• ADVANCES IN ATMOSPHERIC SCIENCES, 2018, 35(2): 146-157
    doi: 10.1007/s00376-017-7070-x
    Comparison between MODIS-derived Day and Night Cloud Cover and Surface Observations over the North China Plain
    Xiao ZHANG1,3, Saichun TAN1,2,, Guangyu SHI1

    Abstract:

    Satellite and human visual observation are two of the most important observation approaches for cloud cover. In this study, the total cloud cover (TCC) observed by MODIS onboard the Terra and Aqua satellites was compared with Synop meteorological station observations over the North China Plain and its surrounding regions for 11 years during daytime and 7 years during nighttime. The Synop data were recorded eight times a day at 3-h intervals. Linear interpolation was used to interpolate the Synop data to the MODIS overpass time in order to reduce the temporal deviation between the satellite and Synop observations. Results showed that MODIS-derived TCC had good consistency with the Synop observations; the correlation coefficients ranged from 0.56 in winter to 0.73 in summer for Terra MODIS, and from 0.55 in winter to 0.71 in summer for Aqua MODIS. However, they also had certain differences. On average, the MODIS-derived TCC was 15.16% higher than the Synop data, and this value was higher at nighttime (15.58%-16.64%) than daytime (12.74%-14.14%). The deviation between the MODIS and Synop TCC had large seasonal variation, being largest in winter (29.53%-31.07%) and smallest in summer (4.46%-6.07%). Analysis indicated that cloud with low cloud-top height and small cloud optical thickness was more likely to cause observation bias. Besides, an increase in the satellite view zenith angle, aerosol optical depth, or snow cover could lead to positively biased MODIS results, and this affect differed among different cloud types.

    Key words: cloud cover; MODIS; cloud-top height; cloud optical thickness; aerosol optical depth; view zenith angle;
    摘要: 地面和卫星观测是目前云观测中两个最重要的观测途径. MODIS作为被动遥感卫星, 华北地区经常出现的雾霾天气中厚气溶胶团对太阳辐射的强迫作用将进一步影响其对云的观测. 目前的研究中, 对于华北地区卫星和地面观测的对比及影响其观测差异的可能因素, 以及不同云类型下二者的云量观测差异的研究仍旧不足. 本次研究对由MODIS卫星和地面观测站观测的华北地区总云量(TCC)进行了对比. 研究表明, MODIS观测的总云量略大于地面观测. 其中, 夜间二者差别(16.64%)大于日间(14.14%), 而冬季差别(31.07%)明显大于夏季(6.07%). 而云顶高度较低以及光学厚度较小的云更容易出现较大的观测偏差. 影响观测差异的原因还有卫星观测角, 气溶胶光学厚度以及积雪. 对于多数类型的云, 卫星观测角, 气溶胶光学厚度以及积雪量越大, 其云量观测差异也越大. 其中, 垂直发展较为旺盛的积雨云以及破碎状的云受卫星观测角的影响比层状云更大; 而不能覆盖全天空的云相对覆盖全天空的云受到气溶胶光学厚度的影响更大.
    关键词: 云量 ; MODIS ; 云顶高度 ; 云光学厚度 ; 气溶胶光学厚度 ; 卫星观测角

    1. Introduction

    Clouds are an important element in climate dynamics, atmospheric radiation, as well as atmospheric physics (Warren et al., 2007). Clouds can strongly affect the radiation balance of Earth, as they have a cooling effect due to the enhancement of planetary albedo and a heating effect resulting from the greenhouse effect of clouds (Ramanathan et al., 1989). The cloud fraction reflects the cloud's spatial domain and is a crucial factor in energy exchange in the climate system (Lu et al., 2015). Thus, it is essential to detect the spatial distribution and temporal variation of total cloud cover (TCC).

    Ground-based observation and satellite remote sensing are the two most commonly used cloud observation methods and have high spatial coverage and long time series (Lu et al., 2015). Ground-based observation includes human visual observations and ground-based automatic cloud detection (Kazantzidis et al., 2012). Since ground-based automatic cloud detection is restricted by short time series and low spatial coverage, human visual observation is still the most important source of cloud information (Kotarba, 2009; Feister et al., 2010; Huo and Lu, 2012; Lu et al., 2015). Visual observation is conducted at meteorological stations, which are also called Synop stations. This is the most traditional observation approach to obtain long-term cloud fraction data, and offers a relatively dense spatial coverage.

    Satellite remote sensing is another important observation approach to obtain cloud fraction data. Compared with Synop observations, satellite data are not influenced by subjective factors. This observation method also provides the opportunity to obtain continuous and spatially uniform observations of cloud conditions (Kästner et al., 2004; Fontana et al., 2013). Nevertheless, the data quality varies with the different characteristics of satellites, such as the spectral, spatial and temporal resolution of the sensors (Fontana et al., 2013). In recent decades, satellite remote sensing has been developing rapidly and is considered to be the most important method of remote sensing in cloud detection. The MODIS instrument, onboard the Aqua and Terra satellites, is a passive imager with 36 spectral channels and a spatial resolution of 250 to 1000 m. Previous studies have shown that MODIS has higher cloud recognition capabilities, as well as better calibration and geometry, compared with other operational sensors (Platnick et al., 2003; Lu et al., 2015). Comparisons between MODIS and other satellites have indicated that the observational quality of MODIS represents an improvement over ISCCP and AVHRR (Heidinger et al., 2002; Kotarba, 2015).

    Satellite-derived TCC has been compared with visual surface observations (Meerkötter et al., 2004; Kotarba, 2009; Fontana et al., 2013; Ma et al., 2014; Lu et al., 2015) and ground-based instruments (Key et al., 2004; An and Wang, 2015) in different regions of the world. The results show good consistency between satellite and surface observations in some regions (Kästner et al., 2004; Meerkötter et al., 2004), but also that MODIS tends to overestimate the cloud cover when compared with the surface observations in other regions (Kotarba, 2009; Fontana et al., 2013). The satellite-observed TCC is generally higher in winter and lower in summer, as determined from the observations of ISCCP, AVHRR and MODIS (Rossow et al., 1993; Kästner et al., 2004; Kotarba, 2009). (Meerkötter et al., 2004) pointed out that, in areas with serious haze pollution in the Mediterranean, the satellite-observed cloud cover is much higher than in clean areas. Research in China has shown that the consistency between satellite and visual-surface-observed TCC is probably affected by air pollution and snow cover (Lu et al., 2015). Also, the cloud cover from satellite and surface observations has been reported to show greater deviation over the North China Plain (NCP) compared with other regions (Ma et al., 2014).

    The NCP is an area with serious air pollution. Rapid economic growth over the past three decades has resulted in severe atmospheric pollution and frequent haze events (Che et al., 2014; Chen and Wang, 2015; Li, 2016). The aggravated pollution is accompanied by high aerosol loading levels (Qiu and Yang, 2000; Luo et al., 2001; Li et al., 2013; Zhang et al., 2013) and reductions in visibility (Che et al., 2007) and solar radiation (Che et al., 2005; Liang and Xia, 2005; Xia, 2010). In regions with high aerosol optical depth(AOD), the so-called shadowing effect caused by aerosols will lead to a smaller Synop-detected value of cloud fraction compared with the true value (Lu et al., 2015). Another important affect caused by high AOD is that MODIS tends to misjudge aerosol plumes as cloud in regions with heavy aerosol concentrations (Shang et al., 2014; Mao et al., 2015). However, comparisons between satellite and visual surface observations are still rare over areas with high atmospheric pollution like the NCP, particularly over the long term and in recent high-haze years.

    In this paper, we present a detailed comparison of MODIS cloud cover data with Synop observations over the NCP and its surrounding regions during the period from December 2002 to November 2013 in daytime, and December 2002 to November 2009 at nighttime. We assess the discrepancies between the two datasets over high haze pollution regions and analyze these discrepancies with respect to cloud with different cloud-top heights (CTHs) and cloud optical thicknesses (COTs). The possible factors (particularly in terms of aerosol) related to the discrepancies between MODIS and Synop data are discussed for different cloud types.

    2. Data and methods

    Eleven years (December 2002 to November 2013) of MODIS-derived TCC and Synop TCC data during daytime and seven years (December 2002 to November 2009) during nighttime were used to analyze the observational consistency of the two datasets over the NCP and its surrounding regions. Five provinces (Liaoning, Hebei, Shandong, Shanxi and Henan) and two municipalities (Beijing and Tianjin) were chosen as the research area, as shown in Fig. 1a, because there is a high AOD center and frequent haze pollution during winter over these regions (Wang et al., 2015a, 2015b, 2015c, 2015d).

    2.1. Cloud fraction from MODIS

    The satellite-observed TCC was derived from MODIS onboard the Terra and Aqua satellites, which passes over each region of the world twice a day in daytime and at nighttime. For Terra, the overpass time is around 1130 LST (Local Standard Time, UTC+8) during the daytime and 2330 LST at nighttime. For Aqua, meanwhile, the overpass time is around 1330 LST during daytime and 0130 LST at nighttime. The MODIS collection 6 MYD06/MOD06 and MYDATML2/MODATML2 cloud and aerosol products were used, downloaded from the Level 1 and Atmospheric Archive and Distribution System (http://ladsweb.nascom.nasa.gov/). The cloud detection results were recorded into 1-km (at nadir) spatial resolution MODIS cloud mask. According to the cloudiness likelihood of a given pixel, it was labeled as "cloudy", "uncertain——probably cloudy", "probably clear" or "confidently clear". The first two conditions were regarded as cloudy and the latter two as clear when calculating the cloud fraction (Platnick et al., 2003). The cloud mask product was generated into cloud fractions at 5-km resolution by calculating the proportion of cloudy pixels from every 25-pixel cloud mask group (Menzel et al., 2008).

