• Aumann, H. H., and Coauthors, 2003: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens., 41, 253264, https://doi.org/10.1109/TGRS.2002.808356.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baum, B. A., P. F. Soulen, K. I. Strabala, M. D. King, S. A. Ackerman, W. P. Menzel, and P. Yang, 2000: Remote sensing of cloud properties using MODIS Airborne Simulator imagery during SUCCESS. 2. Cloud thermodynamic phase. J. Geophys. Res., 105, 11 78111 792, https://doi.org/10.1029/1999JD901090.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baum, B. A., W. P. Menzel, R. A. Frey, D. C. Tobin, R. E. Holz, S. A. Ackerman, A. K. Heidinger, and P. Yang, 2012: MODIS cloud-top property refinements for Collection 6. J. Appl. Meteor. Climatol., 51, 11451163, https://doi.org/10.1175/JAMC-D-11-0203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boisvert, L. N., and J. C. Stroeve, 2015: The Arctic is becoming warmer and wetter as revealed by the Atmospheric Infrared Sounder. Geophys. Res. Lett., 42, 44394446, https://doi.org/10.1002/2015GL063775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cavalieri, D. J., and C. L. Parkinson, 2012: Arctic sea ice variability and trends, 1979-2010. Cryosphere, 6, 881889, https://doi.org/10.5194/tc-6-881-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cesana, G., J. E. Kay, H. Chepfer, J. M. English, and G. de Boer, 2012: Ubiquitous low-level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP. Geophys. Res. Lett., 39, L20804, https://doi.org/10.1029/2012GL053385.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chahine, M. T., and Coauthors, 2006: Improving weather forecasting and providing new data on greenhouse gases. Bull. Amer. Meteor. Soc., 87, 911926, https://doi.org/10.1175/BAMS-87-7-911.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cho, H. M., S. L. Nasiri, and P. Yang, 2009: Application of CALIOP measurements to the evaluation of cloud phase derived from MODIS infrared channels. J. Appl. Meteor. Climatol., 48, 21692180, https://doi.org/10.1175/2009JAMC2238.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fetzer, E. J., 2012: A multi-sensor water vapor climate data record using cloud classification. NASA MEaSUREs Project, accessed 19 July 2020, https://disc.gsfc.nasa.gov/datasets?keywords=Fetzer&page=1&project=MEaSUREs.

  • Goloub, P., M. Herman, H. Chepfer, J. Riedi, G. Brogniez, P. Couvert, and G. Seze, 2000: Cloud thermodynamical phase classification from the POLDER spaceborne instrument. J. Geophys. Res., 105, 14 74714 759, https://doi.org/10.1029/1999JD901183.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guillaume, A., B. H. Kahn, E. J. Fetzer, Q. Yue, G. J. Manipon, B. D. Wilson, and H. Hua, 2019: Footprint-scale cloud type mixtures and their impacts on Atmospheric Infrared Sounder cloud property retrievals. Atmos. Meas. Tech., 12, 43614377, https://doi.org/10.5194/amt-12-4361-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach H., and Coauthors, 2018: Operational global reanalysis: Progress, future directions and synergies with NWP. ERA Rep. Series 27, 65 pp., https://www.ecmwf.int/node/18765.

