Articles | Volume 12, issue 1
https://doi.org/10.5194/amt-12-389-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-389-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A high-level cloud detection method utilizing the GOSAT TANSO-FTS water vapor saturated band
Nawo Eguchi
CORRESPONDING AUTHOR
Research Institute for Applied Mechanics (RIAM), Kyushu University,
Kasuga Park 6-1, Kasuga, Fukuoka, Japan
Yukio Yoshida
Center for Global
Environmental Research (CGER), National Institute for Environmental Studies
(NIES), Onogawa 16-2, Tsukuba, Ibaraki, Japan
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Akihiro Honda, Nawo Eguchi, and Naoko Saitoh
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Global vegetation models are important tools in estimating the impacts of global climate change. The fate of soil carbon is of the upmost importance as its emissions will enhance the atmospheric carbon dioxide concentration. To evaluate the skill of global vegetation models to model the soil carbon and its responses to environmental factors, it is important to use different data sources. We evaluated two different soil carbon models by using atmospheric carbon dioxide concentrations.
Cited articles
Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M.
J., Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative
transfer modeling: a summary of the AER
codes, J. Quant. Spectrosc. Ra., 91, 233–244, 2005. a
Dessler, A. E. and Yang, P.: The Distribution of Tropical Thin Cirrus
Clouds Inferred from Terra MODIS Data, J. Climate, 16,
1241–1247, https://doi.org/10.1175/1520-0442(2003)16<1241:TDOTTC>2.0.CO;2, 2003. a
Eguchi, N. and Kodera, K.: Impacts of Stratospheric Sudden Warming
Event on Tropical Clouds and Moisture Fields in the TTL: A Case Study,
SOLA, 6, 137–140, https://doi.org/10.2151/sola.2010-035, 2010. a, b
Eguchi, N., Yokota, T., and Inoue, G.: Characteristics of cirrus
clouds from ICESat/GLAS observations, Geophys. Res. Lett.,
34, L09810, https://doi.org/10.1029/2007GL029529, 2007. a, b, c
Eguchi, N., Kodera, K., and Nasuno, T.: A global non-hydrostatic model study of
a downward coupling through the tropical tropopause layer during a stratospheric
sudden warming, Atmos. Chem. Phys., 15, 297–304, https://doi.org/10.5194/acp-15-297-2015, 2015. a, b
Gao, B.-C., Goetz, A. F. H., and Wiscombe, W. J.: Cirrus cloud detection
from Airborne Imaging Spectrometer Data using the 1.38 µm water
vapor band, Geophys. Res. Lett., 20, 301–304,
https://doi.org/10.1029/93GL00106, 1993. a, b, c, d
Gao, B.-C., Kaufman, Y. J., Han, W., and Wiscombe, W. J.: Correction of
Thin Cirrus Path Radiance in the 0.4–1.0 µm Spectral Region
Using the Sensitive 1.375-µm Cirrus Detecting Channel, J.
Geophys. Res., 103, 32169–32176, https://doi.org/10.1029/98JD02006, 1998. a
Gao, B.-C., Yang, P., Han, W., Li, R. R., and Wiscombe, W. J.: An
Algorithm Using Visible and 1.38-µm Channels to Retrieve Cirrus
Cloud Reflectances From Aircraft and Satellite Data, IEEE T.
Geosci. Remote Sens., 40, 1659–1668,
https://doi.org/10.1109/TGRS.2002.802454, 2002. a, b, c, d
Gao, B.-C., Montes, M. J., and Davis, C. O.: Refinement of Wavelength
Calibrations of Hyperspectral Imaging Data Using a Spectrum-Matching
Technique, Remote Sens. Environ., 90, 424–433, https://doi.org/10.1016/j.rse.2003.09.002, 2004. a, b
GOSAT Data Archive Service (GDAS): The GOSAT TANSO-FTS Level 1B data,
https://data2.gosat.nies.go.jp/index_en.html, GOSAT Level 1 Product Description
Document TANSO-FTS Section, P.93 Japan Aerospace Exprolation Agency, MAS130014,
https://data2.gosat.nies.go.jp/doc/document.html#Document,
last access: 19 August 2017. a
Guerlet, S., Butz, A., Schepers, D., Basu, S., Hasekamp, O. P., Kuze, A.,
Yokota, T., Blavier, J.-F., Deutscher, N. M., Griffith, D. W. T., Hase, F.,
Kyro, E., Morino, I., Sherlock, V., Sussmann, R., Galli, A., and Aben, I.:
Impact of aerosol and thin cirrus on retrieving and validating XCO2
from GOSAT shortwave infrared measurements, J. Geophys. Res., 118,
4887–4905, https://doi.org/10.1002/jgrd.50332, 2013. a
Hutchison, K. D., Iisager, B. D., and Hauss, B.: The use of global
synthetic data for pre-launch tuning of the VIIRS Cloud Mask
algorithm, Int. J. Remote Sens., 33, 1400–1423, https://doi.org/10.1080/01431161.2011.571299, 2012. a
Holz, R. E., Platnick, S.,
Holz, R. E., Platnick, S., Meyer, K., Vaughan, M., Heidinger, A., Yang, P.,
Wind, G., Dutcher, S., Ackerman, S., Amarasinghe, N., Nagle, F., and Wang,
C.: Resolving ice cloud optical thickness biases between CALIOP and MODIS
using infrared retrievals, Atmos. Chem. Phys., 16, 5075–5090,
https://doi.org/10.5194/acp-16-5075-2016, 2016. a
Ishida, H., Nakajima, T. Y., Yokota, T., Kikuchi, N., and Watanabe, H.:
Investigation of GOSAT TANSO-CAI Cloud Screening Ability through an
Intersatellite Comparison, J. Appl. Meteorol. Clim., 50, 1571–1586,
https://doi.org/10.1175/2011JAMC2672.1, 2011. a
Kodera, K., Funatsu, B. M., Claud, C., and Eguchi, N.: The role of convective
overshooting clouds in tropical stratosphere-troposphere dynamical coupling,
Atmos. Chem. Phys., 15, 6767–6774, https://doi.org/10.5194/acp-15-6767-2015,
2015. a
Kuze, A., Suto, H., Nakajima, M., and Hamazaki, T.: Thermal and near infrared
sensor for carbon observation Fourier-transform spectrometer on the
Greenhouse Gases Observing Satellite for greenhouse gases monitoring, Appl.