    Fig.1. Monthly mean TCC of 121 Synop stations observed by MODIS and Synop stations during daytime from December 2003 to November 2013.

    For comparison of satellite and surface observations, the usual approach is to average the satellite-derived cloud fraction or cloud mask data within the field of view (FOV) of the surface observation. Previous studies have found that a FOV with a radius of 30 or 35 km agrees better with the observers' FOV at each Synop station (Minnis et al., 2003; Meerkötter et al., 2004; Dybbroe et al., 2005; Fontana et al., 2013). In China, studies have found that satellite and surface observations correlate best when using a FOV with a 35-km radius (Lu et al., 2015). At each Synop station, we calculated the average MODIS cloud fraction within the surrounding 35-km radius to obtain the MODIS-observed TCC from Terra and Aqua, separately.

    2.2. Cloud fraction from surface data

    The surface TCC data are visual estimations of cloud cover and cloud type produced by observers at meteorological observation stations, which are sited in open areas away from buildings and trees in order to ensure the FOV is unaffected. The data were provided by the China Meteorological Sharing Service System (CMDSSS, 2016). In total, 121 Synop stations were chosen in the research area. Synop observations were performed at eight times a day at 3-h intervals——at 0200, 0500, 0800, 1100, 1400, 1700, 2000 and 2300 LST. To minimize the effect of the time differences between Synop and MODIS observations, possible approaches include choosing the Synop TCC nearest to the MODIS overpass time (Lu et al., 2015), calculating the average of two time points adjacent to the MODIS overpass time (Fontana et al., 2013), and interpolating the Synop TCC to the MODIS overpass time (Kotarba, 2009). In this study, the Synop TCC at three times nearest the overpass time (0800, 1100 and 1400 LST during daytime and 2000, 2300 and 0200 LST at nighttime for Terra; 1100, 1400 and 1700 LST during daytime and 2300, 0200 and 0500 at nighttime for Aqua) were interpolated to the satellites' overpass times with linear interpolation in order to reduce the errors caused by observational time deviation.

    In terms of the dark conditions at nighttime seriously influencing the accuracy of visual surface observations (Minnis et al., 2003), the main existing method is to choose observations made at illuminations greater than that from a half-moon at zenith. The illumination of the moonlight from the lunar altitude and phase can be determined by the ephemeris and date (Hahn et al., 1992). The Extended Edited Cloud Report Archive (EECRA) is a dataset compiled based on global surface observation datasets. EECRA offers the relative lunar illuminance and flags denoting sufficient illumination from moonlight, twilight, or sunlight during the period 1971 to 2009 for land-based stations. In this study, 81 stations in or near the research area were chosen. For each Synop station to be compared, the nearest EECRA station was identified and their illuminations considered to be approximately equal.

    Synop observations of cloud types divide the cloud at three levels into 10 types, separately. For the sake of analysis of cloud with different forms, we redivided clouds into 10 categories following the classification method defined by the International Meteorological Organization. The 10 cloud types were: cumulus cloud (Cu), cumulonimbus cloud (Cb), stratocumulus cloud (Sc) stratocumulus cloud (St), nimbostratus cloud (Ns), altostratus cloud (As), altocumulus cloud (Ac), cirrus cloud (Ci), cirrostratus cloud (Cs), and cirrocumulus cloud (Cc).

    2.3. Auxiliary data sets

    For the analysis of the factors influencing observations, five auxiliary datasets of CTH, COT, AOD at 550nm, satellite view zenith angle (VZA), and snow cover, were used. All were derived from Terra and Aqua MODIS Collection 6 data products. The AOD data were derived from the Deep Blue (DB) and Dark Target (DT) combined algorithm, and only the highest quality flag (QF=3) AOD data were used. The DT algorithm was developed to detect AOD over dark surfaces such as vegetation and ocean (Remer et al., 2005; Levy et al., 2007a, 2007b). In contrast, the DB algorithm can retrieve AOD over bright surfaces such as desert and snow (Hsu et al., 2004; Bilal and Nichol, 2015). The DT/DB algorithm is a "best of" AOD product with a wide coverage and high precision (Green et al., 2009; Levy et al., 2013; Bilal and Nichol, 2015). The snow cover data were derived from the MODIS snow and sea ice products MOD10/MYD10, which provide the snow cover and ice cap at a 0.05° resolution (Hall et al., 2006). All these auxiliary data were averaged within the same FOV, like the TCC.

    Fig.2. Climatology of TCC of Aqua MODIS and Synop observations calculated by Aqua MODIS and Synop observations: (a, c, e, g) daytime distribution calculated by observations between December 2002 and November 2013; (b, d, f, h) nighttime distribution calculated by observations between December 2002 and November 2009. Colors of the dots indicate the time-averaged TCC observed by each station. Shading indicates the time-averaged TCC observed by Aqua MODIS. Numbers in (a) represent different regions, 1 for Beijing, 2 for Tianjin, 3 for Shanxi, 4 for Hebei, 5 for Liaoning, 6 for Henan, 7 for Shandong.

    Fig.3. Difference between TCC observed by Terra MODIS (TCCT) and Aqua MODIS (TCCA) (a) during daytime and (b) at nighttime. Each dot represents a comparison of the average value of Terra and Aqua MODIS in one month at a single station. The solid line is the regression line, while the dashed lines represent the 95% confidence intervals.

    Fig.4. Frequency distribution of the deviation of MODIS- and Synop-observed TCC at intervals of 0.2 (a) during daytime over 11 years from December 2002 to November 2013, and (b) at nighttime over 7 years from December 2002 to November 2009, for 121 Synop stations. The blue bars on the left represent the deviation between Terra and Synop observations, and the red bars on the right represent the deviation between Aqua and Synop observations.

    3. Results and discussion
    3.1. Climatology of TCC from Aqua MODIS and Synop observations

    In order to realize the overall distribution of MODIS- and Synop-observed TCC, we first calculated the climatic field as well as the temporal variation of the TCC. As shown in Fig. 1, the cloud fraction showed distinct seasonal changes. The TCC observed by MODIS was generally greater than that from the Synop observations. The latter showed the lowest TCC in winter and highest in summer, yet the MODIS value was high both in summer and winter, and relatively low in spring and winter. The TCC observed by the two methods showed best consistency in summer and greatest deviation in winter. Analysis of the TCC climatic field is shown in Fig. 2. In general, the TCC of the southern part was higher than the northern part, which was roughly the same for MODIS and Synop observations. Meanwhile, it is notable that in winter the MODIS-observed TCC in the northern part was much larger than the Synop observation during daytime, while at nighttime both the MODIS- and Synop-derived TCC showed low values. In the southern part, the MODIS-observed TCC was high both in daytime and at nighttime, while the Synop observation was relatively low.

    3.2. Comparison between TCC from Terra and Aqua MODIS

    We conducted a detailed 11-year (December 2002-November 2013) comparison between the MODIS-derived TCC from the Terra and Aqua satellites. Figure 3 compares the monthly averaged TCC observed by Aqua and Terra for all stations. The correlation coefficient (R) between the TCC derived from Terra MODIS and Aqua MODIS was 0.77 for daytime and 0.72 for nighttime, suggesting that Terra MODIS and Aqua MODIS were highly coherent. The Aqua MODIS observation results were slightly larger than those of Terra MODIS for both daytime and nighttime. This may be affected by the satellites' different overpass times.

    Table 1. Comparisons of daily TCC observed by MODIS with Synop observations during the period from December 2002 to November 2013 for daytime and December 2002 to November 2009 for nighttime.
    Day (1 Dec. 2002 to 30 Nov. 2013) Night (1 Dec. 2002 to 30 Nov. 2009) Day + Night (1 Dec. 2002 to 30 Nov. 2009)
    Terra Aqua Terra Aqua Terra Aqua
    Mean difference (%) 12.74 14.14 15.58 16.64 13.95 15.25
    RMSE (%) 33.84 34.66 36.96 39.02 35.04 36.20
    Correlation coefficient 0.69 0.67 0.65 0.64 0.67 0.65
    Number of observations 347 248 446 500 68 125 91 541 136 138 183 106

    Table 1. Comparisons of daily TCC observed by MODIS with Synop observations during the period from December 2002 to November 2013 for daytime and December 2002 to November 2009 for nighttime.

