• Aberson, S. D., 2003: Targeted observations to improve operational tropical cyclone track forecast guidance. Mon. Wea. Rev., 131, 16131628, https://doi.org/10.1175//2550.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ancell, B. C., 2016: Improving high-impact forecasts through sensitivity-based ensemble subsets: Demonstration and initial tests. Wea. Forecasting, 31, 10191036, https://doi.org/10.1175/WAF-D-15-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ancell, B. C., and C. F. Mass, 2006: Structure, growth rates, and tangent linear accuracy of adjoint sensitivities with respect to horizontal and vertical resolution. Mon. Wea. Rev., 134, 29712988, https://doi.org/10.1175/MWR3227.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ancell, B. C., A. Bogusz, M. J. Lauridsen, and C. J. Nauert, 2018: Seeding chaos: The dire consequences of numerical noise in NWP perturbation experiments. Bull. Amer. Meteor. Soc., 99, 615628, https://doi.org/10.1175/BAMS-D-17-0129.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2007: An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus, 59A, 210224, https://doi.org/10.1111/j.1600-0870.2006.00216.x.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arnold, C., and C. Dey, 1986: Observing-systems simulation experiments: Past, present, and future. Bull. Amer. Meteor. Soc., 67, 687695, https://doi.org/10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, R., 1997: Atmospheric observations and experiments to assess their usefulness in data assimilation (Special Issue: Data assimilation in meteology and oceanography: Theory and practice). J. Meteor. Soc. Japan, 75, 111130, https://doi.org/10.2151/jmsj1965.75.1B_111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barker, D. M., W. Huang, Y.-R. Guo, J. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914, https://doi.org/10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.

    • 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, https://doi.org/10.1175/MWR-D-13-00321.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bergot, T., 1999: Adaptive observations during FASTEX: A systematic survey of upstream flights. Quart. J. Roy. Meteor. Soc., 125, 32713298, https://doi.org/10.1002/qj.49712556108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bergot, T., 2001: Influence of the assimilation scheme on the efficiency of adaptive observations. Quart. J. Roy. Meteor. Soc., 127, 635660, https://doi.org/10.1002/qj.49712757219.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berliner, L., Z. Lu, and C. Snyder, 1999: Statistical design for adaptive weather observations. J. Atmos. Sci., 56, 25362552, https://doi.org/10.1175/1520-0469(1999)056<2536:SDFAWO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berman, J. D., and R. D. Torn, 2019: The impact of initial condition and warm conveyor belt forecast uncertainty on variability in the downstream waveguide in an ECWMF case study. Mon. Wea. Rev., 147, 40714089, https://doi.org/10.1175/MWR-D-18-0333.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berman, J. D., R. D. Torn, G. S. Romine, and M. L. Weisman, 2017: Sensitivity of Northern Great Plains convection forecasts to upstream and downstream forecast errors. Mon. Wea. Rev., 145, 21412163, https://doi.org/10.1175/MWR-D-16-0353.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, B. R., and G. J. Hakim, 2015: Sensitivity of intensifying Atlantic hurricanes to vortex structure. Quart. J. Roy. Meteor. Soc., 141, 25382551, https://doi.org/10.1002/qj.2540.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., J. C. Wyngaard, and J. M. Fritsch, 2003: Resolution requirements for the simulation of deep moist convection. Mon. Wea. Rev., 131, 23942416, https://doi.org/10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., and A. Montani, 1999: Targeting observations using singular vectors. J. Atmos. Sci., 56, 29652985, https://doi.org/10.1175/1520-0469(1999)056<2965:TOUSV>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., S. M. Hitchcock, and K. H. Knopfmeier, 2016: Impact of assimilating pre-convective upsonde observations on short-term forecasts of convection observed during MPEX. Mon. Wea. Rev., 144, 43014325, https://doi.org/10.1175/MWR-D-16-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., G. S. Romine, D. D. Turner, and R. D. Torn, 2019: Impacts of targeted AERI and Doppler lidar wind retrievals on short-term forecasts of the initiation and early evolution of thunderstorms. Mon. Wea. Rev., 147, 11491170, https://doi.org/10.1175/MWR-D-18-0351.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diaconescu, E. P., and R. Laprise, 2012: Singular vectors in atmospheric sciences: A review. Earth-Sci. Rev., 113, 161175, https://doi.org/10.1016/j.earscirev.2012.05.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doyle, J. D., C. A. Reynolds, C. Amerault, and J. Moskaitis, 2012: Adjoint sensitivity and predictability of tropical cyclogenesis. J. Atmos. Sci., 69, 35353557, https://doi.org/10.1175/JAS-D-12-0110.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doyle, J. D., C. Amerault, C. A. Reynolds, and P. A. Reinecke, 2014: Initial condition sensitivity and predictability of a severe extratropical cyclone using a moist adjoint. Mon. Wea. Rev., 142, 320342, https://doi.org/10.1175/MWR-D-13-00201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doyle, J. D., C. A. Reynolds, and C. Amerault, 2019: Adjoint sensitivity analysis of high-impact extratropical cyclones. Mon. Wea. Rev., 147, 45114532, https://doi.org/10.1175/MWR-D-19-0055.1.

