Design Strategies of an Hourly Update 3DVAR Data Assimilation System for Improved Convective Forecasting

Wenxue Tong School of Atmospheric Sciences, Key Laboratory of Meteorological Disaster Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

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Gang Li Nanjing University of Information Science and Technology, Nanjing, China

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Juanzhen Sun National Center for Atmospheric Research, Boulder, Colorado

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Xiaowen Tang School of Atmospheric Sciences, Nanjing University, Nanjing, China

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Ying Zhang National Center for Atmospheric Research, Boulder, Colorado

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Abstract

This study examines two strategies for improving the analysis of an hourly update three-dimensional variational data assimilation (3DVAR) system and the subsequent quantitative precipitation forecast (QPF). The first strategy is to assimilate synoptic and radar observations in different steps. This strategy aims to extract both large-scale and convective-scale information from observations typically representing different scales. The second strategy is to add a divergence constraint to the momentum variables in the 3DVAR system. This technique aims at improving the dynamic balance and suppressing noise introduced during the assimilation process. A detailed analysis on how the new techniques impact convective-scale QPF was conducted using a severe storm case over Colorado and Kansas during 8 and 9 August 2008. First, it is demonstrated that, without the new strategies, the QPF initialized with an hourly update analysis performs worse than its 3-hourly counterpart. The implementation of the two-step assimilation and divergence constraint in the hourly update system results in improved QPF throughout most of the 12-h forecast period. The diagnoses of the analysis fields show that the two-step assimilation is able to preserve key convective-scale as well as large-scale structures that are consistent with the development of the real weather system. The divergence constraint is effective in improving the balance between the momentum control variables in the analysis, which leads to less spurious convection and improved QPF scores. The improvements of the new techniques were further verified by eight convective cases in 2014 and shown to be statistically significant.

Corresponding author address: Wenxue Tong, School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Ningliu Road 219, Nanjing, Jiangsu 210044, China. E-mail: tongwenxue63@gmail.com

Abstract

This study examines two strategies for improving the analysis of an hourly update three-dimensional variational data assimilation (3DVAR) system and the subsequent quantitative precipitation forecast (QPF). The first strategy is to assimilate synoptic and radar observations in different steps. This strategy aims to extract both large-scale and convective-scale information from observations typically representing different scales. The second strategy is to add a divergence constraint to the momentum variables in the 3DVAR system. This technique aims at improving the dynamic balance and suppressing noise introduced during the assimilation process. A detailed analysis on how the new techniques impact convective-scale QPF was conducted using a severe storm case over Colorado and Kansas during 8 and 9 August 2008. First, it is demonstrated that, without the new strategies, the QPF initialized with an hourly update analysis performs worse than its 3-hourly counterpart. The implementation of the two-step assimilation and divergence constraint in the hourly update system results in improved QPF throughout most of the 12-h forecast period. The diagnoses of the analysis fields show that the two-step assimilation is able to preserve key convective-scale as well as large-scale structures that are consistent with the development of the real weather system. The divergence constraint is effective in improving the balance between the momentum control variables in the analysis, which leads to less spurious convection and improved QPF scores. The improvements of the new techniques were further verified by eight convective cases in 2014 and shown to be statistically significant.

Corresponding author address: Wenxue Tong, School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Ningliu Road 219, Nanjing, Jiangsu 210044, China. E-mail: tongwenxue63@gmail.com
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  • Barker, D. M., Huang W. , Guo Y.-R. , Bourgeois J. , and Xiao Q. N. , 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914, doi:10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.

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

    • Search Google Scholar
    • Export Citation
  • Dong, J., Xue M. , and Droegemeier K. , 2011: The analysis and impact of simulated high-resolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter. Meteor. Atmos. Phys., 112, 4161, doi:10.1007/s00703-011-0130-3.

    • Search Google Scholar
    • Export Citation
  • Ek, M. B., Mitchell K. E. , Lin Y. , Rogers E. , Grunmann P. , Koren V. , Gayno G. , and Tarpley J. D. , 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Fritsch, J. M., and Carbone R. E. , 2004: Improving quantitative precipitation forecasts in the warm season: A USWRP research and development strategy. Bull. Amer. Meteor. Soc., 85, 955965, doi:10.1175/BAMS-85-7-955.

