Apr. 30, 2020 Updated
Jan. 2017 JAXA/EORC

1. Introduction

This system distributes the geophysical data including cloud and aerosol properties derived from the “A-train”constellation satellites data, e.g., CloudSat, CALIPSO, and, Aqua. The data are retrieved by the algorithms developed by the JAXA EarthCARE science team (Kyushu University, The National Institute for Environmental Studies, and Tokai University). The system is released to maximize the outcomes of the algorithm development activities using A-train data and prepare for the distribution of the JAXA EarthCARE products before the launch.

2. Data Description

This dataset is generated by Earth Observation Research Center (EORC) of JAXA from the CloudSat/CPR, CALIPSO/CALIOP, and Aqua/MODIS with re-sampling to ideal 240m vertical and 1.1km horizontal grid data set (CloudSat-CALIPSO merged data set) using a method developed by Kyushu University.

Radar Observables

File typeNetCDF4
Latest version1.0
Satellite /SensorCloudSat/CPR
Observation areaGlobal
Resolution240m(vertical), 1.1km(horizontal)
DataRadar reflectivity factor, gaseous attenuation (cumulative)
Original dataCloudSat 2B-GEOPROF R04

Lidar Observables

File typeNetCDF4
Latest version1.0
Satellite /SensorCALIPSO/CALIOP
Observation areaGlobal
Resolution240m(vertical), 1.1km(horizontal)
Data532nm-total attenuated backscattering coefficient, 532nm-cross-pol attenuated backscattering coefficient, 1064nm-total attenuated backscattering coefficient
Original dataCALIPSO Lidar L1b V3

ECMWF Ancillary Atmospheric State Product

File typeNetCDF4
Latest version1.0
Data coverageGlobal
Resolution240m(vertical), 1.1km(horizontal)
DataPressure, temperature, water vapor density, skin temperature
Original dataCloudSat ECMWF-AUX R04

Radar/Lidar Cloud Mask Product

File typeNetCDF4
Latest version1.0
Satellite /SensorCloudSat/CPR、CALIPSO/CALIOP
Observation areaGlobal
Resolution240m(vertical), 1.1km(horizontal)
DataCloud mask (Radar only, Lidar only, Radar and Lidar, Radar or Lidar)

Lidar Cloud Particle Type Product

File typeNetCDF4
Latest version1.0
Satellite /SensorCALIPSO/CALIOP
Observation areaGlobal
Resolution240m(vertical), 1.1km(horizontal)
DataCloud particle type (clear, warm water, super-cooled water, 3D-ice, 2D-plate, mixture of 3D-ice and 2D-plate, unknown1, unknown2) for lidar

Radar/Lidar Cloud Microphysics Property Product

File typeNetCDF4
Latest version1.0
Satellite /SensorCloudSat/CPR、CALIPSO/CALIOP
Observation areaGlobal
Resolution240m(vertical), 1.1km(horizontal)
Data Cloud microphysics (effective radius, ice water content, number concentration of ice, optical thickness of ice clouds, mass mixing ratio of 2D-plate to the total ice) of masked regions by radar and lidar

Lidar Aerosol Mask Product

File typeNetCDF4
Latest versionBeta
Satellite /SensorCALIPSO/CALIOP
Observation areaGlobal
Resolution240m(vertical), 1.1km(horizontal)
Data Feature mask (unknown, clear, cloud, aerosol, subsurface, no data), Masked 532nm-total attenuated backscattering coefficient, Masked 532nm-cross-pol attenuated backscattering coefficient, Masked 1064nm-total attenuated backscattering coefficient, depolarization ratio, color ratio (1064/532nm)

Lidar Aerosol Property Product

File typeNetCDF4
Latest versionBeta
Satellite /SensorCALIPSO/CALIOP
Observation areaGlobal
Resolution240m(vertical), 1.1km(horizontal)
DataExtinction coefficient of water soluble, sea salt, dust

Imager Cloud Property Product (day-time, water-cloud only)

File typeNetCDF4
Latest versionBeta
Satellite /SensorAqua/MODIS
Observation areaGlobal
Resolution1x1km(horizontal)
DataCloud mask, cloud phase, cloud optical thickness, cloud effective radius, cloud top temperature, cloud top height
NoteThis product uses the MODIS-AUX radiances corresponding the center of the CloudSat footprint.

3. Data Format

Detailed table of data format is here.

4. Sample code

Sample codes (C, IDL, Python, Fortran) are available to use this dataset.
Please check the readme file in each directory.

5. Reporting requirement

Please specify the following sentence when you publish a thesis, a report, a paper, etc., by using the products and images supplied by this system.

"The XXX product that was used in this paper was supplied by the EarthCARE Research Product Monitor（https://www.eorc.jaxa.jp/EARTHCARE/A-train/A-train_monitor.html）, Japan Aerospace Exploration Agency (JAXA)."

