the Collection 6 (C6) algorithm to retrieve aerosol optical depth (AOD) and aerosol size parameters from MODIS-observed spectral reflectance. While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. The C6 aerosol data set will be created from three separate retrieval algorithms that operate over different surface types. These are the two "Dark Target"(DT) algorithms for retrieving (1) over ocean (dark in visible and longer wavelengths) and (2) over vegetated/dark-soiled land (dark in the visible), plus the "Deep Blue"(DB) algorithm developed originally for retrieving (3) over desert/arid land (bright in the visible). Here, we focus on DT-ocean and DT-land (# 1 and # 2). We have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H2O, O-3, CO2, etc.) absorption corrections, while relaxing the solar zenith angle limit (up to <= 84 degrees) to increase poleward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season/location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence on the surface reflectance, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information. At the same time, we quantified how "upstream"changes to instrument calibration, land/sea masking and cloud masking will also impact the statistics of global AOD, and affect Terra and Aqua differently. For Aqua, all changes will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. We compared preliminary data to surface-based sun photometer data, and show that C6 should improve upon C5. C6 will include a merged DT/DB product over semi-arid land surfaces for reduced-gap coverage and better visualization, and new information about clouds in the aerosol field. Responding to the needs of the air quality community, in addition to the standard 10 km product, C6 will include a global (DT-land and DT-ocean) aerosol product at 3 km resolution.
    DOI:10.5194/amt-6-2989-2013      URL     [Cited within:1]
    [13] Li, Z., Coauthors, 2009: Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: A review and perspective.Ann. Geophys.27,2755-2770,https://doi.org/10.5194/angeo-27-2755-2009.
    As a result of increasing attention paid to aerosols in climate studies, numerous global satellite aerosol products have been generated. Aerosol parameters and underlining physical processes are now incorporated in many general circulation models (GCMs) in order to account for their direct and indirect effects on the earth's climate, through their interactions with the energy and water cycles. There exists, however, an outstanding problem that these satellite products have substantial discrepancies, that must be lowered substantially for narrowing the range of the estimates of aerosol's climate effects. In this paper, numerous key uncertain factors in the retrieval of aerosol optical depth (AOD) are articulated for some widely used and relatively long satellite aerosol products including the AVHRR, TOMS, MODIS, MISR, and SeaWiFS. We systematically review the algorithms developed for these sensors in terms of four key elements that influence the quality of passive satellite aerosol retrieval: calibration, cloud screening, classification of aerosol types, and surface effects. To gain further insights into these uncertain factors, the NOAA AVHRR data are employed to conduct various tests, which help estimate the ranges of uncertainties incurred by each of the factors. At the end, recommendations are made to cope with these issues and to produce a consistent and unified aerosol database of high quality for both environment monitoring and climate studies.
    DOI:10.5194/angeo-27-2755-2009      URL     [Cited within:1]
    [14] Li J., B. E. Carlson, O. Dubovik, and A. A. Lacis, 2014: Recent trends in aerosol optical properties derived from AERONET measurements.Atmos. Chem. Phys.14,12 271-12 289,https://doi.org/10.5194/acp-15-1599-2015.
    The Aerosol Robotic Network (AERONET) has been providing high-quality retrievals of aerosol optical properties from the surface at worldwide locations for more than a decade. Many sites have continuous and consistent records for more than 10 years, which enables the investigation of long-term trends in aerosol properties at these locations. In this study, we present the results of a trend analysis at selected stations with long data records. In addition to commonly studied parameters such as aerosol optical depth (AOD) and ngstrm exponent (AE), we also focus on inversion products including absorption aerosol optical depth (ABS), single-scattering albedo (SSA) and the absorption ngstrm exponent (AAE). Level 2.0 quality assured data are the primary source. However, due to the scarcity of level 2.0 inversion products resulting from the strict AOD quality control threshold, we have also analyzed level 1.5 data, with some quality control screening to provide a reference for global results. Two statistical methods are used to detect and estimate the trend: the Mann揔endall test associated with Sen's slope and linear least-squares fitting. The results of these statistical tests agree well in terms of the significance of the trend for the majority of the cases. The results indicate that Europe and North America experienced a uniform decrease in AOD, while significant (>90%) increases in these two parameters are found for North India and the Arabian Peninsula. The AE trends turn out to be different for North America and Europe, with increases for the former and decreases for the latter, suggesting opposite changes in fine/coarse-mode fraction. For level 2.0 inversion parameters, Beijing and Kanpur both experienced an increase in SSA. Beijing also shows a reduction in ABS, while the SSA increase for Kanpur is mainly due the increase in scattering aerosols. Increased absorption and reduced SSA are found at Solar_Village. At level 1.5, most European and North American sites also show positive SSA and negative ABS trends, although the data are more uncertain. The AAE trends are less spatially coherent due to large uncertainties, except for a robust increase at three sites in West Africa, which suggests a possible reduction in black carbon. Overall, the trends do not exhibit obvious seasonality for the majority of parameters and stations.
