Advanced Search
Article Contents

Comparison of Cloud Properties between CloudSat Retrievals and Airplane Measurements in Mixed-Phase Cloud Layers of Weak Convective and Stratus Clouds


doi: 10.1007/s00376-015-4287-4

  • Cloud microphysical properties including liquid and ice particle number concentration (NC), liquid water content (LWC), ice water content (IWC) and effective radius (RE) were retrieved from CloudSat data for a weakly convective and a widespread stratus cloud. Within the mixed-phase cloud layers, liquid-phase fractions needed to be assumed in the data retrieval process, and one existing linear (p1) and two exponential (p2 and p3) functions, which estimate the liquid-phase fraction as a function of subfreezing temperature (from -20°C to 0°C), were tested. The retrieved NC, LWC, IWC and RE using p1 were on average larger than airplane measurements in the same cloud layer. Function p2 performed better than p1 or p3 in retrieving the NCs of cloud droplets in the convective cloud, while function p1 performed better in the stratus cloud. Function p3 performed better in LWC estimation in both convective and stratus clouds. The REs of cloud droplets calculated using the retrieved cloud droplet NC and LWC were closer to the values of in situ observations than those retrieved directly using the p1 function. The retrieved NCs of ice particles in both convective and stratus clouds, on the assumption of liquid-phase fraction during the retrieval of liquid droplet NCs, were closer to those of airplane observations than on the assumption of function p1.
  • 加载中
  • Austin R., 2007: Level 2B radar-only cloud water content (2B-CWC-RO) process description document. Version: 5.1, CloudSat Project Report, A NASA Earth System Science Pathfinder Mission, 1- 24.
    [ Available online at http://www.cloudsat.cira.colostate.edu/sites/default/files/products/files/2B-CWC-RO_PDICD.P_R04.20071021.pdf.]
    Austin R. T., A. J. Heymsfield, and G. L. Stephens, 2009: Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature. J. Geophys. Res., 114(D8),D00A23, doi: 10.1029/2008JD010049.
    Barker H. W., A. V. Korolev, D. R. Hudak, J. W. Strapp, K. B. Strawbridge, and M. Wolde, 2008: A comparison between CloudSat and aircraft data for a multilayer, mixed phase cloud system during the Canadian CloudSat-CALIPSO Validation Project. J. Geophys. Res., 113(D8),D00A16, doi: 10.1029/ 2008JD009971.
    Bouniol D., A. Protat, A. Plana-Fattori M. Giraud, J.-P. Vinson, and N. Grand, 2008: Comparison of airborne and spaceborne 95-GHz radar reflectivities and evaluation of multiple scattering effects in spaceborne measurements. J. Atmos. Oceanic Technol., 25( 11), 1983- 1995.
    Carey L. D., J. G. Niu, P. Yang, J. A. Kankiewicz, V. E. Larson, and T. H. V. Haar, 2008: The vertical profile of liquid and ice water content in midlatitude mixed-phase altocumulus clouds. J. Appl. Meteor. Climatol., 47( 10), 2487- 2495.
    Crosier J., Coauthors, 2011: Observations of ice multiplication in a weakly convective cell embedded in supercooled mid-level stratus. Atmos. Chem. Phys., 11, 257-273.
    Delano\"e, J., A. Protat, O. Jourdan, J. Pelon, M. Papazzoni, R. Dupuy, J.-F. Gayet, C. Jouan, 2013: Comparison of airborne in situ, airborne radar-lidar, and spaceborne radar-lidar retrievals of polar ice cloud properties sampled during the polarcat campaign. J.Atmos. Oceanic Technol., 30( 2), 57- 73.
    Deng M., G. G. Mace, Z. E. Wang, and R. P. Lawson, 2013: Evaluation of several a-train ice cloud retrieval products with in situ measurements collected during the SPARTICUS campaign. J. Appl. Meteor. Climatol., 52( 5), 1014- 1030.
    Devasthale A., M. A. Thomas, 2012: Sensitivity of cloud liquid water content estimates to the temperature-dependent thermodynamic phase: A global study using CloudSat data. J.Climate, 25( 20), 7297- 7307.
    Fleishauer R. P., V. E. Larson, and T. H. V. Haar, 2002: Observed microphysical structure of midlevel, mixed-phase clouds. J. Atmos. Sci., 59( 11), 1779- 1804.
    Gao W. H., C.-H. Sui, and Z. J. Hu, 2014: A study of macrophysical and microphysical properties of warm clouds over the Northern Hemisphere using CloudSat/CALIPSO data. J. Geophys. Res., 119( 7), 3268- 3280.
    Hallett J., S. C. Mossop, 1974: Production of secondary ice particles during the riming process. Nature, 249, 26- 28.
    Heymsfield A., D. Winker, M. Avery, M. Vaughan, G. Diskin, M. Deng, V. Mitev, and R. Matthey, 2014: Relationships between ice water content and volume extinction coefficient from in situ observations for temperatures from 0° to-86°: Implications for spaceborne lidar retrievals. J. Appl. Meteor. Climatol.,53(3), 479-505.
    Hobbs P. V., A. L. Rangno, M. Shupe, and T. Uttal, 2001: Airborne studies of cloud structures over the Arctic Ocean and comparisons with retrievals from ship-based remote sensing measurements. J. Geophys. Res., 106( D14), 15 029- 15 044.
    Hogan R. J., P. R. Field, A. J. Illingworth, R. J. Cotton, and T. W. Choularton, 2002: Properties of embedded convection in warm-frontal mixed-phase cloud from aircraft and polarimetric radar. Quart. J. Roy. Meteor. Soc., 128( 580), 451- 476.
    Hogan R. J., M. D. Behera, E. J. O'Connor, and A. J. Illingworth, 2004: Estimate of the global distribution of stratiform supercooled liquid water clouds using the LITE lidar. Geophys. Res. Lett., 31(6),L05106, doi: 10.1029/2003GL018977.
    Hu Y. X., S. Rodier, K. M. Xu, W. B. Sun, J. P. Huang, B. Lin, P. W. Zhai, and D. Josset, 2010: Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements. J. Geophys. Res., 115 (D4),D00H34, doi: 10.1029/2009JD012384.
    Korolev A. V., G. A. Isaac, S. G. Cober, J. W. Strapp, and J. Hallett, 2003: Microphysical characterization of mixed-phase clouds. Quart. J. Royal Meteor. Soc., 129( 587), 39- 65.
    Mace G. G., R. Marchand , Q. Q. Zhang, and G. Stephens, 2007: Global hydrometeor occurrence as observed by CloudSat: Initial observations from summer 2006. Geophys. Res. Lett., 34(10),L09808, doi: 10.1029/2006GL029017.
    Mazin I. P., 1995: Cloud water content in continental clouds of middle latitudes. Atmospheric Research, 35( 2-4), 283- 297.
    McFarquhar G. M., A. J. Heymsfield, 1998: The definition and significance of an effective radius for ice clouds. J. Atmos. Sci., 55( 11), 2039- 2052.
    McFarquhar G. M., G. Zhang, M. R. Poellot, G. L. Kok, R. McCoy, T. Tooman, A. Fridlind, and A. J. Heymsfield, 2007: Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 1. Observations. J. Geophys. Res., 112,D24201, doi: 10.1029/2007JD008633.
    Molthan A. L., W. A. Petersen, 2011: Incorporating ice crystal scattering databases in the simulation of millimeter-wavelength radar reflectivity. J. Atmos. Oceanic Technol., 28( 4), 337- 351.
    Nasiri S. L., B. H. Kahn, 2008: Limitations of bispectral infrared cloud phase determination and potential for improvement. J. Appl. Meteor. Climatol., 47( 11), 2895- 2910.
    Protat A., Coauthors, 2009: Assessment of CloudSat reflectivity measurements and ice cloud properties using ground-based and airborne cloud radar observations. J. Atmos. Oceanic Technol., 26( 10), 1717- 1741.
    Protat A., J. Delano\"e, E. J. O'Connor, and T. S. L'Ecuyer, 2010: The evaluation of CloudSat and CALIPSO ice microphysical products using ground-based cloud radar and lidar observations. J. Atmos. Oceanic Technol., 27( 6), 793- 810.
