• Ancell, B. C., and G. J. Hakim, 2007: Comparing adjoint- and ensemble-sensitivity analysis with applications to observation targeting. Mon. Wea. Rev., 135, 41174134, doi:10.1175/2007MWR1904.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. D. Torn, and A. Arellano, 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
  • Anthes, R. A., Y. H. Kuo, D. P. Baumhefner, R. M. Errico, and T. W. Bettge, 1985: Predictability of mesoscale atmospheric motions. Advances in Geophysics, Vol. 28, Academic Press, 159202, doi:10.1016/S0065-2687(08)60188-0.

    • Crossref
    • Export Citation
  • Barker, D. M., and et al. , 2012: The Weather Research and Forecasting Model’s Community Variational/Ensemble Data Assimilation System: WRFDA. Bull. Amer. Meteor. Soc., 93, 831843, doi:10.1175/BAMS-D-11-00167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barkmeijer, J., R. Buizza, T. N. Palmer, K. Puri, and J. F. Mahfouf, 2001: Tropical singular vectors computed with linearized diabatic physics. Quart. J. Roy. Meteor. Soc., 127, 685708, doi:10.1002/qj.49712757221.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bednarczyk, C. N., and B. C. Ancell, 2015: Ensemble sensitivity analysis applied to a southern plains convective event. Mon. Wea. Rev., 143, 230249, doi:10.1175/MWR-D-13-00321.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bei, N., and F. Zhang, 2007: Impacts of initial condition errors on mesoscale predictability of heavy precipitation along the Mei-Yu front of China. Quart. J. Roy. Meteor. Soc., 133, 8399, doi:10.1002/qj.20.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bergmaier, P. T., and B. Geerts, 2015: Characteristics and synoptic environment of drylines occurring over the higher terrain of southeastern Wyoming. Wea. Forecasting, 30, 17331748, doi:10.1175/WAF-D-15-0061.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., C. A. Doswell III, and L. J. Wicker, 1993: STORMTIPE: A forecasting experiment using a three-dimensional cloud model. Wea. Forecasting, 8, 352362, doi:10.1175/1520-0434(1993)008<0352:SAFEUA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., and T. N. Palmer, 1995: The singular vector structure of the atmospheric global circulation. J. Atmos. Sci., 52, 14341456, doi:10.1175/1520-0469(1995)052<1434:TSVSOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campbell, P. C., B. Geerts, and P. T. Bergmaier, 2014: A dryline in southeast Wyoming. Part I: Multiscale analysis using observations and modeling on 22 June 2010. Mon. Wea. Rev., 142, 268289, doi:10.1175/MWR-D-13-00049.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, E. K., M. Zheng, and K. Raeder, 2013: Medium-range ensemble sensitivity analysis of two extreme Pacific extratropical cyclones. Mon. Wea. Rev., 141, 211231, doi:10.1175/MWR-D-11-00304.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., J. Correia, P. T. Marsh, and F. Kong, 2013: Verification of convection-allowing WRF Model forecasts of the planetary boundary layer using sounding observations. Wea. Forecasting, 28, 842862, doi:10.1175/WAF-D-12-00103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crook, N. A., 1996: Sensitivity of moist convection forced by boundary layer processes to low-level thermodynamic fields. Mon. Wea. Rev., 124, 17671785, doi:10.1175/1520-0493(1996)124<1767:SOMCFB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doswell, C. A., 1980: Synoptic-scale environments associated with high plains severe thunderstorms. Bull. Amer. Meteor. Soc., 61, 13881400, doi:10.1175/1520-0477(1980)061<1388:SSEAWH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and M. Gingrich, 2014: Atmospheric predictability: Why butterflies are not of practical importance. J. Atmos. Sci., 71, 24762488, doi:10.1175/JAS-D-14-0007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and J. A. Weyn, 2016: Thunderstorms do not get butterflies. Bull. Amer. Meteor. Soc., 97, 237243, doi:10.1175/BAMS-D-15-00070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durran, D. R., P. A. Reinecke, and J. D. Doyle, 2013: Large-scale errors and mesoscale predictability in Pacific Northwest snowstorms. J. Atmos. Sci., 70, 14701487, doi:10.1175/JAS-D-12-0202.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Easterling, D. R., and P. J. Robinson, 1985: The diurnal variation of thunderstorm activity in the United States. J. Climate Appl. Meteor., 24, 10481058, doi:10.1175/1520-0450(1985)024<1048:TDVOTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Errico, R. M., 1997: What is an adjoint model? Bull. Amer. Meteor. Soc., 78, 25772591, doi:10.1175/1520-0477(1997)078<2577:WIAAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Errico, R. M., and T. Vukicevic, 1992: Sensitivity analysis using an adjoint of the PSU–NCAR mesoscale model. Mon. Wea. Rev., 120, 16441660, doi:10.1175/1520-0493(1992)120<1644:SAUAAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Errico, R. M., K. D. Raeder, and L. Fillion, 2003: Examination of the sensitivity of forecast precipitation rates to possible perturbations of initial conditions. Tellus, 55A, 88105, doi:10.1034/j.1600-0870.2003.201394.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hakim, G. J., and R. D. Torn, 2008: Ensemble synoptic analysis. Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting: A Tribute to Fred Sanders, Meteor. Monogr., No. 55, Amer. Meteor. Soc., 147–161.