    DOI:10.5194/acp-14-12271-2014      URL     [Cited within:8]
    [15] Lyapustin, A., Coauthors, 2014: Scientific impact of MODIS C5 calibration degradation and C6+ improvements.Atmospheric Measurement Techniques7,4353-4365,https://doi.org/10.5194/amt-7-4353-2014.
    The Collection 6 (C6) MODIS land and atmosphere datasets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra, and to lesser extent, in MODIS Aqua geophysical datasets. Sensor degradation is largest in the Blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and ngstrm Exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS dataset which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as de-trending and Terra揂qua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm over deserts, we have also developed a de-trending and cross-calibration method which removes residual decadal trends on the order of several tenths of one percent of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1揃4, and provides a good consistency between the two MODIS sensors. MAIAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra ?NDVI ~ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit Terra provides considerably more cloud-free observations compared to the afternoon Aqua measurements.
    DOI:10.5194/amt-7-4353-2014      URL     [Cited within:1]
    [16] Mishchenko M. I., I. V. Geogdzhayev, W. B. Rossow, B. Cairns, B. E. Carlson, A. A. Lacis, L. Liu, and L. D. Travis, 2007: Long-term satellite record reveals likely recent aerosol trend.Science315,1543,https://doi.org/10.1126/science.1136709.
    Analysis of the long-term Global Aerosol Climatology Project data set reveals a likely decrease of the global optical thickness of tropospheric aerosols by as much as 0.03 during the period from 1991 to 2005. This recent trend mirrors the concurrent global increase in solar radiation fluxes at Earth's surface and may have contributed to recent changes in surface climate.
    DOI:10.1126/science.1136709      PMID:17363666      URL     [Cited within:3]
    [17] Mishchenko M. I., L. Liu, I. V. Geogdzhayev, J. Li, B. E. Carlson, A. A. Lacis, B. Cairns, and L. D. Travis, 2012: Aerosol retrievals from channel-1 and -2 AVHRR radiances: Long-term trends updated and revisited.Journal of Quantitative Spectroscopy and Radiative Transfer113,1974-1980,https://doi.org/10.1016/j.jqsrt.2012.05.006..
    78 GACP record of aerosol optical thickness and 03ngstr02m exponent is extended by 6 months. 78 The most recent 4-year segment reveals no significant short-term tendencies. 78 Unfreezing complex refractive index can substantially affect aerosol retrievals.
    DOI:10.1016/j.jqsrt.2012.05.006      URL     [Cited within:]
    [18] Petrenko M., C. Ichoku, and G. Leptoukh, 2012: Multi-sensor Aerosol Products Sampling System (MAPSS).Atmospheric Measurement Techniques5,913-926,https://doi.org/10.5194/amt-5-913-2012.
    Global and local properties of atmospheric aerosols have been extensively observed and measured using both spaceborne and ground-based instruments, especially during the last decade. Unique properties retrieved by the different instruments contribute to an unprecedented availability of the most complete set of complimentary aerosol measurements ever acquired. However, some of these measurements remain underutilized, largely due to the complexities involved in analyzing them synergistically. To characterize the inconsistencies and bridge the gap that exists between the sensors, we have established a Multi-sensor Aerosol Products Sampling System (MAPSS), which consistently samples and generates the spatial statistics (mean, standard deviation, direction and rate of spatial variation, and spatial correlation coefficient) of aerosol products from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS. Samples of satellite aerosol products are extracted over Aerosol Robotic Network (AERONET) locations as well as over other locations of interest such as those with available ground-based aerosol observations. In this way, MAPSS enables a direct cross-characterization and data integration between Level-2 aerosol observations from multiple sensors. In addition, the available well-characterized co-located ground-based data provides the basis for the integrated validation of these products. This paper explains the sampling methodology and concepts used in MAPSS, and demonstrates specific examples of using MAPSS for an integrated analysis of multiple aerosol products.
