GCM-Oriented CALIPSO Cloud Product

 

The CALIPSO-GOCCP (GCM Oriented Cloud Calipso Product) is designed to evaluate GCM cloudiness.

CALIPSO-GOCCP contains observational cloud diagnostics fully consistent with the ones simulated by the ensemble "GCM+lidar simulator": it has been built in using the same horizontal and vertical resolutions, and the same cloud detection thresholds. The lidar simulator is part of COSP (CFMIP Observation Simulator Package) and can be downloaded at: https://github.com/CFMIP/COSPv2.0

CALIPSO-GOCCP is used for the CFMIP pilot model intercomparison study, it has been processed at LMD/IPSL with the support of NASA/CNES, ICARE and ClimServ.

CALIPSO-GOCCP is derived from CALIPSO L1/NASA products (NASA Langley ASDC - CALIPSO Data Sets, data availability and quicklooks can be found here) and contains the following types of files:

 

  • 1a) 3D_CloudFraction: Gridded profiles
  •     --> Cloud fraction profile, Clear fraction profile, Uncertain fraction profile
  • 1b) 3D_CloudFraction_phase: Gridded cloud phase profiles
  •     --> Liquid Cloud fraction profile, Ice Cloud fraction profile, Uncertain Cloud phase fraction profile
  • 1c) 3D_CloudFraction_phase_temp: Gridded cloud phase profiles (temperature)
  •     --> Same as (1b) but profiles as a function of temperature instead of altitude
  • 1d) 3D_CloudFraction_OPAQ: Gridded OPAQ product profiles (new in v3.X)
  •     --> Opaque Cloud fraction profile, Non-opaque Cloud fraction profile, z_opaque fraction profile, Lidar Opacity fraction profile

  • 2a) MapLowMidHigh: Cloud cover maps at three levels (Low, Mid, High following ISCCP definition), gridded
  •     --> Total Cloud cover,Low Cloud cover, Mid Cloud cover, High Cloud cover
  • 2b) MapLowMidHighphase: Cloud phase cover maps at three levels, gridded
  •     --> Same as (2a) but for Liquid Cloud cover,Ice Cloud cover, Undefined phase Cloud cover
  • 2c) Map_OPAQ: OPAQ product maps, gridded (new in v3.X)
  •     --> Opaque Cloud cover, Thin Cloud cover, z_opaque (altitude of opacity), and more: Cloud temperature, Cloud altitude, Thin Cloud emissivity, ...

  • 3a) SR_histo: Height-Intensity Histogram (similar as CFAD for Cloudsat), gridded
  •     --> For each lat-lon grid-box, 15-bin SR intensities and 40-bin altitude levels occurence matrix
  • 3b) SR_histophase: SR_histo phase, gridded
  •     --> Same as (3a) but for Liquid Cloud, Ice Cloud, Uncertain phase Cloud
  • 3c) SR_histo_OPAQ: SR_histo OPAQ, gridded (new in v3.X)
  •     --> Same as (3a) but for Opaque Cloud, Non-opaque Cloud

  • 4a) Instant_SR : Profiles along the satellite flight track, half orbit files (day/night)
  •     --> SR profiles, Color Ratio profiles, Depolarization Ratio profiles, Cloud mask
  • 4b) Instant_SRphase : Cloud phase profiles, half orbit files
  •     --> Cloud phase mask
  • 4c) Instant_OPAQ : OPAQ profiles, half orbit files (new in v3.X)
  •     --> OPAQ product mask

     

    References relative to GOCCP developments:

    - T. Vaillant de Guélis, H. Chepfer, V. Noel, R. Guzman, D.M. Winker and R. Plougonven, 2017b: "Using space lidar observations to decompose longwave cloud radiative effect variations over the last decade", Geophys. Res. Lett., doi:10.1002/2017GL074628, http://onlinelibrary.wiley.com/doi/10.1002/2017GL074628/abstract (uses GOCCP v3.1.1)

    - T. Vaillant de Guélis, H. Chepfer, V. Noel, R. Guzman, P. Dubuisson, D.M. Winker and S. Kato, 2017a: "The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated", Atmos. Meas. Tech., 10, 4659-4685, doi:10.5194/amt-10-4659-2017, https://www.atmos-meas-tech.net/10/4659/2017/amt-10-4659-2017.html (uses GOCCP v3.1.1)

