Climate Dynamics

, Volume 39, Issue 12, pp 2769–2787 | Cite as

Evaluation of cloud properties in the NOAA/NCEP global forecast system using multiple satellite products

  • Hyelim Yoo
  • Zhanqing LiEmail author
Part of the following topical collections:
  1. Topical Collection on Climate Forecast System Version 2 (CFSv2)


Knowledge of cloud properties and their vertical structure is important for meteorological studies due to their impact on both the Earth’s radiation budget and adiabatic heating within the atmosphere. The objective of this study is to evaluate bulk cloud properties and vertical distribution simulated by the US National Oceanic and Atmospheric Administration National Centers for Environmental Prediction Global Forecast System (GFS) using three global satellite products. Cloud variables evaluated include the occurrence and fraction of clouds in up to three layers, cloud optical depth, liquid water path, and ice water path. Cloud vertical structure data are retrieved from both active (CloudSat/CALIPSO) and passive sensors and are subsequently compared with GFS model results. In general, the GFS model captures the spatial patterns of hydrometeors reasonably well and follows the general features seen in satellite measurements, but large discrepancies exist in low-level cloud properties. More boundary layer clouds over the interior continents were generated by the GFS model whereas satellite retrievals showed more low-level clouds over oceans. Although the frequencies of global multi-layer clouds from observations are similar to those from the model, latitudinal variations show discrepancies in terms of structure and pattern. The modeled cloud optical depth over storm track region and subtropical region is less than that from the passive sensor and is overestimated for deep convective clouds. The distributions of ice water path (IWP) agree better with satellite observations than do liquid water path (LWP) distributions. Discrepancies in LWP/IWP distributions between observations and the model are attributed to differences in cloud water mixing ratio and mean relative humidity fields, which are major control variables determining the formation of clouds.


Cloud fraction NCEP global forecast system Liquid water path Ice water path Cloud optical depth 



This study is supported by NOAA through CICS, MOST (2013CB955804), NASA (NNX08AH71G) and DOE (DESC0007171), and with helps from Drs. Yu-Tai Hou, Brad Ferrier, Shrinivas Moorthi, and Steve Lord from the NOAA/National Center for Environmental Prediction (NCEP).