Optics, 48, 6716–6733, https://doi.org/10.1364/AO.48.006716, 2009. a
Kuze, A., Suto, H., Shiomi, K., Urabe, T., Nakajima, M., Yoshida, J.,
Kawashima, T., Yamamoto, Y., Kataoka, F., and Buijs, H.: Level 1 algorithms
for TANSO on GOSAT: processing and on-orbit calibrations, Atmos. Meas. Tech.,
5, 2447–2467, https://doi.org/10.5194/amt-5-2447-2012, 2012. a
Kuze, A., Suto, H., Shiomi, K., Kawakami, S., Tanaka, M., Ueda, Y., Deguchi,
A., Yoshida, J., Yamamoto, Y., Kataoka, F., Taylor, T. E., and Buijs, H. L.:
Update on GOSAT TANSO-FTS performance, operations, and data products after
more than 6 years in space, Atmos. Meas. Tech., 9, 2445–2461,
https://doi.org/10.5194/amt-9-2445-2016, 2016. a
MacQueen, B. J.: On the Asymptotic Behavior of k-means, Defense
Technical Information Center, 1965. a
McGill, J. B., Etienne, R. S., Gray, J. S., Alonso, D., Anderson, M. J.,
Benecha, H. K., Dornelas, M., Enquist, B. J., Green, J. L., He, F., Hurlbert,
A. H., Magurran, A. E., Marquet, P. A., Maurer, B. A, Ostling, A., Soykan, C.
Y., Ugland, K. I., and Whit, E. P.: Species abundance distributions: moving
beyond single prediction theories to integration within an ecological
framework, Ecol. Lett., 10, 995–1015, https://doi.org/10.1111/j.1461-0248.2007.01094.x,
2007. a
Nakajima, T. and Tanaka, M.: Matrix formulations for the radiative transfer
of solar radiation in a plane-parallel scattering atmosphere, J. Quant.
Spectrosc. Ra., 35, 13–21, 1986. a
NASA Langley Research Center Atmospheric
Science Data Center: The CALIPSO level 2 5 km cloud layer product,
https://eosweb.larc.nasa.gov/project/calipso/cal_lid_l2_05kmclay-standard-v4-10,
last access: 19 February 2018. a
Nazaryan, H., McCormick, M. P., and Menze, M. P.: Global characterization of
cirrus clouds using CALIPSO data, J. Geophys. Res., 113, D16211,
https://doi.org/10.1029/2007JD009481, 2008. a
Sassen, K., Wang, Z., and Liu, D.: 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, 2008. a
Someya, Y., Imasu, R., Saitoh, N., Ota, Y., and Shiomi, K.: A development of
cloud top height retrieval using thermal infrared spectra observed with GOSAT
and comparison with CALIPSO data, Atmos. Meas. Tech., 9, 1981–1992,
https://doi.org/10.5194/amt-9-1981-2016, 2016. a
Vaughan, M., Pitts, M., Trepte, C., Winker, D., Detweiler, P., Garnier, A.,
Getzewich, B., Hunt, W., Lambeth, J., Lee, K.-P., Lucker, P., Murray, T.,
Rodier, S., Tremas, T., Bazureau, A., and Pelon, J.:
Cloud-Aerosol LIDAR Infrared Pathfinder Satellite Observations (CALIPSO)
data management system data products catalog, Release 4.10, NASA Langley
Research Center Document PC-SCI-503, 185 pp., available at:
https://www-calipso.larc.nasa.gov/products/CALIPSO_DPC_Rev4x10.pdf,
last access: 31 August 2018. a
Winker, D. M., Hunt, W. H., and McGill, M. J.: Initial performance assessment
of CALIOP, Geophys. Res. Lett., 34, L19803, https://doi.org/10.1029/2007GL030135, 2007. a
Winker, D. M., Pelon, J., Coakley Jr., J. A., Ackerman, S. A., Charlson, R.
J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T.
L., Le Treut, H., Mccormick, M. P., Mégie, G., Poole, L., Powell, K.,
Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO Mission : A
Global 3D View of Aerosols and Clouds, Bulletin of the American
Meteorological Society, 91, 1211–1229, https://doi.org/10.1175/2010BAMS3009.1, 2010. a
Yoshida, Y., Ota, Y., Eguchi, N., Kikuchi, N., Nobuta, K., Tran, H., Morino,
I., and Yokota, T.: Retrieval algorithm for CO2 and CH4
column abundances from short-wavelength infrared spectral observations by the
Greenhouse gases observing satellite, Atmos. Meas. Tech., 4, 717–734,
https://doi.org/10.5194/amt-4-717-2011, 2011. a
Short summary
A detection method for high-level cloud, such as ice clouds, is developed using the water vapor saturated channels (2 μm) of the solar reflected spectrum observed by the TANSO-FTS on board GOSAT. The clouds detected by this method are optically relatively thin (0.01 or less) and located at high altitudes. Approximately 85 % of the results from this method for clouds with a cloud-top altitude above 5 km agree with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud classification.
A detection method for high-level cloud, such as ice clouds, is developed using the water vapor...