    Table 2. Comparisons of daily MODIS TCC and Synop observations for four seasons during the period from December 2002 to November 2013 for daytime and December 2002 to November 2009 for nighttime.
    Spring (March-May) Summer (June-August) Autumn (September-November) Winter (December in the last year-February in the present year)
    Parameter Terra Aqua Terra Aqua Terra Aqua Terra Aqua
    Mean difference(%) Day 7.70 8.44 2.31 4.29 12.97 14.66 29.42 29.60
    Night 11.98 13.04 6.97 7.99 10.68 12.46 30.15 32.94
    Day + Night 9.64 10.75 4.46 6.07 11.63 13.64 29.53 31.07
    RMSE (%) Day 30.05 30.33 25.13 24.86 32.14 33.62 45.75 46.51
    Night 33.96 36.07 32.85 33.72 33.59 34.01 47.05 49.26
    Day + Night 31.85 32.25 28.69 29.47 32.36 33.78 46.42 47.59
    Correlation coefficient Day 0.75 0.74 0.78 0.76 0.74 0.72 0.59 0.57
    Night 0.71 0.69 0.67 0.65 0.73 0.72 0.54 0.52
    Day + Night 0.72 0.71 0.73 0.71 0.73 0.71 0.56 0.55
    Number of observations Day 87 099 112 657 90 797 112 211 87 499 112 365 81 853 109 267
    Night 15 867 23 483 16 864 23 451 18 356 21 675 17 126 22 843
    Day + Night 31 726 46 745 33 704 46 894 36 707 43 302 34 241 45 639

    Table 2. Comparisons of daily MODIS TCC and Synop observations for four seasons during the period from December 2002 to November 2013 for daytime and December 2002 to November 2009 for nighttime.

    3.3. Comparison between daily MODIS and Synop observations

    A comparison between the MODIS and Synop TCC was conducted daily during the period December 2002-November 2013, and the statistical results are shown in Fig. 4 and Table 1. The positive differences between the MODIS and Synop observations were significantly more than the negative ones, and 55% of all differences during daytime and 50% of all differences at nighttime ranged from 0% to 20% (Fig. 4), indicating that the MODIS-observed data were generally greater than the Synop data. The mean difference (D ms) between the MODIS- and Synop-observed data was 13.95% for Terra and 15.25% for Aqua (Table 1).

    Table 1 explicitly shows that the deviation at nighttime was greater than that during daytime. The D ms at nighttime was 2% to 3% higher than that during daytime, and the RMSE at nighttime was 3% to 4% higher than during daytime. The R during daytime was 0.69 and 0.67 for Terra and Aqua, respectively, which was higher than the R at nighttime (0.65 and 0.64). This may be affected by the lack of a visible channel, which would reduce the accuracy of the MODIS observation (Kotarba, 2009).

    3.4. Seasonal variations between MODIS and Synop TCC

    The difference between MODIS and Synop TCC also varied with season (Table 2). As shown in Table 2, the deviation between the two datasets was greatest in winter. In winter, the D ms and RMSE were the largest among the four seasons; the D ms reached 29.53% and 31.07% and the RMSE 46.42% and 47.59% for Terra and Aqua, respectively. Meanwhile, the R in winter was smallest among the four seasons, being only 0.56 and 0.55 for Terra and Aqua, respectively. The R was similar to a comparison of MODIS and Synop TCC in Poland; however, the D ms was much higher than that in Poland, which was 7.28% in January 2004 (Kotarba, 2009).

    In contrast, the difference between the MODIS and Synop TCC was smallest and most consistent in summer. Both the D ms (2.31%-7.99%) and RMSE (24.86%-33.72%) were much smaller than in the other three seasons, and the R was relatively high (0.65-0.78). The mean D ms during daytime and at nighttime in our study regions was 4.46% for Terra and 6.07% for Aqua, which is comparable to the 4.38% in Poland in July 2004 (Kotarba, 2009). Previous research in China found similar results. (Ma et al., 2014) found that the D ms calculated by full-year data was 15.09% in North China, while the D ms decreased to 5.29% after removal of the winter data. (Lu et al., 2015) found that in the China area the correlation between the two observation results was highest in summer (0.736) and lowest in winter (0.667).

    The deviation between MODIS and Synop TCC in spring and autumn was between that of summer and winter, and did not show any great difference. The high R, ranging from 0.69 to 0.75, suggested good consistency between MODIS and Synop TCC. Table 2 shows that in all seasons the D ms and RMSE during daytime were much smaller than at nighttime and the R during daytime was much higher than that at nighttime, indicating that the TCC observed during daytime was much better than that at nighttime.

    3.5. Relationship between the cloud fraction deviation and CTH/COT

    Considering different cloud types may influence both MODIS and Synop observations and further influence the D ms, two physical characteristics——CTH and COT, which are important parameters to distinguish different cloud types——were chosen to discuss their relationship with D ms. Because of the lack of COT observations at night, both discussions focus on the data during daytime only.

    Fig.5. (a) Average CTH under conditions of different D ms. (b) Cumulative frequency distribution of D ms under different CTH at intervals of 200 m.

    Fig.6. (a) Average COT under conditions of different D ms. (b) Cumulative frequency distribution of D ms under different COT at intervals of 4.

    Figure 5a shows the average CTH under different D ms levels. It can be clearly seen from the figure that when the D ms was less than zero the average CTH was at a relatively high value. In the area that the D ms was near zero the average CTH was at a peak. Meanwhile, when the D ms was greater than 0.2 the CTH showed a sharp decrease with an increase in the D ms. When the D ms was close to 1 the average CTH was near 1 km.

    Figure 5b facilitates further discussion on the D ms distribution for clouds with different CTH. The figure shows that under conditions with lower CTH the distribution frequency of bigger D ms was much higher, and D ms values greater than 0.2 mainly appeared when CTH was less than 2 km. With an increase in CTH the proportion of bigger D ms values reduced rapidly. This result shows that MODIS more easily detects cloud with low CTH, which Synop observations were otherwise unable to detect.

    The other characteristic, COT, is discussed based on the results in Fig. 6. Figure 6a shows that the average COT with D ms near zero was obviously higher. In contrast, the average COT with deviation larger than 0.2 was generally low in value. The distribution of D ms under different COT is presented in Fig. 6b. As can be seen in the figure, large deviation mainly occurred under conditions with low COT. When COT was greater than 20, nearly all deviations were smaller than 0.2.Meanwhile, when COT was smaller than 12, the frequency of deviations greater than 0.2 was nearly half.

    It can be inferred from the analysis above that MODIS tends to detect cloud with low CTH and small COT that is otherwise undetected by Synop observations, meaning there may be cases that Cu and Sc clouds are detected by MODIS but undetected or underestimated by Synop observations. Given that previous research has proven that MODIS tends to judge the layer of aerosols at low altitude as cloud (Shang et al., 2014; Mao et al., 2015), it is possible that MODIS in the present study judged the aerosol layer as cloud, leading to the high D ms. Another possibility is that the surface FOV was larger for high cloud; surface observations can see high cloud in a larger radius than low cloud, which increases the surface-observed high cloud fraction.

    3.6. Possible reasons for the difference between MODIS and Synop

    To explore the possible factors influencing the consistency and deviation between MODIS and Synop TCC observations, we analyzed the relationship between the deviation with AOD, VZA, and snow cover.

    Fig.7. Average D ms calculated for different cloud types under conditions of different VZA at interval of 5°. Each bar color represents a cloud type (see legend). The stems of each colored bar represent the VZA frequency distribution.

    Fig.8. D ms of TCC in (a) spring, (b) summer, (c) autumn and (d) winter. Color of each dot indicates the time-averaged deviation level of each station. Shading indicates the time-averaged Aqua MODIS AOD at 550 nm.

    Fig.9. Average D ms calculated for different (a) seasons and (b-d) cloud types under conditions of different AOD at intervals of 0.25. Each bar color represents a cloud type (see legend). The stems marked in the middle of each colored bar represent the AOD frequency distribution, corresponding to the ticks of the right-hand y-axis.

    The averaged D ms in different VZA intervals (Fig. 7a) showed that the deviation of TCC increased with an increase in VZA. Under conditions of VZA <30°, the D ms basically maintained at a low level (<7%); whereas, at VZA >30°, the D ms increased systematically with VZA. The averaged D ms at the largest VZA interval (23.45%) was 19.68% greater than the D ms at the smallest VZA interval (3.77%). The regression result also showed that larger VZA would lead to larger MODIS observations. This is consistent with previous studies (Maddux et al., 2010; An and Wang, 2015). The larger VZA would decrease the clear space between clouds, especially for thick clouds like convective clouds, on account of the vertical sides of the clouds would be viewed by the satellite. This effect was especially obvious for convective clouds or broken clouds. Another possible reason was that pixels with larger VZA have larger size and longer observation path lengths, which may increase the satellite-observed TCC.

    Analysis of the relationships between cloud types and VZA (Figs. 7b-d) showed that high VZA would lead to the D ms of most categories of clouds being higher when the VZA was higher. Besides, observations of broken clouds were more likely to be affected by the VZA. Cu, Cb and Ac showed significant increasing trend as the VZA became larger. In contrast, cloud covering the whole sky had a relative stable observation result. The trends of Sc, St, Ns and As were not as obvious as the other types of cloud. It is worth noting that the D ms of most cloud types was positive, while that of Ns and As was near zero, possibly because both MODIS and Synop observations were near to 1 under these conditions; plus, Cs was negative in every VZA, which was possibly because MODIS had a relative weak detection ability for thin ice cloud, as proven by (Holz et al., 2008).

