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Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS
Journal of Quantitative Spectroscopy & Radiative Transfer ∎ (∎∎∎∎) ∎∎∎–∎∎∎
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Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS Wencai Wang a,n, Lifang Sheng a, Xu Dong b, Wenjun Qu a, Jilin Sun a, Hongchun Jin c, Timothy Logan d a
Physical Oceanography Laboratory, Ocean University of China, Qingdao, China Jiaxing Meteorological Bureau, Jiaxing, China Beijing DX Wind Power Technology, Beijing, China d Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA b c
a r t i c l e i n f o
abstract
Article history: Received 8 January 2016 Received in revised form 14 March 2016 Accepted 25 March 2016
Dust aerosol effect on the retrievals of dusty cloud top height (DCTH) are analyzed over Northwest China using cloud products from MODerate Resolution Imaging Spectroradiometer (MODIS) on Aqua, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and CloudSat for the Spring season of March–May over the years 2007–2011. An excellent agreement is found between CloudSat and CALIPSO derived DCTHs for all cloud types, suggesting that the effect of dust aerosols plays a small role in DCTHs determination for lidar and radar measurements. However, the presence of dust aerosols greatly affects the retrievals of DCTHs for MODIS compared with pure clouds and the active sensors derived results. The differences of DCTHs retrieving from CloudSat and MODIS range from 2.30 to 6.8 km. Likewise, the differences of DCTHs retrieving from CALIPSO and MODIS range from 2.66 to 6.78 km. In addition, the results show that the differences in DCTHs for active and passive sensors are dependent on cloud type. On the whole, dust aerosols have the largest effect on cloud top heights (CTH) retrieved of nimbostratus (Ns), followed by altocumulus (Ac) and altostratus (As), the last is cirrus (Ci) over Northwest China. Our results also indicate that the accuracy of MODIS-derived retrievals reduces accompanied with a decrease of height. & 2016 Elsevier Ltd. All rights reserved.
Keywords: Dust Cloud top height Satellite
1. Introduction Clouds have a large effect on the energy budget and climate change [1,2]. They influence the climate mainly through reflecting incoming solar and absorbing outgoing thermal radiation. Measurements experiment indicates that the minor changes in cloud microphysical properties (such as cloud particle size and cloud liquid water content) and macrophysical properties (such as cloud coverage and
n Correspondence to: College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, Shandong 266100, China. E-mail address: [email protected] (W. Wang).
cloud height) can lead to significant effects on climate [3]. Many studies indicate that dust aerosols can change cloud macrophysical and microphysical properties by indirect and semi-direct effects, such as altering cloud coverage, cloud particle size, and cloud liquid water content, resulting in a warming effect of dusty clouds [4–11]. Moreover, Jin et al. [12,13] use satellite data to analyze the impact of dust on the retrievals of cloud phase, and find that dust aerosols have a great effect on cloud phase retrievals. They also prove that CALIPSO has a better detection of ice phase clouds compared with passive sensors. The cloud heights indicate the hydrodynamic and thermodynamic structure of the atmosphere [14],
http://dx.doi.org/10.1016/j.jqsrt.2016.03.034 0022-4073/& 2016 Elsevier Ltd. All rights reserved.
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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influence the radiative and latent heating fluxes, which in turn, affect the large-scale atmospheric circulation. Accurate cloud top height (CTH) data are very important to understand how clouds affect the energy budget and the climate system. Lidar and radar have different sensitivity to cloud particle size since they use different wavelengths. Radar has higher sensitivity to relatively larger particles, while lidar has higher sensitivity to small cloud particles [15,16]. Thus, it is most likely that the dusty cloud top height (DCTH) retrievals by radar and lidar are different due to the presence and impact of dust aerosol on clouds, since many studies indicate that dust can reduce cloud particle size [6,9,10]. Moreover, the accuracy of passive and active measurements of DCTHs may have significant differences because of the existence of dust aerosols. However, there are few studies focused on this issue due to the lack of observations. Dust aerosols originated from the Taklimakan and the Gobi Deserts in Northwest China can reach a height of a few kilometers, enabling the dust to mix with clouds and become involved in cloud development [17–22]. Although much investigation has been dedicated to dust and the interactions between dust and cloud over Northwest China [6,7,9,10,17,23,24], there are few studies that consider dust aerosol impact on the determination of DCTHs from satellite retrievals, especially dust aerosol impact on different cloud types. The focus of this paper, therefore, is to investigate dust aerosol impact on the determination of DCTH retrievals for different cloud type using the A-Train satellite observations over the dust source regions of Northwest China. The satellite data products and the classification of dusty clouds are described in Sections 2 and 3. Analysis and results are given in Section 4. The conclusions follow in Section 5.
2. Data and methods CALIPSO, Aqua, and CloudSat are part of the A-Train satellite constellation with a 1:30 PM equator crossing time. Aqua flys ahead of CALIPSO and CloudSat with only 75 and 54 s, respectively, therefore, the three satellites can provide nearly simultaneous observations. In this paper, we combine the cloud products from MODIS on Aqua, the CALIPSO level 2 data, and the CloudSat vertical profiles and cloud mask data to identify dusty clouds and to investigate dust aerosols effect on the determination of DCTHs. 2.1. CALIPSO CALIPSO was launched on 28 April 2006 to provide a variety of information of aerosols and clouds globally. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on CALIPSO uses the wavelengths at 532 and 1064 nm to acquire elastic backscatter's vertical profiles during day and night, it also uses liner depolarization at 532 nm to discriminate cloud phase and nonspherical aerosol particles [25–27]. The vertical and horizontal resolution for CALIOP are 30 m and 333 m from surface to the height of 8.2 km, above 8.2 km, the values are 60 m and 1 km, respectively. Previous studies indicate that the CALIOP can
obtain cloud top boundaries accurately based on the profile of the attenuated scattering ratio [28,29]. Moreover, the Vertical Feature Mask (VFM) products from CALIPSO can distinguish cloud and aerosol type and give their positions and boundaries. In this paper, we used CALIPSO VFM and Level 2 products to distinguish aerosol and cloud types and obtain the CTHs. 2.2. CloudSat CloudSat was launched on 28 April 2006 with CALIPSO and carried a millimeter wavelength Cloud Profiling Radar (CPR). The satellite's horizontal resolution is 1.4 km (crosstrack) and 2.5 km (along-track), the vertical resolution is about 480 m. The CPR on CloudSat is more than 1000 times more sensitive to cloud than the existing weather radars. Moreover, CPR can penetrate through clouds. However, CPR often misses tenuous clouds which consist of only small liquid water droplets since it is insensitive to small hydrometeors. The 2B-GEOPROF products of CloudSat define a cloudy range bin with a confidence mask value ranges from 0 to 40. The Cloud mask with values of 6 suggest clouds approximately 50% of the time, and the Cloud mask Z30 are confidently associated with clouds [30], so we only use the clouds with the pixels of cloud mask values Z30 in our study. 2.3. MODIS MODIS resides on the Aqua and Terra satellite, with 36 spectral bands distributed from 0.415 μm to 14.235 μm and spatial resolution of 250 m, 500 m, and 1 km. Since Aqua, CALIPSO and CloudSat are part of the A-Train satellite constellations, here we only use MODIS products on Aqua (Level 2 MYD06) to obtain CTHs. The MODIS cloud top pressures are calculated by the CO2-slicing technique [31,32], more information are described in King et al. [33] and Platnick et al. [34]. The MODIS CTHs are then calculated using cloud top pressures and National Center for Environmental Prediction (NCEP) reanalysis profiles of temperature. In this study, dusty cloud (a mixture of dust and cloud) is defined as cloud contaminated by dust with the same criterion using combined CALIPSO and CloudSat data in Wang et al. [9]. If the height difference between cloud layer and dust layer is less than 50 m in the same region (i.e., the height difference between the base height for cloud layer and the top height for dust layer is less than 50 m), we define the clouds as dusty clouds. Because the three satellites described above have different horizontal resolution, we follow the CloudSat orbit and use the nearest-neighbor approach to spatially collocate the coincident observations between CloudSat, MODIS and CALIPSO. Moreover, to better compare the accuracy of passive and active measurements of DCTHs, only the single-layered dusty clouds with the pixels of CloudSat level 2 CPR cloud mask values Z30 [30,35] and CTHs above 2.2 km are considered in our research.
