PUBLICATIONS. Journal of Geophysical Research: Atmospheres. Investigating the impact of haze on MODIS cloud detection

Transcription

1 PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE Key Points: We explored the impact of haze on MODIS cloud detection by comparing MODIS, CALIPSO, and CloudSat The hit rate between MODIS and CALIPSO decreases with the AOD increase MODIS cloud detection algorithm tends to misidentify heavy aerosols as clouds Investigating the impact of haze on MODIS cloud detection Feiyue Mao 1,2,3,4,5, Miaomiao Duan 2,6, Qilong Min 2,7, Wei Gong 2,3,4, Zengxin Pan 2, and Guangyi Liu 8 1 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China, 2 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China, 3 Collaborative Innovation Center for Geospatial Technology, Wuhan, China, 4 Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Wuhan, China, 5 School of Resource and Environmental Science, Wuhan University, Wuhan, China, 6 The 2nd Institute of Surveying and Mapping of Hebei Province, Shijiazhuang, China, 7 Atmospheric Sciences Research Center, State University of New York at Albany, Albany, New York, USA, 8 Smart Grid Operation Research Center, China Electric Power Research Institute, Beijing, China Correspondence to: M. Duan and Z. Pan, miaomiaoduan@whu.edu.cn; pzx@whu.edu.cn Citation: Mao, F., M. Duan, Q. Min, W. Gong, Z. Pan, and G. Liu (2015), Investigating the impact of haze on MODIS cloud detection, J. Geophys. Res. Atmos., 120, 12,237 12,247, doi: / 2015JD Received 21 APR 2015 Accepted 19 NOV 2015 Accepted article online 23 NOV 2015 Published online 14 DEC 2015 Abstract The cloud detection algorithm for passive sensors is usually based on a fuzzy logic system with thresholds determined from previous observations. In recent years, haze and high aerosol concentrations with high aerosol optical depth (AOD) occur frequently in China and may critically impact the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection. Thus, we comprehensively explore this impact by comparing the results from MODIS/Aqua (passive sensor), Cloud-Aerosol Lidar with Orthogonal Polarization/CALIPSO (lidar sensor), and Cloud Profiling Radar/CloudSat (microwave sensor) of the A-Train suite of instruments using an averaged AOD as an index for an aerosol concentration value. Case studies concerning the comparison of the three sensors indicate that MODIS cloud detection is reduced during haze events. In addition, statistical studies show that an increase in AOD creates an increase in the percentage of uncertain flags and a decrease in hit rate, a consistency index between consecutive sets of cloud retrievals. On average, AOD values lower than 0.1 give hit rate values up to 80.0% and uncertainty values lower than 16.8%, while AOD values greater than 1.0 reduce the hit rate below to 66.6% and increase the percentage of uncertain flags up to 46.6%. Therefore, we can conclude that the ability of MODIS cloud detection is weakened by large concentrations of aerosols. This suggests that use of the MODIS cloud mask, and derived higher-level products, in situations with haze requires caution. Further improvement of this retrieval algorithm is desired as haze studies based on MODIS products are of great interest in a number of related fields American Geophysical Union. All Rights Reserved. 1. Introduction Clouds play a dominant role in the energy and water cycle of our planet due to their large coverage over the Earth. They control the Earth s radiation budget, by reflecting shortwave radiation from the Sun and emitting long-wave radiation and drive the hydrological cycle of the Earth system through the exchange of water between the surface and the atmosphere. Therefore, the accurate characterization of clouds is of considerable scientific significance [Min et al., 2014; Qiu et al., 2014; Zeng et al., 2010]. Satellite observations offer a unique opportunity to detect clouds and cloud properties on both global and regional scales. NASA s Earth Observing System (EOS) anchored several active and passive satellite sensors with unprecedented observing capabilities in a formation called the afternoon constellation or A-Train [Stephens et al., 2002]. The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) sensor on board the CALIPSO satellite, and Cloud Profiling Radar (CPR) on board the CloudSat satellite are all part of the A-Train and have been regarded as among the most powerful sensors for detecting clouds over the whole Earth. Due to its long temporal record, wide swath/sampling, and broad spectral range, MODIS retrievals are the most commonly used products for a range of applications. Among all MODIS products, the MODIS cloud mask is the most fundamental and can usually be used as a basis of land, ocean, and atmosphere products retrieving [King et al., 2003; Platnick et al., 2003; Remer et al., 2005]. The MODIS cloud detection algorithm is based on a fuzzy logic system in which the thresholds are determined from the statistics of previous observations. This implies that the thresholds are likely to change in different regions and under different conditions [Ackerman et al., 2008, 2010, 1998; Frey et al., 2008]. Since the radiation effect of heavy aerosol loading is similar to that of cloudy conditions, MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,237