    Figure 8 shows the spatial distribution of Aqua MODIS AOD and the averaged D ms between MODIS and Synop at each Synop station in the four seasons. Because of the lack of AOD observations at nighttime, only observations during daytime were analyzed. The D ms was averaged from the D ms of Terra and Aqua MODIS. In all seasons, the distribution of the averaged D ms was consistent with the distribution of AOD. Stations with low D ms values mainly distributed in the northwestern area and Shandong's coastal area, which were the low AOD value areas. In contrast, the D ms in central and western Shandong, central and eastern Henan, as well as southern Hebei, were generally higher than in other areas.

    Note that in the Liaoning area the D ms was slightly larger than in areas with the same AOD value (Fig. 8). This phenomenon became quite obvious in winter (Fig. 8d). In winter, the D ms in Liaoning was even larger than that in the border regions of Shandong, Hebei and Henan, where AODs were largest. This might be influenced by snow cover and the low solar height angle due to Liaoning being located at high latitudes.

    To further investigate the impact of AOD on the difference between satellite and Synop TCC observations for each cloud type, the D ms values at different AOD intervals were calculated (Fig. 9). Figure 9a shows that the D ms was greater at high AODs. In all seasons, the D ms tended to increase generally with an increase in AOD. In summer, the D ms increased monotonically with increasing AOD over the entire AOD range. In spring, autumn and winter, the D ms increased with AOD values at AOD <1.5, whereas the D ms showed no remarkable change and even dropped slightly with increasing AOD at AOD >1.5. That may have been caused by a small amount of high AODs (Fig. 9) or the environment was not so different to satellite and visual surface observations at AOD >1.5.

    Analysis of different cloud types (Figs. 9b-d) showed that the D ms of most cloud types increased with AOD. The most obvious were Cu, Ac, Ci and Cs, which did not cover the whole sky. Misjudging the aerosol layer as cloud by MODIS may be the reason behind this phenomenon.

    To further investigate the influence of snow cover, the relationship between snow cover and the D ms in winter is shown in Fig. 10. The distribution of D ms values showed consistency with the distribution of snow cover. In winter, the main areas with high D ms values appeared in the provinces of Liaoning and Shandong. The above analysis shows that high AODs in Shandong induced high D ms values; however, Liaoning had much lower AOD values. The large snow coverage in Liaoning may have resulted in the higher D ms.

    Fig.10. Difference between MODIS- and Synop-observed TCC in winter. Color of each dot represents the deviation level of each station. The shading indicates the snow coverage.

    4. Summary

    This study compared MODIS (Terra and Aqua) and Synop surface observed cloud fraction over the NCP and its surrounding regions during the period from December 2002 to November 2013 during daytime, and from December 2002 to November 2009 at nighttime. The comparison showed that certain differences existed between MODIS- and Synop-observed TCC. MODIS observed a significantly higher value, and this phenomenon was more obvious at nighttime. At nighttime, the mean difference between Synop and Terra/ Aqua MODIS was 15.58% and 16.64%, respectively; and this was greater than during daytime, being 12.74% and 14.14% for Terra and Aqua, respectively. The regression correlation coefficient between Synop and Terra/Aqua MODIS at nighttime was 0.65 and 0.64, respectively, which was smaller than during daytime (0.69 and 0.67 for Terra and Aqua, respectively). The comparison also revealed considerable changes in different seasons. The mean differences for Terra MODIS and Aqua MODIS in winter (29.53% and 31.07%, respectively) were much higher than in the other three seasons (ranging from 4.46% to 13.64%), and the correlation coefficients in winter (0.56 and 0.55 for Terra and Aqua, respectively) were less than in the other three seasons (ranging from 0.71 to 0.73).

    Analysis of the effect of cloud characteristics on the observational deviation found that CTH and COT had an obvious influence on D ms. Cloud with low CTH was more likely to cause a higher MODIS observational result and lower Synop observational result, while this frequency reduced significantly when the CTH was lower than 4 km. Another point is that observations with significant deviations mainly occurred when COT was less than 12.

    Analysis showed that a large VZA would lead to a larger MODIS-observed TCC, and this effect was more obvious for clouds occurring in clumps than cloud covering the whole sky. Besides, thin clouds like Cs would lead to a negative D ms, and a high VZA value would improve the MODIS detection. Similar results were seen for the effect of AOD. The spatial distribution of the difference between MODIS and Synop matched well with the AOD distribution, and the difference increased with an increase in AOD. The difference in the NCP and its surrounding regions was higher than that in Poland, Europe (Kotarba, 2009), suggesting that high pollution may induce a greater MODIS TCC. In addition, high snow coverage may affect MODIS observations, thus resulting in a high difference in northern areas.

    Acknowledgements. This work was supported by the National Natural Science Foundation of China (Grant Nos. 41590874 and 41590875) and the Ministry of Science and Technology of China (Grant No. 2014CB953703). The MODIS cloud and aerosol properties were provided by the Level 1 and Atmosphere Archive and Distribution System of the NASA Goddard Space Flight Center. We are grateful to the China Meteorological Administration for providing the visual surface cloud cover data.