    - R. Guzman, H. Chepfer, V. Noel, T. Vaillant de Guélis, J.E. Kay, P. Raberanto, G. Cesana, M.A. Vaughan, and D.M. Winker, 2017: "Direct atmosphere opacity observations from CALIPSO provide new constraints on cloud-radiation interactions", J. Geophys. Res. Atmos., 122, 1066-1085, doi:10.1002/2016JD025946, http://onlinelibrary.wiley.com/doi/10.1002/2016JD025946/full (uses GOCCP v3.1.1)

    - G. Cesana, H. Chepfer, D. Winker, B. Getzewich, X. Cai, O. Jourdan, G. Mioche, H. Okamoto, Y. Hagihara, V. Noel, and M. Reverdy, 2016: "Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO", J. Geophys. Res. Atmos., 121, 5788-5808, doi:10.1002/2015JD024334, http://onlinelibrary.wiley.com/doi/10.1002/2015JD024334/full

    - G. Cesana and H. Chepfer, 2013: "Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP", J. Geophys. Res. Atmos., 118, 7922-7937, doi:10.1002/jgrd.50376, http://onlinelibrary.wiley.com/doi/10.1002/jgrd.50376/full

    - H. Chepfer, G. Cesana, D. Winker, B. Getzewich, M. Vaughan, and Z. Liu, 2013: "Comparison of two different cloud climatologies derived from CALIOP-attenuated backscattered measurements (Level 1): The CALIPSO-ST and the CALIPSO-GOCCP", J. Atmos. Oce. Tech., 30.4, 725-744, doi:10.1175/JTECH-D-12-00057.1, [PDF]http://journals.ametsoc.org/doi/full/10.1175/JTECH-D-12-00057.1

    - G. Cesana, J. E. Kay, H. Chepfer, J. M. English, and G. deBoer (2012), Ubiquitous low-level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett., 39, L20804, doi:10.1029/2012GL053385 http://onlinelibrary.wiley.com/doi/10.1029/2012GL053385/full

    - G. Cesana and H. Chepfer, 2012: "How well do climate models simulate cloud vertical structure? A comparison between CALIPSO-GOCCP satellite observations and CMIP5 models", Geophys. Res. Lett., 39.20, doi:10.1029/2012GL053153, http://onlinelibrary.wiley.com/doi/10.1029/2012GL053153/full

    - H. Chepfer, S. Bony, D. M. Winker, G. Cesana, J.-L. Dufresne, P. Minnis, C.J. Stubenrauch, and S. Zeng, 2010: "The GCM Oriented CALIPSO Cloud Product (CALIPSO-GOCCP)", J. Geophys. Res., 115, D00H16, doi: 10.1029/2009JD012251, [PDF]http://onlinelibrary.wiley.com/doi/10.1029/2009JD012251/full

    - H. Chepfer, S. Bony, D. M. Winker, M. Chiriaco, J.-L. Dufresne, and G. Seze, 2008: "Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model", Geophys. Res. Lett., vol. 35, L15704, doi: 10.1029/2008GL034207, [PDF]http://onlinelibrary.wiley.com/doi/10.1029/2008GL034207/full

     

    Informations and updates:
    Please send us an e-mail if you download data, so we will keep you informed of CALIPSO-GOCCP updates.
    Mail to: goccp@lmd.polytechnique.fr

     


     

    1a) 3D_CloudFraction

    - 3D_CloudFraction file contains the following monthly (or seasonal) fractions in longitude/latitude/altitude boxes: cloud fraction, clearfraction, and uncertain fraction. The sum of this fraction in a longitude/latitude/altitude box is equal to 1 (The file contains also an information on the fully attenuated point. The way to calculate this fraction is not fixed yet)

    - The following figure is an example of what you can do with the CALIPSO-GOCCP 3D_CloudFraction DATA for the period JJA 2006-2009 (a) (c) (d) and JFM 2007-2009 (b).

    3d_cloudfraction_jja06-09_avg_cfmip2bis.png

     

    File contents:

    Size: 6.4Mo

    Dimensions:

  • longitude= 96 / 144 / 180 / 360
  • latitude = 72 / 72 / 90 / 180
  • altitude = 40
  • nv = 2
  • time = 1
  • Variables :

  • longitude(longitude)
  • latitude(latitude)
  • alt_mid(altitude)
  • alt_bound(nv,altitude)
  • time(time)
  • clcalipso(time,altitude,latitude,longitude)
  • clrcalipso(time,altitude,latitude,longitude)
  • uncalipso(time,altitude,latitude,longitude)
  •  


     

    1b) 3D_CloudFraction_phase

    - The name of the 3D cloud fraction phase files begin with 3D_CloudFraction_Phase330m_* and contain liquid/ice/undefined-Phase cloud fractions and the relative percentage of ice in cloud with respect to the total condensate.

