• Benjamin, S. G., B. E. Schwartz, E. J. Szoke, and S. E. Koch, 2004: The value of wind profiler data in U.S. weather forecasting. Bull. Amer. Meteor. Soc., 85, 18711886, https://doi.org/10.1175/BAMS-85-12-1871.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., B. D. Jamison, W. R. Moninger, S. R. Sahm, B. E. Schwartz, and T. W. Schlatter, 2010: Relative short-range forecast impact from aircraft, profiler, radiosonde, VAD, GPS-PW, METAR, and mesonet observations via the RUC hourly assimilation cycle. Mon. Wea. Rev., 138, 13191343, https://doi.org/10.1175/2009MWR3097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berger, H., 2004: Satellite wind superobbing. EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF) Doc. NWPSAF-MO-VS-016, 33 pp., https://www.ssec.wisc.edu/~howardb/Papers/superob_nwpsaf_final.pdf.

  • Blumberg, W. G., T. J. Wagner, D. D. Turner, and J. Correia, 2017: Quantifying the accuracy and uncertainty of diurnal thermodynamic profiles and convection indices derived from the Atmospheric Emitted Radiance Interferometer. J. Appl. Meteor. Climatol., 56, 27472766, https://doi.org/10.1175/JAMC-D-17-0036.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonner, W. D., 1968: Climatology of the low level jet. Mon. Wea. Rev., 96, 833850, https://doi.org/10.1175/1520-0493(1968)096<0833:COTLLJ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bormann, N., A. J. Geer, and P. Bauer, 2011: Estimates of observation-error characteristics in clear and cloudy regions for microwave imager radiances from numerical prediction models. Quart. J. Roy. Meteor. Soc., 137, 20142023, https://doi.org/10.1002/qj.833.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, R., 2016: FP3 Ellis, KS radiosonde data, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6GM85DZ.

    • Crossref
    • 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
  • Corfidi, S. F., S. J. Corfidi, and D. M. Schultz, 2008: Elevated convection and castellanus: Ambiguities, significance, and questions. Wea. Forecasting, 23, 12801303, https://doi.org/10.1175/2008WAF2222118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C. A., B. Brown, and R. Bullock, 2006: Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Mon. Wea. Rev., 134, 17721784, https://doi.org/10.1175/MWR3145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Degelia, S. K., X. Wang, D. J. Stensrud, and A. Johnson, 2018: Understanding the impact of radar and in situ observations on the prediction of a nocturnal convection initiation event on 25 June 2013 using an ensemble-based multiscale data assimilation system. Mon. Wea. Rev., 146, 18371859, https://doi.org/10.1175/MWR-D-17-0128.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delgado, R., and K. Vermeesch, 2016: FP2 UMBC surface weather station data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6SJ1HSG.

    • Crossref
    • Export Citation
  • Dowell, D. C., F. Zhang, L. J. Wicker, C. Synder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 19822005, https://doi.org/10.1175/1520-0493(2004)132<1982:WATRIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, J., and Coauthors, 2014: NCEP regional ensemble update: Current systems and planned storm-scale ensembles. 26th Conf. on Weather Analysis and Forecasting/22nd Conf. on Numerical Weather Prediction, Atlanta, GA, Amer. Meteor. Soc., J1.4, https://ams.confex.com/ams/94Annual/webprogram/Paper239030.html.

  • Earth System Research Laboratory, 2016: The High-Resolution Rapid Refresh (HRRR). NOAA Earth System Research Laboratory, accessed 15 February 2017, https://rapidrefresh.noaa.gov/hrrr/.

  • Ek, M., K. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta Model. J. Geophys. Res., 108, 88518867, https://doi.org/10.1029/2002JD003296.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geer, A. J., and P. Bauer, 2011: Observation errors in all-sky data assimilation. Quart. J. Roy. Meteor. Soc., 137, 20242037, https://doi.org/10.1002/qj.830.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geerts, B., and Coauthors, 2017: The 2015 Plains Elevated Convection At Night (PECAN) field project. Bull. Amer. Meteor. Soc., 98, 767786, https://doi.org/10.1175/BAMS-D-15-00257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, B. N., 1995: Elevated cold-sector severe thunderstorms: A preliminary study. Natl. Wea. Dig., 19, 2531.

