Constraining Ensemble Forecasts of Discrete Convective Initiation with Surface Observations

Luke E. Madaus University of Washington, Seattle, Washington

Search for other papers by Luke E. Madaus in
Current site
Google Scholar
PubMed
Close
and
Gregory J. Hakim University of Washington, Seattle, Washington

Search for other papers by Gregory J. Hakim in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Predicting when and where individual convective storms will develop remains an elusive challenge. Previous studies have suggested that surface observations can capture convective-scale features relevant to the convective initiation (CI) process, and new surface observing platforms such as crowdsourcing could significantly increase surface observation density in the near future. Here, a series of observing system simulation experiments (OSSEs) are performed to determine the required density of surface observations necessary to constrain storm-scale forecasts of CI. Ensemble simulations of an environment where CI occurs are cycled hourly using the CM1 model while assimilating synthetic surface observations at varying densities. Skillful and reliable storm-scale forecasts of CI are produced when surface observations of at least 4-km—and particularly with 1-km—density are assimilated, but only for forecasts initiated within 1 h of CI. Time scales of forecast improvement in surface variables suggest that hourly cycling is at the upper limit for CI forecast improvement. In addition, the structure of the assimilation increments, ensemble calibration in these experiments, and challenges of convective-scale assimilation are discussed.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Current affiliation: National Center for Atmospheric Research, Boulder, Colorado.

Corresponding author: Luke E. Madaus, lmadaus@atmos.washington.edu

Abstract

Predicting when and where individual convective storms will develop remains an elusive challenge. Previous studies have suggested that surface observations can capture convective-scale features relevant to the convective initiation (CI) process, and new surface observing platforms such as crowdsourcing could significantly increase surface observation density in the near future. Here, a series of observing system simulation experiments (OSSEs) are performed to determine the required density of surface observations necessary to constrain storm-scale forecasts of CI. Ensemble simulations of an environment where CI occurs are cycled hourly using the CM1 model while assimilating synthetic surface observations at varying densities. Skillful and reliable storm-scale forecasts of CI are produced when surface observations of at least 4-km—and particularly with 1-km—density are assimilated, but only for forecasts initiated within 1 h of CI. Time scales of forecast improvement in surface variables suggest that hourly cycling is at the upper limit for CI forecast improvement. In addition, the structure of the assimilation increments, ensemble calibration in these experiments, and challenges of convective-scale assimilation are discussed.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Current affiliation: National Center for Atmospheric Research, Boulder, Colorado.

Corresponding author: Luke E. Madaus, lmadaus@atmos.washington.edu
Save
  • Anderson, J., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 7283, doi:10.1111/j.1600-0870.2008.00361.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J., 2012: Localization and sampling error correction in ensemble Kalman filter data assimilation. Mon. Wea. Rev., 140, 23592371, doi:10.1175/MWR-D-11-00013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J., B. Wyman, S. Zhang, and T. Hoar, 2005: Assimilation of surface pressure observations using an ensemble filter in an idealized global atmospheric prediction system. J. Atmos. Sci., 62, 29252938, doi:10.1175/JAS3510.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 12831296, doi:10.1175/2009BAMS2618.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 13, doi:10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and J. M. Fritsch, 2002: A benchmark simulation for moist nonhydrostatic numerical models. Mon. Wea. Rev., 130, 29172928, doi:10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burton, P., 2013: IFS documention—Cy40r1, Operational implementation 22 November 2013. Part I: Observations. Tech. Rep., European Center for Medium-Range Weather Forecasts, 76 pp. [Available online at http://www.ecmwf.int/sites/default/files/IFS_CY40R1_Part1.pdf.]

  • Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. Tech. Rep. NASA/TM-1999-10460, NASA, 38 pp.

  • Chou, M. D., and M. J. Suarez, 2001: A thermal infrared radiation parameterization for atmospheric studies. Tech. Rep. NASA/TM-2001-104606, NASA, 55 pp.

  • Dabberdt, W. F., and Coauthors, 2005: Multifunctional mesoscale observing networks. Bull. Amer. Meteor. Soc., 86, 961982, doi:10.1175/BAMS-86-7-961.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Droegemeier, K., 1990: Toward a science of storm-scale prediction. Preprints, 16th Conf. on Severe Local Storms, Kananaskis, Alberta, Canada, Amer. Meteor. Soc., 256–262.

  • Duda, J. D., and W. A. Gallus, 2013: The impact of large-scale forcing on skill of simulated convective initiation and upscale evolution with convection-allowing grid spacings in the WRF. Wea. Forecasting, 28, 9941018, doi:10.1175/WAF-D-13-00005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fowle, M. A., and P. J. Roebber, 2003: Short-range (0–48 h) numerical prediction of convective occurrence, mode, and location. Wea. Forecasting, 18, 782794, doi:10.1175/1520-0434(2003)018<0782:SHNPOC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gallus, W. A., J. Correia, and I. Jankov, 2005: The 4 June 1999 derecho event: A particularly difficult challenge for numerical weather prediction. Wea. Forecasting, 20, 705728, doi:10.1175/WAF883.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaspari, G., and S. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757, doi:10.1002/qj.49712555417.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, doi:10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., 2014: Cloud Dynamics. 2nd ed. Academic Press, 496 pp.

  • Hu, M., H. Shao, D. Stark, and K. Newman, Eds., 2013: Gridpoint Statistical Interpolation (GSI) version 3.2 user’s guide. Tech. Rep., Developmental Testbed Center, 187 pp. [Available online at http://www.dtcenter.org/com-GSI/users/docs/users_guide/GSIUserGuide_v3.2.pdf; error variances obtained from file nam_errtable.r3dv.]