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Fig. 1. Vertical profiles of dusty cloud, 25 April 2008. (a) CALIPSO Vertical feature mask; (b) CALIPSO cloud type; (c) CALIPSO aerosol type; and (d) CloudSat cloud mask image. (The CTH from CALIPSO, CloudSat and MODIS are plotted as blue, green and magenta square).
3. Dusty cloud case study A typical dusty cloud case observed on 25 April 2008 is displayed in Fig. 1. From the CALIPSO VFM information displayed in Fig. 1a, a cloud (light blue) is shown to be surrounded by aerosols (orange). The cloud types are presented in Fig.1b, indicating the cloud type is cirrus. Fig. 1c confirms that the aerosol surrounding the cloud is dust. Lastly, the red color (denoting cloud maskZ30) in Fig. 1d shows high confidence in identification of clouds by CloudSat, confirming the existence of clouds in this scene. Based on the aforementioned analysis from Fig. 1, the cloud surrounded by dust aerosols in this scene is defined as a dusty cloud with the criterion in Wang et al. [9]. The DCTHs determined by CALIPSO, CloudSat and MODIS are overlain on the panels in Fig. 1d. Fig. 1d shows that the DCTHs determined by CloudSat (green square) generally agree well with (but are a little higher than) those determined by CALIPSO (blue square). The mean
difference of DCTHs retrieved from CloudSat and CALIPSO is 0.68 km, and the maximum value can reach to 1.53 km, greater than the values for pure clouds [36], indicating that dust aerosols may have an impact on cloud CTHs retrieved. The vertical velocity at the 500 hPa level (figure not shown) within dusty cloud area in Fig. 1 indicate that dust particles were lifted into the atmosphere via convective updrafts during this storm event. Since dust can heat atmosphere and burn clouds by converting the absorbed radiation to thermal energy [8] a portion of small cloud particles could evaporate by dust aerosols warming effect [37], while the large cloud particles can survive which can be detected by radar [16]. Thus, the DCTHs determined by CloudSat should be larger than that derived from CALIPSO, which is in accordance with our analysis above. Our conclusions also indicate that the direct and semi-direct effects of dust aerosol may have an influence on the determination of DCTHs for CloudSat and CALIPSO.
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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The MODIS retrieval values (magenta square plotted in Fig. 1d) of DCTHs are not quite aligned with the retrieval values by CALIPSO and CloudSat, the maximum difference is 3.72 km between CloudSat and MODIS, and 3.71 km between CALIPSO and MODIS. The MODIS CTHs are calculated using cloud top pressures and NCEP reanalysis profiles of temperature. Dust aerosols direct effects can warm the dust aerosol's local environment via the radiation absorption, resulting in a higher temperature at the top of (and possibly above) the clouds, thus the DCTHs are underestimated. Previous researches indicate that East Asian dust aerosols are more absorptive compared with Saharan dust [38–40], so the heating effect of Asian dust are much more than dust in other regions. Huang et al. [41] find that the heating rate due to dust aerosols can reach 5.5 K/day at the height of 5 km. Moreover, Wang et al. [37] also indicate that the heating rate by dust aerosols can reach 5.89 K during a strong dust storm. However, the optically thick clouds are found to have greater IR sensitivity for cloud top pressure [35], therefore for the thick dusty clouds, the DCTHs retrieved from MODIS should be close to those values obtained by CALIPSO and CloudSat, which can be seen in the current dusty cloud case study over the region between 39 °N and 39.4 °N in Fig. 1d. To better estimate dust aerosol effect on the determination of DCTHs between active and passive sensors, 26 dusty clouds cases are analyzed in our research (Table 1). Moreover, we also select pure cloud (cloud existence without dust aerosols) cases to collocate with the dusty clouds, and compare the results with the dusty clouds to discuss whether the differences are due to dust aerosols. The methods described in Section 2 are applied to select Table 1 Dusty cloud observations Northwest China.
during March to May 2007–2011 in
Case
Data
UTC
Latitude(°N)
Longitude(°E)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
2007/3/5 2007/3/8 2007/3/22 2007/5/7 2007/5/14 2008/3/16 2008/3/29 2008/4/3 2008/4/4 2008/4/20 2008/4/21 2008/4/25 2008/5/13 2008/5/16 2008/5/18 2009/3/17 2009/3/24 2009/3/27 2009/4/1 2009/4/19 2009/4/24 2009/5/15 2010/3/14 2010/4/5 2011/3/1 2011/3/6
19 20 20 19 19 19 19 19 20 20 19 20 20 20 20 19 20 18 17 19 20 5 18 19 18 19
35.36–35.68 42.38–42.72 36.77–37.70 35.04–35.60 36.53–36.85 38.37–38.91 34.41–37.71 36.39–38.07 36.17–36.71 42.00–42.80 42.04–42.75 38.49–40.00 35.11–35.89 37.29–39.96 39.44–39.71 37.32–37.79 36.20–36.44 35.72–36.20 43.85–44.68 35.3–35.63 35.79–39.20 44.04–44.50 36.01–36.36 38.34–38.69 35.60–36.00 38.52–43.07
95.48–96.58 89.92–90.03 85.06–85.34 106.22–106.38 105.09–105.19 97.97–98.14 105.08–105.47 100.48–100.92 89.55–89.70 91.33–91.60 105.25–105.49 85.57–86.04 87.69–87.91 80.60–81.43 84.36–84.44 94.52–94.67 92.55–92.73 109.51–109.65 107.48–107.74 107.78–107.87 78.57–79.59 109.20–109.36 109.61–109.72 94.84–94.95 109.49–109.61 105.73–107.20
dusty clouds using datasets from the spring season (March to May) for the years 2007–2011 over Northwest China region (35°N–45°N, 70°E–110°E). Data beyond 2011 was not used in this study due to CloudSat going off its original trajectory, making it difficult to collocate observations with CALIPSO and Aqua.