    • 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., 1997: What is an adjoint model? Bull. Amer. Meteor. Soc., 78, 25772591, https://doi.org/10.1175/1520-0477(1997)078<2577:WIAAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 14310 162, https://doi.org/10.1029/94JC00572.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, J., C. Fu, D. J. Stensrud, and J. S. Kain, 2016: OSSEs for an ensemble-3DVAR data assimilation system with radar observations of convective storms. J. Atmos. Sci., 73, 24032426, https://doi.org/10.1175/JAS-D-15-0311.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garcies, L., and V. Homar, 2009: Ensemble sensitivities of the real atmosphere: Application to Mediterranean intense cyclones. Tellus, 61A, 394406, https://doi.org/10.1111/j.1600-0870.2009.00392.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garcies, L., and V. Homar, 2010: An optimized ensemble sensitivity climatology of Mediterranean intense cyclones. Nat. Hazards Earth Syst. Sci., 10, 24412450, https://doi.org/10.5194/nhess-10-2441-2010.

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

    • 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
  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550560, https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2.

    • 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, https://doi.org/10.1175/MWR-D-15-0338.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hill, A. J., C. C. Weiss, and B. C. Ancell, 2018: Towards improving forecasts of severe convection along the dryline through targeted observing with ensemble sensitivity analysis. 29th Conf. on Severe Local Storms, Stowe, VT, Amer. Meteor. Soc., 14.2, https://ams.confex.com/ams/29SLS/webprogram/Paper348727.html.

  • Hill, A. J., C. C. Weiss, and D. C. Dowell, 2020: Assimilating near-surface observations from a portable mesoscale network of StickNet platforms during VORTEX-SE with the high-resolution rapid refresh ensemble. Severe Local Storms Symp., Boston, MA, Amer. Meteor. Soc., 369006, https://ams.confex.com/ams/2020Annual/webprogram/Paper369006.html.

  • 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, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., and M. M. Coutinho, 2005: Moist singular vectors and the predictability of some high impact European cyclones. Quart. J. Roy. Meteor. Soc., 131, 581601, https://doi.org/10.1256/qj.04.48.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houtekamer, P., and H. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796811, https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Insurance Information Institute, 2019: Facts statistics: Tornadoes and thunderstorms. Accessed 13 October 2019, https://www.iii.org/fact-statistic/tornadoes-and-thunderstorms.

  • Joly, A., and Coauthors, 1997: The Fronts and Atlantic Storm-Track Experiment (FASTEX): Scientific objectives and experimental design. Bull. Amer. Meteor. Soc., 78, 19171940, https://doi.org/10.1175/1520-0477(1997)078<1917:TFAAST>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joly, A., and Coauthors, 1999: Overview of the field phase of the Fronts and Atlantic Storm-Track Experiment (FASTEX) project. Quart. J. Roy. Meteor. Soc., 125, 31313163, https://doi.org/10.1002/qj.49712556103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • 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, https://doi.org/10.1175/BAMS-D-11-00264.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalman, R., 1960: A new approach to linear filtering and prediction problems. J. Basic Eng., 82, 3545, https://doi.org/10.1115/1.3662552.

