Impacts of assimilating all or GOES-like AHI infrared channels radiances on QPFs over Eastern China

References

• Dragosavac, M. 2007. BUFR User’s Guide. ECMWF Technical note. Online at: http://www.wmo.int/pages/prog/gcos/documents/gruanmanuals/ECMWF/bufr_user_guide.pdf (last accessed 8 August 2014).
   Google Scholar
• Gravelle, C. M., Mecikalski, J. R., Line, W. E., Bedka, K. M., Petersen, R. A. and co-authors. 2016. Demonstration of a GOES-R satellite convective toolkit to ‘bridge the gap’ between severe weather watches and warnings: an example from the 20 May 2013 Moore, Oklahoma, tornado outbreak. Bull. Amer. Meteor. Soc. 97, 69–84.10.1175/BAMS-D-14-00054.1
   Web of Science ®Google Scholar
• Guedj, S., Karbou, F. and Rabier, F. 2011. Land surface temperature estimation to improve the assimilation of SEVIRI radiances over land. J. Geophys. Res. 116, D14107.10.1029/2011JD015776
   Web of Science ®Google Scholar
• Han, Y., Weng, F., Liu, Q. and van Delst, P. 2007. A fast radiative transfer model for SSMIS upper atmosphere sounding channels. J. Geophys. Res. 112, D11121. DOI:10.1029/2006JD008208.
   Web of Science ®Google Scholar
• Heidinger, A. 2011. ABI cloud mask. NOAA NESDIS STAR Algorithm Theoretical Basis Doc., 93 pp. Online at: http://www.goes-r.gov/products/ATBDs/baseline/Cloud_CldMask_v2.0_no_color.pdf
   Google Scholar
• Heidinger, A. K., Evan, A. T., Foster, M. J. and Walther, A. 2012. A Naive Bayesian cloud-detection scheme derived from CALIPSO and applied within PATMOS-x. J. Appl. Meteor. Climatol. 51, 1129–1144.10.1175/JAMC-D-11-02.1
   Web of Science ®Google Scholar
• Hong, S.-Y. and Dudhia, J. 2003. Testing of a new non-local boundary layer vertical diffusion scheme in numerical weather prediction applications. In: 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, Seattle, WA.
   Google Scholar
• Hong, S.-Y. and Lim, J.-O. J. 2006. The WRF singlemoment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc. 42, 129–151.
   Google Scholar
• Kain, J. S. 2004. The Kain–Fritsch convective parameterization: an update. J. Appl. Meteor. 43, 170–181.10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2
   Google Scholar
• Kain, J. S. and Fritsch, J. M. 1990. A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci. 47, 2784–2802.10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2
   Web of Science ®Google Scholar
• Kain, J. S. and Fritsch, J. M. 1993. Convective parameterization for mesoscale models: the Kain-Fritsch scheme. In The Representation of Cumulus Convection in Numerical Models. Meteorological Monographs, No. 24. Boston: American Meteorological Society, pp. 165–170.
   Google Scholar
• Köpken, C., Kelly, G. and Thépaut, J.-N. 2004. Assimilation of Meteosat radiance data within the 4D-Var system at ECMWF: assimilation experiments and forecast impact. Q. J. R. Meteorolo. Soc. 130, 2277–2292.10.1256/qj.02.230
   Web of Science ®Google Scholar
• Mecikalski, J. R. and Bedka, K. M. 2006. Forecasting convective initiation by monitoring the evolution of moving convection in daytime GOES imagery. Mon. Wea. Rev. 134, 49–78.10.1175/MWR3062.1
   Web of Science ®Google Scholar
• Mecikalski, J. R., Williams, J. K., Jewett, C. P., Ahijevych, D., LeRoy, A. and co-authors. 2015. Probabilistic 0–1-h convective initiation nowcasts that combine geostationary satellite observations and numerical weather prediction model data. J. Appl. Meteor. Climatol. 54, 1039–1059.10.1175/JAMC-D-14-0129.1
   Web of Science ®Google Scholar
• Okamoto, K. 2013. Assimilation of overcast cloudy infrared radiances of the geostationary MTSAT-1R imager. Q. J. R. Meteorol. Soc. 139, 715–730.10.1002/qj.v139.672
   Web of Science ®Google Scholar
• Purser, R. J., Wu, W.-S., Parrish, D. F. and Roberts, N. M. 2003a. Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev. 131, 1524–1535.10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2
   Web of Science ®Google Scholar
• Purser, R. James, Wu, W.-S., Parrish, D. F. and Roberts, N. M. 2003b. Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev. 131, 1536–1548.10.1175//2543.1
   Web of Science ®Google Scholar
• Qin, Z., Zou, X. and Weng, F. 2013. Evaluating added benefits of assimilating GOES Imager radiance data in GSI for coastal QPFs. Mon. Wea. Rev. 141, 75–92.10.1175/MWR-D-12-00079.1
   Web of Science ®Google Scholar
• Shao, H., Derber, J., Huang, X.-Y., Hu, M., Newman, K. and co-authors. 2016. Bridging research to operations transitions: status and plans of community GSI. Bull. Amer. Meteor. Soc. 97, 1427–1440. DOI:10.1175/BAMS-D-13-00245.1.