    • Crossref
    • Export Citation
  • Hall, M. C. G., D. G. Cacuci, and M. E. Schlesinger, 1982: Sensitivity analysis of a radiative convective model by the adjoint method. J. Atmos. Sci., 39, 20382050, doi:10.1175/1520-0469(1982)039<2038:SAOARC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hane, C. E., C. L. Ziegler, and H. B. Bluestein, 1993: Investigation of the dryline and convective storms initiated along the dryline: Field experiments during COPS-91. Bull. Amer. Meteor. Soc., 74, 21332145, doi:10.1175/1520-0477(1993)074<2133:IOTDAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hane, C. E., M. E. Baldwin, H. B. Bluestein, T. M. Crawford, and R. M. Rabin, 2001: A case study of severe storm development along a dryline within a synoptically active environment. Part I: Dryline motion and an Eta Model forecast. Mon. Wea. Rev., 129, 21832204, doi:10.1175/1520-0493(2001)129<2183:ACSOSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanley, K. E., D. J. Kirshbaum, N. M. Roberts, and G. Leoncini, 2013: Sensitivities of a squall line over central Europe in a convective-scale ensemble. Mon. Wea. Rev., 141, 112133, doi:10.1175/MWR-D-12-00013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hill, A. J., C. C. Weiss, and B. C. Ancell, 2016: Ensemble sensitivity analysis for mesoscale forecasts of dryline convection initiation. Mon. Wea. Rev., 144, 41614182, doi:10.1175/MWR-D-15-0338.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., and C. Schär, 2007: Atmospheric predictability at synoptic versus cloud-resolving scales. Bull. Amer. Meteor. Soc., 88, 17831793, doi:10.1175/BAMS-88-11-1783.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., M. E. McIntyre, and A. W. Robertson, 1985: On the use and significance of isentropic potential vorticity maps. Quart. J. Roy. Meteor. Soc., 111, 877946, doi:10.1002/qj.49711147002.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945, doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 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.]

  • Klimowski, B. A., M. J. Bunkers, M. R. Hjelmfelt, and J. N. Covert, 2003: Severe convective windstorms over the northern High Plains of the United States. Wea. Forecasting, 18, 502519, doi:10.1175/1520-0434(2003)18<502:SCWOTN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langland, R. H., R. L. Elsberry, and R. M. Errico, 1995: Evaluation of physical processes in an idealized extratropical cyclone using adjoint sensitivity. Quart. J. Roy. Meteor. Soc., 121, 13491386, doi:10.1002/qj.49712152608.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21A, 289307, doi:10.3402/tellusa.v21i3.10086.