    • Search Google Scholar
    • Export Citation
  • Fritsch, J. M., and Coauthors, 1998: Quantitative precipitation forecasting: Report of the Eighth Prospectus Development Team, U.S. Weather Research Program. Bull. Amer. Meteor. Soc., 79, 285299, doi:10.1175/1520-0477(1998)079<0285:QPFROT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gao, J., and Stensrud D. J. , 2012: Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeor classification. J. Atmos. Sci., 69, 10541065, doi:10.1175/JAS-D-11-0162.1.

    • Search Google Scholar
    • Export Citation
  • Gao, J., Xue M. , Shapiro A. , and Droegemeier K. K. , 1999: A variational method for the analysis of three-dimensional wind fields from two Doppler radars. Mon. Wea. Rev., 127, 21282142, doi:10.1175/1520-0493(1999)127<2128:AVMFTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hu, M., Xue M. , and Brewster K. , 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, doi:10.1175/MWR3092.1.

    • Search Google Scholar
    • Export Citation
  • Hu, M., Xue M. , Gao J. , and Brewster K. , 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, doi:10.1175/MWR3093.1.

    • Search Google Scholar
    • Export Citation
  • Huang, X., and Lynch P. , 1993: Diabatic digital filtering initialization: Application to the HIRLAM model. Mon. Wea. Rev., 121, 589603, doi:10.1175/1520-0493(1993)121<0589:DDFIAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., Delamere J. S. , Mlawer E. J. , Shephard M. W. , Clough S. A. , and Collins W. D. , 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, doi:10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation
  • Janjić, Z., 2002: Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note 437, 61 pp. [Available online at http://www.emc.ncep.noaa.gov/officenotes/newernotes/on437.pdf.]

  • Kain, J. S., and Fritsch J. M. , 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, E. K. A. Emanuel and D. J. Raymond, Ed., Amer. Meteor. Soc., 165–170.

  • Lee, M.-S., Kuo Y.-H. , Barker D. M. , and Lim E. , 2006: Incremental analysis updates initialization technique applied to 10-km MM5 and MM5 3DVAR. Mon. Wea. Rev., 134, 13891404, doi:10.1175/MWR3129.1.

    • Search Google Scholar
    • Export Citation
  • Lei, T., Xue M. , and Yu T.-Y. , 2008: Multi-scale analysis and prediction of the 8 May 2003 Oklahoma City tornadic supercell storm assimilating radar and surface network data using EnKF. Preprints, 13th Conf. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, Phoenix, AZ, Amer. Meteor. Soc., 6.4. [Available online at https://ams.confex.com/ams/89annual/techprogram/paper_150404.htm.]

  • Li, Y., Wang X. , and Xue M. , 2012: Assimilation of radar radial velocity data with the WRF hybrid ensemble–3DVAR system for the prediction of Hurricane Ike (2008). Mon. Wea. Rev., 140, 35073524, doi:10.1175/MWR-D-12-00043.1.

    • Search Google Scholar
    • Export Citation
  • Lilly, D. K., 1990: Numerical prediction of thunderstorms—Has its time come? Quart. J. Roy. Meteor. Soc., 116, 779798, doi:10.1002/qj.49711649402.

    • Search Google Scholar
    • Export Citation
  • Lim, E., and Sun J. , 2010: A velocity dealiasing technique using rapidly updated analysis from a four-dimensional variational Doppler radar data assimilation system. J. Atmos. Oceanic Technol., 27, 11401152, doi:10.1175/2010JTECHA1300.1.

    • Search Google Scholar
    • Export Citation
  • Lynch, P., and Huang X.-Y. , 1992: Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev., 120, 10191034, doi:10.1175/1520-0493(1992)120<1019:IOTHMU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mittermaier, M., and Roberts N. , 2010: Intercomparison of spatial forecast verification methods: Identifying skillful spatial scales using the fractions skill score. Wea. Forecasting, 25, 343354, doi:10.1175/2009WAF2222260.1.

    • Search Google Scholar
    • Export Citation
  • Schenkman, A. D., Xue M. , Shapiro A. , Brewster K. A. , and Gao J. , 2011: Impact of CASA radar and Oklahoma mesonet data assimilation on the analysis and prediction of tornadic mesovortices in an MCS. Mon. Wea. Rev., 139, 34223445, doi:10.1175/MWR-D-10-05051.1.