The EarthCARE Research A-train Product Monitor secretariat is collecting related literatures. It would be very appreciated if you could send a reprint or a copy of your research outcome to the secretariat.

JAXA/EORC EarthCARE Research A-train Product Monitor secretariat
Location: Japan Aerospace Exploration Agency (JAXA)
Space Technology Directorate I, Earth Observation Research Center (EORC)
2-1-1 Sengen, Tsukuba, Ibaraki, 305-8505
Fax: +81-29-868-2961
E-mail:

6. Terms of Use of data

Please refer to "Terms of Use of Research Data" (https://earth.jaxa.jp/policy/en.html) for the use of data.

7. References

  • CloudSat-CALIPSO collocation algorithm (merged data set algorithm):

    Hagihara, Y., H. Okamoto, and R. Yoshida (2010): Development of a combined CloudSat-CALIPSO cloud mask to show global cloud distri-bution. Journal of Geophysical Research Atmospheres, 115(D4), D00H33.
    DOI: 10.1029/2009JD012344

  • Cloud mask algorithm (CloudSat/CALIPSO):

    Hagihara, Y., H. Okamoto, and R. Yoshida (2010): Development of a combined CloudSat-CALIPSO cloud mask to show global cloud distri-bution. Journal of Geophysical Research Atmospheres, 115(D4), D00H33.
    DOI: 10.1029/2009JD012344

    Okamoto, H., T. Nishizawa, T. Takemura, K. Sato, H. Kumagai, Y. Ohno, N. Sugimoto, A. Shimizu, I. Matsui, and T. Nakajima (2008): Vertical cloud properties in the tropical western Pacific Ocean: Validation of the CCSR/NIES/FRCGC GCM by shipborne radar and lidar. Journal of Geophysical Research Atmospheres, 113(D24), D24213.
    DOI: 10.1029/2008JD009812

    Okamoto, H., et al. (2007): Vertical cloud structure observed from shipborne radar and lidar: Mid-latitude case study during the MR01/K02 cruise of the R/V Mirai. Journal of Geophysical Research Atmospheres, 112(D8), D08216.
    DOI: 10.1029/2006JD007628

  • Cloud particle type algorithm (CALIPSO):

    Hirakata, M., H. Okamoto, Y. Hagihara, T. Hayasaka, and R. Oki (2014): Comparison of global and seasonal characteristics of cloud phase and horizontal ice plates derived from CALIPSO with MODIS and ECMWF. Journal of Atmospheric and Oceanic Technology, 31(10), pp.2114-2130.
    DOI: 10.1175/JTECH-D-13-00245.1

    Yoshida, R., H. Okamoto, Y. Hagihara, and H. Ishimoto (2010): Global analysis of cloud phase and ice crystal orientation from cloud-aerosol lidar and infrared pathfinder satellite observation (CALIPSO) data using attenuated backscattering and depolarization ratio. Journal of Geophysical Research Atmospheres, 115(D4), D00H32.
    DOI: 10.1029/2009JD012334

  • Cloud microphysics algorithm (CloudSat/CALIPSO):

    Sato, K., and H. Okamoto (2011): Refinement of global ice microphysics using spaceborne active sensors. Journal of Geophysical Research Atmospheres, 116(D20), D20202.
    DOI: 10.1029/2011JD015885

    Okamoto, H., K. Sato, and Y. Hagihara (2010): Global analysis of ice microphysics from CloudSat and CALIPSO: Incorporation of specular reflection in lidar signals. Journal of Geophysical Research Atmospheres, 115(D22), D22209.
    DOI: 10.1029/2009JD013383

    Sato, K., H. Okamoto, M. K. Yamamoto, S. Fukao, H. Kumagai, Y. Ohno, H. Horie, and M. Abo (2009): 95-GHz Doppler radar and lidar synergy for simultaneous ice microphysics and in-cloud vertical air motion retrieval. Journal of Geophysical Research Atmospheres, 114(D3), D03203.
    DOI: 10.1029/2008JD010222

  • Aerosol mask and aerosol properties algorithm (CALIPSO):

    Nishizawa, T., H. Okamoto, T. Takemura, N. Sugimoto, I. Matsui, and A. Shimizu (2008): Aerosol retrieval from two-wavelength backscatter and one-wavelength polarization lidar measurement taken during the MR01K02 cruise of the R/V Mirai and evaluation of a global aerosol transport model. Journal of Geophysical Research Atmospheres, 113(D21), D21201.
    DOI: 10.1029/2007JD009640

    Nishizawa, T., H. Okamoto, N. Sugimoto , I. Matsui, A. Shimizu , K. Aoki (2007): An algorithm that retrieves aerosol properties from dual-wavelength polarized lidar measurements. Journal of Geophysical Research Atmospheres, 112(D6), D06212.
    DOI: 10.1029/2006JD007435