2 Table 1. Comparison of Three Cloud Results MODIS CloudSat CALIPSO Advantages Large FOV region, use many bands Vertical structure of clouds, be able to penetrate thick cloud Disadvantages Passive, be mainly sensitive to the top of Trajectory measurement, easily to ignore moderately thick cloud thin cloud Vertical structure of clouds, be more sensitive to thin cloud Trajectory measurement, easily to be attenuated by thick cloud this threshold system results in inaccuracy of cloud detection for such conditions. As such, it may be a problematic issue for MODIS cloud detection and subsequent applications under heavy aerosol conditions [Brennan et al., 2005] Haze events in China have increased in frequency since 1980 [Ding and Liu, 2014; Quan et al., 2011]. During January 2013, an extreme weather event with heavy haze pollution nearly encompassed the whole of China [Cheng et al., 2014]. Heavy emissions due to the rapid development of economy, unfavorable dispersion due to local geographical conditions such as wind speed and planetary boundary layer height, and the depression of cold air advection due to unusual atmospheric circulation are the main causes of severe haze pollution [Liu et al., 2004; Mao et al., 2013; Tie et al., 2015; Wang et al., 2014b]. More alarmingly, the pollution has become a regional problem rather than just condensed to localized events [Wang et al., 2014a]. In addition, haze pollution is often accompanied by heavy aerosol loadings which have impacts on climate through radiative forcing [Neubauer et al., 2014]. There is a concern that the haze in China may impact the accuracy of satellite cloud detections, so it is important to investigate the impact of heavy aerosol loading on MODIS cloud detection to ensure the accuracy of the cloud mask and related MODIS data during frequent heavy haze days. This study focuses on exploring the impact of haze on satellite cloud detection over China, with a particular focus on the MODIS cloud mask, by using synergetic cloud observations from MODIS, CALIOP, and CPR as Y Liu et al. [2010] used in the Arctic area. The evaluation of cloud detection is a first step toward understanding the quality of current satellite cloud cover retrievals and providing support and guidelines for the subsequent use of satellite data over China. The availability of near-concurrent CALIPSO and CloudSat cloud data in the A-Train provides a unique opportunity to assess the cloud detection limit of the current MODIS algorithm during haze days, which is useful for a region without ground-based validation measurements. Satellite data and comparison methodology utilized in this study are detailed in section 2. The results, including the case analysis and statistical study, are reported in section 3. We further analyze and discuss possible reasoning and solutions in section Data and Methodology 2.1. Satellite Observations The Afternoon Constellation of Earth Observing System (EOS) operated by NASA and its partners, called A-Train for short, is a group of active and passive satellites that follow one after another, with a separation ranging from seconds to minutes, along the same orbital track. The A-Train polar orbiting satellites cross the equator northbound at about 1:30 P.M. at local time. The three A-Train sensors, MODIS/Aqua, CPR/CloudSat, and CALIOP/CALIPSO, are able to observe the same scene near concurrently. All three sensors derive cloud products from different detection mechanisms with different algorithms. The cloud products used are the MODIS cloud mask result from MYD35_L2 collection 5, the CloudSat cloud mask results from its standard cloud product 2B-GEOPROF R04, and the CALIOP Vertical Feature Mask (VFM) version As listed in Table 1, each cloud data set has its own inherent advantages and disadvantages. The most considerable advantage of MODIS is that it can provide a large swath of measurements, which is significant for projects requiring large data sampling MODIS On Board Aqua MODIS is an imaging spectroradiometer with the ability to characterize the spatial and spectral characteristics of the global cloud field. MODIS has 36 channels spanning the spectral range from μm to μm and has a cross-track swath of 2330 km with spatial resolutions of 250 m to 1 km. The advantage of a large swath width with higher resolution is that it provides a more large-scale cloud map as compared to that of active sensors. The MODIS cloud mask algorithm uses a fuzzy-logic scheme and radiances from 22 spectral MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,238