  1. Agee EM (1984) Observations from space and thermal convection: a historical perspective. Bull Am Meteor Soc 65:938–949CrossRefGoogle Scholar
  2. Barnes WL, Pagano TS, Salomonson VV (1998) Prelaunch characteristics of the moderate resolution imaging spectroradiometer (MODIS) on EOS-AM1. IEEE Trans Geosci Remote Sens 36:1088–1100CrossRefGoogle Scholar
  3. Baum BA, Spinhirne JD (2000) Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS. 3. Cloud overlap. J Geophys Res 105:11793–11804Google Scholar
  4. Baum BA, Uttal T, Poellot M, Ackerman TP, Alvarez JM, Intrieri J, Starr DO’C, Titlow J, Tovinkere V, Clothiaux E (1995) Satellite remote sensing of multiple cloud layers. J Atmos Sci 52:4210–4230Google Scholar
  5. Bodas-Salcedo A, Webb MJ, Brooks ME, Ringer MA, William KD, Milton SF, Wilson DR (2008) Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities. J Geophys Res 113:D00A13. doi: 10.1029/2007JD009620
  6. Chang FL, Li Z (2005a) A new method for detection of cirrus-overlapping-low clouds and determination of their optical properties. J Atmos Sci 62:3993–4009CrossRefGoogle Scholar
  7. Chang FL, Li Z (2005b) A near global climatology of single-layer and overlapped clouds and their optical properties retrieved from TERRA/MODIS data using a new algorithm. J Clim 18:4752–4771CrossRefGoogle Scholar
  8. Chou MD, Suarez MJ, Ho CH, Yan HMM, Lee KT (1998) Parameterizations for cloud overlapping and shortwave single scattering properties for use in general circulation and cloud ensemble models. J Clim 11:202–214CrossRefGoogle Scholar
  9. Han J, Pan HL (2011) Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Wea Forecast 26:520–533CrossRefGoogle Scholar
  10. Harshvardhan RD, Randall DA, Corsetti TG, Dazlich DA (1989) Earth radiation budget and cloudiness simulations with a general circulation model. J Atmos Sci 46:1922–1942CrossRefGoogle Scholar
  11. Heymsfield AJ, McFarquhar GM (1996) High albedos of cirrus in the tropical pacific warm pool: microphysical interpretations from CEPEX and from Kwajalein, Marshall Islands. J Atmos Sci 53:2424–2451CrossRefGoogle Scholar
  12. Im E, Durden SL, Wu C (2006) Cloud profiling radar for the CloudSat mission. IEEE Aerosp Electron Syst Mag 20:15–18CrossRefGoogle Scholar
  13. Jin Y, Rossow WB, Wylie DP (1996) Comparison of the climatologies of high-level clouds from HIRS and ISCCP. J Clim 9:2850–2879CrossRefGoogle Scholar
  14. Kahn BH, Chahine MT, Stephens GL, Mace GG, Marchand RT, Wang Z, Barnet CD, Eldering A, Holz RE, Kuehn RE, Vane DG (2007) Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount. Atmos Chem Phys Discuss 7:13915–13958CrossRefGoogle Scholar
  15. Klein SA, Jakob C (1999) Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon Wea Rev 127:2514–2531CrossRefGoogle Scholar
  16. Kuettner JP (1971) Cloud bands in the earth’s atmosphere. Tellus 23:404–425CrossRefGoogle Scholar
  17. Li Z, Leighton H (1991) Scene identification and its effect on cloud radiative forcing in the Arctic. J Geophys Res 96:9175–9188CrossRefGoogle Scholar
  18. Mace GG et al (2006) Cloud radiative forcing at the atmospheric radiation measurement program climate research facility: 1. Technique, validation, and comparison to satellite derived diagnostic quantities. J Geophys Res 111:D11S90. doi: 10.1029/2005JD005921
  19. Mace GG, Zhang Q, Vaughn M, Marchand R, Stephens G, Trepte C, Winker D (2009) A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data. J Geophys Res 114:D00A26. doi: 10.1029/2007JD009755
  20. Ou SC, Liou KN, Baum BA (1996) Detection of multilayer cirrus cloud systems using AVHRR data: verification based on FIRE-II IFO composite measurements. J Appl Meteorol 35:178–191CrossRefGoogle Scholar
  21. Pincus R, Platnick S, Ackerman S, Hemler R, Hofmann R (2012) Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators. J Clim. doi: 10.1175/JCLI-D-11-00267.1
  22. Platnick S, King MD, Ackerman SA, Menzel WP, Baum BA, Riedi JC, Frey RA (2003) The MODIS cloud products: algorithms and examples from Terra. IEEE Trans Geosci Remote Sens 41:459–473CrossRefGoogle Scholar
  23. Sassen K, Wang Z (2008) Classifying clouds around the globe with the CloudSat radar: 1-year of results. Geophys Res Lett 35:L04805. doi: 10.1029/2007GL032591 CrossRefGoogle Scholar
  24. Sheu RS, Curry JA, Liu G (1997) Vertical stratification of tropical cloud properties as determined from satellite. J Geophys Res 102:4231–4245CrossRefGoogle Scholar
  25. Starr DO’C, Cox SK (1985) Cirrus clouds. Part II: numerical experiments on the formation and maintenance of cirrus. J Atmos Sci 42:2682–2694Google Scholar
  26. Stephens GL (2005) Cloud feedbacks in the climate system: a critical review. J Clim 18:237–273CrossRefGoogle Scholar
  27. Stephens GL, Vane DG, Boain RJ, Mace GG, Sassen K, Wang Z, Illingworth AJ, O’Connor EJ, Rossow WB, Durden SL, Miller SD, Austin RT, Benedetti A, Mitrescu C (2002) The cloudsat mission and the A-train. Bull Am Meteorol Soc 83:1771–1790CrossRefGoogle Scholar
  28. Sun R, Moorthi S, Xiao H, Mechoso CR (2010) Simulation of low clouds in the Southeast Pacific by the NCEP GFS: sensitivity to vertical mixing. Atmos Chem Phys 10:12261–12272. doi: 10.5194/acp-10-12261-2010 CrossRefGoogle Scholar
  29. Weare BC, AMIP Modeling Groups (1996) Evaluation of the vertical structure of zonally averaged cloudiness and its variability in the atmospheric model intercomparison project. J Clim 9:3419–3431Google Scholar
  30. Winker DM, Hunt WH, McGill MJ (2007) Initial performance assessment of CALIOP. Geophys Res Lett 34:L19803. doi: 10.1029/2007GL030135 CrossRefGoogle Scholar
  31. Wylie D, Eloranta E, Spinhirne JD, Palm SP (2007) A comparison of cloud cover statistics from the GLAS lidar with HIRS. J Clim 20:4968–4981. doi: 10.1175/JCLI4269.1 CrossRefGoogle Scholar
  32. Xi B, Dong X, Minnis P, Khaiyer MM (2010) A 10 year climatology of cloud fraction and vertical distribution derived from both surface and GOES observations over the DOE ARM SGP site. J Geophys Res 115:D12124. doi: 10.1029/2009JD012800 CrossRefGoogle Scholar
  33. Xu KM, Randall DA (1996) A semiempirical cloudiness parameterization for use in climate models. J Atmos Sci 53:3084–3102CrossRefGoogle Scholar
  34. Yang F, Pan HL, Krueger SK, Moorthi S, Lord SJ (2006) Evaluation of the NCEP global forecast system at the ARM SGP site. Mon Wea Rev 134:3668–3690CrossRefGoogle Scholar
  35. Zhang MH et al (2005) Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. J Geophys Res 110:D15S02. doi: 10.1029/2004JD005021
  36. Zuidema P (1998) The 600–800 mb minimum in tropical cloudiness observed during TOGA COARE. J Atmos Sci 55:2220–2228CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Atmospheric and Oceanic ScienceUniversity of MarylandCollege ParkUSA
  2. 2.State Key Laboratory of Earth Surface Processes and Resource Ecology, GCESSBeijing Normal UniversityBeijingChina

Personalised recommendations