  • Kerr, C. A., and X. Wang, 2020: Ensemble-based targeted observation method applied to radar radial velocity observations on idealized supercell low-level rotation forecasts: A proof of concept. Mon. Wea. Rev., 148, 877890, https://doi.org/10.1175/MWR-D-19-0197.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kerr, C. A., D. J. Stensrud, X. Wang, C. A. Kerr, D. J. Stensrud, and X. Wang, 2019: Diagnosing convective dependencies on near-storm environments using ensemble sensitivity analyses. Mon. Wea. Rev., 147, 495517, https://doi.org/10.1175/MWR-D-18-0140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khare, S. P., and J. L. Anderson, 2006: An examination of ensemble filter based adaptive observation methodologies. Tellus, 58A, 179195, https://doi.org/10.1111/j.1600-0870.2006.00163.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H. M., and B. J. Jung, 2009: Singular vector structure and evolution of a recurving tropical cyclone. Mon. Wea. Rev., 137, 505524, https://doi.org/10.1175/2008MWR2643.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lange, H., and G. C. Craig, 2014: The impact of data assimilation length scales on analysis and prediction of convective storms. Mon. Wea. Rev., 142, 37813808, https://doi.org/10.1175/MWR-D-13-00304.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langland, R. H., 2005: Issues in targeted observing. Quart. J. Roy. Meteor. Soc., 131, 34093425, https://doi.org/10.1256/qj.05.130.

  • Langland, R. H., and Coauthors, 1999: The North Pacific Experiment (NORPEX-98): Targeted observations for improved North American weather forecasts. Bull. Amer. Meteor. Soc., 80, 13631384, https://doi.org/10.1175/1520-0477(1999)080<1363:TNPENT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LeDimet, F., and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A, 97110, https://doi.org/10.1111/j.1600-0870.1986.tb00459.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Limpert, G. L., and A. L. Houston, 2018: Ensemble sensitivity analysis for targeted observations of supercell thunderstorms. Mon. Wea. Rev., 146, 17051721, https://doi.org/10.1175/MWR-D-17-0029.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., Q. Xiao, and B. Wang, 2009: An ensemble-based four-dimensional variational data assimilation scheme. Part II: Observing system simulation experiments with Advanced Research WRF (ARW). Mon. Wea. Rev., 137, 16871704, https://doi.org/10.1175/2008MWR2699.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madaus, L. E., and G. J. Hakim, 2017: Constraining ensemble forecasts of discrete convective initiation with surface observations. Mon. Wea. Rev., 145, 25972610, https://doi.org/10.1175/MWR-D-16-0395.1.

    • 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, https://doi.org/10.1175/MWR-D-13-00269.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Majumdar, S. J., 2016: A review of targeted observations. Bull. Amer. Meteor. Soc., 97, 22872303, https://doi.org/10.1175/BAMS-D-14-00259.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masutani, M., and Coauthors, 2010: Observing system simulation experiments at the National Centers for Environmental Prediction. J. Geophys. Res., 115, D07101, https://doi.org/10.1029/2009JD012528.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McMurdie, L. A., and B. Ancell, 2014: Predictability characteristics of land-falling cyclones along the North American west coast. Mon. Wea. Rev., 142, 301319, https://doi.org/10.1175/MWR-D-13-00141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. Clough, 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
  • Oliphant, T. E., 2007: Python for scientific computing. Comput. Sci. Eng., 9, 1020, https://doi.org/10.1109/MCSE.2007.58.

  • Palmer, T. N., R. Gelaro, J. Barkmeijer, and R. Buizza, 1998: Singular vectors, metrics, and adaptive observations. J. Atmos. Sci., 55, 633653, https://doi.org/10.1175/1520-0469(1998)055<0633:SVMAAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, C. A., J. D. Doyle, R. M. Hodur, and H. Jin, 2010: Naval research laboratory multiscale targeting guidance for T-PARC and TCS-08. Wea. Forecasting, 25, 526544, https://doi.org/10.1175/2009WAF2222292.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, C. A., J. D. Doyle, F. M. Ralph, and R. Demirdjian, 2019: Adjoint sensitivity of North Pacific atmospheric river forecasts. Mon. Wea. Rev., 147, 18711897, https://doi.org/10.1175/MWR-D-18-0347.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romine, G. S., C. S. Schwartz, R. D. Torn, and M. L. Weisman, 2016: Impact of assimilating dropsonde observations from MPEX on ensemble forecasts of severe weather events. Mon. Wea. Rev., 144, 37993823, https://doi.org/10.1175/MWR-D-15-0407.1.