  • Hu, Y., and Coauthors, 2009: CALIPSO/CALIOP cloud phase discrimination algorithm. J. Atmos. Oceanic Technol., 26, 22932309, https://doi.org/10.1175/2009JTECHA1280.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Intrieri, J. M., C. W. Fairall, M. D. Shupe, P. O. G. Persson, E. L Andreas, P. S. Guest, and R. E. Moritz, 2002: An annual cycle of Arctic surface cloud forcing at SHEBA. J. Geophys. Res., 107, 8039, https://doi.org/10.1029/2000JC000439.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, H. C., and S. L. Nasiri, 2014: Evaluation of AIRS cloud-thermodynamic-phase determination with CALIPSO. J. Appl. Meteor. Climatol., 53, 10121027, https://doi.org/10.1175/JAMC-D-13-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kahn, B. H., and Coauthors, 2008: Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount. Atmos. Chem. Phys., 8, 12311248, https://doi.org/10.5194/acp-8-1231-2008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kahn, B. H., S. L. Nasiri, M. M. Schreier, and B. A. Baum, 2011: Impacts of sub-pixel cloud heterogeneity on infrared thermodynamic phase assessment. J. Geophys. Res., 116, D20201, https://doi.org/10.1029/2011JD015774.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kahn, B. H., and Coauthors, 2014: The Atmospheric Infrared Sounder version 6 cloud products. Atmos. Chem. Phys., 14, 399426, https://doi.org/10.5194/acp-14-399-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and T. L’Ecuyer, 2013: Observational constraints on Arctic Ocean clouds and radiative fluxes during the early 21st century. J. Geophys. Res. Atmos., 118, 72197236, https://doi.org/10.1002/JGRD.50489.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Key, J., and J. Intrieri, 2000: Cloud particle phase determination with the AVHRR. J. Appl. Meteor., 39, 17971804, https://doi.org/10.1175/1520-0450-39.10.1797.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marchand, R., G. G. Mace, T. Ackerman, and G. Stephens, 2008: Hydrometeor detection using CloudSat—An Earth-orbiting 94-GHz cloud radar. J. Atmos. Oceanic Technol., 25, 519533, https://doi.org/10.1175/2007JTECHA1006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marchant, B., S. Platnick, K. Meyer, G. T. Arnold, and J. Riedi, 2016: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP. Atmos. Meas. Tech., 9, 15871599, https://doi.org/10.5194/amt-9-1587-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. de Boer, G. Feingold, J. Harrington, M. D. Shupe, and K. Sulia, 2012: Resilience of persistent Arctic mixed-phase clouds. Nat. Geosci., 5, 1117, https://doi.org/10.1038/ngeo1332.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nasiri, S. L., and B. H. Kahn, 2008: Limitations of bispectral infrared cloud phase determination and potential for improvement. J. Appl. Meteor. Climatol., 47, 28952910, https://doi.org/10.1175/2008JAMC1879.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, C. A., X. Chen, Q. Yue, and X. Huang, 2019: The spectral dimension of Arctic outgoing longwave radiation and greenhouse efficiency trends from 2003 to 2016. J. Geophys. Res. Atmos., 124, 84678480, https://doi.org/10.1029/2019JD030428.

    • Search Google Scholar
    • Export Citation
  • Platnick, S., and Coauthors, 2013: MODIS cloud optical properties: User guide for the Collection 6 level-2 MOD06/MYD06 product and associated level-3 datasets. MODIS MOD06 User Guide, 145 pp., https://modis-images.gsfc.nasa.gov/_docs/C6MOD06OPUserGuide.pdf.

  • Platnick, S., and Coauthors, 2017: The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE Trans. Geosci. Remote Sens., 55, 502525, https://doi.org/10.1109/TGRS.2016.2610522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riedi, J., S. Platnick, B. A. Baum, F. Thieuleux, C. Oudard, F. Parol, J. M. Nicolas, and P. Dubuisson, 2010: Cloud thermodynamic phase inferred from merged POLDER and MODIS data. Atmos. Chem. Phys., 10, 11 85111 865, https://doi.org/10.5194/acp-10-11851-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sassen, K., Z. Wang, and D. Liu, 2008: Global distribution of cirrus clouds from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements. J. Geophys. Res., 113, D00A12, https://doi.org/10.1029/2008JD009972.

    • Search Google Scholar
    • Export Citation
  • Schreier, M. M., B. H. Kahn, A. Eldering, D. A. Elliott, E. Fishbein, F. W. Irion, and T. S. Pagano, 2010: Radiance comparisons of MODIS and AIRS using spatial response information. J. Atmos. Oceanic Technol., 27, 13311342, https://doi.org/10.1175/2010JTECHA1424.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shupe, M. D., 2011: Clouds at Arctic atmospheric observatories. Part II: Thermodynamic phase characteristics. J. Appl. Meteor. Climatol., 50, 645661, https://doi.org/10.1175/2010JAMC2468.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shupe, M. D., and J. M. Intrieri, 2004: Cloud radiative forcing of the Arctic surface: The influence of cloud properties, surface albedo, and solar zenith angle. J. Climate, 17, 616628, https://doi.org/10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanelli, S., S. L. Durden, E. Im, K. S. Pak, D. G. Reinke, P. Partain, J. M. Haynes, and R. T. Marchand, 2008: CloudSat’s cloud profiling radar after two years in orbit: Performance, calibration, and processing. IEEE Trans. Geosci. Remote Sens., 46, 35603573, https://doi.org/10.1109/TGRS.2008.2002030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. R., B. H. Kahn, R. O. Green, S. A. Chien, E. M. Middleton, and D. Q. Tran, 2018: Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015. Atmos. Meas. Tech., 11, 10191030, https://doi.org/10.5194/amt-11-1019-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, T., E. J. Fetzer, S. Wong, B. H. Kahn, and Q. Yue, 2016: Validation of MODIS cloud mask and multilayer flag using CloudSat–CALIPSO cloud profiles and a cross-reference of their cloud classifications. J. Geophys. Res. Atmos., 121, 11 62011 635, https://doi.org/10.1002/2016JD025239.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Z., D. Vane, G. Stephens, and D. Reinke, 2013: CloudSat Project: Level 2 combined radar and lidar cloud scenario classification product process description and interface control document. California Institute of Technology, Jet Propulsion Laboratory Doc., 61 pp., http://www.cloudsat.cira.colostate.edu/sites/default/files/products/files/2B-CLDCLASS-LIDAR_PDICD.P_R04.20120522.pdf.