   Web of Science ®Google Scholar
• Shen, Y., Zhao, P., Pan, Y. and Yu, J. 2014. A high spatiotemporal gauge-satellite merged precipitation analysis over China. J. Geophys. Res. Atmos. 119, 3063–3075. DOI:10.1002/2013JD020686.
   Web of Science ®Google Scholar
• Stengel, M., Undén, P., Lindskog, M., Dahlgren, P., Gustafsson, N. and co-authors. 2009. Assimilation of SEVIRI infrared radiances with HIRLAM 4D-Var. Q. J. R. Meteorol. Soc. 135, 2100–2109.10.1002/(ISSN)1477-870X
   Web of Science ®Google Scholar
• Szyndel, M. D. E., Thépaut, J.-N. and Kelly, G. 2005. Evaluation of potential benefit of SEVIRI water vapour radiance data from Meteosat-8 into global numerical weather prediction analyses. Atmos. Sci. Lett. 6, 105–111.10.1002/(ISSN)1530-261X
   Web of Science ®Google Scholar
• Velden, C. S. 1996. Winds derived from geostationary satellite moisture channel observations: applications and impact on numerical weather prediction. Meteor. Atmos. Phys. 60, 37–46.10.1007/BF01029784
   Web of Science ®Google Scholar
• Velden, C. S., Hayden, C. M., Nieman, S. J., Menzel, W. P., Wanzong, S. and Goerss, J. S. 1997. Upper-tropospheric winds derived from geostationary satellite water vapor observations. Bull. Amer. Meteor. Soc. 78, 173–195.10.1175/1520-0477(1997)078<0173:UTWDFG>2.0.CO;2
   Web of Science ®Google Scholar
• Velden, C. S., Olander, T. L. and Wanzong, S. 1998. The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part I: dataset methodology, description, and case analysis. Mon. Wea. Rev. 126, 1202–1218.10.1175/1520-0493(1998)126<1202:TIOMGW>2.0.CO;2
   Web of Science ®Google Scholar
• Weng, F. 2007. Advances in radiative transfer modeling in support of satellite data assimilation. J. Atmos. Sci. 64, 3799–3807.10.1175/2007JAS2112.1
   Web of Science ®Google Scholar
• Wu, Wan-Shu, James Purser, R. and Parrish, David F. 2002. Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev. 130, 2905–2916.10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2
   Web of Science ®Google Scholar
• Zhuge, X. and Zou, X. 2016. Test of a modified infrared only ABI cloud mask algorithm for AHI radiance observations. J. App. Meteor. Climatol. 55, 2529–2546. DOI:10.1175/JAMC-D-16-0254.1.
   Google Scholar
• Zou, X., Qin, Z. and Weng, F. 2011. Improved coastal precipitation forecasts with direct assimilation of GOES-11/12 imager radiances. Mon. Wea. Rev. 139, 3711–3729.10.1175/MWR-D-10-05040.1
   Web of Science ®Google Scholar
• Zou, X., Qin, Z. and Zheng, Y. 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.
   Web of Science ®Google Scholar
• Zou, X., Zhuge, X. and Weng, F. 2016. Characterization of bias of Advanced Himawari Imager infrared observations from NWP background simulations using CRTM and RTTOV. J. Ocean Atmos. Tech. 33, 2553–2567. DOI:10.1175/JTECH-D-16-0105.1.
   Google Scholar