4. Analysis and results Fig. 2a–c shows comparisons of CTHs retrieved from active and positive sensors both for dusty and pure clouds. Table 2a lists the mean differences and standard deviations (STDE) of CTHs retrieved from CALIPSO, CloudSat and MODIS for dusty clouds, the pure clouds results are given in Table 2b. The CTHs derived from CALIPSO and CloudSat in Fig. 2a are in good accordance both for dusty and pure clouds as indicated by the high values of R2 (0.977 for dusty clouds and 0.996 for pure clouds). However, CTHs derived from CloudSat are a little larger than those derived from CALIPSO especially for dusty clouds. The results in Tables 2a and 2b show that the differences of satellite retrieved CTHs between CloudSat and CALIPSO is 0.21 km for dusty clouds and 0.07 km for pure clouds, indicating that dust aerosols influence the CTHs retrievals. Lidar and radar have different sensitivity to cloud particle size since they use different wavelength, radar have higher sensitivity to relatively larger particles present near the cloud tops than does lidar. Thus, radar-determined CTHs are greater than lidar-determined CTHs. For dusty clouds, since dust can heat atmosphere and burn clouds by converting the absorbed radiation to thermal energy, a portion of small cloud particles could evaporate by dust aerosols warming effect. Moreover, the number concentrations of large particles are too small that only radar can pick up the signal. Thus, the DCTHs determined by CloudSat are larger than that derived from CALIPSO. Similar comparisons of CTHs derived from CloudSat and MODIS are plotted in Fig. 2b, and show that MODIS underestimates the DCTHs compared with CloudSat (possibly due to the presence of dust aerosols as discussed before), the mean values of CTHs derived from MODIS are 1.41 km smaller than the values derived from CloudSat, while the difference is 0.59 km for pure clouds as shown in Tables 2a and 2b. In Fig. 2c, we similarly conclude that the DCTHs detected by MODIS are obviously different with the CALIPSO retrieval values (with differences as large as 6.78 km). The mean difference and the STDE of the CTHs for dusty clouds are 1.2 km and 1.29, while the values are 0.52 km and 0.53 for pure clouds. The results and discussions above indicate that MODIS has a less accurate for DCTHs retrieved compare with CALIPSO and CloudSat. The discrepancy between the passive MODIS instrument and the active CALIPSO and CloudSat instruments for determination of DCTHs seems to be notably affected by dust aerosols. The possible reasons are summarized follow. Firstly, during a dust storm, the updraft and the evaporation process by the warming effect of dust aerosols make a portion of small particles evaporate; secondly, the hygroscopic grow of the dust aerosol due to higher liquid water content associated with the cloud
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Fig. 2. Scatterplots of CloudSat derived CTHs and matched CALIPSO derived CTHs (a); (b) for CloudSat and MODIS; (c) for CALIPSO and MODIS.
Table 2a Differences and standard deviations (STDE) of CTHs retrieved from CALIPSO, CloudSat and MODIS for dusty clouds cases in Table 1.
All cloud type As Ac Ci Ns
DCTHs(CloudSat)–DCTHs (CALIPSO)
DCTHs(CloudSat)–DCTHs (MODIS)
DCTHs(CALIPSO)–DCTHs (MODIS)
Differences (km)
STDE
Differences (km)
STDE
Differences (km)
STDE
0.21 0.20 0.16 0.09 0.29
0.33 0.33 0.28 0.33 0.32
1.41 1.39 1.06 0.77 1.73
1.31 1.36 0.96 1.25 1.16
1.20 1.19 0.90 0.68 1.44
1.29 1.36 1.11 1.13 1.11
Table 2b Differences and standard deviations (STDE) of CTHs retrieved from CALIPSO, CloudSat and MODIS for pure clouds.
All cloud type As Ac Ci Ns
PCTHs(CloudSat)–PCTHs (CALIPSO)
PCTHs(CloudSat)–PCTHs (MODIS)
PCTHs(CALIPSO)–PCTHs (MODIS)
Differences (km)
STDE
Differences (km)
STDE
Differences (km)
STDE
0.07 0.08 0.07 0.06 0.07
0.15 0.17 0.15 0.13 0.14
0.59 0.42 0.51 0.71 0.78
0.54 0.42 0.45 0.63 0.59
0.52 0.34 0.44 0.65 0.70
0.53 0.41 0.43 0.62 0.60
droplet and its long durative contact with the moisturerich cloud environment. Subsequently, the number concentrations of large particles are so small that only radar can pick up the signal. Finally, when considering dust aerosol's direct and semi-direct effects, a warming effect will tend to lead to an overestimation of the DCTH temperature, and thus the pressure of the dusty clouds top
will also be overestimated, which can lead to underestimation of DCTHs from MODIS (derived from the dusty clouds top pressure). To quantify dust aerosols effect on the determination of CTHs from active and passive sensors. The relative frequency of CTH differences detected from different sensors for dusty and pure clouds is shown in Fig. 3a–c. The
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Fig. 3. Histograms of CTHs differences between CloudSat and CALIPSO (a), CloudSat and MODIS (b), CALIPSO and MODIS (c) for dusty and pure clouds from the data presented in Fig. 2.