    - CF3D Phase: Cloud phase vertical distribution observed by CALIPSO-GOCCP in JFM. Ice cloud fraction (left column), liquid (center) cloud fraction, and ice fraction with respect to the total condensate (right).

    CF3D_Phase_CFMIP-OBS.png

     

    New Variables :

  • clcalipso_liq(time,altitude,latitude,longitude)
  • clcalipso_ice(time,altitude,latitude,longitude)
  • clcalipso_un(time,altitude,latitude,longitude)
  • clcalipso_RPIC(time,altitude,latitude,longitude)
  •  


     

    1c) 3D_CloudFraction_phase_temp

    - There are also 3D_CloudFraction_Temp330m_* files that contain cloud fractions for all clouds and for liquid/ice clouds as a function of the temperature instead of height and for each longitude/latitude grid box. The temperature bins and ranged every 3°C. The temperature is taken from GMAO data (Global Modeling and Assimilation Office, Bey et al., 2001), which is part of CALIPSO level 1 ancillary data. For each CALIOP level 1 profile, we interpolate the GMAO temperature over the 480m-vertical levels of CALIPSO-GOCCP, and use it as the cloudy pixel temperature.

    - Cloud temperature distribution annual mean observed by CALIPSO-GOCCP for 2012: a) Total cloud fraction, b) Ice cloud fraction, c) liquid cloud fraction and d) Relative percentage of ice with respect to the total condensate.

    CF3DTEMP.png

     

    New Variables :

  • cltemp(time,altitude,latitude,longitude)
  • cltemp_liq(time,altitude,latitude,longitude)
  • cltemp_ice(time,altitude,latitude,longitude)
  • cltemp_phase(time,altitude,latitude,longitude)
  •  


     

    1d) 3D_CloudFraction_OPAQ (new in v3.X)

    - The name of the 3D cloud fraction OPAQ files begin with 3D_CloudFraction_OPAQ330m_*. They contain:

  • cloud/clear/uncertain fraction of opaque/non-opaque profiles computed with respect to the total valid (excluding the fully attenuated) values per bin.
  • z_opaque fraction of opaque profiles with respect to the total valid (including the fully attenuated) values per bin.
  • atmospheric opacity fraction of the CALIPSO lidar (corresponding to the z_opaque fraction integrated from the TOA downwards).
  • The relations between these 3D fraction variables are the following:

    clcalipso + clrcalipso + uncalipso = 1 (as in GOCCP v2.X)

    clcalipso = clcalipso_opaque + clcalipso_notopaque (new cloud fraction partition in GOCCP v3.X)

    clrcalipso = clrcalipso_opaque + clrcalipso_notopaque (new clear fraction partition in GOCCP v3.X)

    uncalipso = uncalipso_opaque + uncalipso_notopaque (new uncertain fraction partition in GOCCP v3.X)

     

    Zonal cloud fraction distributions and opacity distribution observed by CALIPSO-GOCCP from GOCCP v3.1.2 nighttime data for June 2010: (a) "clcalipso", (b) "clcalipso_opaque", (c) "clcalipso_notopaque" and (d) "calipsoopacity".

    Python routine: CloudFraction_3D_month.py

    GOCCP_v3.1.2_CF3D_zonal_062010_night.png

     

    New Variables :

  • clcalipso_opaque (Opaque cloud profiles cloud fraction) [time,altitude,latitude,longitude]
  • clrcalipso_opaque (Opaque cloud profiles clear fraction) [time,altitude,latitude,longitude]
  • uncalipso_opaque (Opaque cloud profiles uncertain fraction) [time,altitude,latitude,longitude]
  • clcalipso_notopaque (Not Opaque cloud profiles cloud fraction) [time,altitude,latitude,longitude]
  • clrcalipso_notopaque (Not Opaque cloud profiles clear fraction) [time,altitude,latitude,longitude]
  • uncalipso_notopaque (Not Opaque cloud profiles uncertain fraction) [time,altitude,latitude,longitude]
  • calipsozopaque (Opaque cloud profiles z_opaque fraction) [time,altitude,latitude,longitude]
  • calipsoopacity (CALIPSO lidar opacity fraction from TOA) [time,altitude,latitude,longitude]
  •  

    Ftp data access:

  • 3D_CloudFraction_2x2xL40 !! Use this grid for the CMIP5/CFMIP-2 experiment. !!
  • 3D_CloudFraction_1x1xL40 !! Use this grid ONLY for the GEWEX Cloud Assessment. !!
  •  

    Matlab routine: 3D_CloudFraction

     


     

    2a) MapLowMidHigh

    - MapLowMidHigh file contains maps of Low-Mid-High cloud fractions, total cloud fraction and clear fraction:

  • Low level clouds :           P° > 680 hPa                             z < 3.2 km          (Hydrostatic equilibrium)
  • Mid level clouds :     680 hPa > P° > 440 hPa        3.2 km < z < 6.5 km
  • High level clouds :         P° < 440 hPa                              z > 6.5 km     
  • - The total cloud/clear fraction (called cltcalipso for cloud and clccalipso for clear) enable to know the cloud/clear fraction present in a longitude/latitude column.

