Editorial Type:
Article
Article Type:
Research Article

Test of a Modified Infrared-Only ABI Cloud Mask Algorithm for AHI Radiance Observations

X. Zhuge School of Atmospheric Sciences and Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, Nanjing, China, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

Search for other papers by X. Zhuge in
Current site
Google Scholar
PubMed Close
and
X. Zou Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

Search for other papers by X. Zou in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Nov 2016
DOI:
https://doi.org/10.1175/JAMC-D-16-0254.1
Page(s):
2529–2546
Restricted access

Abstract

Assimilation of infrared channel radiances from geostationary imagers requires an algorithm that can separate cloudy radiances from clear-sky ones. An infrared-only cloud mask (CM) algorithm has been developed using the Advanced Himawari Imager (AHI) radiance observations. It consists of a new CM test for optically thin clouds, two modified Advanced Baseline Imager (ABI) CM tests, and seven other ABI CM tests. These 10 CM tests are used to generate composite CMs for AHI data, which are validated by using the Moderate Resolution Imaging Spectroradiometer (MODIS) CMs. It is shown that the probability of correct typing (PCT) of the new CM algorithm over ocean and over land is 89.73% and 90.30%, respectively and that the corresponding leakage rates (LR) are 6.11% and 4.21%, respectively. The new infrared-only CM algorithm achieves a higher PCT and a lower false-alarm rate (FAR) over ocean than does the Clouds from the Advanced Very High Resolution Radiometer (AVHRR) Extended System (CLAVR-x), which uses not only the infrared channels but also visible and near-infrared channels. A slightly higher FAR of 7.92% and LR of 6.18% occurred over land during daytime. This result requires further investigation.

Denotes Open Access content.

Corresponding author address: Dr. X. Zou, Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, MD 20740-3823. E-mail: xzou1@umd.edu

Abstract

Assimilation of infrared channel radiances from geostationary imagers requires an algorithm that can separate cloudy radiances from clear-sky ones. An infrared-only cloud mask (CM) algorithm has been developed using the Advanced Himawari Imager (AHI) radiance observations. It consists of a new CM test for optically thin clouds, two modified Advanced Baseline Imager (ABI) CM tests, and seven other ABI CM tests. These 10 CM tests are used to generate composite CMs for AHI data, which are validated by using the Moderate Resolution Imaging Spectroradiometer (MODIS) CMs. It is shown that the probability of correct typing (PCT) of the new CM algorithm over ocean and over land is 89.73% and 90.30%, respectively and that the corresponding leakage rates (LR) are 6.11% and 4.21%, respectively. The new infrared-only CM algorithm achieves a higher PCT and a lower false-alarm rate (FAR) over ocean than does the Clouds from the Advanced Very High Resolution Radiometer (AVHRR) Extended System (CLAVR-x), which uses not only the infrared channels but also visible and near-infrared channels. A slightly higher FAR of 7.92% and LR of 6.18% occurred over land during daytime. This result requires further investigation.

Denotes Open Access content.

Corresponding author address: Dr. X. Zou, Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, MD 20740-3823. E-mail: xzou1@umd.edu
Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Ackerman, S., and Coauthors, 2006: Discriminating clear-sky from cloud with MODIS algorithm theoretical basis document (MOD35). Cooperative Institute for Meteorological Satellite Studies Tech. Rep., 125 pp. [Available online at http://modis-atmos.gsfc.nasa.gov/_docs/MOD35:MYD35_ATBD_C005.pdf.]

  • Bessho, K., and Coauthors, 2016: An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. J. Meteor. Soc. Japan, 94, 151–183, doi:10.2151/jmsj.2016-009.

    • Search Google Scholar
    • Export Citation

  • Heidinger, A., and W. C. Straka III, 2013: Algorithm theoretical basis document: ABI cloud mask. NOAA/NESDIS Center for Satellite Applications and Research Tech. Rep., 93 pp. [Available online at http://www.star.nesdis.noaa.gov/goesr/docs/ATBD/Cloud_Mask.pdf.]

  • Heidinger, A., A. T. Evan, M. J. Foster, and A. Walther, 2012: A naive Bayesian cloud-detection scheme derived from CALIPSO and applied within PATMOS-x. J. Appl. Meteor. Climatol., 51, 1129–1144, doi:10.1175/JAMC-D-11-02.1.

    • Search Google Scholar
    • Export Citation

  • Hocking, J., P. Francis, and R. Saunders, 2010: Cloud detection in Meteosat second generation imagery at the Met Office. Met Office Tech. Rep. 540, 40 pp. [Available online at http://research.metoffice.gov.uk/research/nwp/publications/papers/technical_reports/reports/540.pdf.]

  • Kelly, G. A., M. Tomassini, and M. Matricardi, 1996: Meteosat cloud-cleared radiances for use in three/four-dimensional variational data assimilation. Proc. Third Int. Winds Workshop, Ascona, Switzerland, International Winds Working Group, 105–116. [Available online at http://cimss.ssec.wisc.edu/iwwg/iww3/p105-116_Kelly-Meteosat.pdf.]

  • Köpken, C., G. Kelly, and J.-N. Thépaut, 2004: Assimilation of Meteosat radiance data within the 4D-Var system at ECMWF: Assimilation experiments and forecast impact. Quart. J. Roy. Meteor. Soc., 130, 2277–2292, doi:10.1256/qj.02.230.

