Assimilating Cloud Optical Depth for Applications to Extreme Weather Events

Juxiang Peng aHubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan, Hubei, China

Search for other papers by Juxiang Peng in
Current site
Google Scholar
PubMed
Close
,
Yuanfu Xie bGuangdong-Hong Kong-Macao Greater Bay Area Weather Research Center for Monitoring Warning and Forecasting (Shenzhen Institute of Meteorological Innovation), Shenzhen, China

Search for other papers by Yuanfu Xie in
Current site
Google Scholar
PubMed
Close
, and
Zhaoping Kang aHubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan, Hubei, China

Search for other papers by Zhaoping Kang in
Current site
Google Scholar
PubMed
Close
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

This paper reports the assimilation of cloud optical depth datasets into a variational data assimilation system to improve cloud ice, cloud water, rain, snow, and graupel analysis in extreme weather events for improving forecasts. A cloud optical depth forward operator was developed and implemented in the Space and Time Multiscale Analysis System (STMAS), a multiscale three-dimensional variational analysis system. Using this improved analysis system, the NOAA GOES-15 Daytime Cloud Optical and Microphysical Properties (DCOMP) cloud optical depth products were assimilated to improve the microphysical states. For an 8-day period of extreme weather events in September 2013 in Colorado, the United States, the impact of the cloud optical depth assimilation on the analysis results and forecasts was evaluated. The DCOMP products improved the cloud ice and cloud water predictions significantly in convective and lower levels. The DCOMP products also reduced errors in temperature and relative humidity data at the top (250–150 hPa) and bottom (850–700 hPa) layers. With the cloud ice improvement at higher layers, the DCOMP products provided better forecasts of cloud liquid at low layers (900–700 hPa), temperature and wind at all layers, and relative humidity at middle and bottom layers. Furthermore, for this extreme weather event, both equitable threat score (ETS) and bias were improved throughout the 12-h period, with the most significant improvement observed in the first 3 h. This study will raise the expectation of cloud optical depth product assimilation in operational applications.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yuanfu Xie, yuanfu_xie@yahoo.com

Abstract

This paper reports the assimilation of cloud optical depth datasets into a variational data assimilation system to improve cloud ice, cloud water, rain, snow, and graupel analysis in extreme weather events for improving forecasts. A cloud optical depth forward operator was developed and implemented in the Space and Time Multiscale Analysis System (STMAS), a multiscale three-dimensional variational analysis system. Using this improved analysis system, the NOAA GOES-15 Daytime Cloud Optical and Microphysical Properties (DCOMP) cloud optical depth products were assimilated to improve the microphysical states. For an 8-day period of extreme weather events in September 2013 in Colorado, the United States, the impact of the cloud optical depth assimilation on the analysis results and forecasts was evaluated. The DCOMP products improved the cloud ice and cloud water predictions significantly in convective and lower levels. The DCOMP products also reduced errors in temperature and relative humidity data at the top (250–150 hPa) and bottom (850–700 hPa) layers. With the cloud ice improvement at higher layers, the DCOMP products provided better forecasts of cloud liquid at low layers (900–700 hPa), temperature and wind at all layers, and relative humidity at middle and bottom layers. Furthermore, for this extreme weather event, both equitable threat score (ETS) and bias were improved throughout the 12-h period, with the most significant improvement observed in the first 3 h. This study will raise the expectation of cloud optical depth product assimilation in operational applications.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yuanfu Xie, yuanfu_xie@yahoo.com
Save
  • Albers, S. C., J. A. McGinley, D. L. Birkenheuer, and J. R. Smart, 1996: The Local Analysis and Prediction System (LAPS): Analyses of clouds, precipitation, and temperature. Wea. Forecasting, 11, 273287, https://doi.org/10.1175/1520-0434(1996)011<0273:TLAAPS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Auligne, T., A. Lorenc, Y. Michel, T. Montmerle, A. Jones, M. Hu, and J. Dudhia, 2011: Toward a new cloud analysis and prediction system. Bull. Amer. Meteor. Soc., 92, 207210, https://doi.org/10.1175/2010BAMS2978.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauer, P., A. J. Geer, P. Lopez, and D. Salmond, 2010: Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation. Quart. J. Roy. Meteor. Soc., 136, 18681885, https://doi.org/10.1002/qj.659.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benedetti, A., and M. Janisková, 2008: Assimilation of MODIS cloud optical depths in the ECMWF model. Mon. Wea. Rev., 136, 17271746, https://doi.org/10.1175/2007MWR2240.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and Coauthors, 2004: An hourly assimilation forecast cycle: The RUC. Mon. Wea. Rev., 132, 495518, https://doi.org/10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Briggs, W. L., V. E. Henson, and S. F. McCormick, 2000: A Multigrid Tutorial. 2nd ed. Society for Industrial and Applied Mathematics, xii + 187 pp., Https://Doi.Org/10.1137/1.9780898719505.