    - The following figure is an example of what you can do with the CALIPSO-GOCCP MapLowMidHigh DATA for the period JJA 2006-2009.

     

    maplowmidhigh_jja06-09_avg_cfmip2bis.png

     

    File contents:

    Size: 140ko

    Dimensions:

  • longitude= 96 / 144 / 180 / 360
  • latitude = 72 / 72 / 90 / 180
  • toplvl = 3
  • time = 1
  • Variables :

  • longitude(longitude)
  • latitude(latitude)
  • toplvl(toplvl)
  • time(time)
  • cllcalipso(time,latitude,longitude)
  • clmcalipso(time,latitude,longitude)
  • clhcalipso(time,latitude,longitude)
  • cltcalipso(time,latitude,longitude)
  • clccalipso(time,latitude,longitude)
  •  


     

    2b) MapLowMidHighphase

    - The name of the cloud phase map files begin with MapLowMidHigh_Phase330m_*. They contain:

  • liquid/ice/undefined-Phase of low/mid/high/total cloud covers.
  • Another variable which is the relative percentage of ice in cloud with respect to the total condensate for low/mid/high/total level
  • - Map Phase: Ice and Liquid cloud maps observed by CALIPSO-GOCCP in JFM: Liquid cloud covers observed (a-c) and Ice cloud covers observed (g-i) at high, mid, low altitudes.

     

    Map_Phase_CFMIP-OBS.png

     

    New Variables :

  • cllcalipso_liq(time,latitude,longitude)
  • cllcalipso_ice(time,latitude,longitude)
  • cllcalipso_un(time,latitude,longitude)
  • cllcalipso_RPIC(time,latitude,longitude)
  • clmcalipso_liq(time,latitude,longitude)
  • clmcalipso_ice(time,latitude,longitude)
  • clmcalipso_un(time,latitude,longitude)
  • clmcalipso_RPIC(time,latitude,longitude)
  • clhcalipso_liq(time,latitude,longitude)
  • clhcalipso_ice(time,latitude,longitude)
  • clhcalipso_un(time,latitude,longitude)
  • clhcalipso_RPIC(time,latitude,longitude)
  • cltcalipso_liq(time,latitude,longitude)
  • cltcalipso_ice(time,latitude,longitude)
  • cltcalipso_un(time,latitude,longitude)
  • cltcalipso_RPIC(time,latitude,longitude)
  •  


     

    2c) Map_OPAQ (new in v3.X)

    - The name of the OPAQ product map files begin with Map_OPAQ330m_*. They contain:

  • cover/temperature of opaque/thin clouds.
  • temperature of z_opaque, the altitude of full opacity of the lidar.
  • altitude of opaque/thin clouds and z_opaque with respect to sea level.
  • altitude of opaque/thin cloud and z_opaque with respect to surface elevation.
  • thin cloud emissivity.
  • Version 3.0 (nighttime data only)

    New variables from v3.X are all derived from the OPAQ algorithm which relies on a simple paradigm applied to each single profile: if a surface echo is detected near the ground, the profile is either declared as clear-sky or as a thin cloud profile if a cloud is detected within the profile. If the surface echo is not detected, the profile is declared as an opaque cloud profile. This section briefly describes the new variables introduced in GOCCP v3.0 but all the variables that existed in version 2.X are still available and are unchanged.

    This new partition of clouds into opaque and thin clouds allows us to describe the mean atmospheric states in three distinct covers: (a) the opaque cloud cover, (b) the thin/broken cloud cover and (c) the clear-sky.

    Variables of the example figure below: (a) "cltcalipso_opaque", (b) "cltcalipso_thin" and (c) "clccalipso" from 2D Maps files.