differences in CTHs between CloudSat and CALIPSO occurs approximately 99.6% within 71 km for dusty clouds and 100% for pure clouds, the differences and STDE in Table 2a also indicates that both CALIPSO and CloudSat can retrieve CTH well with the existence of dust aerosols compared with MODIS. However, the presence of dust aerosols greatly affects retrievals of CTHs for MODIS. The differences of DCTHs between CloudSat and MODIS can range from 2.30 to 6.8 km, while the differences in pure cloud top heights (PCTH) between CloudSat and MODIS occurs approximately 100% within 71 km as shown in Fig. 3b. Likewise, the differences of DCTHs between CALIPSO and MODIS range from 2.66 to 6.78 km for all cloud types, while the differences in PCTHs between CALIPSO and MODIS occurs approximately 100% within 71 km. That is, MODIS obviously underestimates DCTHs compared with CALIPSO and CloudSat due to the existence of dust. Previous studies suggest that dust aerosols have different effects on different cloud types [10,42–44]. In order to assess the accuracy of active and passive sensors in retrieval of CTHs for different cloud types, we classify cloud types into altostratus (As), altocumulus (Ac), cirrus (Ci) and nimbostratus (Ns) according to the product of cloud classification (2B-CLDCLASS) detected in CloudSat. In addition, since thin Ci clouds are difficult for the radar to detect because of its small ice particles, we combined CloudSat and CALIPSO to detect and classify the Ci cloud types. Similar analyses are conducted for the four listed cloud types and the results are shown in Figs. 4a–f and 5a–f. For each cloud type, the differences of DCTHs between the active and passive sensors are different. On the whole, the distribution of DCTHs differences peak around 1 km, and the DCTHs detected by CloudSat are greater than CALIPSO and MODIS. For Ci (Fig. 4a–c), the CTHs differences between CALIPSO and CloudSat are all within 71 km, the negative and positive difference of DCTHs between CloudSat and CALIPSO are 46% and 54%. For pure clouds,
the values are 35% and 65%, respectively. The frequency distributions of CTHs differences derived from MODIS and CloudSat are show in Fig. 4b, the CTHs differences detected by CloudSat and MODIS centered on 1 km both for dusty (89%) and pure clouds (86.14%). Moreover, the distribution frequencies between dusty and pure clouds are mainly the same. The results in Fig. 4c also show that the CTHs difference between CALIPSO and MODIS occurring 83.80% at 1 km for pure clouds and 80% at 1 km for dusty clouds. The results in Fig. 4a–c indicate that dust aerosols have a small effect on CTHs retrieved for Ci. For Ns (Fig. 4d–f), the CTHs retrieved by active and passive sensors between dusty and pure clouds are obviously different. In addition, there are few larger difference values occur for Ns, the differences of DCTHs between CALIPSO and MODIS can reach to 6.78 km. Likewise, the differences of DCTHs between CloudSat and MODIS can up to 6.8 km. Moreover, more significant differences between MODIS and CloudSat, MODIS and CALIPSO retrievals can be found in Ns with the R2 of 0.46 and 0.50 (figures not shown), respectively. However, for pure clouds, the CTHs differences are mainly centered on 1 km. Our results show that dust aerosol have a larger effect on Ns CTHs retrieval than for Ci, the results in Table 2a also support this conclusion. Similar comparisons for As and Ac are plotted in Fig. 5a–f. The peak of the CTHs difference center on 1 km, and the skew to the positive side is strong both for As and Ac. The frequency distributions of CTHs difference detected from CloudSat and CALIPSO for As between dusty and pure clouds are much the same as shown in Fig. 5a, differing only 7.87% for 1 km and 7.61% for 1 km. Similarly, for Ac, the values are 10.14% and 10.14%. Moreover, positive CTHs difference are more frequent for dusty clouds as show in Fig. 5b–c and Fig. 5e–f, suggesting that MODIS underestimate CTHs due to the existence of dust. However, there are also few larger difference values occur for As, and the values of difference can exceed 5 km,
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Fig. 4. Same as Fig. 3 but for Ci (a–c) and Ns (d–f).
implying that satellite-retrieved DCTHs for As is more inaccurate than for Ac overall. Weisz et al. [35] indicate that for pure clouds, MODIS retrieval CTHs obviously disagree with CALIPSO for Ci but they have a good agreement for As, our results for pure clouds in Table 2b are also consistent with their results. However, the results in Figs. 4 and 5 and Table 2a show that for dusty clouds, CTHs retrievals by MODIS disagree more significantly with the values retrievals by CALIPSO for As than for Ci, this is because the impact of dust aerosols as we discussed above, and partially related to vertical distributions of dust aerosols. In summary, dust aerosols have a greatest effect on Ns CTHs retrieved since dust aerosols mainly centered on the height of Ns, followed by As and Ac, and the last is Ci where dust aerosols less existence. Generally speaking, dust aerosols mainly distribute from surface to the height of 7 km, and decrease with height over Northwest China [21,45,46], then dust aerosols effect on CTHs retrieved may also correlate with height. The vertical Profiles of CTHs difference between CloudSat/ CALIPSO and MODIS as a function of the MODIS retrieved CTHs are displayed in Fig. 6 both for dusty and pure clouds. For pure clouds, the difference of CTHs retrieved by MODIS between CloudSat and CALIPSO are almost less than 1 km for all cloud height. However, for dusty clouds, the accuracy of MODIS-derived retrievals reduces with the decreasing of heights. The DCTHs differences between CloudSat/CALIPSO and MODIS greater than 1 km are
mainly distributed below 7 km where high concentrations of dust aerosols are most often present. The results and discussions above further prove that dust aerosols effect the detection of CTHs by satellite, especially for MODIS.
5. Conclusion CTHs play an important role in understanding clouds impact on the energy budget and the earth climate system. However, there is a lack of knowledge about dust aerosol impacts on determination of different clouds type CTHs over Northwest China. This paper assessed the precision of DCTHs derived from CALIPSO, CloudSat and Aqua MODIS over dust sources regions in Northwest China using March–May dataset from 2007 to 2011. Our results indicate that the active CALIPSO and CloudSat instruments produce better DCTHs retrievals compared to the passive MODIS sensor. Most of the CALIPSO-derived retrievals are highly correlated with the CloudSat-derived DCTHs for all cloud types with the existence of dust aerosols. However, the presence of dust greatly affects retrievals of DCTHs for MODIS. The differences of DCTHs between CloudSat and MODIS range from 2.30 to 6.8 km. Likewise, the differences of DCTHs between CALIPSO and MODIS range from 2.66 to 6.78 km for all cloud type. In addition, the results also show that the differences in DCTHs are dependent on
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Fig. 5. Same as Fig. 3 but for As (a–c) and Ac (d–f).
cloud type. For Ci, the negative and positive difference of DCTHs between CloudSat and CALIPSO are 46% and 54%, respectively. For low-level and mid-level clouds (As, Ac, and Ns), the main part of differences between CloudSat and CALIPSO are positive. Moreover, MODIS underestimates DCTHs when compared with CALIPSO and CloudSat, especially for Ns clouds. On the whole, dust aerosols have the largest effect on CTHs retrieved of Ns, followed by As and Ac, the last is Ci. Moreover, the accuracy of MODIS-derived retrievals reduces with the decreasing of height, because dust aerosols mainly heat the low and middle atmospheric levels where they are most often present. We have to emphasize that our current investigation are limited to single-layered dusty clouds, Li et al. [47,48] find that As and Ac prevail over Northwest China and tend to coexist with other cloud types. Additional analysis of multilayered dusty clouds, larger scale monitoring and particularly detailed cloud resolving model simulation, are needed in the future research.
Acknowledgments
Fig. 6. The vertical profiles of CTHs difference between CloudSat/CALIPSO and MODIS for dusty and pure clouds with the MODIS retrieved CTHs.
This research is partially supported by the National Natural Science Foundation of China (41505013, 41375032) and partially supported the National Natural
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Science Foundation of Shandong Province (BS2015HZ019). We also thank Atmospheric Sciences Data Center for providing the CALIPSO products, the NASA Earth Observing System Data and Information System for providing the MODIS data, the CloudSat Data Processing Center for providing the CloudSat data, and the NCEP for providing the reanalysis data.