    • Search Google Scholar
    • Export Citation
  • Martius, O., C. Schwierz, and H. C. Davies, 2006: A refined Hovmöller diagram. Tellus, 58A, 221226, doi:10.1111/j.1600-0870.2006.00172.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCarthy, J., and S. E. Koch, 1982: The evolution of an Oklahoma dryline. Part I: A meso- and subsynoptic-scale analysis. J. Atmos. Sci., 39, 225236, doi:10.1175/1520-0469(1982)039<0225:TEOAOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851875, doi:10.1029/RG020i004p00851.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, doi:10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • National Climatic Data Center, 2016: Storm events database. National Climatic Data Center, accessed 5 May 2016. [Available online at https://www.ncdc.noaa.gov/stormevents/.]

  • Nuss, W. A., and D. K. Miller, 2001: Mesoscale predictability under various synoptic regimes. Nonlinear Processes Geophys., 8, 429438, doi:10.5194/npg-8-429-2001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, S. K., 1999: Nonlinearity and predictability of convective rainfall associated with water vapor perturbations in a numerically simulated storm. J. Geophys. Res., 104, 31 57531 587, doi:10.1029/1999JD900446.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rabier, F., P. Courtier, and O. Talagrand, 1992: An application of adjoint models to sensitivity analysis. Beitr. Phys. Atmos., 65, 177192.

    • 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
  • Romine, G. S., C. S. Schwartz, C. Snyder, J. L. Anderson, and M. L. Weisman, 2013: Model bias in a continuously cycled assimilation system and its influence on convection-permitting forecasts. Mon. Wea. Rev., 141, 12631284, doi:10.1175/MWR-D-12-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salazar, J. L., A. Hopf, R. F. Contreras, B. Philips, E. J. Knapp, D. McLaughlin, J. Brotzge, and K. Brewster, 2009: Coverage comparison of short range radar networks vs. conventional weather radars: Case study in the northwestern United States. 2009 IEEE Int. Geosci. Remote Sens. Symp., IGARSS 2009, Cape Town, Africa, IEEE, Vol. 2, 964967, doi:10.1109/IGARSS.2009.5418261.

    • Crossref
    • Export Citation
  • Schwartz, C. S., G. S. Romine, M. L. Weisman, R. A. Sobash, K. R. Fossell, K. W. Manning, and S. B. Trier, 2015: A real-time convection-allowing ensemble prediction system initialized by mesoscale ensemble Kalman filter analyses. Wea. Forecasting, 30, 11581181, doi:10.1175/WAF-D-15-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and M. L. Weisman, 2009: The impact of positive-definite moisture transport on NWP precipitation forecasts. Mon. Wea. Rev., 137, 488494, doi:10.1175/2008MWR2583.1.

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

    • Crossref
    • Export Citation
  • Tegen, I., P. Hollrig, M. Chin, I. Fung, D. Jacob, and J. Penner, 1997: Contribution of different aerosol species to the global aerosol extinction optical thickness: Estimates from model results. J. Geophys. Res., 102, 23 89523 915, doi:10.1029/97JD01864.

    • 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, doi:10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 17791800, doi:10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and G. J. Hakim, 2008: Ensemble-based sensitivity analysis. Mon. Wea. Rev., 136, 663677, doi:10.1175/2007MWR2132.1.