    Python routine: Covers.py

    GOCCP_v3.0_covers_2008-2015.png

     

    Z_opaque maps

    The altitude of opacity z_opaque, as defined by the OPAQ algorithm, can only be defined for opaque cloud profiles. Hence, a mean z_opaque altitude is computed for each 2° x 2° lat-lon gridpoint from all opaque cloud profiles (and only opaque cloud profiles) sounded in a 2° x 2° lat-lon gridpoint. The significance of this mean z_opaque value is strongly dependent on the opaque cloud cover, and it is particularly meaningful where opaque cloud covers are large.

    Variables of the example figure below: (a) "zopaque" for all opaque cloud covers and (b) "zopaque" only where "cltcalipso_opaque" > 0.3, from 2D Maps files.

    Python routine for z_opaque: Z_opaque_maps.py

    Python routine for climatologies: climato_Maps_OPAQ_variables_v3.1.2.py

    GOCCP_v3.0_zopaque_2008-2015.png

     

    Version 3.1.1 (nighttime data only)

    GOCCP v3.1.1 has new map variables for the Opaque and Thin clouds as defined in the Vaillant de Guélis et al., 2017a reference above. These new 2D fields include the temperature and the altitude of Opaque and Thin clouds, and the emissivity of Thin clouds.

    Variables of the example figure below: (a) "cltcalipso_opaque", (b) "cltcalipso_thin", (c) "cltcalipso_opaque_z", (d) "cltcalipso_thin_z", and (f) "cltcalipso_thin_emis" from Map_OPAQ v3.1.1 files.

    Python routine: Maps_OPAQ_altitude_v3.1.1.py

    Maps_OPAQ_altitude_2008-2015_night_v3.1.1.png

     

    Version 3.1.2 (consistent daytime and nighttime data)

    The GOCCP v3.1.2 dataset is significantly different from v3.1.1. In order to have the OPAQ variables defined in the previous GOCCP v3.X for daytime, the surface detection threshold has been adapted to the noisier daytime data. The surface detection threshold is now higher for nighttime data because we choose to have a unique and consistent detection threshold for daytime and nighttime opaque clouds, which is hence constrained by the noisier daytime data. The major difference with GOCCP v3.1.1 is that more opaque clouds are detected during nighttime because of the higher surface detection threshold (see figure below).

    Variables of the example figure below: (a) and (b) "cltcalipso_opaque", (c) and (d) "cltcalipso_thin", (e) and (f) "cltcalipso_thin_emis" from Map_OPAQ files for v3.1.1 and v3.1.2, respectively.

    Python routine: Comp_OPAQ_v3.1.1_v3.1.2.py

    Comp_Maps_OPAQ_2008-2015_night_v3.1.1_v3.1.2.png

     

    Besides having all the OPAQ variables for the daytime data, which are consistent with the new surface detection threshold used for the nighttime data in GOCCP v3.1.2, all altitude maps exist now with respect to the Surface Elevation (variables having a "_se" at the end of the variable name). The figure below shows some of the OPAQ maps for the daytime multi-year mean where we can see the new z_opaque computed with respect to the Surface Elevation plotted in subplot (e).

    Variables of the example figure below: (a) "cltcalipso_opaque", (b) "cltcalipso_thin", (c) "cltcalipso_opaque_z", (d) "cltcalipso_thin_z", (e) "zopaque_se", and (f) "cltcalipso_thin_emis" from Map_OPAQ v3.1.2 files.

    Python routine: Maps_OPAQ_altitude_v3.1.2.py

    Maps_OPAQ_altitude_2008-2015_day_v3.1.2.png

     

    Finally, as developped in Vaillant de Guélis et al. 2017a and Vaillant de Guélis et al. 2017b, the TOA LongWave Cloud Radiative Effect (LWCRE) can be estimated by using the 5 following GOCCP cloud variables only: opaque cloud cover ("cltcalipso_opaque"), opaque cloud altitude ("cltcalipso_opaque_z"), thin cloud cover ("cltcalipso_thin"), thin cloud altitude ("cltcalipso_thin_z") and thin cloud emissivity ("cltcalipso_thin_emis"). The relation between these cloud variables and the LWCRE is the following:

    LWCRE = LWCRE_opaque_clouds + LWCRE_thin_clouds

                    = 11×cltcalipso_opaque×cltcalipso_opaque_z + 11×cltcalipso_thin×cltcalipso_thin_z×cltcalipso_thin_emis

    The figure below shows the 2008-2015 multi-year mean of the day+night LWCRE from (a) the CERES EBAF Ed.4.0 product, (b) the CALIPSO-derived estimate from the 5 GOCCP v3.1.2 cloud variables and (c) their difference.