    DOI:10.5194/amt-5-913-2012      URL     [Cited within:2]
    [19] Pozzer A., A. de Meij, J. Yoon, H. Tost, A. K. Georgoulias, and M. Astitha, 2015: AOD trends during 2001-2010 from observations and model simulations.Atmos. Chem. Phys.15,5521-5535,https://doi.org/10.5194/acp-15-5521-2015.
    The aerosol optical depth (AOD) trend between 2001 and 2010 is estimated globally and regionally from observations and results from simulations with the EMAC (ECHAM5/MESSy Atmospheric Chemistry) model. Although interannual variability is applied only to anthropogenic and biomass-burning emissions, the model is able to quantitatively reproduce the AOD trends as observed by the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor, while some discrepancies are found when compared to MISR (Multi-angle Imaging SpectroRadiometer) and SeaWIFS (Sea-viewing Wide Field-of-view Sensor) observations. Thanks to an additional simulation without any change in emissions, it is shown that decreasing AOD trends over the US and Europe are due to the decrease in the emissions, while over the Sahara Desert and the Middle East region, the meteorological changes play a major role. Over Southeast Asia, both meteorology and emissions changes are equally important in defining AOD trends. Additionally, decomposing the regional AOD trends into individual aerosol components reveals that the soluble components are the most dominant contributors to the total AOD, as their influence on the total AOD is enhanced by the aerosol water content.
    DOI:10.5194/acp-15-5521-2015      URL     [Cited within:3]
    [20] Qin Y., R. M. Mitchell, 2009: Characterisation of episodic aerosol types over the Australian continent.Atmospheric Chemistry and Physics9,1943-1956,https://doi.org/10.5194/acp-9-1943-2009.
    Classification of Australian continental aerosol types resulting from episodes of enhanced source activity, such as smoke plumes and dust outbreaks, is carried out via cluster analysis of optical properties obtained from inversion of sky radiance distributions at Australian aerosol ground stations using data obtained over the last decade. The cluster analysis distinguishes four significant classes, which are identified on the basis of their optical properties and provenance as determined by satellite imagery and back-trajectory analysis. The four classes are identified respectively as aged smoke, fresh smoke, coarse dust and a super-absorptive aerosol. While the first three classes show similarities with comparable aerosol types identified elsewhere, the super-absorptive aerosol has no obvious foreign prototype. The class identified as coarse dust shows a prominent depression in single scattering albedo in the blue spectral region due to absorption by hematite, which is shown to be more abundant in central Australian dust relative to the quot;dust beltquot;of the Northern Hemisphere. The super-absorptive class is distinctive in view of its very low single scattering albedo (~0.7 at 500 nm) and variable enhanced absorption at 440 nm. The strong absorption by this aerosol requires a high black carbon content while the enhanced blue-band absorption may derive from organic compounds emitted during the burning of specific vegetation types. This aerosol exerts a positive radiative forcing at the top of atmosphere (TOA), with a large deposition of energy in the atmosphere per unit aerosol optical depth. This contrasts to the other three classes where the TOA forcing is negative. Optical properties of the four types will be used to improve the representation of Australian continental aerosol in climate models, and to enhance the accuracy of satellite-based aerosol retrievals over Australia.
    DOI:10.5194/acp-9-1943-2009      URL     [Cited within:1]
    [21] Sayer A. M., N. C. Hsu, C. Bettenhausen, and M. -J. Jeong, 2013: Validation and uncertainty estimates for MODIS Collection 6 "Deep Blue" aerosol data.J. Geophys. Res.,118,7864-7872,https://doi.org/10.1002/jgrd.50600.
    [1] This work evaluates various approaches to compute the second order ionospheric correction (SOIC) to Global Positioning System (GPS) measurements. When estimating the reference frame using GPS, applying this correction is known to primarily affect the realization of the origin of the Earth's reference frame along the spin axis (Z coordinate). Therefore, the Z translation relative to the International Terrestrial Reference Frame 2008 is used as the metric to evaluate various published approaches to determining the slant total electron content (TEC) for the SOIC: getting the slant TEC from GPS measurements, and using the vertical total electron content (TEC) given by a Global Ionospheric Model (GIM) to transform it to slant TEC via a mapping function. All of these approaches agree to 1 mm if the ionospheric shell height needed in GIM-based approaches is set to 600 km. The commonly used shell height of 450 km introduces an offset of 1 to 2 mm. When the SOIC is not applied, the Z axis translation can be reasonably modeled with a ratio of +0.23芒聙聣mm/TEC units of the daily median GIM vertical TEC. Also, precise point positioning (PPP) solutions (positions and clocks) determined with and without SOIC differ by less than 1 mm only if they are based upon GPS orbit and clock solutions that have consistently applied or not applied the correction, respectively. Otherwise, deviations of few millimeters in the north component of the PPP solutions can arise due to inconsistencies with the satellite orbit and clock products, and those deviations exhibit a dependency on solar cycle conditions.