    • Crossref
    • 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., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Smith, N. H., and B. C. Ancell, 2017: Ensemble sensitivity analysis of wind ramp events with applications to observation targeting. Mon. Wea. Rev., 145, 25052522, https://doi.org/10.1175/MWR-D-16-0306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobash, R., and D. Stensrud, 2013: The impact of covariance localization for radar data on EnKF analyses of a developing MCS: Observing system simulation experiments. Mon. Wea. Rev., 141, 36913709, https://doi.org/10.1175/MWR-D-12-00203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobash, R., C. S. Schwartz, G. S. Romine, K. R. Fossell, and M. L. Weisman, 2015: Severe weather prediction using storm surrogates from an ensemble forecasting system. Wea. Forecasting, 31, 255271, https://doi.org/10.1175/WAF-D-15-0138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Szunyogh, I., and Z. Toth, 2002: Propagation of the effect of targeted observations: The 2000 winter storm reconnaissance program. Mon. Wea. Rev., 130, 11441165, https://doi.org/10.1175/1520-0493(2002)130<1144:POTEOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519543, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., 2010: Ensemble-based sensitivity analysis applied to African easterly waves. Wea. Forecasting, 25, 6178, https://doi.org/10.1175/2009WAF2222255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., 2014: The impact of targeted dropwindsonde observations on tropical cyclone intensity forecasts of four weak systems during PREDICT. Mon. Wea. Rev., 142, 28602878, https://doi.org/10.1175/MWR-D-13-00284.1.

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

  • Torn, R. D., and G. J. Hakim, 2009: Initial condition sensitivity of western Pacific extratropical transitions determined using ensemble-based sensitivity analysis. Mon. Wea. Rev., 137, 33883406, https://doi.org/10.1175/2009MWR2879.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and D. Cook, 2013: The role of vortex and environment errors in genesis forecasts of Hurricanes Danielle and Karl (2010). Mon. Wea. Rev., 141, 232251, https://doi.org/10.1175/MWR-D-12-00086.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/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, https://doi.org/10.1175/MWR3187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., G. S. Romine, and T. J. Galarneau, 2017: Sensitivity of dryline convection forecasts to upstream forecast errors for two weakly forced MPEX cases. Mon. Wea. Rev., 145, 18311852, https://doi.org/10.1175/MWR-D-16-0457.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wile, S. M., J. P. Hacker, and K. H. Chilcoat, 2015: The potential utility of high-resolution ensemble sensitivity analysis for observation placement during weak flow in complex terrain. Wea. Forecasting, 30, 15211536, https://doi.org/10.1175/WAF-D-14-00066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, B., F. Zhang, Q. Zhang, J. Poterjoy, and Y. Weng, 2013: Observing strategy and observation targeting for tropical cyclones using ensemble-based sensitivity analysis and data assimilation. Mon. Wea. Rev., 141, 14371453, https://doi.org/10.1175/MWR-D-12-00188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yussouf, N., and D. J. Stensrud, 2010: Impact of phased-array radar observations over a short assimilation period: Observing system simulation experiments using an ensemble Kalman filter. Mon. Wea. Rev., 138, 517538, https://doi.org/10.1175/2009MWR2925.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zack, J., E. Natenberg, S. Young, G. V. Knowe, K. Waight, J. Manobainco, and C. Kamath, 2010a: Application of ensemble sensitivity analysis to observation targeting for short-term wind speed forecasting in the Tehachapi region winter season. Lawrence Livermore National Laboratory Tech. Rep. LLNL-TR-460956, 60 pp., https://computing.llnl.gov/projects/starsapphire-data-driven-modeling-analysis/LLNL-TR-460956.pdf.