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Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS Wencai Wang a,n, Lifang Sheng a, Xu Dong b, Wenjun Qu a, Jilin Sun a, Hongchun Jin c, Timothy Logan d a
Physical Oceanography Laboratory, Ocean University of China, Qingdao, China Jiaxing Meteorological Bureau, Jiaxing, China Beijing DX Wind Power Technology, Beijing, China d Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA b c
a r t i c l e i n f o
abstract
Article history: Received 8 January 2016 Received in revised form 14 March 2016 Accepted 25 March 2016
Dust aerosol effect on the retrievals of dusty cloud top height (DCTH) are analyzed over Northwest China using cloud products from MODerate Resolution Imaging Spectroradiometer (MODIS) on Aqua, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and CloudSat for the Spring season of March–May over the years 2007–2011. An excellent agreement is found between CloudSat and CALIPSO derived DCTHs for all cloud types, suggesting that the effect of dust aerosols plays a small role in DCTHs determination for lidar and radar measurements. However, the presence of dust aerosols greatly affects the retrievals of DCTHs for MODIS compared with pure clouds and the active sensors derived results. The differences of DCTHs retrieving from CloudSat and MODIS range from 2.30 to 6.8 km. Likewise, the differences of DCTHs retrieving from CALIPSO and MODIS range from 2.66 to 6.78 km. In addition, the results show that the differences in DCTHs for active and passive sensors are dependent on cloud type. On the whole, dust aerosols have the largest effect on cloud top heights (CTH) retrieved of nimbostratus (Ns), followed by altocumulus (Ac) and altostratus (As), the last is cirrus (Ci) over Northwest China. Our results also indicate that the accuracy of MODIS-derived retrievals reduces accompanied with a decrease of height. & 2016 Elsevier Ltd. All rights reserved.
Keywords: Dust Cloud top height Satellite
1. Introduction Clouds have a large effect on the energy budget and climate change [1,2]. They influence the climate mainly through reflecting incoming solar and absorbing outgoing thermal radiation. Measurements experiment indicates that the minor changes in cloud microphysical properties (such as cloud particle size and cloud liquid water content) and macrophysical properties (such as cloud coverage and
n Correspondence to: College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, Shandong 266100, China. E-mail address: [email protected] (W. Wang).
cloud height) can lead to significant effects on climate [3]. Many studies indicate that dust aerosols can change cloud macrophysical and microphysical properties by indirect and semi-direct effects, such as altering cloud coverage, cloud particle size, and cloud liquid water content, resulting in a warming effect of dusty clouds [4–11]. Moreover, Jin et al. [12,13] use satellite data to analyze the impact of dust on the retrievals of cloud phase, and find that dust aerosols have a great effect on cloud phase retrievals. They also prove that CALIPSO has a better detection of ice phase clouds compared with passive sensors. The cloud heights indicate the hydrodynamic and thermodynamic structure of the atmosphere [14],
http://dx.doi.org/10.1016/j.jqsrt.2016.03.034 0022-4073/& 2016 Elsevier Ltd. All rights reserved.
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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influence the radiative and latent heating fluxes, which in turn, affect the large-scale atmospheric circulation. Accurate cloud top height (CTH) data are very important to understand how clouds affect the energy budget and the climate system. Lidar and radar have different sensitivity to cloud particle size since they use different wavelengths. Radar has higher sensitivity to relatively larger particles, while lidar has higher sensitivity to small cloud particles [15,16]. Thus, it is most likely that the dusty cloud top height (DCTH) retrievals by radar and lidar are different due to the presence and impact of dust aerosol on clouds, since many studies indicate that dust can reduce cloud particle size [6,9,10]. Moreover, the accuracy of passive and active measurements of DCTHs may have significant differences because of the existence of dust aerosols. However, there are few studies focused on this issue due to the lack of observations. Dust aerosols originated from the Taklimakan and the Gobi Deserts in Northwest China can reach a height of a few kilometers, enabling the dust to mix with clouds and become involved in cloud development [17–22]. Although much investigation has been dedicated to dust and the interactions between dust and cloud over Northwest China [6,7,9,10,17,23,24], there are few studies that consider dust aerosol impact on the determination of DCTHs from satellite retrievals, especially dust aerosol impact on different cloud types. The focus of this paper, therefore, is to investigate dust aerosol impact on the determination of DCTH retrievals for different cloud type using the A-Train satellite observations over the dust source regions of Northwest China. The satellite data products and the classification of dusty clouds are described in Sections 2 and 3. Analysis and results are given in Section 4. The conclusions follow in Section 5.
2. Data and methods CALIPSO, Aqua, and CloudSat are part of the A-Train satellite constellation with a 1:30 PM equator crossing time. Aqua flys ahead of CALIPSO and CloudSat with only 75 and 54 s, respectively, therefore, the three satellites can provide nearly simultaneous observations. In this paper, we combine the cloud products from MODIS on Aqua, the CALIPSO level 2 data, and the CloudSat vertical profiles and cloud mask data to identify dusty clouds and to investigate dust aerosols effect on the determination of DCTHs. 2.1. CALIPSO CALIPSO was launched on 28 April 2006 to provide a variety of information of aerosols and clouds globally. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on CALIPSO uses the wavelengths at 532 and 1064 nm to acquire elastic backscatter's vertical profiles during day and night, it also uses liner depolarization at 532 nm to discriminate cloud phase and nonspherical aerosol particles [25–27]. The vertical and horizontal resolution for CALIOP are 30 m and 333 m from surface to the height of 8.2 km, above 8.2 km, the values are 60 m and 1 km, respectively. Previous studies indicate that the CALIOP can
obtain cloud top boundaries accurately based on the profile of the attenuated scattering ratio [28,29]. Moreover, the Vertical Feature Mask (VFM) products from CALIPSO can distinguish cloud and aerosol type and give their positions and boundaries. In this paper, we used CALIPSO VFM and Level 2 products to distinguish aerosol and cloud types and obtain the CTHs. 2.2. CloudSat CloudSat was launched on 28 April 2006 with CALIPSO and carried a millimeter wavelength Cloud Profiling Radar (CPR). The satellite's horizontal resolution is 1.4 km (crosstrack) and 2.5 km (along-track), the vertical resolution is about 480 m. The CPR on CloudSat is more than 1000 times more sensitive to cloud than the existing weather radars. Moreover, CPR can penetrate through clouds. However, CPR often misses tenuous clouds which consist of only small liquid water droplets since it is insensitive to small hydrometeors. The 2B-GEOPROF products of CloudSat define a cloudy range bin with a confidence mask value ranges from 0 to 40. The Cloud mask with values of 6 suggest clouds approximately 50% of the time, and the Cloud mask Z30 are confidently associated with clouds [30], so we only use the clouds with the pixels of cloud mask values Z30 in our study. 2.3. MODIS MODIS resides on the Aqua and Terra satellite, with 36 spectral bands distributed from 0.415 μm to 14.235 μm and spatial resolution of 250 m, 500 m, and 1 km. Since Aqua, CALIPSO and CloudSat are part of the A-Train satellite constellations, here we only use MODIS products on Aqua (Level 2 MYD06) to obtain CTHs. The MODIS cloud top pressures are calculated by the CO2-slicing technique [31,32], more information are described in King et al. [33] and Platnick et al. [34]. The MODIS CTHs are then calculated using cloud top pressures and National Center for Environmental Prediction (NCEP) reanalysis profiles of temperature. In this study, dusty cloud (a mixture of dust and cloud) is defined as cloud contaminated by dust with the same criterion using combined CALIPSO and CloudSat data in Wang et al. [9]. If the height difference between cloud layer and dust layer is less than 50 m in the same region (i.e., the height difference between the base height for cloud layer and the top height for dust layer is less than 50 m), we define the clouds as dusty clouds. Because the three satellites described above have different horizontal resolution, we follow the CloudSat orbit and use the nearest-neighbor approach to spatially collocate the coincident observations between CloudSat, MODIS and CALIPSO. Moreover, to better compare the accuracy of passive and active measurements of DCTHs, only the single-layered dusty clouds with the pixels of CloudSat level 2 CPR cloud mask values Z30 [30,35] and CTHs above 2.2 km are considered in our research.