3 bands to quantify a given pixel of the Earth s surface to be confident cloudy, probably cloudy, uncertainty clear, or confident clear [Ackerman et al., 2008, 2010, 1998; Frey et al., 2008]. In this study we employed the MYD35_L2 collection 5 product that contains cloud mask results with spatial resolution of 1 km CPR On Board CloudSat CPR is an active radar sensor on board CloudSat that trails the Aqua satellite by an average of about 60 s. This nadir-looking radar with a frequency of 94 GHz measures the return power backscattered by clouds as a function of distance to the radar. CPR provides vertical profiles with a resolution of 480 m along the flight trajectory of A-Train and the instantaneous footprint of CPR is a circle of approximately 1.4 km in diameter. The CPR cloud mask algorithm differentiates return power due to scattering by cloud particles from those only containing noise. The cloud mask from the CloudSat standard cloud product, 2B-GEOPROF R04, contains values from 0 to 40 assigned to each vertical bin [Marchand et al., 2008], where mask values of 30 and 40 are usually considered cloudy with very low false detection rates. Even though CloudSat is sensitive to most clouds, several types of clouds have return signals that fall below the minimum detectable limit, such as high thin cirrus, altocumulus, and continental stratus [Marchand et al., 2008]. Additionally, backscatter data from the lowest 500 m are often unusable due to the strong surface clutter signal CALIOP On Board CALIPSO CALIOP is an active sensor on board CALIPSO that lags about 15 s behind CloudSat. It is a nadir-looking, polarization-sensitive, elastic backscatter lidar that uses a diode-pumped Nd:YAG laser transmitting at dual wavelengths of 532 and 1064 nm. Similar to CPR, CALIOP also provides vertical profiles of aerosols and some clouds and discriminates between the two by combining the total backscatter radiation measured at 1064 nm and the degree of linear depolarization at 532 nm [Liu et al., 2009; Mao et al., 2012]. The laser pulses of CALIOP illuminate a circle with 70 m in diameter at the Earth s surface and produce footprints every 1/3 km along the ground with a vertical resolution of 30 m at altitudes below 8.2 km. For this analysis, we used the CALIOP VFM version 3.01 data which provide feature classification within a vertical profile. Clouds, aerosols, and other features can be identified in the VFM data [Liu et al., 2009; Ma et al., 2015]. Cloudy profiles in level 2 products of CALIOP are identified based on the cloud-aerosol discrimination (CAD) method developed by Z Liu et al. [2004]. Color ratio (i.e., the ratio of 1064 nm and 532 nm backscatters) is sensitive to the particle size of cloud and aerosol. In addition, the backscattered intensities of cloud are generally stronger than those of aerosol; therefore, the color ratio and backscattered intensities are used for cloudaerosol discrimination in the CAD method. In the cloud-aerosol discrimination, the misclassification of optically thick aerosols as clouds is possible because these properties (color ratio and backscatter intensity) are fairly close to those of clouds under high aerosol loading condition (optical depth > 1 2) [Liu et al., 2008]. However, inspection of the cloud-aerosol mask has shown that layers are correctly identified as cloud or aerosol about 90% or better [Liu et al., 2009]; hence, the CALIPSO VFM is accurate enough and commonly used for validation of MODIS cloud detection [Liu et al., 2008]. Although any combination of the cloud detection results from the three sensors can provide complementary information of cloud cover due to their unique cloud detection principles, it is important and useful to assess cloud detection accuracy through their intercomparisons. The A-Train constellation orbit retrieves data over the same area about every 16 days so it is possible to get a series of MODIS, CloudSat, and CALIPSO synchronous cloud data for each transit observation. For example, a MODIS cloud mask image of 2330 km by 1354 km can be obtained in this fashion for the majority of China while heavily populated cities, such as Wuhan and Beijing, can be captured using the smaller swaths of the CloudSat cloud mask track and CALIPSO VFM Methodology Figure 1 shows the distribution of annual average aerosol optical depth (AOD) on 1 1 grids over China, which is the mean column AOD computed based on all the daily aerosol profiles of CALIPSO level 2 version 3.02 product in The areas surrounding the provincial capitals are collocated with strong haze pollution. Thus, in order to fairly explore the impact of haze on satellite cloud detection over China, we select two trajectories intersecting Beijing, as shown in Figure 1. The trajectories in red and blue are for daytime and nighttime, respectively, both of which cross the area of maximum haze pollution. In our opinion, those trajectories are the optimal trajectory for the haze studies over China, which repeat every 16 days. MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,239