    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and Coauthors, 2009: Next-day convection-allowing WRF model guidance: A second look at 2-km versus 4-km grid spacing. Mon. Wea. Rev., 137, 33513372, doi:10.1175/2009MWR2924.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Klemp J. B. , 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, doi:10.1016/j.jcp.2007.01.037.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D68S4MVH.

  • Smith, T. L., Benjamin S. G. , Brown J. M. , Weygandt S. , Smirnova T. , and Schwartz B. , 2008: Convection forecasts from the hourly updated, 3-km High Resolution Rapid Refresh (HRRR) model. Preprints, 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., 11.1. [Available online at https://ams.confex.com/ams/24SLS/techprogram/paper_142055.htm.]

  • Sokol, Z., and Zacharov P. , 2012: Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity. Quart. J. Roy. Meteor. Soc., 138, 10721082, doi:10.1002/qj.970.

    • Search Google Scholar
    • Export Citation
  • Sun, J., 2005: Initialization and numerical forecasting of a supercell storm observed during STEPS. Mon. Wea. Rev., 133, 793813, doi:10.1175/MWR2887.1.

    • Search Google Scholar
    • Export Citation
  • Sun, J., and Crook N. A. , 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 16421661, doi:10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sun, J., Chen M. , and Wang Y. , 2010: A frequent-updating analysis system based on radar, surface, and mesoscale model data for the Beijing 2008 Forecast Demonstration Project. Wea. Forecasting, 25, 17151735, doi:10.1175/2010WAF2222336.1.

    • Search Google Scholar
    • Export Citation
  • Sun, J., Trier S. B. , Xiao Q. , Weisman M. L. , Wang H. , Ying Z. , Xu M. , and Zhang Y. , 2012: Sensitivity of 0–12-h warm-season precipitation forecasts over the central United States to model initialization. Wea. Forecasting, 27, 832855, doi:10.1175/WAF-D-11-00075.1.

    • Search Google Scholar
    • Export Citation
  • Sun, J., and Coauthors, 2014: Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bull. Amer. Meteor. Soc., 95, 409426, doi:10.1175/BAMS-D-11-00263.1.

    • Search Google Scholar
    • Export Citation
  • Sun, J., Wang H. , Tong W. , Zhang Y. , Lin C.-Y. , and Xu D. , 2016: Comparison of the impacts of momentum control variables on high-resolution variational data assimilation and precipitation forecasting. Mon. Wea. Rev., 144, 149169, doi:10.1175/MWR-D-14-00205.1.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., Field P. R. , Rasmussen R. M. , and Hall W. D. , 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, doi:10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation
  • Vendrasco, E., Sun J. , Herdies D. L. , and De Angelis C. F. , 2015: Constraining a 3DVAR radar data assimilation system with large-scale analysis to improve short-range precipitation forecast. J. Appl. Meteor. Climatol., 55, 673690, doi:10.1175/JAMC-D-15-0010.1.

    • Search Google Scholar
    • Export Citation
  • Wang, H., Sun J. , Fan S. , and Huang X. Y. , 2013: Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of four summertime convective events. J. Appl. Meteor. Climatol., 52, 889902, doi:10.1175/JAMC-D-12-0120.1.

    • Search Google Scholar
    • Export Citation
  • Weygandt, S. S., Benjamin S. G. , Smirnova T. G. , and Brown J. M. , 2008: Assimilation of radar reflectivity data using a diabatic digital filter within the Rapid Update Cycle. Preprints, 12th Conf. on IOAS-AOLS, New Orleans, LA, Amer. Meteor. Soc., 8.4. [Available online at https://ams.confex.com/ams/88Annual/techprogram/paper_134081.htm.]

  • Wilson, J. W., Feng Y. , Chen M. , and Roberts R. D. , 2010: Nowcasting challenges during the Beijing Olympics: Successes, failures, and implications for future nowcasting systems. Wea. Forecasting, 25, 16911714, doi:10.1175/2010WAF2222417.1.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., and Sun J. , 2007: Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002. Mon. Wea. Rev., 135, 33813404, doi:10.1175/MWR3471.1.

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

    • Search Google Scholar
    • Export Citation
  • Zou, X., Kuo Y.-H. , and Guo Y.-R. , 1995: Assimilation of atmospheric radio refractivity using a nonhydrostatic adjoint model. Mon. Wea. Rev., 123, 22292250, doi:10.1175/1520-0493(1995)123<2229:AOARRU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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