  • Jacques, A. A., J. D. Horel, E. T. Crosman, and F. L. Vernon, 2015: Central and eastern U.S. surface pressure variations derived from the USArray network. Mon. Wea. Rev., 143, 14721493, doi:10.1175/MWR-D-14-00274.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, T. A., D. Stensrud, L. Wicker, P. Minnis, and R. Palikonda, 2015: Simultaneous radar and satellite data storm-scale assimilation using an ensemble Kalman filter approach for 24 May 2011. Mon. Wea. Rev., 143, 165194, doi:10.1175/MWR-D-14-00180.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and Coauthors, 2013: A feasibility study for probabilistic convection initiation forecasts based on explicit numerical guidance. Bull. Amer. Meteor. Soc., 94, 12131225, doi:10.1175/BAMS-D-11-00264.1.

    • Crossref
    • 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
  • Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 10651092, doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madaus, L. E., and G. J. Hakim, 2016: Observable surface anomalies preceding simulated isolated convective initiation. Mon. Wea. Rev., 144, 22652284, doi:10.1175/MWR-D-15-0332.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madaus, L. E., G. J. Hakim, and C. F. Mass, 2014: Utility of dense pressure observations for improving mesoscale analyses and forecasts. Mon. Wea. Rev., 142, 23982413, doi:10.1175/MWR-D-13-00269.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Markowski, P., C. Hannon, and E. Rasmussen, 2006: Observations of convection initiation failure from the 12 June 2002 IHOP deployment. Mon. Wea. Rev., 134, 375405, doi:10.1175/MWR3059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mass, C. F., and L. E. Madaus, 2014: Surface pressure observations from smartphones: A potential revolution for high-resolution weather prediction? Bull. Amer. Meteor. Soc., 95, 13431349, doi:10.1175/BAMS-D-13-00188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mass, C. F., D. Ovens, K. Westrick, and B. A. Colle, 2002: Does increasing horizontal resolution produce more skillful forecasts? Bull. Amer. Meteor. Soc., 83, 407430, doi:10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., P. Minnis, and R. Palikonda, 2013: Use of satellite derived cloud properties to quantify growing cumulus beneath cirrus clouds. Atmos. Res., 120–121, 192201, doi:10.1016/j.atmosres.2012.08.017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muller, C. L., L. Chapman, S. Johnston, C. Kidd, S. Illingworth, G. Foody, A. Overeem, and R. R. Leigh, 2015: Crowdsourcing for climate and atmospheric sciences: Current status and future potential. Int. J. Climatol., 35, 31853203, doi:10.1002/joc.4210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Owen, J., 1966: A study of thunderstorm formation along dry lines. J. Appl. Meteor., 5, 5863, doi:10.1175/1520-0450(1966)005<0058:ASOTFA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., D. M. Schultz, and R. Romero, 2002: Synoptic regulation of the 3 May 1999 tornado outbreak. Wea. Forecasting, 17, 399429, doi:10.1175/1520-0434(2002)017<0399:SROTMT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., L. M. Cronce, W. F. Feltz, K. M. Bedka, M. J. Pavolonis, and A. K. Heidinger, 2011: Nowcasting convective storm initiation using satellite-based box-averaged cloud-top cooling and cloud-type trends. J. Appl. Meteor. Climatol., 50, 110126, doi:10.1175/2010JAMC2496.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snook, N., M. Xue, and Y. Jung, 2015: Multiscale EnKF assimilation of radar and conventional observations and ensemble forecasting for a tornadic mesoscale convective system. Mon. Wea. Rev., 143, 10351057, doi:10.1175/MWR-D-13-00262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., and D. J. Stensrud, 2015: Assimilating surface mesonet observations with the EnKF to improve ensemble forecasts of convection initiation on 29 May 2012. Mon. Wea. Rev., 143, 37003725, doi:10.1175/MWR-D-14-00126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and J. M. Fritsch, 1994: Mesoscale convective systems in weakly forced large-scale environments. Part III: Numerical simulations and implications for operational forecasting. Mon. Wea. Rev., 122, 20842104, doi:10.1175/1520-0493(1994)122<2084:MCSIWF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 14871499, doi:10.1175/2009BAMS2795.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoelinga, M. T., 2005: Simulated equivalent reflectivity factor as currently formulated in RIP: Description and possible improvements. The Pennsylvania State University, 5 pp. [Available online at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.522.925&rep=rep1&type=pdf.]

  • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., and J. Simpson, 1993: Goddard Cumulus Ensemble Model. Part I: Model description. Terr. Atmos. Oceanic Sci., 4, 3571, doi:10.3319/TAO.1993.4.1.35(A).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weckwerth, T. M., 2000: The effect of small-scale moisture variability on thunderstorm initiation. Mon. Wea. Rev., 128, 40174030, doi:10.1175/1520-0493(2000)129<4017:TEOSSM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weckwerth, T. M., and D. B. Parsons, 2006: A review of convection initiation and motivation for IHOP 2002. Mon. Wea. Rev., 134, 522, doi:10.1175/MWR3067.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheatley, D. M., and D. J. Stensrud, 2010: The impact of assimilating surface pressure observations on severe weather events in a WRF mesoscale ensemble system. Mon. Wea. Rev., 138, 16731694, doi:10.1175/2009MWR3042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 11731185, doi:10.1175/1520-0469(2003)060<1173:EOMCOM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ziegler, C. L., T. J. Lee, and R. A. Pielke, 1997: Convective initiation at the dryline: A modeling study. Mon. Wea. Rev., 125, 10011026, doi:10.1175/1520-0493(1997)125<1001:CIATDA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 160 48 5
PDF Downloads 128 45 4