4 Figure 1. The selected trajectory in red and blue with the distribution of annual average AOD over China in 2013, where the star dots are the locations of Beijing and capitals of other provinces. To explore the influence of haze, we compared MODIS cloud detection results with the collocated CloudSat/ CALIOP cloud detection data at a pixel-to-pixel level. We assigned the MODIS flags cloudy and probably cloudy to cloudy, while the other two flags (confident clear and uncertainty clear) as clear in this study. Additionally, CPR cloud mask values of 30 and 40 with very low false alarm rates and CALIOP cloud features are used. Finally, the overall agreement for one piece of MODIS cloud mask image between cloud results from MODIS and CPR or CALIOP is expressed as the hit rate: ðn cld þ N clr Þ hit rate ¼ 100% : N tol where N cld is the number of cloud pixels in agreement, N clr is the number of clear pixels in agreement, and N tol is the total number of collocated pixels. We opted to use hit rate to measure the agreement between every two satellite cloud data. We use the averaged CALIPSO AOD of a trajectory for denoting the level of haziness. For the calculation of averaged CALIPSO AOD, the AOD of unpenetrated lidar signals due to optically thick layers is excluded to avoid bias. We do not use the MODIS AOD retrieval as it is based on the MODIS cloud mask, the object of study. It should be considered that using average AOD from CALIPSO, trajectories could mitigate discrepancies in the instrument s differentiation between clouds and aerosols. In the following study, we implement comparisons of three cloud results using year-long data and then implement comparisons of MODIS and CALIPSO data from year 2007 to 2013 for further investigations. It should be noticed that the resolution of MODIS data is 1 km, but the instantaneous footprints of CALIPSO and Cloudsat are approximately 70 m and 1.4 km, respectively. Thus, the difference of the field of view (FOV) of MODIS, CALIPSO, and Cloudsat may lead to a target detected by a larger FOV observation not being detected by a smaller FOV observation, a situation which will bring adverse effects to our study purpose. 3. Results and Discussions The calendar year beginning in January 2007 of coincident MODIS, CPR, and CALIOP cloud detection retrievals was compared. The comparisons were implemented every 16 days, which provided cases to evaluate the accuracy of the different cloud detection results under various conditions. Moreover, we made two statistical comparisons for the year-long data set: one between MODIS and CPR and another between MODIS and CALIOP. In addition, though with the absence of CPR cloud data, 7 years ( ) of collocated MODIS and CALIOP cloud results are compared. This allows for the statistical investigation of the trend of the capability of cloud detection for further study Case Studies Case With Low Aerosol Loading The first comparison case was under clean conditions on 9 December Figure 2a displays the MODIS cloud mask image over China at 18:25 UTC, with the solid line indicating the trajectory line of CloudSat and CALIPSO. Additionally, the color of the trajectory line in Figure 2a indicates the agreement between the cloud products of MODIS and CALIPSO, with gray for the agreement (either cloudy or clear) and red for disagreement, respectively. Figure 2b shows a CALIPSO VFM image including information for clouds and aerosols. Figure 2c shows the coincident vertical profile of CloudSat along the same track. Additionally, Figure 2d shows the CALIPSO AOD, as well as the cloud and aerosol mask of CALIPSO, MODIS, and CloudSat. Additionally, this figure shows the agreement between the cloud and aerosol masks of MODIS versus CALIPSO and MODIS versus CloudSat, with gray for the agreement and red for disagreement, respectively. MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,240

5 Figure 2. Data comparison at 18:25 UTC 9 December (a) MODIS cloud mask and the agreement line between MODIS and CALIPSO, with gray for the agreement and red for disagreement, respectively, (b) CALIPSO vertical feature mask, (c) cloud mask from CloudSat data, and (d) CALIPSO AOD and the cloud and aerosol mask of CALIPSO, MODIS, and CloudSat, as well as the agreement between cloud and aerosol mask of MODIS versus CALIPSO and MODIS versus CloudSat, with gray for the agreement and red for disagreement, respectively. Averaged CALIPSO AOD along this orbit track was 0.16, indicating low aerosol loading. CPR and CALIOP provide complementary cloud results due to their different observation mechanisms. For geometrically thick clouds the lidar signal tends to be fully attenuated, and the cloud base is left undetected, such as the cloud between 43 and 38 N (labeled A). However, CPR provides a full profile of thick clouds as well as the cloud base information. All the same, both CPR and CALIOP have significant cloud detection ability and are usually considered to have stronger capabilities than that of a passive sensor [Kahn et al., 2008; Wu et al., 2009]. Along this track, all the sensors, including MODIS, are able to detect the clouds satisfactorily. The hit rate of MODIS versus CALIPSO is about 84.6%, and MODIS versus CloudSat is about 96.6%. The most likely cause of the higher difference of MODIS versus CloudSat than MODIS versus CALIPSO is the poor ability of both CloudSat and MODIS to detect broken low clouds, resulting in missing information of some specific clouds between 32 to 24 N. The high hit rate between MODIS and CALIPSO indicates high consistency of the two kinds of cloud and aerosol detection results under clear conditions Case With Intermediate Aerosol Loading Figure 3 shows the case comparison with intermediate aerosol pollution on 26 January CALIOP, shown in Figure 3b, indicated a consecutive aerosol layer from 40 N to 30 N and some low broken clouds, especially between 42 N and 40 N (in the green square frame in Figure 3d). These broken clouds were missed by the vertical feature profiles of CPR (Figure 3c). Contrarily, MODIS cloud mask flags most of the same pixels with broken clouds as clear in the green circle (Figure 3b). The hit rate between MODIS and CALIPSO is only 62.4%. Additionally, the discrepancy between MODIS and the other two cloud detection methods mostly occurred in the area around 35 N where aerosol loading was heavy. The mutual influence on cloud and MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,241