    We computed the day+night LWCRE from GOCCP v3.1.2 by doing the average between the daytime and the nighttime estimates. The uncertainty for the CERES EBAF total 24-h average global mean LW TOA flux is 3.7 W/m2 [Loeb et al., 2009, Journal of Climate].

    Python routines: Diff_EBAF-CRELW_v3.1.2.py       plot_Diff_EBAF-CRELW_day+night_v3.1.2.py

    Diff_CRELW_Maps_2008-2015_v3.1.2_day+night.png

    New Variables (ASL=Above Sea Level; SE=Surface Elevation):

  • cltcalipso_thin  (thin cloud cover)  [time,latitude,longitude]
  • cltcalipso_thin_temp  (thin cloud temperature)  [time,latitude,longitude]
  • cltcalipso_thin_z  (thin cloud altitude ASL)  [time,latitude,longitude]
  • cltcalipso_thin_z_se  (thin cloud altitude with respect to SE)  [time,latitude,longitude]
  • cltcalipso_thin_emis  (thin cloud emissivity)  [time,latitude,longitude]
  • cltcalipso_opaque  (opaque cloud cover)  [time,latitude,longitude]
  • cltcalipso_opaque_temp  (opaque cloud temperature)  [time,latitude,longitude]
  • cltcalipso_opaque_z  (opaque cloud altitude ASL)  [time,latitude,longitude]
  • cltcalipso_opaque_z_se  (opaque cloud altitude with respect to SE)  [time,latitude,longitude]
  • zopaque  (altitude of opacity ASL)  [time,latitude,longitude]
  • zopaque_se  (altitude of opacity with respect to SE)  [time,latitude,longitude]
  • zopaque_temp  (altitude of opacity temperature)  [time,latitude,longitude]
  • calipso_notopaque  (thin cloud cover + clear sky)  [time,latitude,longitude]
  • nsidc  (ice/snow surface cover)  [time,latitude,longitude]
  •  

    Ftp data access:

  • MapLowMidHigh_2x2xL40 !! Use this grid for the CMIP5/CFMIP-2 experiment. !!
  • MapLowMidHigh_1x1xL40 !! Use this grid ONLY for the GEWEX Cloud Assessment. !!
  •  

    Matlab routine: MapLowMidHigh

     


     

    3a) SR_histograms

    - SR_histo file contains the number of points encontered in each SR bin (= cfad_lidarsr532_Occ & cfad_lidarsr532_Occ2).

    - The 19 SR interval boundaries are : -888,-777,-776,0, 0.01, 1.2, 3, 5, 7, 10, 15, 20, 25, 30, 40, 50, 60, 80, 10000.

    - In the simulator outputs, the three first interval boxes (-888,-777,-776 to 0) do not have their counterpart in the simulator outputs, they are associated to the following situation:

  • First box (SR=-888) corresponds to pixels located below the surface elevation.
  • Second box (SR=-777) corresponds to pixels rejected.
  • Third box (-776<SR<0) corresponds to noisy pixels.
  • - SR_histo is defined for each longitude/latitude/altitude box.

    - The following figures are examples of what you can do with the CALIPSO-GOCCP SR_histo DATA for the period JJA 2006-2009:

     

  • The first graphic is a representation of the SR histograms as a function of the altitude.
  • sr_histo_jja_cfmip2_avgbis.png

     

  • We can use the SR_histo CALIPSO-GOCCP data on particular regions such as the graphic below.
  • sr_histo_jja_region_cfmip2_avgbis.png

     

  • On the third graphic, the cloud fraction is rebuilt from the SR_histovalues. Thus, we can chose the treshold of SR and follow the evolutionof the optically thicker or thinner clouds.
  • There is some differences between this method and the cloud fractionderived from 3D_CloudFraction files. Indeed, in the 3D_cloudFraction files we introduce a treshold called DeltaAtb which is equal to : atb - atbmol, during the calculation of the cloud fraction.The aim of the DeltaAtb is to reduce the noise in the stratosphere. This treshold has been set to 2.5E-03 km-1.sr-1. So, in the3D_CloudFraction files a cloudy point has to be higher than the cloudy treshold but also higher than the DeltaAtb treshold, whereas inthe SR_histo files the DeltaAtb treshold is not used.
  • 3dcf_srhist_jja_cfmip2_avgbis.png

     

    File contents:

    Size: 33Mo

    Dimensions:

  • longitude= 96 / 144 / 180 / 360
  • latitude = 72 / 72 / 90 / 180
  • altitude = 40
  • nv = 2
  • box = 15
  • box2 = 3
  • time = 1
  • Variables :

  • longitude(longitude)
  • latitude(latitude)
  • alt_mid(altitude)
  • alt_bound(nv,altitude)
  • srbox_mid(box)
  • srbox_mid2(box2)
  • srbox_bound(nv,box)
  • srbox_bound2(nv,box2)
  • time(time)
  • cfad_lidarsr532_Occ(time,box,altitude,latitude,longitude)
  • cfad_lidarsr532_Occ2(time,box2,altitude,latitude,longitude)
  •  


     

    3b) SR_histograms phase

    - The name of the SR_histo phase files begin with SR_histo_Phase330m_* and contain liquid/ice/undefined-Phase SR histograms of clouds.