  • Cloud retrieval algorithm (MODIS):
    (Cloud flag)

    Letu, H., T. M. Nagao, T. Y. Nakajima, and Y. Matsumae (2014): Method for validating cloud mask obtained from satellite measurements using ground-based sky camera. Applied optics, 53(31), pp.7523-7533.
    DOI: 10.1364/AO.53.007523

    Ishida, H., T. Y. Nakajima, T. Yokota, N. Kikuchi, and H. Watanabe (2011): Investigation of GOSAT TANSO-CAI cloud screening ability through an inter-satellite comparison. Journal of Applied Meteorology and Climatology, 50(7), pp.1571-1586.
    DOI: 10.1175/2011JAMC2672.1

    Nakajima, T. Y., T. Tsuchiya, H. Ishida, and H. Shimoda (2011): Cloud detection performance of spaceborne visible-to-infrared multispectral imagers. Applied Optics, 50(17), pp.2601-2616.
    DOI: 10.1364/AO.50.002601

    Ishida, H., and T. Y. Nakajima (2009): Development of an unbiased cloud detection algorithm for a spaceborne multispectral imager. Journal of Geophysical Research Atmospheres, 114(D7), D07206.
    DOI: 10.1029/2008JD010710

    (Cloud retrieval)

    Kawamoto, K., T. Nakajima, and T. Y. Nakajima (2001): A global Determination of Cloud Microphysics with AVHRR Remote Sensing. Journal of Climate, 14(9), pp.2054-2068.
    DOI: 10.1175/1520-0442(2001)014<2054:AGDOCM>2.0.CO;2

    Nakajima, T. Y., and T. Nakajima (1995): Wide-Area Determination of Cloud Microphysical Properties from NOAA AVHRR Measurements for FIRE and ASTEX Regions. Journal of the Atmospheric Sciences, 52(23), pp.4043-4059.
    DOI: 10.1175/1520-0469(1995)052<4043:WADOCM>2.0.CO;2

8. Related Papers

  • 2019

    Seiki, T., Kodama, C., Satoh, M., Hagihara, Y., & Okamoto, H. (2019): Characteristics of ice clouds over mountain regions detected by CALIPSO and CloudSat satellite observations. Journal of Geophysical Research Atmospheres, 124(20), pp.10858-10877.
    DOI: 10.1029/2019JD030519

    Kikuchi, M., & Suzuki, K. (2019): Characterizing vertical particle structure of precipitating cloud system from multiplatform measurements of A‐Train constellation. Geophysical Research Letters, 46(2), pp.1040-1048.
    DOI: 10.1029/2018GL081244

  • 2018

    Yamauchi, A, Kawamoto, K, Okamoto, H. (2018): Differences in the fractions of ice clouds between eastern and western parts of Eurasian continent using CALIPSO in January 2007. Atmospheric Science Letters, 19(3), e807.
    DOI: 10.1002/asl.807

  • 2016

    Cesana, G., H. Chepfer, D. Winker, B. Getzewich, X. Cai, O. Jourdan, G. Mioche, H.Okamoto, Y. Hagihara, V. Noel, M. Reverdy (2016): Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO. Journal of Geophysical Research Atmospheres, 121(10), pp.5788-5808.
    DOI: 10.1002/2015JD024334

    Takahashi, N., T. Hayasaka and H. Okamoto (2016): Difference of ice cloud microphysical properties between western and eastern tropical Pacific regions derived from CloudSat and CALIPSO measurements. SOLA, 12, pp.91-95.
    DOI: 10.2151/sola.2016-021

    Hashino, T., Satoh, M., Hagihara, Y., Kato, S., Kubota, T., Matsui, T., Nasuno, T., Okamoto, H., and Sekiguchi, M. (2016): Evaluating Arctic cloud radiative effects simulated by NICAM with A‐train. Journal of Geophysical Research Atmospheres, 121(12), pp.7041-7063.
    DOI: 10.1002/2016JD024775

  • 2015

    Illingworth, A., H. Barker, A. Beljaars, M. Ceccaldi, H. Chepfer, N. Clerbaux, J. Cole, J. Delanoe, C. Domenech, D. Donovan, S. Fukuda, M. Hirakata, R. Hogan, A. Huenerbein, P. Kollias, T. Kubota, T. Nakajima, T. Nakajima, T. Nishizawa, Y. Ohno, H. Okamoto, R. Oki, K. Sato, M. Satoh, M. Shephard, A. Velazquez-Blazquez, U. Wandinger, T. Wehr, and G. van Zadelhoff (2015): The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation. Bulletin of the American Meteorological Society, 96(8), pp.1311-1332.
    DOI: 10.1175/BAMS-D-12-00227.1