    Stein T. H. M., J. Delano\"e, and R. J. Hogan, 2011: A comparison among four different retrieval methods for ice-cloud properties using data from CloudSat, CALIPSO, and MODIS. J. Appl. Meteor. Climatol., 50( 10), 1952- 1969.
    Stephens G.L., Coauthors, 2002: The CloudSat mission and the a-train: A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83( 12), 1771- 1790.
    Tsushima Y., Coauthors, 2006: Importance of the mixed-phase cloud distribution in the control climate for assessing the response of clouds to carbon dioxide increase: A multi-model study. Climate Dyn., 27( 2-3), 113- 126.
    Wang L., C. C. Li, Z. G. Yao, Z. L. Zhao, Z. G. Han, and Q. Wei, 2014: Application of aircraft observations over Beijing in cloud microphysical property retrievals from CloudSat. Adv. Atmos. Sci.,31(5), 926-937, doi: 10.1007/s00376-013-3156-2.
    Wood N., 2008: Level 2B radar-visible optical depth cloud water content (2B-CWC-RVOD) process description document. Version 5.1, CloudSat Project Report, A NASA Earth System Science Pathfinder Mission, 1- 26.
    [ Available online at http://www.cloudsat.cira.colostate.edu/sites/default/files/products/files/2B-CWC-RVOD_PDICD.P_R04.20081023.pdf.]
    Yin J. F., D. H. Wang, and G. Q. Zhai, 2011: Long-term in situ measurements of the cloud-precipitation microphysical properties over East Asia. Atmospheric Research, 102( 1-2), 206- 217.
    You L. G., Y. G. Liu, 1995: Some microphysical characteristics of cloud and precipitation over China. Atmospheric Research, 35( 2-4), 271- 281.
    Zhang D. G., X. L. Guo, D. L. Gong, and Z. Y. Yao, 2011: The observational results of the clouds microphysical structure based on the data obtained by 23 sorties between 1989 and 2008 in Shandong Province. Acta Meteorologica Sinica, 69, 195- 207. (in Chinese)
    Zhang D. M., Z. E. Wang, and D. Liu, 2010: A global view of midlevel liquid-layer topped stratiform cloud distribution and phase partition from CALIPSO and CloudSat measurements. J. Geophys. Res., 115 (D4),D00H13, doi: 10.1029/2009JD 012143.
  • [1] ZHAO Zhen, LEI Hengchi, 2014: Aircraft Observations of Liquid and Ice in Midlatitude Mixed-Phase Clouds, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 604-610.  doi: 10.1007/s00376-013-3083-2
    [2] ZONG Rong, LIU Liping, YIN Yan, 2013: Relationship between Cloud Characteristics and Radar Reflectivity Based on Aircraft and Cloud Radar Co-observations, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1275-1286.  doi: 10.1007/s00376-013-2090-7
    [3] WANG Lei, LI Chengcai, YAO Zhigang, ZHAO Zengliang, HAN Zhigang, and WEI Qiang, 2014: Application of Aircraft Observations over Beijing in Cloud Microphysical Property Retrievals from CloudSat, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 926-937.  doi: 10.1007/s00376-013-3156-2
    [4] Jiefan YANG, Hengchi LEI, Tuanjie HOU, 2017: Observational Evidence of High Ice Concentration in a Shallow Convective Cloud Embedded in Stratiform Cloud over North China, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 509-520.  doi: 10.1007/s00376-016-6079-x
    [5] B. S. K. REDDY, K. R. KUMAR, G. BALAKRISHNAIAH, K. R. GOPAL, R. R. REDDY, V. SIVAKUMAR, S. Md. ARAFATH, A. P. LINGASWAMY, S. PAVANKUMARI, K. UMADEVI, Y. N. AHAMMED, 2013: Ground-Based In Situ Measurements of Near-Surface Aerosol Mass Concentration over Anantapur: Heterogeneity in Source Impacts, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 235-246.  doi: 10.1007/s00376-012-1234-5
    [6] Xiaoning XIE, He ZHANG, Xiaodong LIU, Yiran PENG, Yangang LIU, 2018: Role of Microphysical Parameterizations with Droplet Relative Dispersion in IAP AGCM 4.1, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 248-259.  doi: 10.1007/s00376-017-7083-5
    [7] LI Lijuan, Yuqing WANG, WANG Bin, ZHOU Tianjun, 2008: Sensitivity of the Grid-point Atmospheric Model of IAP LASG (GAMIL1.1.0) Climate Simulations to Cloud Droplet Effective Radius and Liquid Water Path, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 529-540.  doi: 10.1007/s00376-008-0529-z
    [8] Tuanjie HOU, Hengchi LEI, Youjiang HE, Jiefan YANG, Zhen ZHAO, Zhaoxia HU, 2021: Aircraft Measurements of the Microphysical Properties of Stratiform Clouds with Embedded Convection, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 966-982.  doi: 10.1007/s00376-021-0287-8
    [9] Liu Chunlei, Yao Keya, 1997: Particle Size Truncation Effect on the Inference of Effective Particle Diameter, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 350-354.  doi: 10.1007/s00376-997-0055-4
    [10] Yao Keya, Liu Chunlei, 1996: ICE Particle Size and Shape Effect on Solar Energy Scattering Angular Distribution, ADVANCES IN ATMOSPHERIC SCIENCES, 13, 505-510.  doi: 10.1007/BF03342040
    [11] Anqi CAI, Xiaolei ZOU, 2019: Latitudinal and Scan-dependent Biases of Microwave Humidity Sounder Measurements and Their Dependences on Cloud Ice Water Path, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 557-569.  doi: 10.1007/s00376-019-8190-2
    [12] Yaodeng CHEN, Ruizhi ZHANG, Deming MENG, Jinzhong MIN, Lina ZHANG, 2016: Variational Assimilation of Satellite Cloud Water/Ice Path and Microphysics Scheme Sensitivity to the Assimilation of a Rainfall Case, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1158-1170.  doi: 10.1007/s00376-016-6004-3
    [13] Jiefan YANG, Fei YAN, Hengchi LEI, Shuo JIA, Xiaobo DONG, Xiangfeng HU, 2024: Aircraft Observation and Simulation of the Supercooled Liquid Water Layer in a Warm Conveyor Belt over North China, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 529-544.  doi: 10.1007/s00376-023-3068-8
    [14] HUANG Jianping, 2006: Analysis of Ice Water Path Retrieval Errors Over Tropical Ocean, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 165-180.  doi: 10.1007/s00376-006-0165-4
    [15] Wenyue HE, Bo SUN, Huijun WANG, 2021: Dominant Modes of Interannual Variability in Atmospheric Water Vapor Content over East Asia during Winter and Their Associated Mechanisms, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1706-1722.  doi: 10.1007/s00376-021-0014-5
    [16] Zou Jinshang, Liu Huilan, 1986: DISTRIBUTION OF WATER VAPOR CONTENT (WVC) AND ITS SEASONAL VARIATION OVER THE MAINLAND OF CHINA, ADVANCES IN ATMOSPHERIC SCIENCES, 3, 385-395.  doi: 10.1007/BF02678659
    [17] Liu Yangang, 1995: On the Generalized Theory of Atmospheric Particle Systems, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 419-438.  doi: 10.1007/BF02657003
    [18] Yan XIA, Yongyun HU, Jiping LIU, Yi HUANG, Fei XIE, Jintai LIN, 2020: Stratospheric Ozone-induced Cloud Radiative Effects on Antarctic Sea Ice, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 505-514.  doi: 10.1007/s00376-019-8251-6
    [19] ZHONG Zhong, ZHAO Ming, SU Bingkai, TANG Jianping, 2003: On the Determination and Characteristics of Effective Roughness Length for Heterogeneous Terrain, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 71-76.  doi: 10.1007/BF03342051
    [20] Yong-Sang CHOI, Chang-Hoi HO, Sang-Woo KIM, Richard S. LINDZEN, 2010: Observational Diagnosis of Cloud Phase in the Winter Antarctic Atmosphere for Parameterizations in Climate Models, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1233-1245.  doi: 10.1007/s00376-010-9175-3