  • Grell, G. A., and S. R. Freitas, 2013: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 13, 23 84523 893, https://doi.org/10.5194/acpd-13-23845-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haghi, K. R., and Coauthors, 2019: Bore-ing into nocturnal convection. Bull. Amer. Meteor. Soc., 100, 11031121, https://doi.org/10.1175/BAMS-D-17-0250.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanesiak, J., and D. Turner, 2016a: FP3 University of Manitoba Doppler lidar wind profile data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D60863P5.

    • Crossref
    • Export Citation
  • Hanesiak, J., and D. Turner, 2016b: FP6 University of Manitoba Doppler lidar VAD winds data, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D64F1NTN.

    • Crossref
    • Export Citation
  • Hartung, D. C., J. A. Otkin, R. A. Petersen, D. D. Turner, and W. F. Feltz, 2011: Assimilation of surface-based boundary layer profiler observations during a cool-season weather event using an observing system simulation experiment. Part II: Forecast assessment. Mon. Wea. Rev., 139, 23272346, https://doi.org/10.1175/2011MWR3623.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hitchcock, S. M., M. C. Coniglio, and K. H. Knopfmeier, 2016: Impact of MPEX observations on ensemble analyses and forecasts of the 31 May 2013 convective event over Oklahoma. Mon. Wea. Rev., 144, 28892913, https://doi.org/10.1175/MWR-D-15-0344.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holdridge, D., and D. Turner, 2015: FP6 Hesston, KS radiosonde data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6765CD0.

    • Crossref
    • Export Citation
  • Hong, S., and J. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129151.

  • Horel, J., and Coauthors, 2002: Mesowest: Cooperative Mesonets in the western United States. Bull. Amer. Meteor. Soc., 83, 211225, https://doi.org/10.1175/1520-0477(2002)083<0211:MCMITW>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horgan, K. L., D. M. Schultz, J. E. Hales, S. F. Corfidi, and R. H. Johns, 2007: A five-year climatology of elevated severe convective storms in the United States east of the Rocky Mountains. Wea. Forecasting, 22, 10311044, https://doi.org/10.1175/WAF1032.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., and F. Zhang, 2016: Review of the ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 144, 44894532, https://doi.org/10.1175/MWR-D-15-0440.1.

    • 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, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johns, R. H., and C. A. Doswell, 1992: Severe local storms forecasting. Wea. Forecasting, 7, 588612, https://doi.org/10.1175/1520-0434(1992)007<0588:SLSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., and X. Wang, 2017: Design and implementation of a GSI-based convection-allowing ensemble data assimilation and forecast system for the PECAN field experiment. Part I: Optimal configurations for nocturnal convection prediction using retrospective cases. Wea. Forecasting, 32, 289315, https://doi.org/10.1175/WAF-D-16-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, J. R. Carley, L. J. Wicker, and C. Karstens, 2015: A comparison of multiscale GSI-based EnKF and 3DVar data assimilation using radar and conventional observations for midlatitude convective-scale precipitation forecasts. Mon. Wea. Rev., 143, 30873108, https://doi.org/10.1175/MWR-D-14-00345.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, and S. K. Degelia, 2017: Design and implementation of a GSI-based convection-allowing ensemble data assimilation and forecast system for the PECAN field experiment. Part II: Overview and evaluation of real-time system. Wea. Forecasting, 32, 12271251, https://doi.org/10.1175/WAF-D-16-0201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, K. R. Haghi, and D. B. Parsons, 2018: Evaluation of forecasts of a convectively generated bore using an intensively observed case study from PECAN. Mon. Wea. Rev., 146, 30973122, https://doi.org/10.1175/MWR-D-18-0059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kawabata, T., H. Seko, K. Saito, T. Kuroda, K. Tamiya, T. Tsuyuki, Y. Honda, and Y. Wakazuki, 2007: An assimilation and forecasting experiment of the Nerima Heavy Rainfall with a Cloud-Resolving Nonhydrostatic 4-Dimensional Variational Data Assimilation System. J. Meteor. Soc. Japan, 85, 255276, https://doi.org/10.2151/jmsj.85.255.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kawabata, T., H. Iwai, H. Seko, Y. Shoji, K. Saito, S. Ishii, and K. Mizutani, 2014: Cloud-resolving 4D-Var assimilation of Doppler wind lidar data on a meso-gamma-scale convective system. Mon. Wea. Rev., 142, 44844498, https://doi.org/10.1175/MWR-D-13-00362.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, P., D. Turner, E. Smith, and J. Gebauer, 2016: Mobile PISA 1 OU/NSSL CLAMPS radiosonde data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6416VDH.