    • Crossref
    • Export Citation
  • Zack, J., E. Natenberg, S. Young, G. V. Knowe, K. Waight, J. Manobianco, and C. Kamath, 2010b: Application of ensemble sensitivity analysis to observation targeting for short-term wind speed forecasting in the Washington-Oregon region. Lawrence Livermore National Laboratory Tech. Rep. LLNL-TR-458086, 67 pp., https://computing.llnl.gov/projects/starsapphire-data-driven-modeling-analysis/LLNL-TR-458086.pdf.

    • Crossref
    • Export Citation
  • Zack, J., E. Natenberg, S. Young, J. Manobianco, and C. Kamath, 2010c: Application of ensemble sensitivity analysis to observation targeting for short-term wind speed forecasting. Lawrence Livermore National Laboratory Tech. Rep. LLNL-TR-42442, 32 pp., https://doi.org/10.2172/972845.

    • Crossref
    • Export Citation
  • Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 12381253, https://doi.org/10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., Z. Meng, and A. Aksoy, 2006: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: Perfect model experiments. Mon. Wea. Rev., 134, 722736, https://doi.org/10.1175/MWR3101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, M., E. K. M. Chang, and B. A. Colle, 2013: Ensemble sensitivity tools for assessing extratropical cyclone intensity and track predictability. Wea. Forecasting, 28, 11331156, https://doi.org/10.1175/WAF-D-12-00132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Factors Influencing Ensemble Sensitivity–Based Targeted Observing Predictions at Convection-Allowing Resolutions

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  • 1 Department of Geosciences, Texas Tech University, Lubbock, Texas
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Abstract

Ensemble sensitivity analysis (ESA) is applied to select types of observations, in various locations and in advance of forecast convection, to systematically evaluate the effectiveness of ESA-based observation targeting for 10 convection forecasts. To facilitate the analysis, observing system simulation experiments and perfect models are utilized to generate synthetic targeted observations of temperature and pressure for future assimilation with an ensemble prediction system. Various observation assimilation experiments are carried out to assess the impacts of nonlinearity, covariance localization, and numerical noise on ESA-based observation-impact predictions. It is discovered that localization applied during data assimilation restricts targeted-observation increments onto the forecast responses of composite reflectivity and 3-hourly accumulated precipitation, making impact predictions poor. In addition, numerical noise introduced by nonlinear perturbation evolution tends to reduce the correlations between observed and predicted impacts; small, random-perturbation experiments often yielded similar impacts on forecasts as targeted observations. Nonlinearity also manifests in the observation impacts when comparing targeted observations with nontargeted, randomly chosen observations; random observations have seemingly the same impact on forecasts as targeted observations. The results, under idealized conditions and simplified ensemble configurations, demonstrate that ESA-based targeting for nonlinear convection forecasts may be most applicable at short time scales. Important implications for ESA-based targeting methods employed with real-time ensemble systems are also discussed.

Current affiliation: Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado.

© 2020 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: Aaron Hill, aaron.hill@colostate.edu

Abstract

Ensemble sensitivity analysis (ESA) is applied to select types of observations, in various locations and in advance of forecast convection, to systematically evaluate the effectiveness of ESA-based observation targeting for 10 convection forecasts. To facilitate the analysis, observing system simulation experiments and perfect models are utilized to generate synthetic targeted observations of temperature and pressure for future assimilation with an ensemble prediction system. Various observation assimilation experiments are carried out to assess the impacts of nonlinearity, covariance localization, and numerical noise on ESA-based observation-impact predictions. It is discovered that localization applied during data assimilation restricts targeted-observation increments onto the forecast responses of composite reflectivity and 3-hourly accumulated precipitation, making impact predictions poor. In addition, numerical noise introduced by nonlinear perturbation evolution tends to reduce the correlations between observed and predicted impacts; small, random-perturbation experiments often yielded similar impacts on forecasts as targeted observations. Nonlinearity also manifests in the observation impacts when comparing targeted observations with nontargeted, randomly chosen observations; random observations have seemingly the same impact on forecasts as targeted observations. The results, under idealized conditions and simplified ensemble configurations, demonstrate that ESA-based targeting for nonlinear convection forecasts may be most applicable at short time scales. Important implications for ESA-based targeting methods employed with real-time ensemble systems are also discussed.

Current affiliation: Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado.

© 2020 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: Aaron Hill, aaron.hill@colostate.edu
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