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Fig. 1. Vertical profiles of dusty cloud, 25 April 2008. (a) CALIPSO Vertical feature mask; (b) CALIPSO cloud type; (c) CALIPSO aerosol type; and (d) CloudSat cloud mask image. (The CTH from CALIPSO, CloudSat and MODIS are plotted as blue, green and magenta square).
3. Dusty cloud case study A typical dusty cloud case observed on 25 April 2008 is displayed in Fig. 1. From the CALIPSO VFM information displayed in Fig. 1a, a cloud (light blue) is shown to be surrounded by aerosols (orange). The cloud types are presented in Fig.1b, indicating the cloud type is cirrus. Fig. 1c confirms that the aerosol surrounding the cloud is dust. Lastly, the red color (denoting cloud maskZ30) in Fig. 1d shows high confidence in identification of clouds by CloudSat, confirming the existence of clouds in this scene. Based on the aforementioned analysis from Fig. 1, the cloud surrounded by dust aerosols in this scene is defined as a dusty cloud with the criterion in Wang et al. [9]. The DCTHs determined by CALIPSO, CloudSat and MODIS are overlain on the panels in Fig. 1d. Fig. 1d shows that the DCTHs determined by CloudSat (green square) generally agree well with (but are a little higher than) those determined by CALIPSO (blue square). The mean
difference of DCTHs retrieved from CloudSat and CALIPSO is 0.68 km, and the maximum value can reach to 1.53 km, greater than the values for pure clouds [36], indicating that dust aerosols may have an impact on cloud CTHs retrieved. The vertical velocity at the 500 hPa level (figure not shown) within dusty cloud area in Fig. 1 indicate that dust particles were lifted into the atmosphere via convective updrafts during this storm event. Since dust can heat atmosphere and burn clouds by converting the absorbed radiation to thermal energy [8] a portion of small cloud particles could evaporate by dust aerosols warming effect [37], while the large cloud particles can survive which can be detected by radar [16]. Thus, the DCTHs determined by CloudSat should be larger than that derived from CALIPSO, which is in accordance with our analysis above. Our conclusions also indicate that the direct and semi-direct effects of dust aerosol may have an influence on the determination of DCTHs for CloudSat and CALIPSO.
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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The MODIS retrieval values (magenta square plotted in Fig. 1d) of DCTHs are not quite aligned with the retrieval values by CALIPSO and CloudSat, the maximum difference is 3.72 km between CloudSat and MODIS, and 3.71 km between CALIPSO and MODIS. The MODIS CTHs are calculated using cloud top pressures and NCEP reanalysis profiles of temperature. Dust aerosols direct effects can warm the dust aerosol's local environment via the radiation absorption, resulting in a higher temperature at the top of (and possibly above) the clouds, thus the DCTHs are underestimated. Previous researches indicate that East Asian dust aerosols are more absorptive compared with Saharan dust [38–40], so the heating effect of Asian dust are much more than dust in other regions. Huang et al. [41] find that the heating rate due to dust aerosols can reach 5.5 K/day at the height of 5 km. Moreover, Wang et al. [37] also indicate that the heating rate by dust aerosols can reach 5.89 K during a strong dust storm. However, the optically thick clouds are found to have greater IR sensitivity for cloud top pressure [35], therefore for the thick dusty clouds, the DCTHs retrieved from MODIS should be close to those values obtained by CALIPSO and CloudSat, which can be seen in the current dusty cloud case study over the region between 39 °N and 39.4 °N in Fig. 1d. To better estimate dust aerosol effect on the determination of DCTHs between active and passive sensors, 26 dusty clouds cases are analyzed in our research (Table 1). Moreover, we also select pure cloud (cloud existence without dust aerosols) cases to collocate with the dusty clouds, and compare the results with the dusty clouds to discuss whether the differences are due to dust aerosols. The methods described in Section 2 are applied to select Table 1 Dusty cloud observations Northwest China.
during March to May 2007–2011 in
Case
Data
UTC
Latitude(°N)
Longitude(°E)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
2007/3/5 2007/3/8 2007/3/22 2007/5/7 2007/5/14 2008/3/16 2008/3/29 2008/4/3 2008/4/4 2008/4/20 2008/4/21 2008/4/25 2008/5/13 2008/5/16 2008/5/18 2009/3/17 2009/3/24 2009/3/27 2009/4/1 2009/4/19 2009/4/24 2009/5/15 2010/3/14 2010/4/5 2011/3/1 2011/3/6
19 20 20 19 19 19 19 19 20 20 19 20 20 20 20 19 20 18 17 19 20 5 18 19 18 19
35.36–35.68 42.38–42.72 36.77–37.70 35.04–35.60 36.53–36.85 38.37–38.91 34.41–37.71 36.39–38.07 36.17–36.71 42.00–42.80 42.04–42.75 38.49–40.00 35.11–35.89 37.29–39.96 39.44–39.71 37.32–37.79 36.20–36.44 35.72–36.20 43.85–44.68 35.3–35.63 35.79–39.20 44.04–44.50 36.01–36.36 38.34–38.69 35.60–36.00 38.52–43.07
95.48–96.58 89.92–90.03 85.06–85.34 106.22–106.38 105.09–105.19 97.97–98.14 105.08–105.47 100.48–100.92 89.55–89.70 91.33–91.60 105.25–105.49 85.57–86.04 87.69–87.91 80.60–81.43 84.36–84.44 94.52–94.67 92.55–92.73 109.51–109.65 107.48–107.74 107.78–107.87 78.57–79.59 109.20–109.36 109.61–109.72 94.84–94.95 109.49–109.61 105.73–107.20
dusty clouds using datasets from the spring season (March to May) for the years 2007–2011 over Northwest China region (35°N–45°N, 70°E–110°E). Data beyond 2011 was not used in this study due to CloudSat going off its original trajectory, making it difficult to collocate observations with CALIPSO and Aqua.