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    DOI:10.1175/JTECH-D-11-00006.1      URL     [Cited within:1]
    [18] Kästner, M., P. Bissolli, K. Hoppner, 2004: Comparison of a satellite based Alpine cloud climatology with observations of synoptic stations.Meteor. Z.,13,233-243,https://doi.org/10.1127/0941-2948/2004/0013-0233.
    A five-year cloud climatology (1992 to 1996) of the Alpine region in a 15-km resolution has been evaluated by means of the APOLLO cloud detection algorithm applied to daytime AVHRR data of several NOAA satellites. The study area comprises three different climatic regions, the moderate climate north of the Alps, the Alpine climate and the Mediterranean climate in the Po-valley. Synoptic observations of the total cloud cover at 40 stations have been compared to the satellite based monthly mean data. Hourly ground observations allowed to estimate the variance in the monthly mean diurnal cycle of total cloud cover due to the fact that the satellite overpass time shifts from noon to afternoon for the NOAA-11 platform and for different NOAA satellites as well. This time shift of satellite observation effects the cloud climatology only slightly, because the changes of the cloud cover between 11 and 16 UTC are in most cases considerably smaller than the yearto-year variability. Furthermore, these cloud cover variations due to the time of the day are in monthly means below the validation accuracy. The comparison of monthly means reveals an overestimation of the satellite cloud cover of about 10% mainly due to additional detection of thin cirrus. A good agreement is found in the Alpine and rural moderate climates (corr. coeff. r > 0.75), whereas the cloud detection in the satellite data is too high in the Mediterranean zone due to urban and aerosol haze effects. In both data sets a rather small amplitude of the annual cycle of cloud cover results in the mountains compared to the lowlands. The high spatial variability of cloud cover in mountainous terrain is obvious with the satellite data and is substantiated by the sparse synoptic stations within the Alps. Eine 5-j01hrige (1992–1996) Wolkenklimatologie der Alpenregion in einer 15-km-Aufl02sung wurde mit dem APOLLO Wolkenerkennungsalgorithmus erzeugt. Sie basiert auf t01glichen AVHRR-Daten (Mittags überflüge) von verschiedenen NOAA-Satelliten. Das Auswertegebiet umfasst drei verschiedene Klimaregionen: das gem0108igte Klima n02rdlich der Alpen, das Alpenklima und das mediterrane Klima in der Po-Ebene. Synoptische Beobachtungen der Gesamtwolkenbedeckung an 40 SYNOP-Stationen wurden mit den satellitengestützten, monatlich gemittelten Daten verglichen. Die stündlichen Stationsbeobachtungen erm02glichen eine Absch01tzung der 02nderung der Gesamtbew02lkung im Tagesgang und damit den Einfluss aus unterschiedlichen 05berflugzeiten der Satelliten. Dies ist insbesondere für den NOAA-11-Satelliten wichtig, dessen 05berflugzeit sich im Laufe der Jahre von Mittag auf Nachmittag verschob, bei den anderen NOAA-Satelliten ist es 01hnlich. Es zeigte sich, dass diese Zeitverschiebung der Satellitenbeobachtung die Ergebnisse der Wolkenklimatologie nur wenig beeinflusst, weil die Ver01nderungen der Wolkenbedeckung zwischen 11 und 16 UTC im Monatsmittel in den meisten F01llen betr01chtlich kleiner als die Jahr-zu-Jahr Variabilit01t sind. Ferner liegen diese tageszeitlichen Ver01nderungen unterhalb der Validierungsgenauigkeit. Der Vergleich der Monatsmittelwerte zeigt eine 05bersch01tzung der Satelliten-Wolkenbedeckung von ca. 10% zu den Bodenstationen, haupts01chlich bedingt durch die zus01tzliche Erkennung von dünnem Zirrus in den Satellitendaten. Insgesamt besteht eine recht gute 05bereinstimmung für das alpine und das gem0108igte Klima in l01ndlichen Gebieten (Korr. koeff. r0,75), w01hrend in der Mittelmeerzone und in Gro08st01dten die Satellitendaten die Bew02lkung übersch01tzen, was auf Aerosol- und Dunsteffekte zurückzuführen ist. Beide Datens01tze weisen eine geringe Amplitude des Jahresgangs der Bew02lkung über Bergen im Vergleich zum Flachland auf. Die hohe r01umliche Variabilit01t des Bedeckungsgrades im gebirgigen Gel01nde wird besonders deutlich in den Satellitendaten und gleichfalls best01tigt durch die r01umlich weniger gut aufgel02sten Stationsbeobachtungen im Alpengebiet.
    DOI:10.1127/0941-2948/2004/0013-0233      URL     [Cited within:1]
    [19] Kazantzidis A., P. Tzoumanikas, A. F. Bais, S. Fotopoulos, and G. Economou, 2012: Cloud detection and classification with the use of whole-sky ground-based images.Atmos. Res.,113,80-88,https://doi.org/10.1016/j.atmosres.2012.05.005.
    A simple whole sky imaging system, based on a commercial digital camera with a fish-eye lens and a hemispheric dome, is used for the automatic estimation of total cloud coverage and classification. For the first time, a multi color criterion is applied on sky images, in order to improve the accuracy in detection of broken and overcast clouds under large solar zenith angles. The performance of the cloud detection algorithm is successfully compared with ground based weather observations. A simple method is presented for the detection of raindrops standing on the perimeter of hemispheric dome. Based on previous works on cloud classification, an improved k-Nearest-Neighbor algorithm is presented, based not only on statistical color and textural features, but taking also into account the solar zenith angle, the cloud coverage, the visible fraction of solar disk and the existence of raindrops in sky images. The successful detection percentage of the classifier ranges between 78 and 95% for seven cloud types.
    DOI:10.1016/j.atmosres.2012.05.005      URL     [Cited within:1]
    [20] Key E. L., P. J. Minnett, and R. A. Jones, 2004: Cloud distributions over the coastal Arctic Ocean: Surface-based and satellite observations.Atmos. Res.,72,57-88,https://doi.org/10.1016/j.atmosres.2004.03.029.
    All-weather Arctic cloud analyses primarily derived from a surface-based hemispheric all-sky imager are compared against ISCCP D-1 cloud amount, type, and phase during the sunlit polar season. Increasing surface temperatures and decreasing ice cover over the past decade have altered heat and moisture fluxes around the Arctic, providing conditions more conducive for cloud generation. Shipboard and ice camp measurements from field experiments conducted over an 8-year period show cloudy skies in 7095% of the record. Most of these occurrences are stratiform or multi-level, multi-form cloud, increasing in amount with time through the season. Collocated ISCCP retrievals underestimate cloud amount at small solar zenith angles and overestimate at large angles, sometimes by as much as 50%. Satellite assessments of cloud form classify 95% of scenes as having multiple cloud types, the majority of which are mid-level ice cloud and low-level liquid cloud. Despite large discrepancies in diurnal cloud amount, regional averages of ISCCP pixel cloudiness over the length of the experiments agree within 5% of surface observations.
    DOI:10.1016/j.atmosres.2004.03.029      URL     [Cited within:1]
    [21] Kotarba A. Z., 2009: A comparison of MODIS-derived cloud amount with visual surface observations.Atmos. Res.,92,522-530,https://doi.org/10.1016/j.atmosres.2009.02.001.
    Two main sources for global cloud climatologies are visual surface observations and observations made by spaceborne sensors. Satellite observations compared with surface data show in most cases differences ranging from 610215% up to 61021%, depending on sensor and observation conditions. These differences are partially controlled by sensors' cloud detection capabilities — a higher number of spectral bands and higher spatial resolution are believed to allow discrimination of clouds from land/ocean/snow background. A Moderate-Resolution Imaging Spectroradiometer (MODIS) produces images of the atmosphere in 36 spectral bands with a spatial resolution of 250–1000m, thus having a capacity for cloud detection far more advanced than other operating sensors. In this study, instantaneous MODIS cloud observations were compared with surface data for Poland for January (winter) and July (summer) 2004. It was found that MODIS observed 4.38% greater cloud amount in summer conditions and 7.28% in winter conditions. Differences were greater at night (7–8%) than in daytime (0.5–7%) and correlations ranged between 0.577 (winter night) and 0.843 (winter day, summer day and night).
    DOI:10.1016/j.atmosres.2009.02.001      URL     [Cited within:9]
    [22] Kotarba A. Z., 2015: Evaluation of ISCCP cloud amount with MODIS observations.Atmos. Res.,153,310-317,https://doi.org/10.1016/j.atmosres.2014.09.006.
    The goal of the International Satellite Cloud Climatology Project (ISCCP) is to provide global cloud amount statistics for atmospheric radiation flux modeling, which is a key element of climate change studies. However, ISCCP estimates rely on two spectral channels only, while the most advanced satellite sensors offer over 20 spectral bands, and thus a higher probability of correct cloud detection. We validated the accuracy of ISCCP mean monthly cloud amount statistics using the state-of-the-art, 36-spectral channel Moderate-resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites. Based on the MODIS Level 2 Cloud Mask we developed a dedicated Level 3 product for Central Europe (2004–2009). For the first time, MODIS swath data were projected onto an ISCCP equal-area grid, which guaranteed an exact geometrical agreement between both climatologies. Results showed that there was a close correlation between ISCCP and MODIS data (ρ02=020.872, α02=020.99), especially at warmer part of the year (ρ02≥020.940, α02>020.99). However, ISCCP estimations were found to be unreliable in wintertime when surface was covered with snow. The presence of snow resulted in a significant underestimate of cloud amount by 0.45 for individual ISCCP grid boxes. Our results suggest that MODIS cloud climatology is more reliable when estimates of mean monthly cloud amount are required.
    DOI:10.1016/j.atmosres.2014.09.006      URL     [Cited within:1]
    [23] Levy R. C., L. A. Remer, and O. Dubovik, 2007a: Global aerosol optical properties and application to Moderate Resolution Imaging Spectro radiometer aerosol retrieval over land.J. Geophys. Res.,112,D13210,https://doi.org/10.1029/2006JD007815.
    [Cited within:]
    [24] Levy R. C., L. A. Remer, S. Mattoo, E. F. Vermote, and Y. J. Kaufman, 2007b: Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectro radiometer spectral reflectance.J. Geophys. Res.,112,D13211,https://doi.org/10.1029/2006JD007811.
    [1] Since first light in early 2000, operational global quantitative retrievals of aerosol properties over land have been made from Moderate Resolution Imaging Spectroradiometer (MODIS) observed spectral reflectance. These products have been continuously evaluated and validated, and opportunities for improvements have been noted. We have replaced the surface reflectance assumptions, the set of aerosol model optical properties, and the aerosol lookup table (LUT). This second-generation operational algorithm performs a simultaneous inversion of two visible (0.47 and 0.66 μ m) and one shortwave-IR (2.12 μ m) channel, making use of the coarse aerosol information content contained in the 2.12 μ m channel. Inversion of the three channels yields three nearly independent parameters, the aerosol optical depth ( τ ) at 0.55 μ m, the nondust or fine weighting ( η ), and the surface reflectance at 2.12 μ m. Retrievals of small-magnitude negative τ values (down to 610.05) are considered valid, thus balancing the statistics of τ in near zero τ conditions. Preliminary validation of this algorithm shows much improved retrievals of τ , where the MODIS/Aerosol Robotic Network τ (at 0.55 μ m) regression has an equation of: y = 1.01x + 0.03, R = 0.90. Global mean τ for the test bed is reduced from 650.28 to 650.21.