    • Search Google Scholar
    • Export Citation

  • Menzel, W. P., and J. F. W. Purdom, 1994: Introducing GOES-I: The first of a new generation of geostationary operational environmental satellites. Bull. Amer. Meteor. Soc., 75, 757–781, doi:10.1175/1520-0477(1994)075<0757:IGITFO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Miyamura, K., 2007: MTSAT-2 systems (in Japanese). Meteorological Satellite Center Tech. Note 49, 55–58. [Available online at http://www.data.jma.go.jp/mscweb/technotes/msctechrep49-4.pdf.]

  • Munro, R., C. Köpken, G. Kelly, J.-N. Thépaut, and R. Saunders, 2004: Assimilation of Meteosat radiance data within the 4D-Var system at ECMWF: Data quality monitoring, bias correction and single-cycle experiments. Quart. J. Roy. Meteor. Soc., 130, 2293–2313, doi:10.1256/qj.02.229.

    • Search Google Scholar
    • Export Citation

  • Ojha, S. P., R. Singh, and M. V. Shukla, 2016: Algorithm for generating cloud-mask from multi-channel satellite data. Quart. J. Roy. Meteor. Soc., 142, 1351–1358, doi:10.1002/qj.2738.

    • Search Google Scholar
    • Export Citation

  • Saunders, R., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiance observations. Quart. J. Roy. Meteor. Soc., 125, 1407–1425, doi:10.1002/qj.1999.49712555615.

    • Search Google Scholar
    • Export Citation

  • Schmetz, J., P. Pili, S. Tjemkes, D. Just, J. Kerkmann, S. Rota, and A. Ratier, 2002: An introduction to Meteosat Second Generation (MSG). Bull. Amer. Meteor. Soc., 83, 977–992, doi:10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Schmit, T. J., E. M. Prins, A. J. Schreiner, and J. J. Gurka, 2001: Introducing the GOES-M imager. Natl. Wea. Dig., 25 (3–4), 28–37. [Available online at http://nwafiles.nwas.org/digest/papers/2001/Vol25No34/Pg28-Schmit.pdf.]

    • Search Google Scholar
    • Export Citation

  • Sherlock, V., 1999: ISEM-6: Infrared surface emissivity model for RTTOV-6. Met Office NWP SAF Tech. Rep. 299, 17 pp. [Available online at https://library.metoffice.gov.uk/M10326UK/OPAC/Details/Record.aspx?BibCode=55344264.]

  • Singh, R., G. Kelly, R. Brugge, and A. O’Neill, 2011: A comparison of the performance of Kalpana and Meteosat-7 WV radiances in the ECMWF NWP model. Quart. J. Roy. Meteor. Soc., 137, 1573–1580, doi:10.1002/qj.848.

    • Search Google Scholar
    • Export Citation

  • Singh, R., S. P. Ojha, C. M. Kishtawal, P. K. Pal, and A. S. K. Kumar, 2016: Impact of the assimilation of INSAT-3D radiances on short-range weather forecasts. Quart. J. Roy. Meteor. Soc., 142, 120–131, doi:10.1002/qj.2636.

    • Search Google Scholar
    • Export Citation

  • Stowe, L. L., P. A. Davis, and E. P. McClain, 1999: Scientific basis and initial evaluation of the CLAVR-1 global clear/cloud classification algorithm for the Advanced Very High Resolution Radiometer. J. Atmos. Oceanic Technol., 16, 656–681, doi:10.1175/1520-0426(1999)016<0656:SBAIEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Szyndel, M. D. E., G. Kelly, and J. N. Thépaut, 2005: Evaluation of potential benefit of assimilation of SEVIRI water vapour radiance data from Meteosat-8 into global numerical weather prediction analyses. Atmos. Sci. Lett., 6, 105–111, doi:10.1002/asl.98.

    • Search Google Scholar
    • Export Citation

  • Zhang, W., J. Xu, C. Dong, and J. Yang, 2006: China’s current and future meteorological satellite systems. Science and Instruments, J. J. Qu et al., Eds., Vol. 1, Earth Science Satellite Remote Sensing, Springer, 392–413.

  • Zou, X., Z. Qin, and F. Weng, 2011: Improved coastal precipitation forecasts with direct assimilation of GOES-11/12 imager radiances. Mon. Wea. Rev., 139, 3711–3729, doi:10.1175/MWR-D-10-05040.1.

    • Search Google Scholar
    • Export Citation

  • Zou, X., Z. Qin, and Y. Zheng, 2015: Improved tropical storm forecasts with GOES-13/15 imager radiance assimilation and asymmetric vortex initialization in HWRF. Mon. Wea. Rev., 143, 2485–2505, doi:10.1175/MWR-D-14-00223.1.

    • Search Google Scholar
    • Export Citation

  • Zou, X., X. Zhuge, and F. Weng, 2016: Characterization of bias of advanced Himawari imager infrared observations from NWP background simulations using CRTM and RTTOV. J. Atmos. Oceanic Technol., 33, 2553–2567, doi:10.1175/JTECH-D-16-0105.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 578 156 17
PDF Downloads 460 135 20