    Iwasaki, S., Luo, Z., J., H. Kubota, T. Shibata, H. Okamoto, Ishimoto, H. (2015): Characteristics of cirrus clouds in the tropical lower stratosphere. Atmospheric Research, 164-165, pp.358-368.
    DOI: 10.1016/j.atmosres.2015.06.009

    Seiki, T., C. Kodama, M. Satoh, T. Hashino, Y. Hagihara, H. Okamoto (2015): Vertical grid spacing necessary for simulating tropical cirrus clouds with a high-resolution atmospheric general circulation model. Geophysical Research Letters, 42(10), pp.4150-4157.
    DOI: 10.1002/2015GL064282

    Hideaki KAWAI, Shoukichi YABU, Yuichiro HAGIHARA, Tsuyoshi KOSHIRO, Hajime OKAMOTO (2015): Characteristics of the Cloud Top Heights of Marine Boundary Layer Clouds and the Frequency of Marine Fog over Mid-Latitudes. Journal of the Meteorological Society of Japan. Ser. II, 93(6), pp.613-628.
    DOI: 10.2151/jmsj.2015-045

  • 2014

    Letu, H., T. M. Nagao, T. Y. Nakajima, and Y. Matsumae (2014): Method for validating cloud mask obtained from satellite measurements using ground-based sky camera. Applied Optics, 53(31), pp.7523-7533.
    DOI: 10.1364/AO.53.007523

    Nakajima, T. Y., T. M. Nagao, H. Letu, and H. Okamoto (2014): Synergistic use of spaceborne active sensors and passive multispectral imagers for investigating cloud evolution processes. Trans. JSASS Aerospace Tech. Japan, 12(ists29), pp.Tn_19-Tn_24.
    DOI: 10.2322/tastj.12.Tn_19

    Nagao, T. M., T. Y. Nakajima, H. Letu, and H. Okamoto (2014): Cloud microphysical properties as seen from spaceborne passive multi-spectral imagers: interpretation in terms of vertical and horizontal inhomogeneity by using modeling and other spaceborne instruments. Trans. JSASS Aerospace Tech. Japan, 12(ists29), pp.Tn_1-Tn_6.
    DOI: 10.2322/tastj.12.Tn_1

    Hagihara, Y., H. Okamoto, Z. Luo (2014): Joint analysis of cloud-top heights from CloudSat and CALIPSO: New insights into cloud-top microphysics. Journal of Geophysical Research Atmospheres, 119(7), pp.4087-4106.
    DOI: 10.1002/2013JD020919

    Yoshitaka JIN, Kenji KAI, Hajime OKAMOTO, Yuichiro HAGIHARA (2014): Improvement of CALIOP Cloud Masking Algorithms for Better Estimation of Dust Extinction Profiles. Journal of the Meteorological Society of Japan. Ser. II, 92(5), pp.433-455.
    DOI: 10.2151/jmsj.2014-502

    Ishimoto, H., Okamoto, K., Okamoto, H., and Sato, K. (2014): One‐dimensional variational (1D‐Var) retrieval of middle to upper tropospheric humidity using AIRS radiance data. Journal of Geophysical Research Atmospheres, 119(12), pp.7633-7654.
    DOI: 10.1002/2014JD021706

    Iwabuchi, H., S. Yamada, S. Katagiri, P. Yang, and H. Okamoto (2014): Radiative and Microphysical Properties of Cirrus Cloud Inferred from Infrared Measurements Made by the Moderate Resolution Imaging Spectroradiometer (MODIS). Part I: Retrieval Method. Journal of Applied Meteorology and Climatology, 53(5), pp.1297-1316.
    DOI: 10.1175/JAMC-D-13-0215.1

  • 2013

    Hashino T., M. Satoh, Y. Hagihara, T. Kubota, T. Matsui, T. Nasuno, H. Okamoto (2013): Evaluating cloud microphysics from NICAM against CloudSat and CALIPSO. Journal of Geophysical Research Atmospheres, 118(13), pp.7273-7292.
    DOI: 10.1002/jgrd.50564

  • 2012

    Watanabe, M., Shiogama, H., Yoshimori, M. et al. (2012): Fast and slow timescales in the tropical low-cloud response to increasing CO2 in two climate models. Climate Dynamics, 39(7-8), pp.1627-1641.
    DOI: 10.1007/s00382-011-1178-y

  • 2010

    Iwasaki, S., Shibata, T., Nakamoto, J., Okamoto, H., Ishimoto, H., and Kubota, H. (2010): Characteristics of deep convection measured by using the A‐train constellation. Journal of Geophysical Research Atmospheres, 115(D6), D06207.
    DOI: 10.1029/2009JD013000

9. Links

10. Acknowledgments

　 The data using this system were obtained from the CloudSat data processing center and the NASA Langley Research Center Atmospheric Science Data Center.

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