6 Figure 3. Same as Figure 2 but using the data at 18:25 UTC 26 January aerosol detection is not a new problem [Eck et al., 2014]. This leads us to preliminarily speculate that poor detection of clouds by MODIS is due to the heavy aerosol loading. When influenced by the thick aerosol presence, the MODIS cloud algorithm failed to detect some specific low broken clouds. The region from 47 N to 40 N has low aerosol loading because it is located on the Mongolian Plateau, where the altitude is usually higher than 1.0 km and is far away from source of air pollution Case With Heavy Aerosol Loading In order to further explore the possible relationship between the occurrences of poor MODIS pixel classification in the presence of aerosols, a case on 10 January 2009 with serious aerosol loading is shown in Figure 4. The trajectory between 37 and 32 N coincides with heavy aerosol loading bringing the AOD up to 1.0, as is shown in Figure 4d. As illustrated in Figures 4a and 4b, both CALIOP and MODIS indicate clouds from about 40 N to 38 N, highlighted in the green circle (Figure 4b), which were missed by CPR due to lack of CPR sensitivity. In addition, the MODIS cloud mask primarily identified the pixels with heavy aerosol loading between 37 and 32 N as cloudy, but CALIOP and CloudSat identified the same pixels as clear (green square frame in Figure 4d). This led to a hit rate of 45.0% between MODIS and CALIPSO. CPR failed to detect some low broken clouds that were detected by both CALIOP and MODIS under conditions of low aerosol loading around 45 N, consistent with that obtained from case Statistical Study The analyses of the above three cases allow us to draw a preliminary conclusion that heavy aerosol loading during hazy days affected the capability of MODIS cloud detection, though statistical analysis is necessary for further confirmation. Thus, the year-long statistics of hit rates between MODIS and CloudSat/CALIPSO are analyzed in terms of aerosol loading. The two groups of hit rates obtained by comparing MODIS with CloudSat and MODIS with CALIPSO in September 2008 July 2009 are separately shown in Figure 5. MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,242

7 Figure 4. Same as Figure 2 but using the data at 18:25 UTC 10 January The AOD values averaged along the corresponding track are also shown in the panel of Figure 5. The 3 days of circled AOD data represent the three cases analyzed. We can see the negative correlation between the hit rate and AOD: the hit rate between MODIS and CALIPSO on 10 January 2009 is the lowest, and the AOD is relatively large, while the hit rate on 9 December 2008 is high, but the AOD is relatively low. Additionally, Figure 5 shows that the trends of the two sets of hit rates are consistent with each other. But the hit rates between MODIS and CALIPSO, with values mostly greater than 50%, are overall higher than those between MODIS and CloudSat. This is no surprise due to the differences in sensor sensitivity to cloud and aerosols as well as those in the vertical and horizontal resolution (resolving power and image registration) between CPR and CALIOP. Clearly, the hit rates for both pairs are lower for heavy aerosol loading, suggesting the decreasing of the accuracy of the MODIS cloud mask with heavy aerosol loadings. Further, the relationship between AOD and hit rate between MODIS and CALIPSO is explored for a data set spanning 7 years ( ), shown in Figure 6, along with a linear fitting curve with a slope of 0.61 and 0.53 in daytime and nighttime, respectively. Notably, MODIS has limitation to detect the cirrus cloud, but CALIPSO does not. The difference between MODIS and CALIPSO in cirrus detection should be independent on the boundary layer aerosol loading. Thus, cirrus samples have been removed from the statistical study in Figure 6. Although the correlation coefficient of the fitting are just about 0.62 in daytime and 0.53 in nighttime, the decrease of hit rate with AOD is clearly evident; the hit rate of MODIS with CALIPSO is likely to be lower with the higher AOD, as the MODIS cloud detection algorithm was based on the statistics of observations over United States under conditions with AOD below 0.4 [Ma et al., 2013; Shinozuka et al., 2007]. While these statistics and algorithm are robust in a global approach, AOD values in China are usually above 0.4, especially in urban areas. For instance, the aerosol loading in Wuhan mostly ranged between 0.4 and 2.0 [Wang et al., 2015]. MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,243