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 23 January 2015
Manuscript revised: 02 May 2015
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Comparison of Cloud Properties between CloudSat Retrievals and Airplane Measurements in Mixed-Phase Cloud Layers of Weak Convective and Stratus Clouds

  • 1. Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044
  • 2. Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044
  • 3. Centre for Atmospheric Science, SEAES, University of Manchester, Manchester M13 9PL, UK

Abstract: Cloud microphysical properties including liquid and ice particle number concentration (NC), liquid water content (LWC), ice water content (IWC) and effective radius (RE) were retrieved from CloudSat data for a weakly convective and a widespread stratus cloud. Within the mixed-phase cloud layers, liquid-phase fractions needed to be assumed in the data retrieval process, and one existing linear (p1) and two exponential (p2 and p3) functions, which estimate the liquid-phase fraction as a function of subfreezing temperature (from -20°C to 0°C), were tested. The retrieved NC, LWC, IWC and RE using p1 were on average larger than airplane measurements in the same cloud layer. Function p2 performed better than p1 or p3 in retrieving the NCs of cloud droplets in the convective cloud, while function p1 performed better in the stratus cloud. Function p3 performed better in LWC estimation in both convective and stratus clouds. The REs of cloud droplets calculated using the retrieved cloud droplet NC and LWC were closer to the values of in situ observations than those retrieved directly using the p1 function. The retrieved NCs of ice particles in both convective and stratus clouds, on the assumption of liquid-phase fraction during the retrieval of liquid droplet NCs, were closer to those of airplane observations than on the assumption of function p1.