  • Yue, Q., B. H. Kahn, E. J. Fetzer, and J. Teixeira, 2011: Relationship between marine boundary layer clouds and lower tropospheric stability observed by AIRS, CloudSat and CALIOP. J. Geophys. Res., 116, D18212, https://doi.org/10.1029/2011JD016136.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 56 56 21
Full Text Views 6 6 1
PDF Downloads 8 8 0

Evaluation of AIRS Cloud Phase Classification over the Arctic Ocean against Combined CloudSat–CALIPSO Observations

View More View Less
  • 1 Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, Michigan
  • 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • 3 Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, Michigan
© Get Permissions
Restricted access

Abstract

Cloud phase retrievals from the Atmospheric Infrared Sounder (AIRS) are evaluated against combined CloudSat–CALIPSO (CCL) observations using four years of data (2007–10) over the Arctic Ocean. AIRS cloud phase is evaluated over sea ice and open ocean separately using collocated CCL and AIRS fields of view (FOVs). In addition, AIRS and CCL cloud phase occurrences are evaluated seasonally, zonally, and with respect to total column water vapor (TCWV) and the temperature difference between 1000 and 300 hPa (ΔT1000−300). Last, collocated MODIS cloud information is implemented in a 1-month case study to assess the relationship between AIRS and CCL phase decisions, cloud cover, and cloud phase throughout the AIRS FOV. Depending on the surface type, AIRS classification skill for single-layer ice and liquid-phase clouds is over the ranges of 85%–95% and 22%–32%, respectively. Most unknown and liquid AIRS phase classifications correspond to mixed-phase clouds. AIRS ice-phase relative occurrence is biased low relative to CCL. However, the liquid-phase relative occurrence is similar between the two instruments. When compared with the CCL climatology, AIRS accurately represents the seasonal cycle of liquid and ice cloud phase across the Arctic as well as the relationship between cloud phase and TCWV and ΔT1000−300 regime in some cases. The more heterogeneous the MODIS cloud macrophysical properties within an AIRS FOV are, the more likely it is that the AIRS FOV is classified as unknown phase.

Corresponding author: Colten A. Peterson, coltenp@umich.edu

Abstract

Cloud phase retrievals from the Atmospheric Infrared Sounder (AIRS) are evaluated against combined CloudSat–CALIPSO (CCL) observations using four years of data (2007–10) over the Arctic Ocean. AIRS cloud phase is evaluated over sea ice and open ocean separately using collocated CCL and AIRS fields of view (FOVs). In addition, AIRS and CCL cloud phase occurrences are evaluated seasonally, zonally, and with respect to total column water vapor (TCWV) and the temperature difference between 1000 and 300 hPa (ΔT1000−300). Last, collocated MODIS cloud information is implemented in a 1-month case study to assess the relationship between AIRS and CCL phase decisions, cloud cover, and cloud phase throughout the AIRS FOV. Depending on the surface type, AIRS classification skill for single-layer ice and liquid-phase clouds is over the ranges of 85%–95% and 22%–32%, respectively. Most unknown and liquid AIRS phase classifications correspond to mixed-phase clouds. AIRS ice-phase relative occurrence is biased low relative to CCL. However, the liquid-phase relative occurrence is similar between the two instruments. When compared with the CCL climatology, AIRS accurately represents the seasonal cycle of liquid and ice cloud phase across the Arctic as well as the relationship between cloud phase and TCWV and ΔT1000−300 regime in some cases. The more heterogeneous the MODIS cloud macrophysical properties within an AIRS FOV are, the more likely it is that the AIRS FOV is classified as unknown phase.

Corresponding author: Colten A. Peterson, coltenp@umich.edu
Save