    DOI:10.1029/2006JD007811      URL     [Cited within:]
    [25] Levy R. C., S. Mattoo, L. A. Munchak, L. A. Remer, A. M. Sayer, and N. C. Hsu, 2013: The Collection 6 MODIS aerosol products over land and ocean.Atmospheric Measurement Techniques Discussions6,159-259,https://doi.org/10.5194/amtd-6-159-2013.
    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:]
    [26] Li X. Y., 2016: Empirical analysis of the smog factors in Beijing-Tianjin-Hebei region.Ecological Economy32,144- 150,https://doi.org/10.3969/j.issn.1671-4407.2016.03.029.(in Chinese)
    [Cited within:1]
    [27] Li, Z., Coauthors, 2013: Aerosol physical and chemical properties retrieved from ground-based remote sensing measurements during heavy haze days in Beijing winter.Atmos. Chem. Phys.13,10 171-10 183,https://doi.org/10.5194/acp-13-10171-2013.
    With the development of economy in the past thirty years, many large cities in the eastern and southwestern China are experiencing increased haze events and atmospheric pollution, causing significant impacts on the regional environment and even climate. However, knowledge on the aerosol physical and chemical properties in heavy haze conditions is still insufficient. In this study, two winter heavy haze events in Beijing occurred in 2011 and 2012 were selected and investigated by using the ground-based remote sensing measurements. We used CIMEL CE318 sun-sky radiometer to derive haze aerosol optical, physical and chemical properties, including aerosol optical depth (AOD), size distribution, complex refractive indices and fractions of chemical components like black carbon (BC), brown carbon (BrC), mineral dust (DU), ammonium sulfate-like (AS) components and aerosol water content (AW). The retrieval results from a total of five haze days showed that the aerosol loading and properties during the two winter haze events were relatively stable. Therefore, a parameterized heavy haze characterization was drawn to present a research case for future studies. The averaged AOD is 3.2 at 440 nm and ngstrm exponent is 1.3 from 440870 nm. The coarse particles occupied a considerable fraction of the bimodal size distribution in winter haze events, with the mean particle radius of 0.21 and 2.9 m for the fine and coarse mode respectively. The real part of the refractive indices exhibited a relatively flat spectral behavior with an average value of 1.48 from 440 to 1020 nm. The imaginary part showed obviously spectral variation with the value at 440 nm (about 0.013) higher than other three wavelengths (e.g. about 0.008 at 675 nm). The chemical composition retrieval results showed that BC, BrC, DU, AS and AW occupied 1%, 2%, 49%, 15% and 33% respectively on average for the investigated haze events. The comparison of these remote sensing results with in situ BC and PMlt;subgt;2.5lt;/subgt; measurements were also presented in the paper.
    DOI:10.5194/acpd-13-5091-2013      URL     [Cited within:1]
    [28] Liang F., X. A. Xia, 2005: Long-term trends in solar radiation and the associated climatic factors over China for 1961-2000.Annales Geophysicae23,2425-2432,https://doi.org/10.5194/angeo-23-2425-2005.
    Long-term trends in downwelling solar irradiance and associated climatic factors over China are studied in the paper. Decreasing trends in global and direct radiation are observed over much of China. The largest decrease occurs in South and East China (east of about 100℃ E and south of about 40℃ N). The spatial pattern of observed trends in diffuse irradiance is complex and inhomogeneous. An intriguing aspect of trends in global and direct irradiance is the rather abrupt decrease in annual and seasonal mean values from 1978 onward. The decreasing trends in solar radiation in China did not persist into the 1990s. The spatial and temporal patterns of trends in sunshine duration are consistent with that of global and direct irradiance. A decreasing trend in rainy days is observed over much of China, which is in agreement with the secular trend in cloud amount. The fact that trends in cloud amount and solar radiation are quite similar suggests that the cloud amount is not the primary cause for the decrease in solar radiation. Visibility in the eastern part of China has deteriorated heavily as a result of the rapid increase in aerosol loading. The statistical analysis showed that atmospheric transmission under clear conditions decreased rapidly. These facts suggest that the rapid increase in aerosol loading should be one of the principle causes for the decrease in solar radiation. The observed diurnal temperature range decreases remarkably in China, which is closely related to the increase in aerosols. The effects of anthropogenic air pollutants on climate should be further studied and included in the simulation of climate and projection of climate scenario.Keywords. Atmospheric composition and structure (Aerosol and particles; General or miscellaneous) Meteorology and atmospheric dynamics (Radiative processes)
    DOI:10.5194/angeo-23-2425-2005      URL     [Cited within:1]
    [29] Lu H., Y. W. Zhang, and J. Cai, 2015: Consistency and differences between remotely sensed and surface observed total cloud cover over China.Int. J. Remote Sens.,36,4160-4176,https://doi.org/10.1080/01431161.2015.1072651.
    In this study, we conducted a comparison between surface-observed total cloud cover (TCCs) and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived total cloud cover (TCCm) over China. A statistical method was applied to estimate the average field of view (FOV) of surface observers, and the radius range of FOV was 20–25, 25–35, 35–50, and 25–4502km for spring, summer, autumn, and winter, respectively. More differences would be added in the comparison when the satellite’s FOV was smaller or larger than the average FOV. Monthly mean TCCs was 74.78%, 74.41%, 66.5%, and 74.06% for each season and the corresponding TCCm was 75.27%, 78.34%, 73.82%, and 82.12%. The correlation between two data sets was stronger in spring (0.727) and summer (0.736) than in autumn (0.710) and winter (0.667). Over 60% of the differences were within the 6110% to 10% range, and more differences occurred for smaller TCCs. As a special feature, we found that the dust, haze, and snow cover over specific regions in China were the possible causes of the significant differences. Generally, these two data sets were in good agreement over China, and can complement each other especially in those significant difference cases to provide more accurate TCC data sets.
    DOI:10.1080/01431161.2015.1072651      URL     [Cited within:10]
    [30] Luo Y. F., D. R. Lu, X. J. Zhou, W. L. Li, and Q. He, 2001: Characteristics of the spatial distribution and yearly variation of aerosol optical depth over China in last 30 years.J. Geophys. Res.,106,14 501-14 513,https://doi.org/10.1029/2001JD900030.
    DOI:10.1029/2001JD900030      URL     [Cited within:]
    [31] Ma J. J., H. Wu, C. Wang, X. Zhang, Z. Q. Li, and X. H. Wang, 2014: Multiyear satellite and surface observations of cloud fraction over China.J. Geophys. Res.,119,7655-7666,https://doi.org/10.1002/2013JD021413.
    Abstract Earth observation satellites can provide systematic and continuous monitoring of clouds. High spatial resolution (565km) cloud fraction data are retrieved from the Moderate Resolution Imaging Spectroradiometer cloud mask products of Terra (1265years) and Aqua (965years) over China. Long-term trends of cloud fraction for morning and afternoon observations clearly reflected seasonal variations. We found more clouds in the afternoon than in the morning. There is a strong correlation between satellite and surface observations of the daily cloud fraction for the period of 2012, with correlation coefficients of 0.678 and 0.7 for morning and afternoon, respectively. However, the analyses of the monthly mean cloud fraction between satellite and surface observations showed a larger discrepancy in the winter. In order to investigate the differences between satellite and ground-based cloud fraction over different underlying surfaces, a statistical test was carried out for six areas. The results indicated statistically significant increased correlations ( p 65<650.0001) between satellite and ground-based cloud fraction in northern China after removing winter data, especially in Northeastern Forest and Taklimakan Desert, while the correlation coefficients in southern China did not show significant changes.
    DOI:10.1002/2013JD021413      URL     [Cited within:3]
    [32] Maddux B. C., S. A. Ackerman, and S. Platnick, 2010: Viewing geometry dependencies in MODIS cloud products.J. Atmos. Oceanic Technol.27,1519-1528,https://doi.org/10.1175/2010JTECHA1432.1.
    Characterizing the earth's global cloud field is important for the proper assessment of the global radiation budget and hydrologic cycle. This characterization can only be achieved with satellite measurements. For complete daily coverage across the globe, polar-orbiting satellites must take observations over a wide range of sensor zenith angles. This paper uses Moderate Resolution Imaging Spectroradiometer (MODIS) Level-3 data to determine the effect that sensor zenith angle has on global cloud properties including the cloud fraction, cloud-top pressure, effective radii, and optical thickness. For example, the MODIS cloud amount increases from 57% to 71% between nadir and edge-of-scan (0903046700°) observations, for clouds observed between 3500°N and 3500°S latitude. These increases are due to a combination of factors, including larger pixel size and longer observation pathlength at more oblique sensor zenith angles. The differences caused by sensor zenith angle bias in cloud properties are not readily apparent in monthly mean regional or global maps because the averaging of multiple satellite overpasses together 'washes out' the zenith angle artifact. Furthermore, these differences are not constant globally and are dependent on the cloud type being observed.
    DOI:10.1175/2010JTECHA1432.1      URL     [Cited within:1]
    [33] Mao F. Y., M. M. Duan, Q. L. Min, W. Gong, Z. X. Pan, and G. Y. Liu, 2015: Investigating the impact of haze on MODIS cloud detection.J. Geophys. Res.,120,12 237-12 247,https://doi.org/10.1002/2015JD023555.
    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.
    DOI:10.1002/2015JD023555      URL     [Cited within:1]
    [34] Meerkötter, R., C. König, P. Bissolli, G. Gesell, H. Mannstein, 2004: A 14-year European Cloud Climatology from NOAA/ AVHRR data in comparison to surface observations.Geophys. Res. Lett.,31,L15103,https://doi.org/10.1029/2004GL020098.