4. Analysis and results Fig. 2a–c shows comparisons of CTHs retrieved from active and positive sensors both for dusty and pure clouds. Table 2a lists the mean differences and standard deviations (STDE) of CTHs retrieved from CALIPSO, CloudSat and MODIS for dusty clouds, the pure clouds results are given in Table 2b. The CTHs derived from CALIPSO and CloudSat in Fig. 2a are in good accordance both for dusty and pure clouds as indicated by the high values of R2 (0.977 for dusty clouds and 0.996 for pure clouds). However, CTHs derived from CloudSat are a little larger than those derived from CALIPSO especially for dusty clouds. The results in Tables 2a and 2b show that the differences of satellite retrieved CTHs between CloudSat and CALIPSO is 0.21 km for dusty clouds and 0.07 km for pure clouds, indicating that dust aerosols influence the CTHs retrievals. Lidar and radar have different sensitivity to cloud particle size since they use different wavelength, radar have higher sensitivity to relatively larger particles present near the cloud tops than does lidar. Thus, radar-determined CTHs are greater than lidar-determined CTHs. For dusty clouds, since dust can heat atmosphere and burn clouds by converting the absorbed radiation to thermal energy, a portion of small cloud particles could evaporate by dust aerosols warming effect. Moreover, the number concentrations of large particles are too small that only radar can pick up the signal. Thus, the DCTHs determined by CloudSat are larger than that derived from CALIPSO. Similar comparisons of CTHs derived from CloudSat and MODIS are plotted in Fig. 2b, and show that MODIS underestimates the DCTHs compared with CloudSat (possibly due to the presence of dust aerosols as discussed before), the mean values of CTHs derived from MODIS are 1.41 km smaller than the values derived from CloudSat, while the difference is 0.59 km for pure clouds as shown in Tables 2a and 2b. In Fig. 2c, we similarly conclude that the DCTHs detected by MODIS are obviously different with the CALIPSO retrieval values (with differences as large as 6.78 km). The mean difference and the STDE of the CTHs for dusty clouds are 1.2 km and 1.29, while the values are 0.52 km and 0.53 for pure clouds. The results and discussions above indicate that MODIS has a less accurate for DCTHs retrieved compare with CALIPSO and CloudSat. The discrepancy between the passive MODIS instrument and the active CALIPSO and CloudSat instruments for determination of DCTHs seems to be notably affected by dust aerosols. The possible reasons are summarized follow. Firstly, during a dust storm, the updraft and the evaporation process by the warming effect of dust aerosols make a portion of small particles evaporate; secondly, the hygroscopic grow of the dust aerosol due to higher liquid water content associated with the cloud
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Fig. 2. Scatterplots of CloudSat derived CTHs and matched CALIPSO derived CTHs (a); (b) for CloudSat and MODIS; (c) for CALIPSO and MODIS.
Table 2a Differences and standard deviations (STDE) of CTHs retrieved from CALIPSO, CloudSat and MODIS for dusty clouds cases in Table 1.
All cloud type As Ac Ci Ns
DCTHs(CloudSat)–DCTHs (CALIPSO)
DCTHs(CloudSat)–DCTHs (MODIS)
DCTHs(CALIPSO)–DCTHs (MODIS)
Differences (km)
STDE
Differences (km)
STDE
Differences (km)
STDE
0.21 0.20 0.16 0.09 0.29
0.33 0.33 0.28 0.33 0.32
1.41 1.39 1.06 0.77 1.73
1.31 1.36 0.96 1.25 1.16
1.20 1.19 0.90 0.68 1.44
1.29 1.36 1.11 1.13 1.11
Table 2b Differences and standard deviations (STDE) of CTHs retrieved from CALIPSO, CloudSat and MODIS for pure clouds.
All cloud type As Ac Ci Ns
PCTHs(CloudSat)–PCTHs (CALIPSO)
PCTHs(CloudSat)–PCTHs (MODIS)
PCTHs(CALIPSO)–PCTHs (MODIS)
Differences (km)
STDE
Differences (km)
STDE
Differences (km)
STDE
0.07 0.08 0.07 0.06 0.07
0.15 0.17 0.15 0.13 0.14
0.59 0.42 0.51 0.71 0.78
0.54 0.42 0.45 0.63 0.59
0.52 0.34 0.44 0.65 0.70
0.53 0.41 0.43 0.62 0.60
droplet and its long durative contact with the moisturerich cloud environment. Subsequently, the number concentrations of large particles are so small that only radar can pick up the signal. Finally, when considering dust aerosol's direct and semi-direct effects, a warming effect will tend to lead to an overestimation of the DCTH temperature, and thus the pressure of the dusty clouds top
will also be overestimated, which can lead to underestimation of DCTHs from MODIS (derived from the dusty clouds top pressure). To quantify dust aerosols effect on the determination of CTHs from active and passive sensors. The relative frequency of CTH differences detected from different sensors for dusty and pure clouds is shown in Fig. 3a–c. The
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Fig. 3. Histograms of CTHs differences between CloudSat and CALIPSO (a), CloudSat and MODIS (b), CALIPSO and MODIS (c) for dusty and pure clouds from the data presented in Fig. 2.