8 Figure 5. The influence of AOD on comparisons of hit rate of MODIS versus CloudSat and MODIS versus CALIPSO (September 2008 July 2009). In order to further investigate how aerosols impact MODIS cloud detection, we calculated the hit rate based on AOD of each pixel rather than averaged AOD of each track. Figure 7 shows the change of hit rate of MODIS and CALIPSO according to different intervals of AOD with the proportion of uncertainty flagged by MODIS cloud mask. Cirrus samples have been removed from the statistical study in Figure 7 as in Figure 6. The hit rate was calculated based on AOD in each range, which was abstracted and reassigned from all images in We use the percentage of the uncertain flags (i.e., probably cloudy and uncertainty clear) in a flags data set to denote the confidence of MODIS cloud detection. Figure 7 shows that, for both of the daytime and nighttime, the hit rate decreased while the percentage of uncertain flags increased with an increase in AOD. The total hit rate reached 80.0%, and the percentage of total uncertain flags reached as low as 16.8% in all time when the value of AOD was lower than 0.1. However, when AOD was greater than 1.0, the total hit rate was generally below 66.6%, and the percentage of uncertain flags went up to 46.6% in all time. Therefore, we conclude that the MODIS cloud detection algorithm is weakened by heavy aerosol loading and failed to classify pixels with confidence. Additionally, Figure 7 shows that the daytime hit rate is commonly lower than that of the nighttime when AOD larger than 0.5, which is due to that the cloud mask is a final combined determination based on many different types of clouds test, which combination is clear-sky conservative [Ackerman et al., 2010]. Thus, because none of the channels can promise a pixel is cloud free when the aerosol loading is heavy, the final cloud mask will tend to mark the pixel as cloudy. Therefore, the visible channels are helpful for determining the existing of a layer but which may not be conducive to enhancing the hit rate because the combining approach is clear-sky conservative. Figure 6. The influence of AOD on hit rate of MODIS versus CALIPSO ( ). Furthermore, case studies indicate that MODIS tends to misidentify heavy aerosols as clouds, but the results show that the MODIS cloud algorithm fails to detect the low broken clouds for both low and intermediate aerosol loading cases, especially for the low aerosol loading case from this study. Thus, we examined the capability of MODIS low broken cloud detection under different aerosol loading condition as shown in Figure 8. CALIPSO classify clouds into eight subtypes cloud, of which the broken cloud (category 3 of subtypes cloud of CALIPSO VFM product) analyzed in Figure 8 is defined as the low-level clouds with cloud layer fraction lower than 0.4 [Liu et al., 2009]. The hit rate in Figure 8 is actually the probability of MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,244

9 Figure 7. The influence of AOD on hit rate of MODIS versus CALIPSO and percentage of uncertain flags of MODIS ( ). This statistics excluded profiles with cirrus or opacity layer. MODIS cloud algorithm to classify a pixel as cloudy when low broken cloud was detected by CALIPSO. Figure 8 shows that the hit rate is mostly lower than 50%. Both of the hit rate and the uncertain flags increase along with the AOD increase, which means that the MODIS cloud algorithm with visible channels tends to classify optically thick mixed target as cloud with low confidence. Furthermore, because the combining approach of cloud mask detection is clear-sky conservative, the visible channels are helpful for determining the existing of a layer, which enhances the hit rate of low broken cloud as shown in Figure 8. The possible reasons for the limited ability of MODIS cloud detection are complicated. First, though collocation was implemented before the comparisons were performed, it was inevitable that some such errors still appear in the results. Despite these possible geographical deviations, the choice of three distinct cases provides a comprehensive comparison that suggests the overall errors lie in the MODIS cloud detection rather than on calibration issues. One additional consideration is that there are often a typical meteorological factors associated with haze events that also impact MODIS detection, such as temperature inversions. Moreover, Figure 8. The same as Figure 7, but only for low broken clouds. MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,245