    - SR histograms Phase : Seasonal two-dimensional histograms of scattering ratio and height from CALIPSO-GOCCP Arctic (70-82 N, ocean-only) observations. a) SON liquid-containing cloud, b) DJF liquid-containing cloud, c) MAM liquid-containing cloud, d) JJA liquid-containing cloud, e-h) as in a-d) but for ice-dominated cloud, i-l) as in a-d) but for unclassified cloud:

    SR_histo_Phase_CFMIP-OBS.png

     

    New Variables :

  • cfad_lidarsr532_liq(time,box,altitude,latitude,longitude)
  • cfad_lidarsr532_ice(time,box,altitude,latitude,longitude)
  • cfad_lidarsr532_un(time,box,altitude,latitude,longitude)
  •  


     

    3c) SR_histograms OPAQ (new in v3.X)

    - The name of the SR_histo OPAQ files begin with SR_histo_OPAQ330m_* and contain opaque/non-opaque profile SR histograms.

    Two-dimensional histograms of scattering ratio (SR) and height from GOCCP v3.1.2 nighttime data for January 2010: (a) "cfad_lidarsr532_Occ", (b) "cfad_lidarsr532_Occ_opaque" and (c) "cfad_lidarsr532_Occ_notopaque".

    Python routine: SR_histo330m_012010_night_GOCCP_v3.1.2.py

    GOCCP_v3.1.2_SRhisto_012010_night.png

     

    New Variables :

  • cfad_lidarsr532_Occ_opaque (Height-intensity histogram for opaque cloud profiles, SR > 0) [time,box,altitude,latitude,longitude]
  • cfad_lidarsr532_Occ2_opaque (Height-intensity histogram for opaque cloud profiles, SR < 0) [time,box2,altitude,latitude,longitude]
  • cfad_lidarsr532_Occ_notopaque (Height-intensity histogram for not-opaque cloud profiles, SR > 0) [time,box,altitude,latitude,longitude]
  • cfad_lidarsr532_Occ2_notopaque (Height-intensity histogram for not-opaque cloud profiles, SR < 0) [time,box2,altitude,latitude,longitude]
  •  

    Ftp data access:

  • SR_histo_2x2xL40 !! Use this grid for the CMIP5/CFMIP-2 experiment. !!
  • SR_histo_1x1xL40 !! Use this grid ONLY for the GEWEX Cloud Assessment. !!
  •  

    Matlab routine: SR_histo

     


     

    4a) Instant_SR_CR_DR

    - Instant_SR_CR_DR file contains the scattering ratio (SR), Color Ratio (CR) and Depolarization Ratio (DR) average over the 40 vertical levels of the CFMIP grid. The horizontal day track resolution is 330m (about 60000 points).

    - The longitude, latitude, hour and SE are saved in the instantaneous files too.

    instant_srcrdr_20070101_nightbis.png

     

    File contents:

    Size: about 10Mo

    Dimensions:

  • altitude = 40
  • it = about 60000
  • Variables :

  • longitude(it)
  • latitude(it)
  • altitude(altitude)
  • heure(it)
  • SE(it) (Surface_Elevation)
  • instant_SR(it,altitude)
  • instant_CR(it,altitude)
  • instant_DR(it,altitude)
  •  


     

    4b) Instant_SR_CR_DR phase

    -The cloud phase mask has been added to the existing instant_SR_CR_DR files (instant_Phase variable).