    • Crossref
    • Export Citation
  • Knupp, K., and R. Wade, 2016: MP2 UAH MIPS 915 MHz profiler NIMA-processed consensus wind and moments data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6B27SJ2.

    • Crossref
    • Export Citation
  • Lakshmanan, V., T. Smith, G. Stumpf, and K. Hondl, 2007: The Warning Decision Support System-Integrated Information. Wea. Forecasting, 22, 596612, https://doi.org/10.1175/WAF1009.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loehrer, S. M., T. A. Edmands, and J. A. Moore, 1996: TOGA COARE upper-air sounding data archive: Development and quality control procedures. Bull. Amer. Meteor. Soc., 77, 26512672, https://doi.org/10.1175/1520-0477(1996)077<2651:TCUASD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Markowski, P., and Y. Richardson, 2010: Mesoscale Meteorology in Midlatitudes. Wiley-Blackwell, 430 pp.

    • Crossref
    • Export Citation
  • Menzies, R. T., and R. M. Hardesty, 1989: Coherent Doppler lidar for measurements of wind fields. Proc. IEEE, 77, 449462, https://doi.org/10.1109/5.24130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minamide, M., and F. Zhang, 2017: Adaptive observation error inflation for assimilating all-sky satellite radiance. Mon. Wea. Rev., 145, 10631081, https://doi.org/10.1175/MWR-D-16-0257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morse, C. S., R. K. Goodrich, and L. B. Cornman, 2002: The NIMA method for improved moment estimation from Doppler spectra. J. Atmos. Oceanic Technol., 19, 274295, https://doi.org/10.1175/1520-0426-19.3.274.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2006: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397407, https://doi.org/10.1007/s10546-005-9030-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • National Research Council, 2009: Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks. National Academies Press, 250 pp., https://doi.org/10.17226/12540.

    • Crossref
    • Export Citation
  • Newsom, R. K., W. A. Brewer, J. M. Wilczak, D. E. Wolfe, S. P. Oncley, and J. K. Lundquist, 2017: Validating precision estimates in horizontal wind measurements from a Doppler lidar. Atmos. Meas. Tech., 10, 12291240, https://doi.org/10.5194/amt-10-1229-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Otkin, J. A., D. C. Hartung, D. D. Turner, R. A. Petersen, W. F. Feltz, and E. Janzon, 2011: Assimilation of surface-based boundary layer profiler observations during a cool-season weather event using an observing system simulation experiment. Part I: Analysis impact. Mon. Wea. Rev., 139, 23092326, https://doi.org/10.1175/2011MWR3622.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, J. M., E. R. Nielsen, M. D. Parker, S. M. Hitchcock, and R. S. Schumacher, 2017: The impact of low-level moisture errors on model forecasts of an MCS observed during PECAN. Mon. Wea. Rev., 145, 35993624, https://doi.org/10.1175/MWR-D-16-0296.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Privé, N. C., R. M. Errico, and K.-S. Tai, 2014: The impacts of increased frequency of rawinsonde observations on forecast skill investigated with an observing system simulation experiment. Mon. Wea. Rev., 142, 18231834, https://doi.org/10.1175/MWR-D-13-00237.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reif, D. W., and H. B. Bluestein, 2017: A 20-year climatology of nocturnal convection initiation over the central and southern Great Plains during the warm season. Mon. Wea. Rev., 145, 16151639, https://doi.org/10.1175/MWR-D-16-0340.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, E., and Coauthors, 2009: The NCEP North American Mesoscale Modeling System: Recent changes and future plans. 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 2A.4, https://ams.confex.com/ams/23WAF19NWP/techprogram/paper_154114.htm.

  • Schwartz, C. S., and R. A. Sobash, 2017: Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations. Mon. Wea. Rev., 145, 33973418, https://doi.org/10.1175/MWR-D-16-0400.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and Coauthors, 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263280, https://doi.org/10.1175/2009WAF2222267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shao, H., and Coauthors, 2016: Bridging research to operations transitions: Status and plans of community GSI. Bull. Amer. Meteor. Soc., 97, 14271440, https://doi.org/10.1175/BAMS-D-13-00245.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shapiro, A., E. Fedorovich, and S. Rahimi, 2016: A unified theory for the Great Plains nocturnal low-level jet. J. Atmos. Sci., 73, 30373057, https://doi.org/10.1175/JAS-D-15-0307.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shin, H., and S.-Y. Hong, 2011: Intercomparison of planetary boundary-layer parameterizations in the WRF Model for a single day from CASES-99. Bound.-Layer Meteor., 139, 261281, https://doi.org/10.1007/s10546-010-9583-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sivaraman, C., L. Ma, L. Riihimaki, P. Muradyan, R. Coulter, S. Collis, and S. Xie, 1990: Radar wind profiler (915RWPPRECIPCON; updated hourly) from Southern Great Plains (SGP) central facility (C1), NW radar wind profiler site (I10), NE radar wind profiler site (8). Atmospheric Radiation Measurement (ARM) climate research facility data archive, accessed 1 June 2018, https://doi.org/10.5439/1025127.