    DOI:10.1002/2013JA019356      URL     [Cited within:3]
    [22] Sen P. K., 1968: Estimates of the regression coefficient based on Kendall's tau.Journal of the American Statistical Association63,1379-1389,https://doi.org/10.1080/01621459.1968.10480934.
    DOI:10.1080/01621459.1968.10480934      URL     [Cited within:1]
    [23] Thomas, G. E., Coauthors, 2010: Validation of the GRAPE single view aerosol retrieval for ATSR-2 and insights into the long term global AOD trend over the ocean.Atmos. Chem. Phys.10,4849-4866,https://doi.org/10.5194/acp-10-4849-2010.
    The Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) project has produced a global data-set of cloud and aerosol properties from the Along Track Scanning Radiometer-2 (ATSR-2) instrument, covering the time period 19952001. This paper presents the validation of aerosol optical depths (AODs) from this product against AERONET sun-photometer measurements, as well as a comparison to the Advanced Very High Resolution Radiometer (AVHRR) optical depth product produced by the Global Aerosol Climatology Project (GACP). lt;brgt;lt;brgt; The GRAPE AOD over ocean is found to be in good agreement with AERONET measurements, with a correlation of 0.79 and a best-fit slope of 1.0amp;plusmn;0.1, but with a positive bias of 0.08amp;plusmn;0.04. Although the GRAPE and GACP datasets show reasonable agreement, there are significant differences. These discrepancies are explored, and suggest that the downward trend in AOD reported by GACP may arise from changes in sampling due to the orbital drift of the AVHRR instruments.
    DOI:10.5194/acpd-9-21581-2009      URL     [Cited within:2]
    [24] Xia X. G., 2011: Variability of aerosol optical depth and -gström wavelength exponent derived from AERONET observations in recent decades.Environ. Res. Lett.,6,044011,https://doi.org/10.1088/1748-9326/6/4/044011.
    Using aerosol loading data from 79 Aerosol Robotic Network (AERONET) stations with observations from more than six years, changes in aerosol optical depth (AOD) and Angstrom wavelength exponent (AWE) were studied. A statistical method was developed to determine whether AOD changes were due to increased background AOD values and/or an increased number of high AOD events. AOD decreased significantly at AERONET sites in northeastern North American and in Western Europe, which was accompanied by decreased AWE. Reduction of AOD there was mainly due to a decreased frequency of high AOD events and an increased frequency of background AOD events. In addition, decreased AOD values for high AOD events also accounted for 65 16–32% of the AOD reduction. This is indicative of significant meteorological effects on AOD variability. AOD trends in other regions were marginal and most were not significant; however, AOD increased significantly at one site in the Sahel and another in Saudi Arabia, predominantly due to the increased frequency of high AOD events and their average AOD.
    DOI:10.1088/1748-9326/6/4/044011      URL     [Cited within:2]
    [25] Yue S., C. Y. Wang, 2002: Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test.Water Resour. Res.,38(6),1068,https://doi.org/10.1029/2001WR000861.
    [Cited within:3]
    [26] Yue S., P. Pilon, B. Phinney, and G. Cavadias, 2002: The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes, 16, 1807- 1829.
    Abstract This study investigated using Monte Carlo simulation the interaction between a linear trend and a lag-one autoregressive (AR(1)) process when both exist in a time series. Simulation experiments demonstrated that the existence of serial correlation alters the variance of the estimate of the Mann揔endall (MK) statistic; and the presence of a trend alters the estimate of the magnitude of serial correlation. Furthermore, it was shown that removal of a positive serial correlation component from time series by pre-whitening resulted in a reduction in the magnitude of the existing trend; and the removal of a trend component from a time series as a first step prior to pre-whitening eliminates the influence of the trend on the serial correlation and does not seriously affect the estimate of the true AR(1). These results indicate that the commonly used pre-whitening procedure for eliminating the effect of serial correlation on the MK test leads to potentially inaccurate assessments of the significance of a trend; and certain procedures will be more appropriate for eliminating the impact of serial correlation on the MK test. In essence, it was advocated that a trend first be removed in a series prior to ascertaining the magnitude of serial correlation. This alternative approach and the previously existing approaches were employed to assess the significance of a trend in serially correlated annual mean and annual minimum streamflow data of some pristine river basins in Ontario, Canada. Results indicate that, with the previously existing procedures, researchers and practitioners may have incorrectly identified the possibility of significant trends. Copyright Environment Canada. Published by John Wiley & Sons, Ltd.