1. Introduction
  • Received 23 January 2015; revised 2 May 2015; accepted 2 June 2015

    Cloud droplets in mixed-phase clouds experience a complicated three-phase transformation, adding difficulties in quantifying cloud properties. (Hallett and Mossop, 1974) conducted experiments in a cloud chamber and demonstrated the production of secondary ice crystals at slightly supercooled temperatures of between -3°C and -9°C, which was later referred to as the Hallett-Mossop process. This process has also been observed in natural clouds by airplane observations (e.g., Hogan et al., 2002; Crosier et al., 2011; Zhang et al., 2011). Due to the complex transformation of liquid and ice particles, the properties of mixed-phase clouds are poorly represented in climate models (Hogan et al., 2004). Further observational studies need to be carried out to advance our understanding of cloud properties in mixed-phase clouds, and the knowledge gained should then be used to constrain GCM cloud parameterizations and reduce uncertainties in cloud feedbacks (Tsushima et al., 2006).

    The 94-GHz (W-band) nadir-looking Cloud Profiling Radar (CPR), aboard the CloudSat satellite launched on 28 April 2006, has provided vast amounts of unprecedented high-resolution data to study cloud microphysical properties all over the world (Stephens et al., 2002). CloudSat releases the Level 2B Radar-only Cloud Water Content (2B-CWC-RO) and Level 2B Radar-Visible Optical Depth Cloud Water Content (2B-CWC-RVOD) products, which provide profiles of cloud microphysical retrievals (Austin, 2007). These products have been used to study clouds properties (Barker et al., 2008; Hu et al., 2010; Zhang et al., 2010; Devasthale and Thomas, 2012; Gao et al., 2014; Wang et al., 2014). However, large discrepancies in cloud properties have been found between CloudSat retrievals and airplane observations in mixed-phase cloud layers. For example, (Barker et al., 2008) found that the crystal number concentrations estimated from CloudSat retrieval products were larger by a factor of 5.0 compared to in situ airplane observations in a mixed-phase boundary layer cloud. (Protat et al., 2010) pointed out that CloudSat retrievals produce ice water content and extinction amounts in a much narrower range than a ground-based method, overestimating the mean vertical profiles of microphysical parameters below the height of 10 km by a factor of greater than 2. (Devasthale and Thomas, 2012) conducted sensitivity analyses on subfreezing-temperature clouds using different liquid and ice fractions and found that the liquid water content (LWC) under temperatures down to -20°C, estimated using a quadratic or sigmoid-shaped function, differed by 20%-40% over the tropics (in terms of seasonal means), by 10%-30% over the midlatitudes, and by up to 50% over high latitudes, compared to using a linear function.

    The purpose of the present study is to evaluate, by comparing with airplane observations, the cloud properties retrieved from CloudSat data for mixed-phase convective and stratus clouds, and to improve the retrieval algorithms in mixed-phase clouds. The knowledge gained from the study is expected to be useful in improving mixed-phase cloud microphysics parameterizations in meteorological models.

2. Data description
  • CloudSat carries a 94 GHz CPR that provides vertically resolved information on clouds at a resolution of 240 m and with a footprint of 1.4 km cross-track by 2.5 km along-track (Mace et al., 2007). Using a priori values (for the liquid and ice particle size distribution parameters in each cloudy bin), CloudSat RO (using radar only) retrievals mainly provide cloud microphysical properties, including cloud LWC and ice water content (IWC), liquid and ice droplet number concentration (NC), and droplet effective radius (RE), based on the Forward Model algorithm and assuming a log-normal size distribution. Simultaneously, retrievals from CloudSat RVOD (Radar-Visible Optical Depth), which combines the radar reflectivity factor and visible optical depth, provide the same cloud microphysics parameters using a priori values. The NC, LWC and RE of liquid cloud droplets, and the NC, IWC and RE of crystal particles, are defined below, in Eqs. (2)-(4) and (5)-(7), respectively. A detailed description of the Forward Model algorithm can be found in (Wood, 2008) and (Austin et al., 2009).

    \begin{eqnarray} \label{eq1} {\rm NC}(r)&=&\dfrac{N_{\rm T}}{\sqrt{2\pi}r\ln\sigma_{\rm g}}\exp\left[\dfrac{-\ln^2(r/r_{\rm g})}{2\ln^2\sigma_{\rm g}}\right] ,\\[1.5mm] \label{eq2} {\rm LWC}&=&\dfrac{4\pi}{3}N_{\rm T}\rho_\omega r_{\rm g}^3\exp\left(\dfrac{9}{2}\ln^2\sigma_{\rm g}\right) ,\\[1mm] \label{eq3} {\rm RE}&=&r_{\rm g}\exp\left(\dfrac{5}{2}\ln^2\sigma_{\rm g}\right) ,\\[-2mm]\nonumber \end{eqnarray} \begin{eqnarray} \label{eq4} {\rm NC}(D)&=&\dfrac{N_{\rm T}}{\sqrt{2\pi}D\ln\sigma_{\rm g}}\exp\left[\dfrac{-\ln^2(D/D_{\rm g})}{2\ln^2\sigma_{\rm g}}\right] ,\\[-0.5mm] \label{eq5} {\rm IWC}&=&\rho_{\rm i}\dfrac{\pi}{6}N_{\rm T}D_{\rm g}^3\exp\left(\dfrac{9}{2}\ln^2\sigma_{\rm g}\right)10^{-3} ,\\ \label{eq6} {\rm RE}&=&\dfrac{1}{2}D_{\rm g}\exp\left(\dfrac{5}{2}\ln^2\sigma_{\rm g}\right)10^3 .\vspace*{-0.5mm} \end{eqnarray}