differences in CTHs between CloudSat and CALIPSO occurs approximately 99.6% within 71 km for dusty clouds and 100% for pure clouds, the differences and STDE in Table 2a also indicates that both CALIPSO and CloudSat can retrieve CTH well with the existence of dust aerosols compared with MODIS. However, the presence of dust aerosols greatly affects retrievals of CTHs for MODIS. The differences of DCTHs between CloudSat and MODIS can range from 2.30 to 6.8 km, while the differences in pure cloud top heights (PCTH) between CloudSat and MODIS occurs approximately 100% within 71 km as shown in Fig. 3b. Likewise, the differences of DCTHs between CALIPSO and MODIS range from 2.66 to 6.78 km for all cloud types, while the differences in PCTHs between CALIPSO and MODIS occurs approximately 100% within 71 km. That is, MODIS obviously underestimates DCTHs compared with CALIPSO and CloudSat due to the existence of dust. Previous studies suggest that dust aerosols have different effects on different cloud types [10,42–44]. In order to assess the accuracy of active and passive sensors in retrieval of CTHs for different cloud types, we classify cloud types into altostratus (As), altocumulus (Ac), cirrus (Ci) and nimbostratus (Ns) according to the product of cloud classification (2B-CLDCLASS) detected in CloudSat. In addition, since thin Ci clouds are difficult for the radar to detect because of its small ice particles, we combined CloudSat and CALIPSO to detect and classify the Ci cloud types. Similar analyses are conducted for the four listed cloud types and the results are shown in Figs. 4a–f and 5a–f. For each cloud type, the differences of DCTHs between the active and passive sensors are different. On the whole, the distribution of DCTHs differences peak around 1 km, and the DCTHs detected by CloudSat are greater than CALIPSO and MODIS. For Ci (Fig. 4a–c), the CTHs differences between CALIPSO and CloudSat are all within 71 km, the negative and positive difference of DCTHs between CloudSat and CALIPSO are 46% and 54%. For pure clouds,
the values are 35% and 65%, respectively. The frequency distributions of CTHs differences derived from MODIS and CloudSat are show in Fig. 4b, the CTHs differences detected by CloudSat and MODIS centered on 1 km both for dusty (89%) and pure clouds (86.14%). Moreover, the distribution frequencies between dusty and pure clouds are mainly the same. The results in Fig. 4c also show that the CTHs difference between CALIPSO and MODIS occurring 83.80% at 1 km for pure clouds and 80% at 1 km for dusty clouds. The results in Fig. 4a–c indicate that dust aerosols have a small effect on CTHs retrieved for Ci. For Ns (Fig. 4d–f), the CTHs retrieved by active and passive sensors between dusty and pure clouds are obviously different. In addition, there are few larger difference values occur for Ns, the differences of DCTHs between CALIPSO and MODIS can reach to 6.78 km. Likewise, the differences of DCTHs between CloudSat and MODIS can up to 6.8 km. Moreover, more significant differences between MODIS and CloudSat, MODIS and CALIPSO retrievals can be found in Ns with the R2 of 0.46 and 0.50 (figures not shown), respectively. However, for pure clouds, the CTHs differences are mainly centered on 1 km. Our results show that dust aerosol have a larger effect on Ns CTHs retrieval than for Ci, the results in Table 2a also support this conclusion. Similar comparisons for As and Ac are plotted in Fig. 5a–f. The peak of the CTHs difference center on 1 km, and the skew to the positive side is strong both for As and Ac. The frequency distributions of CTHs difference detected from CloudSat and CALIPSO for As between dusty and pure clouds are much the same as shown in Fig. 5a, differing only 7.87% for 1 km and 7.61% for 1 km. Similarly, for Ac, the values are 10.14% and 10.14%. Moreover, positive CTHs difference are more frequent for dusty clouds as show in Fig. 5b–c and Fig. 5e–f, suggesting that MODIS underestimate CTHs due to the existence of dust. However, there are also few larger difference values occur for As, and the values of difference can exceed 5 km,
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Fig. 4. Same as Fig. 3 but for Ci (a–c) and Ns (d–f).
implying that satellite-retrieved DCTHs for As is more inaccurate than for Ac overall. Weisz et al. [35] indicate that for pure clouds, MODIS retrieval CTHs obviously disagree with CALIPSO for Ci but they have a good agreement for As, our results for pure clouds in Table 2b are also consistent with their results. However, the results in Figs. 4 and 5 and Table 2a show that for dusty clouds, CTHs retrievals by MODIS disagree more significantly with the values retrievals by CALIPSO for As than for Ci, this is because the impact of dust aerosols as we discussed above, and partially related to vertical distributions of dust aerosols. In summary, dust aerosols have a greatest effect on Ns CTHs retrieved since dust aerosols mainly centered on the height of Ns, followed by As and Ac, and the last is Ci where dust aerosols less existence. Generally speaking, dust aerosols mainly distribute from surface to the height of 7 km, and decrease with height over Northwest China [21,45,46], then dust aerosols effect on CTHs retrieved may also correlate with height. The vertical Profiles of CTHs difference between CloudSat/ CALIPSO and MODIS as a function of the MODIS retrieved CTHs are displayed in Fig. 6 both for dusty and pure clouds. For pure clouds, the difference of CTHs retrieved by MODIS between CloudSat and CALIPSO are almost less than 1 km for all cloud height. However, for dusty clouds, the accuracy of MODIS-derived retrievals reduces with the decreasing of heights. The DCTHs differences between CloudSat/CALIPSO and MODIS greater than 1 km are
mainly distributed below 7 km where high concentrations of dust aerosols are most often present. The results and discussions above further prove that dust aerosols effect the detection of CTHs by satellite, especially for MODIS.
5. Conclusion CTHs play an important role in understanding clouds impact on the energy budget and the earth climate system. However, there is a lack of knowledge about dust aerosol impacts on determination of different clouds type CTHs over Northwest China. This paper assessed the precision of DCTHs derived from CALIPSO, CloudSat and Aqua MODIS over dust sources regions in Northwest China using March–May dataset from 2007 to 2011. Our results indicate that the active CALIPSO and CloudSat instruments produce better DCTHs retrievals compared to the passive MODIS sensor. Most of the CALIPSO-derived retrievals are highly correlated with the CloudSat-derived DCTHs for all cloud types with the existence of dust aerosols. However, the presence of dust greatly affects retrievals of DCTHs for MODIS. The differences of DCTHs between CloudSat and MODIS range from 2.30 to 6.8 km. Likewise, the differences of DCTHs between CALIPSO and MODIS range from 2.66 to 6.78 km for all cloud type. In addition, the results also show that the differences in DCTHs are dependent on
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Fig. 5. Same as Fig. 3 but for As (a–c) and Ac (d–f).
cloud type. For Ci, the negative and positive difference of DCTHs between CloudSat and CALIPSO are 46% and 54%, respectively. For low-level and mid-level clouds (As, Ac, and Ns), the main part of differences between CloudSat and CALIPSO are positive. Moreover, MODIS underestimates DCTHs when compared with CALIPSO and CloudSat, especially for Ns clouds. On the whole, dust aerosols have the largest effect on CTHs retrieved of Ns, followed by As and Ac, the last is Ci. Moreover, the accuracy of MODIS-derived retrievals reduces with the decreasing of height, because dust aerosols mainly heat the low and middle atmospheric levels where they are most often present. We have to emphasize that our current investigation are limited to single-layered dusty clouds, Li et al. [47,48] find that As and Ac prevail over Northwest China and tend to coexist with other cloud types. Additional analysis of multilayered dusty clouds, larger scale monitoring and particularly detailed cloud resolving model simulation, are needed in the future research.
Acknowledgments
Fig. 6. The vertical profiles of CTHs difference between CloudSat/CALIPSO and MODIS for dusty and pure clouds with the MODIS retrieved CTHs.
This research is partially supported by the National Natural Science Foundation of China (41505013, 41375032) and partially supported the National Natural
Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i
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Science Foundation of Shandong Province (BS2015HZ019). We also thank Atmospheric Sciences Data Center for providing the CALIPSO products, the NASA Earth Observing System Data and Information System for providing the MODIS data, the CloudSat Data Processing Center for providing the CloudSat data, and the NCEP for providing the reanalysis data.
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Please cite this article as: Wang W, et al. Dust aerosol impact on the retrieval of cloud top height from satellite observations of CALIPSO, CloudSat and MODIS. J Quant Spectrosc Radiat Transfer (2016), http://dx.doi.org/10.1016/j. jqsrt.2016.03.034i