    DOI:10.1002/hyp.1095      URL     [Cited within:2]
    [27] Yoon J., W. von Hoyningen-Huene, M. Vountas, and J. P. Burrows, 2011: Analysis of linear long-term trend of aerosol optical thickness derived from SeaWiFS using BAER over Europe and South China.Atmos. Chem. Phys.11,12 149-12 167,https://doi.org/10.5194/acp-11-12149-2011.
    The main purposes of the present paper are not only to investigate linear long-term trends of Aerosol Optical Thickness (AOT) at 443 and 555 nm over regions in Europe and South China, but also to show the uncertainty caused by cloud disturbance in the trend analysis of cloud-free aerosol. These research areas are the densely urbanised and often highly polluted regions. The study uses the Bremen AErosol Retrieval (BAER) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data for AOT retrievals in the specified regions from October 1997 to May 2008. In order to validate the individually retrieved AOTs and the corresponding trends, AErosol RObotic NETwork (AERONET) level 2.0 data have been used. The retrieved AOTs were in good agreement with those of AERONET (0.79 ≤ R ≤ 0.88, 0.08 ≤ RMSD ≤ 0.13). The contamination of the aerosol retrievals and/or AERONET observations by thin clouds can significantly degrade the AOT and lead to statistically non-representative monthly-means, especially during cloudy seasons. Therefore an inter-correction method has been developed and applied. The "corrected" trends for both BAER SeaWiFS and AERONET AOT were similar and showed in average a relative difference of ~25.19%. In general terms, negative trends (decrease of aerosol loading) were mainly observed over European regions, with magnitudes up to 0.00453 and 0.00484 yr 1 at 443 and 555 nm, respectively. In contrast, the trend in Pearl River Delta was positive, most likely attributed to rapid urbanization and industrialization. The magnitudes of AOT increased by +0.00761 and +0.00625 yr 1 respectively at 443 and 555 nm.
    DOI:10.5194/acp-11-12149-2011      URL     [Cited within:3]
    [28] Yoon J., W. von Hoyningen-Huene, A. A. Kokhanovsky, M. Vountas, and J. P. Burrows, 2012: Trend analysis of aerosol optical thickness and -gström exponent derived from the global AERONET spectral observations.Atmospheric Measurement Techniques5,1271-1299,https://doi.org/10.5194/amt-5-1271-2012.
    No abstract available.
    DOI:10.5194/amt-5-1271-2012      URL     [Cited within:4]
    [29] Yu H., M. Chin, L. A. Remer, R. G. Kleidman, N. Bellouin, H. Bian, and T. Diehl, 2009: Variability of marine aerosol fine mode fraction and estimates of anthropogenic aerosol component over cloud-free oceans from the Moderate resolution Imaging Spectroradiometer (MODIS).J. Geophys. Res.,114,D10206,https://doi.org/10.1029/2008JD010648.
    [1] In this study, we examine seasonal and geographical variability of marine aerosol fine-mode fraction (fm) and its impacts on deriving the anthropogenic component of aerosol optical depth (0304a) and direct radiative forcing from multispectral satellite measurements. A proxy of fm, empirically derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 data, shows large seasonal and geographical variations that are consistent with the Goddard Chemistry Aerosol Radiation Transport (GOCART) and Global Modeling Initiative (GMI) model simulations. The so-derived seasonally and spatially varying fm is then implemented into a method of estimating 0304a and direct radiative forcing from the MODIS measurements. It is found that the use of a constant value for fm as in previous studies would have overestimated 0304a by about 20% over global ocean, with the overestimation up to 09080445% in some regions and seasons. The 7-year (20010900092007) global ocean average 0304a is 0.035, with yearly average ranging from 0.031 to 0.039. Future improvement in measurements is needed to better separate anthropogenic aerosol from natural ones and to narrow down the wide range of aerosol direct radiative forcing.