    In the above equations, N T is the droplet number density, r is the droplet radius, r g is the geometric mean radius, D is the crystal particle radius, D g is the crystal particle geometric mean radius, σ g is the geometric standard deviation, and ρ w and ρ i are the densities of water and ice crystals, respectively. 2B-CWC-RO and 2B-CWC-RVOD retrievals are performed separately for liquid and ice phases, assuming in each case that the radar profile is due to a single phase of water. The resulting separate liquid and ice profiles are then combined using a scheme based on temperature. In this scheme, the portion of the profile colder than -20°C is deemed pure ice, warmer than 0°C pure liquid, and that between is partitioned linearly into ice and liquid phases according to

    \begin{equation} \label{eq7} p_1=\dfrac{T-T_{\min}}{T_{\max}-T_{\min}} ,\vspace*{-0.5mm} \end{equation}

    where p1 is the liquid-phase fraction, T is the temperature of cloud layers, \(T_\min\) is -20°C, and Tmax is 0°C.

    The distributions of LWC and IWC versus temperature in mixed-phase clouds are complex, as demonstrated by in situ measurements (e.g., You and Liu, 1995; Fleishauer et al., 2002; Korolev et al., 2003), mainly because the riming of ice particles has a nonlinear relationship with cloud temperature, as in the Hallett-Mossop process. The production of secondary ice particles appears to have a peak value at around T=-4°C to T=-8°C, as measured in a cloud chamber (Hallett and Mossop, 1974). Therefore, besides the above linear partition function [p1 in Eq. (8)], two exponential functions [p2 in Eq. (9) and p3 in Eq. (10)] were also used, as sensitivity tests, for the estimation of the liquid-phase fraction:

    \begin{eqnarray} \label{eq8} p_2=\dfrac{\exp(2p_1)-1}{\exp(2)-1} ,\\[-0.5mm] \label{eq9} p_3=\dfrac{\exp(3p_1)-1}{\exp(3)-1} .\vspace*{-1mm} \end{eqnarray}

    The liquid-phase fractions estimated from the three partitioning functions as a function of temperature are shown in Fig. 1. Fractions calculated using the exponential functions (p2 and p3) were lower than those from using the linear function (p1) at the same sub-zero temperatures. The largest difference was on the order of a factor of 2.0, at around T=-8°C.

  • On 18 February 2009, the UK BAe146 Facility for Airborne Atmospheric Measurement (FAAM) airplane observation was conducted as part of the APPRAISE-Clouds project (Crosier et al., 2011). The airplane flew in mixed-phase clouds in the vicinity of the Chilbolton Facility for Atmospheric and Radio Research (CFARR) ground site (51.1145°N, 1.4370°W), which is located in southern England. The flight route is illustrated in Fig. 2. A 3 GHz Doppler-Polarisation Radar (Chilbolton Advanced Meteorological Radar-CAMRa), CFARR, performed range height indicator (RHI) scans along the 253° radial. The BAe146 airplane flew several runs at different altitudes in mixed-phase cloud layers. A summary of the airplane maneuvers during the flight is provided in Table 1.

    Figure 1.  Estimated temperature-dependent cloud liquid-phase fractions using the linear (black line) and exponential (blue and red lines) functions.

    Figure 2.  CloudSat overpass track and flight segments on 18 February 2009 in the UK.

    Cloud droplets (2 μm < diameter < 50 μm) were measured using a wing pylon-mounted Cloud Droplet Probe (CDP) (CDP-100 Version 2, Droplet Measurement Technologies Inc., Boulder, USA). The CDP had a sample area of 0.24 mm2, resulting in sample volume of 28.8 cm3 s-1 at the typical airspeed (120 m s-1). The precision of the lowest measurable concentration from the 1 Hz CDP data was approximately 0.035 cm-3. Large water droplets and ice crystals were measured using a wing pylon-mounted 2DS-128 probe (SPEC Inc., Boulder, USA, referred to as 2DS hereafter). The 2DS is a high volume 2D Optical Array Probe. Details of the data processing techniques used with the CDP and 2DS probes can be found in Crosier et al. (2011, Appendix B). Simultaneous in situ and remote measurements were used to examine the microphysical properties of mixed-phase convective and stratus clouds.

    Figure 3.  (a) The 94 GHz radar reflectivity image from CloudSat on 18 February 2009. (b, c) Reflectivity images from the 3GHz CAMRa (3GHz Doppler-Polarisation Radar, Chilbolton advanced meteorological radar) performed RHI scan along the 253° radial during flight run 2 (R2) and run 6 (R6) of the airplane observations. The black dotted lines in (b, c) indicate the height of R2 and R6, respectively. (d, e) Average reflectivity profiles from (a-c). The red points refer to CAMRa reflectivity during the R2 observation, the light blue points for R6, and the black points for cloud-free reflectivity averaged from CloudSat sites in the range of 100× 100 km.

  • On 18 February 2009 the UK experienced high pressure conditions that resulted in large-scale descent of relatively warm and dry air and a large-scale supercooled cloud with a cloud top temperature of approximately -12°C (Crosier et al., 2011). The cold frontal system initially moved slowly to the west, and then remained stationary during the entire course of the flights. The stationary front marked a boundary line separating warm air to its west and cold air to its east, with the boundary roughly aligned in the north-south direction. As a result, an extensive layer of supercooled mid-level stratus cloud formed. Ice particles falling from the supercooled layer were observed to evaporate at a height of 2.5 km.