    • Crossref
    • 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, E. N., J. A. Gibbs, E. Fedorovich, and T. Bonin, 2015: WRF Model study of the Great Plains low-level jet: Effects of grid spacing and boundary layer parameterizations. 22nd Symp. on Boundary Layers and Turbulence, Salt Lake City, UT, Amer. Meteor. Soc., 14B.1, https://ams.confex.com/ams/32AgF22BLT3BG/webprogram/Paper294866.html.

  • Smith, T. M., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) severe weather and aviation products: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 16171630, https://doi.org/10.1175/BAMS-D-14-00173.1.

    • 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, https://doi.org/10.1175/MWR-D-14-00126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stelten, S., and W. A. Gallus, 2017: Pristine nocturnal convective initiation: A climatology and preliminary examination of predictability. Wea. Forecasting, 32, 16131635, https://doi.org/10.1175/WAF-D-16-0222.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storm, B., J. Dudhia, S. Basu, A. Swift, and I. Giammanco, 2009: Evaluation of the Weather Research and Forecasting Model on forecasting low-level jets: Implications for wind energy. Wind Energy, 12, 8190, https://doi.org/10.1002/we.288.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, J., and S. B. Trier, 2018: Physical processes leading to elevated convection initiation during 25–26 June PECAN: Convective-scale reanalysis based on a radar data assimilation system. Special Symp. on Plains Elevated Convection At Night (PECAN), Austin, TX, Amer. Meteor. Soc., 1.6, https://ams.confex.com/ams/98Annual/webprogram/Paper336167.html.

  • Surcel, M., M. Berenguer, and I. Zawadzki, 2010: The diurnal cycle of precipitation from continental radar mosaics and numerical weather prediction models. Part I: Methodology and seasonal comparison. Mon. Wea. Rev., 138, 30843106, https://doi.org/10.1175/2010MWR3125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., and Coauthors, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97137, https://doi.org/10.1007/s00703-001-0594-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trier, S. B., and D. B. Parsons, 1993: Evolution of environmental conditions preceding the development of a nocturnal mesoscale convective complex. Mon. Wea. Rev., 121, 10781098, https://doi.org/10.1175/1520-0493(1993)121<1078:EOECPT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trier, S. B., J. W. Wilson, D. A. Ahijevych, and R. A. Sobash, 2017: Mesoscale vertical motions near nocturnal convection initiation in PECAN. Mon. Wea. Rev., 145, 29192941, https://doi.org/10.1175/MWR-D-17-0005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trier, S. B., R. D. Roberts, J. Sun, T. M. Weckwerth, and J. W. Wilson, 2018: Physical processes influencing elevated convection initiation during 25–26 June PECAN: Observations and numerical simulations. Special Symp. on Plains Elevated Convection At Night (PECAN), Austin, TX, Amer. Meteor. Soc., 1.5, https://ams.confex.com/ams/98Annual/webprogram/Paper335614.html.

  • Turner, D., 2016a: FP2 AERIoe thermodynamic profile retrieval data, version 1.0. UCAR/NCAR–Earth Observation Laboratory, accessed 1 June 2018, https://doi.org/10.5065/d6x63k9k.

    • Crossref
    • Export Citation
  • Turner, D., 2016b: FP3 AERIoe thermodynamic profile retrieval data, version 2.0. UCAR/NCAR–Earth Observation Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6Z31WV0.

    • Crossref
    • Export Citation
  • Turner, D., 2016c: FP5 AERIoe thermodynamic profile retrieval data, version 2.0. UCAR/NCAR–Earth Observation Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D61V5C5J.

    • Crossref
    • Export Citation
  • Turner, D., 2016d: FP6 ARM surface meteorology data, version 1.0. UCAR/NCAR– Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6RR1WN0.