    DOI:10.1029/2008JD010648      URL     [Cited within:1]
    [30] Zhang J., J. S. Reid, 2010: A decadal regional and global trend analysis of the aerosol optical depth using a data-assimilation grade over-water MODIS and Level 2 MISR aerosol products.Atmos. Chem. Phys.10,10 949-10 963,https://doi.org/10.5194/acp-10-10949-2010.
    Using the ten-year (20002009) DA quality Terra MODIS and MISR aerosol products, as well as 7 years of Aqua MODIS, we studied both regional and global aerosol trends over oceans. This included both natural and data assimilation grade versions of the products. Contrary to some of the previous studies that showed a decreasing trend in aerosol optical depth (AOD) over global oceans, after correcting for what appears to be aerosol signal drift from the radiometric calibration of both MODIS instruments, we found MODIS and MISR agreed on a statistically negligible global trend of 0.0003/per year. Our study also suggests that AODs over the Indian Bay of Bengal, east coast of Asia, and Arabian Sea show statistically significant increasing trends of 0.07, 0.06, and 0.06 per ten years for MODIS, respectively. Similar increasing trends were found from MISR, but with less relative magnitude. These trends reflect respective increases in the optical intensity of aerosol events in each region: anthropogenic aerosols over the east coast of China and Indian Bay of Bengal; and a stronger influence from dust events over the Arabian Sea. Negative AOD trends are found off Central America, the east coast of North America, and the west coast of Africa. However, confidence levels are low in these regions, which indicate that longer periods of observation are necessary to be conclusive.
    DOI:10.5194/acpd-10-18879-2010      URL     [Cited within:1]
    [31] Zhang J. L., J. S. Reid, R. Alfaro-Contreras, and P. Xian, 2017: Has China been exporting less particulate air pollution over the past decade? Geophys.Res. Lett.,44,2941-2948,https://doi.org/10.1002/2017GL072617.
    Particulate matter (PM) pollution from China is transported eastward to Korea and Japan and has been suggested to influence surface air quality on the West Coast of the United States. However, remote sensing studies have been inconclusive as to recent trends in Chinese emissions and transport. We reconciled different passive remote sensing points of view and found that while aerosol optical thickness (AOT) as an indicator of particulate pollution has increased from the start of the observation period (2000) to 2006-2007 from the main Chinese coastal outflow regions, since then there has been a 10-20% decrease in AOT (with respect to 2007). Reductions were observed in spring, summer, and fall seasons. No improvement in exported PM pollution is found for the winter season.
    DOI:10.1002/2017GL072617      URL     [Cited within:3]
    [32] Zhao X.-P., I. Laszlo, W. Guo, A. Heidinger, C. Cao, A. Jelenak, D. Tarpley, and J. Sullivan, 2008: Study of long-term trend in aerosol optical thickness observed from operational AVHRR satellite instrument.J. Geophys. Res.,113,D07201,https://doi.org/10.1029/2007JD009061.
    [1] The long-term trend of aerosol optical thickness (AOT) over the global oceans has been studied by using a nearly 25-year aerosol record from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmosphere extended (PATMOS-x) data set. Both global and regional analyses have been performed to derive the AOT tendencies for monthly, seasonal, and annual mean AOT values at AVHRR 0.63 0204m channel (or Channel-1). A linear decadal change of 0908080.01 is obtained for globally and monthly averaged aerosol optical thickness, 03041, of AVHRR Channel-1. This negative tendency is even more evident for globally and annually averaged 03041 and the magnitude can be up to 0908080.03/decade. Seasonal patterns in the AOT regional long-term trend are evident. In general, negative tendencies are observed for seasonally averaged 03041 in regions influenced by emissions from industrialized countries and the magnitude can be up to 0908080.10/decade. Positive tendencies are observed in regions influenced by emissions from fast developing countries and the magnitude can be up to +0.04/decade. For regions heavily influenced by Saharan desert particles, a negative trend with a maximum magnitude of 0908080.03/decade is detected. However, over regions influenced by smoke from biomass burning, positive tendencies with a maximum magnitude of +0.04/decade are observed. Sensitivity analyses have also been performed to study the effects of radiance calibration, aerosol retrieval algorithm, and spatial resolution of input retrieval radiances on the global aerosol long-term tendencies.
    DOI:10.1029/2007JD009061      URL     [Cited within:1]
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    Key words
    MODIS
    AERONET
    Aerosol Optical Depth
    Mann-Kendall trend test

    Authors
    Xuehua FAN
    Xiang'ao XIA
    Hongbin CHEN