    A 14-year (1990-2003) high resolution European Cloud Climatology has been generated by use of NOAA/AVHRR data. For selected areas we present spatially averaged monthly means of total cloud cover derived from noon overpasses and compare them with surface SYNOP observations. The climatologies do not reveal a significant trend of cloud cover over the 14-year period. However, both data sets show a clear latitudinal variability and a seasonal dependence which is more pronounced in the satellite than in the SYNOP observations. Mean differences between satellite and SYNOP data range from about -2% to -10% in all seasons except summer when the mean difference is as large as -15.3%. As a special feature we notice the broad minimum of cloud cover during the extreme dry and hot summer in 2003 in Central Europe.
    DOI:10.1029/2004GL020098      URL     [Cited within:4]
    [35] Menzel, W. P., Coauthors, 2008: MODIS global cloud-top pressure and amount estimation: Algorithm description and results.Journal of Applied Meteorology and Climatology47,1175-1198,https://doi.org/10.1175/2007JAMC1705.1.
    The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua platforms provides unique measurements for deriving global and regional cloud properties. MODIS has spectral coverage combined with spatial resolution in key atmospheric bands, which is not available on previous imagers and sounders. This increased spectral coverage/spatial resolution, along with improved onboard calibration, enhances the capability for global cloud property retrievals. MODIS operational cloud products are derived globally at spatial resolutions of 5 km (referred to as level-2 products) and are aggregated to a 1℃ equal-angle grid (referred to as level-3 product), available for daily, 8-day, and monthly time periods. The MODIS cloud algorithm produces cloud-top pressures that are found to be within 50 hPa of lidar determinations in single-layer cloud situations. In multilayer clouds, where the upper-layer cloud is semitransparent, the MODIS cloud pressure is representative of the radiative mean between the two cloud layers. In atmospheres prone to temperature inversions, the MODIS cloud algorithm places the cloud above the inversion and hence is as much as 200 hPa off its true location. The wealth of new information available from the MODIS operational cloud products offers the promise of improved cloud climatologies. This paper 1) describes the cloud-top pressure and amount algorithm that has evolved through collection 5 as experience has been gained with in-flight data from NASA Terra and Aqua platforms; 2) compares the MODIS cloud-top pressures, converted to cloud-top heights, with similar measurements from airborne and space-based lidars; and 3) introduces global maps of MODIS and High Resolution Infrared Sounder (HIRS) cloud-top products.
    DOI:10.1175/2007JAMC1705.1      URL     [Cited within:1]
    [36] Minnis P., D. A. Spangenberg, and V. Chakrapani, 2003: Distribution and validation of cloud cover derived from AVHRR data over the Arctic Ocean during the SHEBA year. Proceedingsofthe13thARMScienceTeamMeeting, Broomfield, Colorado.
    [Cited within:2]
    [37] Platnick S., M. D. King, S. 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 Transactionson Geoscience and Remote Sensing, 41, 459-473, https://doi.org/10.1109/TGRS.2002.808301.
    The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of five instruments aboard the Terra Earth Observing System (EOS) platform launched in December 1999. After achieving final orbit, MODIS began Earth observations in late February 2000 and has been acquiring data since that time. The instrument is also being flown on the Aqua spacecraft, launched in May 2002. A comprehensive set of remote sensing algorithms for cloud detection and the retrieval of cloud physical and optical properties have been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. We will describe the various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir. An example of each Level-2 cloud product from a common data granule (5 min of data) off the coast of South America will be discussed. Future efforts will also be mentioned. Relevant points related to the global gridded statistics products (Level-3) are highlighted though additional details are given in an accompanying paper in this issue.
    DOI:10.1109/TGRS.2002.808301      URL     [Cited within:2]
    [38] Qiu J. H., L. Q. Yang, 2000: Variation characteristics of atmospheric aerosol optical depths and visibility in North China during 1980-1994.Atmos. Environ.34,603-609,https://doi.org/10.1016/S1352-2310(99)00173-9
    Using a method developed by Qiu (Qiu, J., 1998. A method to determine atmospheric aerosol optical depth using total direct solar radiation. J. Atmos. Sci. 55, 734–758), 0.75 μm aerosol optical depths at five meteorological observatories in north China during 1980–1994 are retrieved from global direct solar radiation, and variation characteristics of the depths and visibility are analyzed. These observatories are located in the cities of Wulumuqi, Geermu, Harbin, Beijing and Zhengzhou. It is found that during 1980–1994 the aerosol optical depths show an increasing trend at all five sites. During winter the trend is stronger. In winter at Beijing and Wulumuqi, the depth increased by a factor of about two in 15 years. Pollution caused due to the burning of fossil fuel is the main cause of the change. In spring at Geermu the depth is larger and its increase is the quickest among the four seasons, mainly due to desert dust events. The Pinatubo volcanic eruption in 1991 had a significant influence on the aerosol optical depth. The yearly averaged depths over five sites in 1992 after the eruption increased by 0.068 to 0.212, compared to those in 1990, while from 1992 to 1994 they generally show a decreasing trend. In some cities such as Zhengzhou and Geermu, both visibility and optical depth show an increasing trend during 1980–1994, a possible reason for this is that the aerosol particle vertical distribution shifts up in the troposphere. At Geermu, Harbin, Beijing and Zhengzhou, optical depths in summer are larger, which may be because of the growth of aerosol particles growing in the moist summer. Apart from Geermu, at the other four sites visibility in winter is smaller, especially at Wulumuqi and Harbin. At Harbin, visibility in summer is about twice larger than that in winter, but the difference between depths is small, implying the turbid lower troposphere in winter and the larger extinction coefficient in the upper troposphere during summer.
    DOI:10.1016/S1352-2310(99)00173-9      URL     [Cited within:]
    [39] Ramanathan V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmad, and D. Hartmann, 1989: Cloud-radiative forcing and climate: Results from the earth radiation budget experiment.Science243,57-63,https://doi.org/10.1126/science.243.4887.57.
    The study of climate and climate change is hindered by a lack of information on the effect of clouds on the radiation balance of the earth, referred to as the cloud-radiative forcing. Quantitative estimates of the global distributions of cloud-radiative forcing have been obtained from the spaceborne Earth Radiation Budget Experiment (ERBE) launched in 1984. For the April 1985 period, the global shortwave cloud forcing [-44.5 watts per square meter (W/m(2))] due to the enhancement of planetary albedo, exceeded in magnitude the longwave cloud forcing (31.3 W/m(2)) resulting from the greenhouse effect of clouds. Thus, clouds had a net cooling effect on the earth. This cooling effect is large over the mid-and high-latitude oceans, with values reaching -100 W/m(2). The monthly averaged longwave cloud forcing reached maximum values of 50 to 100 W/m(2) over the convectively disturbed regions of the tropics. However, this heating effect is nearly canceled by a correspondingly large negative shortwave cloud forcing, which indicates the delicately balanced state of the tropics. The size of the observed net cloud forcing is about four times as large as the expected value of radiative forcing from a doubling of CO(2). The shortwave and longwave components of cloud forcing are about ten times as large as those for a CO(2) doubling. Hence, small changes in the cloud-radiative forcing fields can play a significant role as a climate feedback mechanism. For example, during past glaciations a migration toward the equator of the field of strong, negative cloud-radiative forcing, in response to a similar migration of cooler waters, could have significantly amplified oceanic cooling and continental glaciation.
    DOI:10.1126/science.243.4887.57      PMID:17780422      URL     [Cited within:1]
    [40] Remer, L. A., Coauthors, 2005: The MODIS aerosol algorithm,products, and validation.J. Atmos. Sci.,62,947-973,https://doi.org/10.1175/JAS3385.1.
    The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard both NASA0964s Terra and Aqua satellites is making near-global daily observations of the earth in a wide spectral range (0.4109“15 0204m). These measurements are used to derive spectral aerosol optical thickness and aerosol size parameters over both land and ocean. The aerosol products available over land include aerosol optical thickness at three visible wavelengths, a measure of the fraction of aerosol optical thickness attributed to the fine mode, and several derived parameters including reflected spectral solar flux at the top of the atmosphere. Over the ocean, the aerosol optical thickness is provided in seven wavelengths from 0.47 to 2.13 0204m. In addition, quantitative aerosol size information includes effective radius of the aerosol and quantitative fraction of optical thickness attributed to the fine mode. Spectral irradiance contributed by the aerosol, mass concentration, and number of cloud condensation nuclei round out the list of available aerosol products over the ocean. The spectral optical thickness and effective radius of the aerosol over the ocean are validated by comparison with two years of Aerosol Robotic Network (AERONET) data gleaned from 132 AERONET stations. Eight thousand MODIS aerosol retrievals collocated with AERONET measurements confirm that one standard deviation of MODIS optical thickness retrievals fall within the predicted uncertainty of 02”0367 = 00±0.03 00±0.050367 over ocean and 02”0367 = 00±0.05 00± 0.150367 over land. Two hundred and seventy-one MODIS aerosol retrievals collocated with AERONET inversions at island and coastal sites suggest that one standard deviation of MODIS effective radius retrievals falls within 02”reff = 00±0.11 0204m. The accuracy of the MODIS retrievals suggests that the product can be used to help narrow the uncertainties associated with aerosol radiative forcing of global climate.
    DOI:10.1175/JAS3385.1      URL     [Cited within:1]
    [41] Rossow W. B., A. W. Walker, and L. C. Garder, 1993: Comparison of ISCCP and other cloud amounts. J. Climate, 6, 2394-2418, https://doi.org/10.1175/1520-0442(1993)006 <2394:COIAOC>2.0.CO;2.