10 low broken clouds that MODIS failed to detect are well-known challenges to passive sensors; this kind of cloud is mostly ignored by passive detection due to the interference of the upper atmosphere. Thus, further studies for comprehensively investigating the above issues are necessary in the future. Acknowledgments This research is supported by the National Science Foundation of China ( ), the Program for Innovative Research Team in University of Ministry of Education of China (IRT1278), the National Science Foundation of Hubei Province (2015CFA002), the China Postdoctoral Science Foundation (2015M570667), the Fundamental Research Funds for the Central Universities ( kf0015), Science and Technology Research Foundation of SGCC (contract DZB ), and Major Projects of Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy (HBSZD ). We thank the MODIS, CloudSat, and CALIPSO science teams for providing excellent and accessible data products that made this study possible. These data were obtained from the NASA LAADS Web ( the CloudSat data processing center ( and the NASA Langley Research Center Atmospheric Science Data Center ( 4. Conclusion This study explored the impact of aerosol loading, particularly heavy haze, on A-Train constellation satellite cloud detection. First, we used an image-to-image comparison of cases as a basic methodology to evaluate the differences of the three sensors (MODIS, CALIOP, and CPR). Meanwhile, the averaged AOD of the corresponding orbit is used as the index for the haze and aerosols. With this methodology, we present some cases with few to heavy aerosols to analyze their effect on cloud detection. We discovered that some discrepancies between the cloud results from various instruments exist during haze weather with large aerosol loading. As a result, the ability of MODIS to detect broken cloud layers is reduced when encountering heavy aerosols. Second, by using the hit rate as the index of the agreement between different cloud data and AOD as the index of aerosol amount, we implemented year-long statistics of hit rates between MODIS and CALIOP/CPR in terms of AOD. Both sets of hit rates are more likely to be lower when AOD is higher and the lowest values of 45.0% for MODIS versus CALIPSO on 10 January 2009 due to serious aerosol loading. This can let us further deduce that the reduced accuracy of the MODIS cloud mask is mainly related to the occurrence of heavy aerosol loadings. Finally, we further explored the relationship between AOD and hit rate between MODIS and CALIPSO for a 7 year data set ( ). We see a clear decrease in hit rate with an increase in AOD. AOD values lower than 0.1 give hit rate values up to 80.0% and uncertainty values lower than 16.8%, while AOD values greater than 1.0 reduce the hit rate below 66.6% and have uncertainty values of up to 46.6% in all time. This finding confirms the conclusion of the prior case and statistical results. This study concludes that the accuracy of the MODIS cloud mask is reduced under hazy conditions, as it failed to detect some aerosols and clouds and was unable to flag pixels with confidence. This discovery suggests to be cautious when using the MODIS cloud mask and subsequently deriving higher-level MODIS products for retrievals in hazy weather conditions. Improvements for MODIS products are still necessary to reduce the impact of haze on MODIS cloud masking as research into haze effects, using MODIS data, continues to be of great interest, and making adjustments to the cloud detection algorithm thresholds for haze events in China is a relatively simple initial step. Combining satellite information with ground-based instruments in further studies shows promise for longer-term advancement on this issue. The findings in this study have global significance, as aerosol loading is not unique to China; further inquiry into global cloud detection will have beneficial impacts on similar studies and improve the entire MODIS cloud detection data set. References Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumley (1998), Discriminating clear sky from clouds with MODIS, J. Geophys. Res., 103(D24), 32,141 32,157, doi: /1998jd Ackerman, S., R. Holz, R. Frey, E. Eloranta, B. Maddux, and M. McGill (2008), Cloud detection with MODIS. Part II: Validation, J. Atmos. Oceanic Technol., 25(7), , doi: /2007jtecha Ackerman, S., K. Strabala, P. Menzel, R. Frey, C. Moeller, and L. Gumley (2010), Discriminating clear-sky from cloud with MODIS algorithm theoretical basis document (MOD35) paper presented at MODIS Cloud Mask Team, Cooperative Institute for Meteorological Satellite Studies, Univ. of Wisconsin, Citeseer. Brennan, J. I., Y. J. Kaufman, I. Koren, and R. R. Li (2005), Aerosol-cloud interaction-misclassification of MODIS clouds in heavy aerosol, IEEE Trans. Geosci. Remote Sens., 43(4), Cheng, H., W. Gong, Z. Wang, F. Zhang, X. Wang, X. Lv, J. Liu, X. Fu, and G. Zhang (2014), Ionic composition of submicron particles (PM 1.0) during the long-lasting haze period in January 2013 in Wuhan, central China, J. Environ. Sci., 26(4), Ding, Y., and Y. Liu (2014), Analysis of long-term variations of fog and haze in China in recent 50 years and their relations with atmospheric humidity, Sci. China Earth Sci., 57(1), Eck, T., B. Holben, J. Schafer, D. Giles, J. Kim, Y. Kim, I. Sano, J. Reid, K. Pickering, and J. Crawford (2014), Observations of the interaction and/or transport of aerosols with cloud or fog during DRAGON campaigns from AERONET ground-based remote sensing, paper presented at EGU General Assembly Conference Abstracts. Frey, R. A., S. A. Ackerman, Y. Liu, K. I. Strabala, H. Zhang, J. R. Key, and X. Wang (2008), Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for Collection 5, J. Atmos. Oceanic Technol., 25(7), , doi: /2008jtecha Kahn, B., M. Chahine, G. Stephens, G. Mace, R. Marchand, Z. Wang, C. Barnet, A. Eldering, R. Holz, and R. Kuehn (2008), Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount, Atmos. Chem. Phys., 8(5), MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,246