    The airplane flew five horizontal legs in mixed-phase cloud layers during the period 1157 to 1319 UTC 18 February 2009 (Table 1). Due to the restrictions of airplane operations by Air Traffic Control, the majority of the airplane's flight time was spent along a radial of 253° from CFARR at distances ranging from 0 km (overpass) to 100 km. A local weak convective cloud was observed at 15-25 km west of the CFARR Radar site station during the CFARR Radar RHI scan. The weak convective cloud is in agreement with the CloudSat Radar identified (see Fig. 3). Figure 3 presents the CloudSat radar reflectivity, CAMRa RHI scan, and average radar reflectivity profiles. The CAMRa RHI scan was performed half way through the airplane constant altitude run.

    Figures 3b and c show the CAMRa scan images at the beginning and end of the airplane observational period in the mixed-phase cloud layers. The three images are broadly consistent with the convective and stratiform regions. The mean profiles of both cloud regions shown in Figs. 3d and e also indicate the good level of agreement between CloudSat and ground-based measurements, albeit some discrepancies are apparent in the reflectivity values of the two average profiles. The discrepancy might have been caused by the different scanning modes and wavelengths of the two radars. The 94 GHz CloudSat radar scans from the top of the atmosphere to the ground, while the 3 GHz site radar scans from the ground to the atmosphere. The differences in radar sensitivity and spatiotemporal dislocation likely contributed to differences in radar reflectivities. The weakly convective and stratiform regions showed robust and homogeneous features (Crosier et al., 2011).

    Figure 4.  Comparisons of LWC, liquid NC and RE between RO and RVOD retrievals and airplane observation data during R2. The left and right regions separated by the red line are associated with the convective and stratus cloud regions, respectively. Panels (a-c) represent in situ LWC, NC and RE, and (d-f) represent the LWC, NC and RE from CloudSat retrievals, respectively.

3. Results and discussion
  • 3.1.1. Airplane run 2 (R2)

    Considering that the complete data from flight leg R2 contain information on both the convective and stratus regions, measurements from R2 were used to evaluate the CloudSat retrievals of NC, LWC and RE. It should be noted that the airplane measurements were conducted about half an hour earlier than the CloudSat overpass, but the cloud regimes maintained a steady state during this time period, as shown by the CFAAR radar (Fig. 3). Figure 4 shows the comparisons as a function of distance from CFARR. The retrievals and airplane observations in the convective region were observed to fluctuate more sharply than those in the stratus cloud region. The trends in the time series of cloud property parameters were similar in the two datasets, although discrepancies of more than 20% existed in the actual values of the parameters, suggesting that the CloudSat retrievals are still effective data to study cloud microphysical characteristics.

    At the distance of 18-20 km, located in the convective region, there were no effective 2B-CWC-RO RE, NC, LWC retrievals, while 2B-CWC-RVOD RE, NC, LWC retrievals showed peaks consistent with the airplane observations. This implies that, in convective cloud regions of this nature, retrievals using both the radar reflectivity factor and visible optical depth are better than using the radar reflectivity factor only. In addition to the default value, 2B-CWC-RO retrievals showed retrievals that were nearly identical to the 2B-CWC-RVOD retrievals at the same cloud layer height.

    In the convective regime, 99% of data samples showed larger average droplet NCs from retrievals than from airplane observations, and the overall average differed by a factor of 2.9. Similarly, the average LWC from the CloudSat retrievals was about 2.6 times larger than that from the airplane observations. The difference in RE between retrieved and airplane observations was much smaller, e.g., only 20% larger from the retrieval. A similar phenomenon was also found for the stratus region, with the retrieved average droplet NC, LWC and RE being 1.5, 5.6 and 2.2 times larger, respectively, than those from the airplane measurements.

    The simple assumption of the linear function of ice/liquid phase partition under sub-zero temperatures in the RO and RVOD retrieval algorithms might have caused the large discrepancies between the retrievals and in situ observations in the mixed-phase cloud layers of the convective and stratus regions, because such a relationship may not accurately capture the mixed-phase cloud structure (e.g., Mazin, 1995; Nasiri and Kahn, 2008; Yin et al., 2011). The distributions of LWC and IWC versus temperature in mixed-phase clouds are complex, mainly because the riming of ice particles has a nonlinear relationship with the cloud temperature, as in the Hallett-Mossop process. This assumption is validated in the next section.

    3.1.2. Cloud droplet property profiles in the convective regime

    The cloud property retrievals from CloudSat were acquired by averaging the values in the convective regime. Note that this may have caused the cloud base and top heights identified from CPR Cloud_mask to possess some discrepancies with the airplane observations. This study focuses mainly on comparing the values of cloud properties in similar cloud layers between the airplane observations and CloudSat retrievals. Ice particles formed within the supercooled layer started evaporating as they fell below 2.5 km, as determined by the airplane observations. Mixed-phase cloud layers above 2.5 km were considered during airplane passes R2 to R5, as shown in Figs. 5, 6, 8 and 9.

    Figure 5.  Comparison of the (a) liquid NC, (b) LWC and (c) RE between those observed by the CDP onboard the airplane and those retrieved from CloudSat using the p1, p2 and p3 functions for the liquid-phase fraction in the convective region. The red triangles show the average LWC, NC and RE of the CDP, with the red dotted lines indicating the range from the minimum LWC, NC and RE of the CDP to the maximum. Blue, orange and black diamonds represent the CloudSat RVOD-retrieved LWC, LNC and RE based on the p1, p2 and p3 functions, respectively. The turquoise diamonds represent the CloudSat RO-retrieved liquid NC, LWC and RE. The blue line indicates the 0°C temperature height.

    Figure 6.  Comparison of (a) liquid NC, (b) LWC and (c) RE between observations and retrievals in the stratus region. Brown and red triangles represent the airplane observations and average values, respectively, from the CDP, with the red dotted lines indicating the range from the minimum LWC, NC and RE of the CDP to the maximum. The blue, orange and black diamonds represent the retrieved values based on p1, p2 and p3, respectively. The blue line indicates the 0°C temperature height.