    A new 8-year global cloud climatology has been produced by the International Satellite Cloud Climatology Project (ISCCP) that provides information every 3 h at 280-km spatial resolution covering the period from July 1983 through June 1991. If cloud detection errors and differences in area sampling are neglected, individual ISCCP cloud amounts agree with individual surface observations to within 15% rms with biases of only a few percent. When measurements of small-scale, broken clouds are isolated in the comparison, the rms differences between satellite and surface cloud amounts are about 25%, similar to the rms difference between ISCCP and Landsat determinations of cloud amount. For broken clouds, the average ISCCP cloud amounts are about 5% smaller than estimated by surface observers (difference between earth cover and sky cover), but about 5% larger than estimated from very high spatial resolution satellite observations (overestimate due to low spatial resolution offset by underestimate due to finite radiance thresholds). Detection errors, caused by errors in the ISCCP clear-sky radiances or incorrect radiance threshold magnitude are the dominant source of error in monthly average cloud amounts. The ISCCP cloud amounts appear to he too low over land by about 10%, somewhat less in summer and somewhat more in winter, and about right (maybe slightly low) over oceans. In polar regions, ISCCP cloud amounts are probably too low by about 15%–25% in summer and 5%–10% in winter. Comparison of the ISCCP climatology to three other cloud climatologies shows excellent agreement in the geographic distribution and seasonal variation of cloud amounts; there is little agreement about day/night contrasts in cloud amount. Notable results from ISCCP are that the global annual mean cloud amount is about 63%, being about 23% higher over oceans than over land, that it varies by <1% rms from month to month, and that it has varied by about 4% on a time wale ≈2–4 years. The magnitude of interannual variations of local (280-km scale) monthly mean cloud amounts is about 7%–9%. Longitudinal contrasts in cloud amount are just as large as latitudinal contrasts. The largest seasonal variation of cloud amount occurs in the tropics, being larger in summer than in winter; the seasonal variation in middle latitudes has the opposite phase. Polar regions may have little seasonal variability in cloud amount. The ISCCP results show slightly more nighttime than daytime cloud amount over oceans and more daytime than nighttime cloud amount over land.
    DOI:10.1175/1520-0442(1993)0062.0.CO;2      URL     [Cited within:1]
    [42] Shang H. Z., L. F. Chen, J. H. Tao, L. Su, and S. L. Jia, 2014: Synergetic use of MODIS cloud parameters for distinguishing high aerosol loadings from clouds over the North China Plain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 4879-4886, https://doi.org/10.1109/JSTARS.2014.2332427.
    The Moderate Resolution Imaging Spectroradiometer (MODIS) standard cloud product is prone to misidentifying areas that are heavily polluted with aerosols as cloudy regions over the North China Plain (NCP) and to retrieving aerosol characteristics as cloud parameters. Based on the differences in physical and optical properties between aerosols and clouds, we propose a new approach to distinguish aerosol-laden areas from cloudy regions using MODIS level 2 cloud properties (e.g., cloud fraction, cloud phase, and cloud top pressure products). The approach was applied to 22 haze-fog cases that occurred in the 2011 and 2012 winters over the NCP. The aerosol identification results were then compared with MODIS-flagged aerosol areas, which were inferred from the noncloud obstruction flag and the suspended dust flag in the MODIS cloud mask product. The results indicated that approximately 60% of the MODIS-flagged aerosol areas were correctly identified using our approach. Among the analyzed cases, two cases exhibited substantial differences; the aerosol areas detected using the newly proposed method were approximately 2.5 times larger than that of the MODIS-flagged area. Further comparisons with aerosol distributions along the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) orbit for the two cases demonstrated that approximately 60%-80% of the CALIOP observed aerosols were identified using our method, while less than 10% of the CALIOP observed aerosols were consistent with the MODIS flagging.
    DOI:10.1109/JSTARS.2014.2332427      URL     [Cited within:1]
    [43] Wang H., G. Y. Shi, X. Y. Zhang, S. L. Gong, S. C. Tan, B. Chen, H. Z. Che, and T. Li, 2015b: Mesoscale modelling study of the interactions between aerosols and PBL meteorology during a haze episode in China Jing-Jin-Ji and its near surrounding region-Part 2: Aerosols' radiative feedback effects.Atmos. Chem. Phys.15,3277-3287,https://doi.org/10.5194/acp-15-3277-2015.
    Two model experiments, namely a control (CTL) experiment without aerosol–radiation feedbacks and a experiment with online aerosol–radiation (RAD) interactions, were designed to study the radiative feedback on regional radiation budgets, planetary boundary layer (PBL) meteorology and haze formation due to aerosols during haze episodes over Jing–Jin–Ji, China, and its near surroundings (3JNS region of China: Beijing, Tianjin, Hebei, East Shanxi, West Shandong and North Henan) with a two-way atmospheric chemical transport model. The impact of aerosols on solar radiation reaching Earth's surface, outgoing long-wave emission at the top of the atmosphere, air temperature, PBL turbulence diffusion, PBL height, wind speeds, air pressure pattern and PM2.5 has been studied focusing on a haze episode during the period from 7 to 11 July 2008. The results show that the mean solar radiation flux that reaches the ground decreases by about 15% in 3JNS and 20 to 25%in the region with the highest aerosol optical depth during the haze episode. The fact that aerosol cools the PBL atmosphere but warms the atmosphere above it leads to a more stable atmospheric stratification over the region, which causes a decrease in turbulence diffusion of about 52% and a decrease in the PBL height of about 33%. This consequently forms a positive feedback on the particle concentration within the PBL and the surface as well as the haze formation. Additionally, aerosol direct radiative forcing (DRF) increases PBL wind speed by about 9% and weakens the subtropical high by about 14 hPa, which aids the collapse of haze pollution and results in a negative feedback to the haze episode. The synthetic impacts from the two opposite feedbacks result in about a 14% increase in surface PM2.5. However, the persistence time of both high PM2.5 and haze pollution is not affected by the aerosol DRF. On the contrary over offshore China, aerosols heat the PBL atmosphere and cause unstable atmospheric stratification, but the impact and its feedback on the planetary boundary layer height, turbulence diffusion and wind is weak, with the exception of the evident impacts on the subtropical high.
    DOI:10.5194/acp-15-3277-2015      URL     [Cited within:]
    [44] Wang, H., Coauthors, 2015c: Mesoscale modeling study of the interactions between aerosols and PBL meteorology during a haze episode in Jing-Jin-Ji (China) and its nearby surrounding region-Part 1: Aerosol distributions and meteorological features.Atmos. Chem. Phys.15,3257-3275,https://doi.org/10.5194/acp-15-3257-2015.
    [Cited within:]
    [45] Wang H. J., H. P. Chen, and J. P. Liu, 2015a: Arctic sea ice decline intensified haze pollution in eastern China.Atmos. Oceanic Sci. Lett.,8,1-9,https://doi.org/10.3878/AOSL20140081.
    URL     [Cited within:1]
    [46] Wang, H. J., Coauthors, 2015d: A review of seasonal climate prediction research in China.Adv. Atmos. Sci.,32,149-168,https://doi.org/10.1007/s00376-014-0016-7.
    The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930 s. Experimental operational climate forecasts have been performed since the late 1950 s,based on historical analog circulation patterns. However, due to the inherent complexity of climate variability, the forecasts produced at that time were fairly inaccurate. Only from the late 1980 s has seasonal climate prediction experienced substantial progress, when the Tropical Ocean and Global Atmosphere project of the World Climate Research program(WCRP) was launched. This paper, following a brief description of the history of seasonal climate prediction research, provides an overview of these studies in China. Processes and factors associated with the climate variability and predictability are discussed based on the literature published by Chinese scientists. These studies in China mirror aspects of the climate research effort made in other parts of the world over the past several decades, and are particularly associated with monsoon research in East Asia. As the climate warms, climate extremes, their frequency, and intensity are projected to change, with a large possibility that they will increase. Thus, seasonal climate prediction is even more important for China in order to effectively mitigate disasters produced by climate extremes, such as frequent floods, droughts, and the heavy frozen rain events of South China.
    DOI:10.1007/s00376-014-0016-7      URL     [Cited within:]
    [47] Warren S. G., R. M. Eastman, and C. J. Hahn, 2007: A survey of changes in cloud cover and cloud types over land from surface observations,1971-96.J. Climate,20,717-738,https://doi.org/10.1175/JCLI4031.1.
    In the middle latitudes of both hemispheres, seasonal anomalies of cloud cover are positively correlated with surface temperature in winter and negatively correlated in summer, as expected if the direction of causality is from clouds to temperature.
    DOI:10.1175/JCLI4031.1      URL     [Cited within:1]
    [48] Xia X., 2010: A closer looking at dimming and brightening in China during 1961-2005.Annales Geophysicae28,1121-1132,https://doi.org/10.5194/angeo-28-1121-2010.
    This study investigates dimming and brightening of surface solar radiation (SSR) during 1961–2005 in China as well as its relationships to total cloud cover (TCC). This is inferred from daily ground-based observational records at 45 pyranometer stations. A statistical method is introduced to study contributions of changes in the frequency of TCC categories and their atmospheric transparency to the secular SSR trend. The surface records suggest a renewed dimming beyond 2000 in North China after the stabilization in the 1990s; however, a slight brightening appears beyond 2000 in South China. Inter-annual variability of SSR is negatively correlated with that of TCC, but there is a positive correlation between decadal variability of SSR and TCC in most cases. The dimming during 1961–1990 is exclusively attributable to decreased atmospheric transparency, a portion of which is offset by TCC frequency changes in Northeast and Southwest China. The dimming during 1961–1990 in Northwest and Southeast China primarily results from decreased atmospheric transparency under all sky conditions and the percentage of dimming stemming from TCC frequency changes is 11% in Northwest and 2% in Southeast China. Decreased atmospheric transparencies during 1991–2005 in North China in most cases lead to the dimming. TCC frequency changes also contribute to the dimming during this period in North China. This feature is more pronounced in summer and winter when TCC frequency changes can account for more than 80% of dimming. In South China, increased atmospheric transparencies lead to the brightening during 1991–2005. A substantial contribution by TCC frequency changes to the brightening is also evident in spring and autumn.
    DOI:10.5194/angeo-28-1121-2010      URL     [Cited within:]
    [49] Zhang, R., Coauthors, 2013: Chemical characterization and source apportionment of PM2.5 in Beijing: Seasonal perspective.Atmos. Chem. Phys.13,7053-7074,https://doi.org/10.5194/acp-13-7053-2013.
    DOI:10.5194/acp-13-7053-2013      URL     [Cited within:]
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    Key words
    cloud cover
    MODIS
    cloud-top height
    cloud optical thickness
    aerosol optical depth
    view zenith angle

    Authors
    Xiao ZHANG
    Saichun TAN
    Guangyu SHI