    • Crossref
    • Export Citation
  • Chen, Y., H. Wang, and J. Z. Min, 2015: Variational assimilation of cloud liquid/ice water path and its impact on NWP. J. Appl. Meteor. Climatol., 54, 18091825, https://doi.org/10.1175/JAMC-D-14-0243.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 22872299, https://doi.org/10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Errico, R. M., P. Bauer, and J.-F. Mahfouf, 2007: Issues regarding the assimilation of cloud and precipitation data. J. Atmos. Sci., 64, 37853798, https://doi.org/10.1175/2006JAS2044.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, Y., 2019: Satellite-borne active and passive instruments for remote sensing of heavy rain in China: A review (in Chinese). Torrential Rain Disasters, 38, 554563, https:doi.org/10.3969/j.issn.1004-9045.2019.05.0016.

    • Search Google Scholar
    • Export Citation
  • Geer, A. J., and P. Bauer, 2011: Observation errors in all-sky data assimilation. Quart. J. Roy. Meteor. Soc., 137, 20242037, https://doi.org/10.1002/qj.830.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geer, A. J., P. Bauer, and P. Lopez, 2010: Direct 4D-Var assimilation of all-sky radiances. Part II: Assessment. Quart. J. Roy. Meteor. Soc., 136, 18861905, https://doi.org/10.1002/qj.681.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gochis, D., and Coauthors, 2015: The Great Colorado Flood of September 2013. Bull. Amer. Meteor. Soc., 96, 14611487, https://doi.org/10.1175/BAMS-D-13-00241.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guidard, V., N. Fourrie, P. Brousseau, and F. Rabier, 2011: Impact of IASI assimilation at global and convective scales and challenges for the assimilation of cloudy scenes. Quart. J. Roy. Meteor. Soc., 137, 19751987, https://doi.org/10.1002/qj.928.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 2014: Cloud Dynamics. Academic Press, 96 pp.

  • Hu, M., M. Xue, and K. Brewster, 2006a: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675698, https://doi.org/10.1175/MWR3092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, M., M. Xue, J. Gao, and K. Brewster, 2006b: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev., 134, 699721, https://doi.org/10.1175/MWR3093.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H. L., and Coauthors, 2015: Real-time applications of the Variational Version of the Local Analysis and Prediction System (vLAPS). Bull. Amer. Meteor. Soc., 96, 20452057, https://doi.org/10.1175/BAMS-D-13-00185.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, T. A., P. Minnis, and R. Palikonda, 2013: Evaluation of a forward operator to assimilate cloud water path into WRF-DART. Mon. Wea. Rev., 141, 22722289, https://doi.org/10.1175/MWR-D-12-00238.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kostka, P. M., M. Weissmann, R. Buras, B. Mayer, and O. Stiller, 2014: Observation operator for visible and near-infrared satellite reflectances. J. Atmos. Oceanic Technol., 31, 12161233, https://doi.org/10.1175/JTECH-D-13-00116.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McNally, A. P., 2009: The direct assimilation of cloud affected satellite infrared radiances in the ECMWF 4D-Var. Quart. J. Roy. Meteor. Soc., 135, 12141229, https://doi.org/10.1002/qj.426.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E., 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated–k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Okamoto, K., 2013: Assimilation of overcast cloudy infrared radiances of the geostationary MTSAT-1R imager. Quart. J. Roy. Meteor. Soc., 139, 715730, https://doi.org/10.1002/qj.1994.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Veen, V. D., 2013: Improving NWP model cloud forecasts using Meteosat Second-Generation imagery. Mon. Wea. Rev., 141, 15451557, https://doi.org/10.1175/MWR-D-12-00021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vukicevic, T., T. Greenwald, M. Zupanski, D. Zupanski, T. Vonder Haar, and A. S. Jones, 2004: Mesoscale cloud state estimation from visible and infrared satellite radiances. Mon. Wea. Rev., 132, 30663077, https://doi.org/10.1175/MWR2837.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walther, A., and A. K. Heidinger, 2012: Implementation of the daytime cloud optical and microphysical properties algorithm (DCOMP) in PATMOS-x. J. Appl. Meteor. Climatol., 51, 13711390, https://doi.org/10.1175/JAMC-D-11-0108.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, Y., S. Koch, J. McGinley, S. Albers, P. E. Bieringer, M. Wolfson, and M. Chan, 2011: A space–time multiscale analysis system: A sequential variational analysis approach. Mon. Wea. Rev., 139, 12241240, https://doi.org/10.1175/2010MWR3338.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yucel, I., W. James Shuttleworth, X. Gao, and S. Sorooshian, 2003: Short-term performance of MM5 with cloud-cover assimilation from satellite observations. Mon. Wea. Rev., 131, 17971810, https://doi.org/10.1175//2565.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., and E. Liu, 2016: All-sky microwave radiance assimilation in NCEP’s GSI analysis system. Mon. Wea. Rev., 144, 47094735, https://doi.org/10.1175/MWR-D-15-0445.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 527 0 0
Full Text Views 1240 897 368
PDF Downloads 433 146 0