Apr. 30, 2020 Updated
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 type | NetCDF4 |
|---|---|
| Latest version | 1.0 |
| Satellite /Sensor | CloudSat/CPR |
| Observation area | Global |
| Resolution | 240m(vertical), 1.1km(horizontal) |
| Data | Radar reflectivity factor, gaseous attenuation (cumulative) |
| Original data | CloudSat 2B-GEOPROF R04 |
Lidar Observables
| File type | NetCDF4 |
|---|---|
| Latest version | 1.0 |
| Satellite /Sensor | CALIPSO/CALIOP |
| Observation area | Global |
| Resolution | 240m(vertical), 1.1km(horizontal) |
| Data | 532nm-total attenuated backscattering coefficient, 532nm-cross-pol attenuated backscattering coefficient, 1064nm-total attenuated backscattering coefficient |
| Original data | CALIPSO Lidar L1b V3 |
ECMWF Ancillary Atmospheric State Product
| File type | NetCDF4 |
|---|---|
| Latest version | 1.0 |
| Data coverage | Global |
| Resolution | 240m(vertical), 1.1km(horizontal) |
| Data | Pressure, temperature, water vapor density, skin temperature |
| Original data | CloudSat ECMWF-AUX R04 |
Radar/Lidar Cloud Mask Product
| File type | NetCDF4 |
|---|---|
| Latest version | 1.0 |
| Satellite /Sensor | CloudSat/CPR、CALIPSO/CALIOP |
| Observation area | Global |
| Resolution | 240m(vertical), 1.1km(horizontal) |
| Data | Cloud mask (Radar only, Lidar only, Radar and Lidar, Radar or Lidar) |
Lidar Cloud Particle Type Product
| File type | NetCDF4 |
|---|---|
| Latest version | 1.0 |
| Satellite /Sensor | CALIPSO/CALIOP |
| Observation area | Global |
| Resolution | 240m(vertical), 1.1km(horizontal) |
| Data | Cloud 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 type | NetCDF4 |
|---|---|
| Latest version | 1.0 |
| Satellite /Sensor | CloudSat/CPR、CALIPSO/CALIOP |
| Observation area | Global |
| Resolution | 240m(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 type | NetCDF4 |
|---|---|
| Latest version | Beta |
| Satellite /Sensor | CALIPSO/CALIOP |
| Observation area | Global |
| Resolution | 240m(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 type | NetCDF4 |
|---|---|
| Latest version | Beta |
| Satellite /Sensor | CALIPSO/CALIOP |
| Observation area | Global |
| Resolution | 240m(vertical), 1.1km(horizontal) |
| Data | Extinction coefficient of water soluble, sea salt, dust |
Imager Cloud Property Product (day-time, water-cloud only)
| File type | NetCDF4 |
|---|---|
| Latest version | Beta |
| Satellite /Sensor | Aqua/MODIS |
| Observation area | Global |
| Resolution | 1x1km(horizontal) |
| Data | Cloud mask, cloud phase, cloud optical thickness, cloud effective radius, cloud top temperature, cloud top height |
| Note | This 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/research_product/ecare_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
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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
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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
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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
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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
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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
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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.
(Cloud retrieval)
DOI: 10.1029/2008JD010710
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: 10.1029/2019JD030519Kikuchi, 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: 10.1002/2015JD024334Takahashi, 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.2016-021Hashino, 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-12-00227.1Iwasaki, 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.2015.06.009Seiki, 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: 10.1002/2015GL064282Hideaki 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.53.007523Nakajima, 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_19Nagao, 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_1Hagihara, 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: 10.1002/2013JD020919Yoshitaka 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.2014-502Ishimoto, 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: 10.1002/2014JD021706Iwabuchi, 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
- JAXA/EORC EarthCARE Web (https://www.eorc.jaxa.jp/EARTHCARE/index.html)
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.