11 King, M. D., W. P. Menzel, Y. J. Kaufman, D. Tanré, B.-C. Gao, S. Platnick, S. A. Ackerman, L. A. Remer, R. Pincus, and P. A. Hubanks (2003), Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS, IEEE Trans. Geosci. Remote Sens., 41(2), Liu, D., Z. Wang, Z. Liu, D. Winker, and C. Trepte (2008), A height resolved global view of dust aerosols from the first year CALIPSO lidar measurements, J. Geophys. Res., 113, D16214, doi: /2007jd Liu, Y., S. A. Ackerman, B. C. Maddux, J. R. Key, and R. A. Frey (2010), Errors in cloud detection over the Arctic using a satellite imager and implications for observing feedback mechanisms, J. Clim., 23(7), Liu, Z., M. A. Vaughan, D. M. Winker, C. A. Hostetler, L. R. Poole, D. Hlavka, W. Hart, and M. McGill (2004), Use of probability distribution functions for discriminating between cloud and aerosol in lidar backscatter data, J. Geophys. Res., 109, D15202, doi: /2004jd Liu, Z., M. Vaughan, D. Winker, C. Kittaka, B. Getzewich, R. Kuehn, A. Omar, K. Powell, C. Trepte, and C. Hostetler (2009), The CALIPSO lidar cloud and aerosol discrimination: Version 2 algorithm and initial assessment of performance, J. Atmos. Oceanic Technol., 26(7), Ma, X., K. Bartlett, K. Harmon, and F. Yu (2013), Comparison of AOD between CALIPSO and MODIS: Significant differences over major dust and biomass burning regions, Atmos. Meas. Tech., 6(9), Ma, Y., W. Gong, and F. Mao (2015), Transfer learning used to analyze the dynamic evolution of the dust aerosol, J. Quant. Spectros. Radiat. Transfer, 153, Mao, F., W. Gong, and Y. Ma (2012), Retrieving the aerosol lidar ratio profile by combining ground- and space-based elastic lidars, Opt. Lett., 37(4), Mao, F., W. Gong, S. Song, and Z. Zhu (2013), Determination of the boundary layer top from lidar backscatter profiles using a Haar wavelet method over Wuhan, China, Opt. Laser Technol., 49, Marchand, R., G. G. Mace, T. Ackerman, and G. Stephens (2008), Hydrometeor detection using Cloudsat An earth-orbiting 94-GHz cloud radar, J. Atmos. Oceanic Technol., 25(4), Min, Q., B. Yin, S. Li, J. Berndt, L. Harrison, E. Joseph, M. Duan, and P. Kiedron (2014), A high-resolution oxygen A-band spectrometer (HABS) and its radiation closure, Atmos. Meas. Tech., 7(6), Neubauer, D., U. Lohmann, C. Hoose, and M. Frontoso (2014), Impact of the representation of marine stratocumulus clouds on the anthropogenic aerosol effect, Atmos. Chem. Phys., 14(21), 11,997 12,022. Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riédi, and R. A. Frey (2003), The MODIS cloud products: Algorithms and examples from Terra, IEEE Trans. Geosci. Remote Sens., 41(2), Quan, J., Q. Zhang, H. He, J. Liu, M. Huang, and H. Jin (2011), Analysisoftheformationoffogandhaze in North China Plain (NCP), Atmos. Chem. Phys., 11(15), , doi: /acp Qiu, B., L. I. Weijiao, M. Zhong, Z. Tang, and C. Chen (2014), Spatiotemporal analysis of vegetation variability and its relationship with climate change in China, Geo-spatial Info. Sci., 17(3), Remer, L. A., Y. Kaufman, D. Tanré, S. Mattoo, D. Chu, J. V. Martins, R.-R. Li, C. Ichoku, R. Levy, and R. Kleidman (2005), The MODIS aerosol algorithm, products, and validation, J. Atmos. Sci., 62(4), Shinozuka, Y., A. D. Clarke, S. G. Howell, V. N. Kapustin, C. S. McNaughton, J. Zhou, and B. E. Anderson (2007), Aircraft profiles of aerosol microphysics and optical properties over North America: Aerosol optical depth and its association with PM2. 5 and water uptake, J. Geophys. Res., 112, D12S20, doi: /2006jd Stephens, G. L., D. G. Vane, R. J. Boain, G. G. Mace, K. Sassen, Z. Wang, A. J. Illingworth, E. J. O Connor, W. B. Rossow, and S. L. Durden (2002), The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation, Bull. Am. Meteorol. Soc., 83(12), Tie, X., Q. Zhang, H. He, J. Cao, S. Han, Y. Gao, X. Li, and X. C. Jia (2015), A budget analysis of the formation of haze in Beijing, Atmos. Environ., 100, Wang, H., S.-C. Tan, Y. Wang, C. Jiang, G.-y. Shi, M.-X. Zhang, and H.-Z. Che (2014a), A multisource observation study of the severe prolonged regional haze episode over eastern China in January 2013, Atmos. Environ., 89, Wang, L., W. Gong, X. Xia, J. Zhu, J. Li, and Z. Zhu (2015), Long-term observations of aerosol optical properties at Wuhan, an urban site in Central China, Atmos. Environ., 101, Wang, Y., L. Yao, L. Wang, Z. Liu, D. Ji, G. Tang, J. Zhang, Y. Sun, B. Hu, and J. Xin (2014b), Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China, Sci. China Earth Sci., 57(1), Wu, D. L., et al. (2009), Vertical distributions and relationships of cloud occurrence frequency as observed by MISR, AIRS, MODIS, OMI, CALIPSO, and CloudSat, Geophys. Res. Lett., 36, L09821, doi: /2009gl Zeng, Y., W. Huang, F. B. Zhan, H. Zhang, and H. Liu (2010), Study on the urban heat island effects and its relationship with surface biophysical characteristics using MODIS imageries, Geo-spatial Info. Sci., 13(1), 1 7. MAO ET AL. IMPACT OF HAZE ON MODIS CLOUD DETECTION 12,247