  • Torn, R. D., and G. S. Romine, 2015: Sensitivity of central Oklahoma convection forecasts to upstream potential vorticity anomalies during two strongly forced cases during MPEX. Mon. Wea. Rev., 143, 40644087, doi:10.1175/MWR-D-15-0085.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 24902502, doi:10.1175/MWR3187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and et al. , 2015: The Mesoscale Predictability Experiment (MPEX). Bull. Amer. Meteor. Soc., 96, 21272149, doi:10.1175/BAMS-D-13-00281.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamaguchi, M., and S. J. Majumdar, 2010: Using TIGGE data to diagnose initial perturbations and their growth for tropical cyclone ensemble forecasts. Mon. Wea. Rev., 138, 36343655, doi:10.1175/2010MWR3176.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamaguchi, M., R. Sakai, M. Kyoda, T. Komori, and T. Kadowaki, 2009: Typhoon ensemble prediction system developed at the Japan Meteorological Agency. Mon. Wea. Rev., 137, 25922604, doi:10.1175/2009MWR2697.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamaguchi, M., D. S. Nolan, M. Iskandarani, S. J. Majumdar, M. S. Peng, and C. A. Reynolds, 2011: Singular vectors for tropical cyclone–like vortices in a nondivergent barotropic framework. J. Atmos. Sci., 68, 22732291, doi:10.1175/2011JAS3727.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., Y. Wang, and K. Hamilton, 2011: Improved representation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme. Mon. Wea. Rev., 139, 34893513, doi:10.1175/MWR-D-10-05091.1.

    • 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
  • Zhang, F., A. M. Odins, and J. W. Nielsen-Gammon, 2006: Mesoscale predictability of an extreme warm-season precipitation event. Wea. Forecasting, 21, 149166, doi:10.1175/WAF909.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Sensitivity of Northern Great Plains Convection Forecasts to Upstream and Downstream Forecast Errors

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  • 1 Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
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Abstract

The role of earlier forecast errors on subsequent convection forecasts is evaluated for a northern Great Plains severe convective event on 11–12 June 2013 during the Mesoscale Predictability Experiment (MPEX) by applying the ensemble-based sensitivity technique to Weather Research and Forecasting (WRF) Model ensemble forecasts with explicit convection. This case was characterized by two distinct modes of convection located 150 km apart in western Nebraska and South Dakota, which formed on either side of an axis of high, lower-tropospheric equivalent potential temperature . Convection forecasts over both regions are found to be sensitive to the position of this axis. The convection in Nebraska is sensitive to the position of the western edge of the axis near an upstream dryline, which modulates the preconvective prior to the diurnal maximum. In contrast, the convection in South Dakota is sensitive to the position of the eastern edge of the axis near a cold front, which also modulates the preconvective in that location. The position of the axis is modulated by the positions of both upstream and downstream mid- to upper-tropospheric potential vorticity anomalies, and can be traced backward in time to the initial conditions. Dropsondes sampling the region prior to convective initiation indicate that ensemble members with better representations of upstream conditions in sensitive regions are associated with better convective forecasts over Nebraska.

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

The National Center for Atmospheric Research is supported by the National Science Foundation.

Corresponding author: Jeremy Berman, jdberman@albany.edu

Abstract

The role of earlier forecast errors on subsequent convection forecasts is evaluated for a northern Great Plains severe convective event on 11–12 June 2013 during the Mesoscale Predictability Experiment (MPEX) by applying the ensemble-based sensitivity technique to Weather Research and Forecasting (WRF) Model ensemble forecasts with explicit convection. This case was characterized by two distinct modes of convection located 150 km apart in western Nebraska and South Dakota, which formed on either side of an axis of high, lower-tropospheric equivalent potential temperature . Convection forecasts over both regions are found to be sensitive to the position of this axis. The convection in Nebraska is sensitive to the position of the western edge of the axis near an upstream dryline, which modulates the preconvective prior to the diurnal maximum. In contrast, the convection in South Dakota is sensitive to the position of the eastern edge of the axis near a cold front, which also modulates the preconvective in that location. The position of the axis is modulated by the positions of both upstream and downstream mid- to upper-tropospheric potential vorticity anomalies, and can be traced backward in time to the initial conditions. Dropsondes sampling the region prior to convective initiation indicate that ensemble members with better representations of upstream conditions in sensitive regions are associated with better convective forecasts over Nebraska.

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

The National Center for Atmospheric Research is supported by the National Science Foundation.

Corresponding author: Jeremy Berman, jdberman@albany.edu
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