    Since the profiles of RO retrievals only differed slightly from those of RVOD retrievals, based on the p1 assumption, we only compared the RVOD retrievals with the airplane observations, but using all of the assumptions (p1, p2 and p3). In addition, NC values ranged by more than one order of magnitude, and thus the averaged values from airplane observations for the same convective cloud layers were used when comparing with retrievals from CloudSat.

    Figure 5a shows that the assumption of exponential functions (p2 and p3) was better than that of the linear function (p1) in retrieving the NCs of cloud droplets. The NCs of cloud droplets retrieved based on the p1 assumption were close to the maximum values of measurements at the heights of the R2, R4 and R5 flight legs, while p2 and p3 produced much closer retrievals to the airplane observations in general. In addition, the NCs retrieved based on the p2 assumption were also better than those based on the p3 assumption.

    The retrieved LWC increased with decreasing altitude, opposite to what was measured by the airplane. The highest proportion of liquid droplet water was typically found in the top levels within the cloud, based on airplane observations, similar to what has been found in midlatitude mixed-phase clouds and Arctic clouds (Hobbs et al., 2001; McFarquhar et al., 2007; Carey et al., 2008). However, the three retrieved profiles of LWC show that the largest values occurred at the height of the melting layer. The ice crystals within the melting layer were potential factors impacting the CloudSat radar reflectivity because melting ice crystals would have caused excessively large radar reflectivity, resulting in larger retrievals than the in situ observations. (Bouniol et al., 2008) suggested a multiple scattering enhancement of at least 2.5 dB in the melting layer of convective systems. In general, the retrieved LWCs based on p3 were the closest to the airplane observations, as compared to those retrieved based on p1 or p2. The retrieved LWCs were closer to the in situ observations in the top layers than in layers near the melting layer. The retrieved LWC based on p3 at the base of the melting layer achieved the maximal value of the airplane observations, which provides a direction to revise the signal of cloud radar in the melting layer of convective clouds.

    The liquid droplet RE was calculated using Eqs. (2)-(4) with NCs and LWCs retrieved based on the p2 or p3 function (Fig. 5c). The results show that the REs calculated based on NC and LWC retrievals were closer to the average values of the airplane observations, with less variance than the CloudSat RO and RVOD RE retrieved based on p1.

    3.1.3. Cloud droplets property profiles in the stratus region

    Comparisons of NC, LWC and RE between the in situ airplane observations and CloudSat retrieval in the widespread mixed stratus cloud are presented in Fig. 6. The RVOD retrieved-NC using the p1 function was closer to the average value of the airplane observations than that using p2 or p3. The values calculated using p2 were smaller than half of the average values from the airplane observations. This implies that the NC of RVOD retrievals can represent the overall level of NC in stratus.

    It is clear that the LWCs retrieved from RVOD using p1 were too large——larger, even, than the maximal value of the flight campaign in stratus cloud. The LWCs observed from the airplane showed larger values at the top layer and smaller values at the base layer. The retrieved value using p3 was the closest to the average value from in situ measurements. The value calculated using p2 was close to the maximum value in stratus layers observed by the airplane.

    Based on the retrieved NC using p1 and the LWC using p3, the RE was calculated using Eqs. (2)-(4). Similar to the convective region, the calculated RE was closer to the airplane measurements than the CloudSat-retrieved RE using p1.

    Note that the retrieved NC and LWC profiles were smoother in the stratus region than in the weak convective region. This implies that the CloudSat radar data can be used for retrieving cloud properties in wide ranges of stratus clouds and presenting the overall characteristics of cloud properties.

    Figure 7.  The (a, c) average and (b, d) maximum ice particle NC distribution from the 2DS data in the (a, b) convective and (c, d) stratus region.

    Figure 8.  Comparison between the retrievals and airplane observations in the convective region: (a) NC profiles from airplane 2DS observations (small size range is 55-165 μm and large size range is 165-1305 μm); (b) retrieved ice particle NC profiles; (c) retrieved IWC profiles.

    Figure 9.  Comparison between the retrievals and observations in the stratus region: (a) NC profiles from airplane 2DS observations; (b) ice particle NC profiles of retrievals; (c) profiles of retrieved IWC.

  • 3.2.1. Ice crystal NC distribution

    The average RE of ice particles from the airplane observations was about 63 μm (in the size range of 2DS, from 55 μm to 65 μm); 55 μm is taken as the smallest size of ice particles here. The ice particle NC in the size range of less than 165 μm differed significantly between the convective and stratus clouds. This size range is referred to as the small size range, while the size range larger than 165 μm is referred to as the large size range. The NC in the small size range was at least one order of magnitude higher than that in the large size range. For example, during the airplane observations of R2 in the convective cloud, the average NC in the small size range was about 2.6 L-1 μm-1, but was only 0.1 L-1 μm-1 in the large size range. Similarly, the average NC was 3.3 and 0.08 L-1 μm-1 in the small and large size ranges, respectively, in the stratus cloud. The difference in the NC distribution between the convective and stratus cloud implies that the mixing effect from turbulence increases the NC of large sizes in convective clouds, which potentially has an impact on retrievals because the uncertainty is significantly amplified due to the fact that radar reflectivity is the sixth power of the droplet diameter, although this is not necessarily true (sixth power) for large crystals in the W-Band due to non-Rayleigh scattering.