    - Instant Cloud Phase: Cloud phase mask CALIPSO-GOCCP for a CALIPSO orbit, January 1st 2007 during night time (latitude between 72°13 and -58°93; longitude between 173°85 and -4°86).

    instant_Phase_CFMIP-OBS.png

     

    New Variables :

  • instant_Cloud(it,altitude)
  • instant_Phase(it,altitude)
  • ATB(it,altitude)
  • ATB_mol(it,altitude)
  • ATB_per(it,altitude)
  • ATB_par(it,altitude)
  • TEMP(it,altitude)
  •  


     

    4c) Instant_OPAQ (new in v3.X)

    At the highest horizontal resolution of the CALIOP lidar, the Instant files provide (a) an updated Cloud mask (instant_Cloud_OPAQ) and (b) a new OPAQ mask (instant_OPAQ). As shown in the figure below, opaque cloud profiles are clearly identified in the OPAQ mask by their full opacity level (green), termed z_opaque, corresponding to the altitude where the lidar beam is fully attenuated and no further information on the atmospheric state can be confidently retrieved below that altitude for that particular profile. Unlike the opaque cloud profiles, clear-sky and thin cloud profiles are completely vertically documented from the lower stratosphere to the surface.

    Variables of the figure below: (a) "Instant_Cloud_OPAQ" and (b) "Instant_OPAQ" masks from an Instant file (15 June 2007, orbit file "T20-42-00ZN", profiles 15454 to 15560).

    Python routine: Convection_case_study.py

    Instant_masks_2007-06-15T20-42-00ZN.png

     

    New Variables :

  • instant_Cloud_OPAQ(it,altitude)
  • instant_OPAQ(it,altitude)
  •  

    Ftp data access: instant_SR_CR_DR

     

    Matlab routine: instant_SRCRDR

     


     

    Update of the 29/04/2013 ==> cloud phase diagnosis

    The list of changes is summarized here after:

    Main changes:

  • In addition to monthly night/day/avg files, daily night/day/avg files (in the daily folder) and yearly files (in the same folder as the monthly files) are now available.
  • The processing period has been extended from june 2006 to december 2012.
  • Cloud phase diagnosis have been added in 3D_CloudFraction, MapLowMidHigh, SR_histo and instant_SR files.
  •  

    New Cloud Phase files:

  • MapLowMidHigh_Phase contain liquid, ice and undefined low/mid/high/tot cloud covers
  • 3D_CloudFraction_Phase contain liquid, ice and undefined vertical cloud fractions
  • 3D_CloudFraction_Temp contain liquid, ice and undefined cloud fractions as a function of the temperature instead of the height
  • SR_histo_Phase contain liquid, ice and undefined histograms of SR
  • instant_SR_CR_DR contain new variables: cloud phase mask, total ATB, perpendicular ATB, parallel ATB, molecular ATB and Temperature. The name of the instant_SR_CR_DR has been changed. It is built such as the CALIPSO level1 orbit files.
  •  

    Update of the 01/2012 ==> CMOR data available

    The list of changes is summarized here after:

  • CMOR data available.
  •  

    Update of the 23/03/2011 ==> CALIPSO-GOCCP version 2.1

     

    The list of changes is summarized here after:

  • improve DAY TIME Signal to Noise Ratio correction:
  • Major changes in "MapLowMidHigh" and "3D_CloudFraction" files (AVG,DAY & "climato" files).
  • Correction of fake detections above 8km, during DAY TIME,in "instant_SR" variable:
  • changes in "SR_histo" DAY/AVG TIME files and "SR_histo_climato" files.
  • Add Color Ratio and Depolarization Ratio in instant_SR files:
  • Name of instant_SR files becomes instant_SR_CR_DR.
  • Color Ratio variable is called "instant_CR", Depolarization Ratio is called "instant_DR".
  • New variables in "SR_histo" files:
  • The previous "cfad_lidarsr532_Occ" variable has been splitted into 2 different variables:
  • the variable "cfad_lidarsr532_Occ" contains (lon,lat,alt,srbox[0 to 10e5]) (= positive bins of SR).
  • the variable "cfad_lidarsr532_Occ2" contains (lon,lat,alt,srbox[-888 to 0]) (= negative bins of SR e.g. negative values, rejected values and surface values).
  • The altitude description has been changed in:
  • "3D_CloudFraction", "SR_histo", "instant_SR_CR_DR" and "climato" files:
  • "alt_mid(40x1)" contains the altitude of the middle of the layer.
  • "alt_bound(40x2)" contains the altitude of the top of the layer and the altitude of the bottom of the layer.
  • The SRbox description has been changed in:
  • "SR_histo" and SR_histo_climato" files:
  • "srbox_mid(15x1)" contains the value of the middle of the SR bin.
  • "srbox_mid2(3x1)" : same as srbox_mid for negative value of SR bin.
  • "srbox_bound(15x2)" contains the higher value of the SR bin and the lower value of the SR bin.
  • "srbox_bound2(3x2)": same as srbox_bound for negative value of SR bin.