    Figure 7 shows significant differences in the number distributions of ice particles between the convective and stratus clouds. In the convective cloud, the NC peaks appeared in the size range from 165 to 1305 μm, except in the top cloud layer. The NC varied more widely in the convective cloud than in the stratus cloud. A peak region of NC in the convective cloud was apparent in the radius range of 165-300 μm. (Zhang et al., 2011) observed a similar phenomenon in Shandong Province, China, through more than 10 airplane deployments during 2006-08. The only exception was in the top layer of the convective cloud, where NCs were similar in the two types of clouds. This implies a weaker effect of turbulence mixing in the top layer than in the lower layers of convective cloud. The log-normal distribution, which the CloudSat RO and RVOD retrievals assumed, for NCs in the large size range, fits better in the convective layers than in the stratus layers.

    3.2.2. Ice crystal profiles in the convection region

    Many studies have focused on combining lidar and radar data to retrieve ice cloud properties (Delano\"e et al., 2013; Deng et al., 2013; Heymsfield et al., 2014), but discrepancies still exist among different products of retrievals. Radar reflectivity is sensitive to ice particle shape, size and distribution (Molthan and Petersen, 2011), which affects IWC retrieval (McFarquhar and Heymsfield, 1998). The estimation of IWC from particle size data requires an assumption of particle mass-dimension, which can potentially cause error on the order of tens of percent (Carey et al., 2008; Protat et al., 2009). If ice particles are modeled as oblate spheroids rather than spheres for radar scattering data, the retrieved IWC is reduced by 50% on average, with a reflectivity factor larger than 0 dBZ (Stein et al., 2011) in clouds. The Cloudsat profiles in convective cloud need to be corrected for attenuation by supercooled liquid water and ice aggregates/graupel particles and multiple scattering prior to their quantitative use (Protat et al., 2009). In the present study, comparison between airplane 2DS data and retrievals was explored by only considering the NCs of ice crystals. The retrieved IWC profiles are shown in Fig. 8.

    As reported in section 3.1.2, a better retrieval effect of liquid droplet NCs was obtained in the convective region when using p2 compared to p1 or p3; for LWC, overall, p3 performed the best. So, the NC of ice particles and IWC were retrieved based on p2 and p3, respectively (see Fig. 8). From the in situ observations, the peak NC of large ice particles appeared in the upper layers, which was opposite to the case of the small size range. Note that the NC profiles of the RO retrieval showed a similar tendency to that of small size particles from the in situ observations, but the average value of the former was about 1.9 times that of the latter. The maximum values of the RO NC retrieval were typically observed to be located in the lower half of the mixed-phase layer, which is similar to the in situ observations reported by (Carey et al., 2008).

    The NC profiles of the RVOD retrieval maintained a similar tendency as the in situ observations of large crystals, which dominated the radar signal and, as a result, affected the corresponding retrievals significantly. The average retrieved NC values of RVOD using p1 and p2 were about 0.7 and 1.2 times, respectively, of the values from in situ observations. This result shows that the NCs of RVOD retrievals using p2 are the closest to observations.

    3.2.3. Ice crystal profiles in the stratus region

    Section 3.1.3 demonstrated that the best retrievals of liquid droplet NCs and LWCs were achieved using p1 and p3, respectively, in the stratus cloud. The crystal NCs were retrieved based on the p1 function, which CloudSat RO and RVOD assumed. In this section, the NCs of crystals and IWC are compared using various functions, as shown in Fig. 9. The average NCs of the RVOD and RO retrievals were 1.9 and 4.9 times, respectively, of the average observations. The average NC of the RO retrieval was close to the maximum profile of the in situ observations. The comparison here implies that the values of the RVOD retrieval using p1 are more appropriate than those of the RO retrieval.

    However, the NC profiles of the RVOD retrieval showed a different tendency to those obtained through the in situ observations, with the minimum of the former appearing in the melting layer where the maximum values were observed by the airplane. A small difference was obtained between the stratus and convective clouds in terms of their NC values of the RVOD and RO retrievals; however, this was not the case in the airplane observations. The contrasting results between the retrievals and airplane observations suggest that the retrieval method for the NCs of ice particles still needs further improvement.

4. Summary
  • The CloudSat retrievals of cloud droplet properties (NC, LWC and RE), based on the assumption of the liquid and ice phase fraction partitioned as a linear function of cloud temperature, were on average larger than airplane observations in both the convective and stratus clouds. The magnitude of the differences between the satellite retrievals and airplane observations depended heavily on the cloud type. The average NC, LWC and RE from the retrievals were about 2.9, 2.6 and 1.2 times of those from the airplane observations in the convective cloud, and were 1.5, 5.6 and 2.2 times of in the stratus cloud. The large discrepancies between the CloudSat retrievals and the airplane observations suggest that the existing linear function used for ice/liquid phase partitioning in retrieving cloud microphysical properties needs further improvement.

    In mixed-phase cloud layers, the relationship between the liquid-phase fraction and temperature is complex due to ice-liquid transformation processes. The exponential function performed better than the linear function when used for retrieving the NC and LWC in the convective cloud, and the LWC in stratus cloud. On the other hand, the linear function was found to be appropriate for retrieving the NC in stratus cloud. The REs calculated based on the LWC and NC retrievals were closer to the airplane observations than those from CloudSat RO and RVOD retrievals using the linear function. Overall, the exponential function p3 is recommended for use in mixed-phase cloud layers when retrieving the NC, LWC and RE, if the cloud type is not clearly identified.

    Large differences in ice particle NCs appeared between the RO and RVOD retrievals when using the linear function, especially in the melting layer and in the upper layers of both convective cloud and stratus cloud. The NCs of the RVOD retrievals were closer to the airplane observations than those of the RO retrievals. Furthermore, the NCs of ice particles retrieved based on the exponential function were closer to the in situ observations than those based on the linear function, in convective cloud layers. However, crystal NC retrievals in the convective cloud deviated significantly from the airplane observations in the melting layer. The attenuation in the melting layer of the CloudSat measurements needs to be corrected. Different weighting coefficients according to the scale of cloud particles and cloud temperatures might be helpful to eliminate the influence of large particles on the process of retrieving cloud microphysical parameters.

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return