    • Crossref
    • Export Citation
  • Turner, D., 2016e: MP1 OU/NSSL CLAMPS Doppler lidar VAD wind data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6BR8QJH.

    • Crossref
    • Export Citation
  • Turner, D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based Atmospheric Emitted Radiance Interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752771, https://doi.org/10.1175/JAMC-D-13-0126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • UCAR/NCAR, 2015a: FP1 ARM central facility radiosonde data, version 1. UCAR/NCAR–Earth Observation Laboratory, accessed 1 June 2018, https://data.eol.ucar.edu/dataset/485.021.

  • UCAR/NCAR, 2015b: FP3 FP4 FP5 QC 5 min surface data, tilt corrected, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6BZ645V.

    • Crossref
    • Export Citation
  • UCAR/NCAR, 2015c: FP4 NCAR/EOL 915 MHz profiler NIMA consensus winds and moments, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6RV0KXH.

    • Crossref
    • Export Citation
  • UCAR/NCAR, 2015d: FP5 NCAR/EOL 915 MHz profiler 30 minute consensus winds and moments data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6H993DQ.

    • Crossref
    • Export Citation
  • UCAR/NCAR, 2016a: FP4 NCAR/EOL QC soundings, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D63776XH.

    • Crossref
    • Export Citation
  • UCAR/NCAR, 2016b: FP5 NCAR/EOL QC soundings, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6ZG6QF7.

    • Crossref
    • Export Citation
  • UCAR/NCAR, 2016c: MP4 NCAR/EOL MISS 915 MHz profiler 30 minute consensus winds and moments and surface meteorology data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6RJ4GPJ.

    • Crossref
    • Export Citation
  • UCAR/NCAR, 2016d: MP4 NCAR/EOL QC soundings, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6707ZNV.

    • Crossref
    • Export Citation
  • UCAR/NCAR, 2017: FP3 NCAR/EOL 449MHz profiler 30 minute consensus winds data, version 1.0 [PRELIMINARY]. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D66W98T7.

    • Crossref
    • Export Citation
  • Vaisala, 2017: Vaisala Radiosonde RS41 Measurement Performance. Ref. B211356EN-B, 28 pp., https://www.vaisala.com/sites/default/files/documents/WEA-MET-RS41-Performance-White-paper-B211356EN-B-LOW-v3.pdf.

  • Vermeesch, K., 2015: FP2 Greensburg, KS radiosonde data, version 1.0. UCAR/NCAR–Earth Observation Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6FQ9TPH.

    • Crossref
    • Export Citation
  • Wagner, T., D. Turner, and R. Newsom, 2016a: MP3 University of Wisconsin SPARC Doppler lidar VAD wind data, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6V9869B.

    • Crossref
    • Export Citation
  • Wagner, T., E. Olson, N. Smith, and W. Feltz, 2016b: MP3 University of Wisconsin SPARC AERIoe thermodynamic profile data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D60Z71HC.

    • Crossref
    • Export Citation
  • Wagner, T., E. Olson, N. Smith, and W. Feltz, 2016c: Mobile PISA 3 UW/SSEC SPARC radiosonde data, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6VH5M7B.

    • Crossref
    • Export Citation
  • Wagner, T., E. Olson, N. Smith, and W. Feltz, 2016d: MP3 University of Wisconsin SPARC surface meteorological data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6N014XZ.

    • Crossref
    • Export Citation
  • Wang, X., D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3DVar-based ensemble–variational hybrid data assimilation for NCEP Global Forecast System: Single-resolution experiments. Mon. Wea. Rev., 141, 40984117, https://doi.org/10.1175/MWR-D-12-00141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., and X. Wang, 2017: Direct assimilation of radar reflectivity without tangent linear and adjoint of the nonlinear observation operator in the GSI-based EnVar system: Methodology and experiment with the 8 May 2003 Oklahoma City tornadic supercell. Mon. Wea. Rev., 145, 14471471, https://doi.org/10.1175/MWR-D-16-0231.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waugh, S., and C. Ziegler, 2017: NSSL mobile mesonet data, version 1.1. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D64M92RG.

    • Crossref
    • Export Citation
  • Wei, M., Z. Toth, R. Wobus, and Y. Zhu, 2008: Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus, 60A, 6279, https://doi.org/10.1111/j.1600-0870.2007.00273.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., C. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0–36-h explicit convective forecasts with the WRF-ARW Model. Wea. Forecasting, 23, 407437, https://doi.org/10.1175/2007WAF2007005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheatley, D. M., N. Yussouf, and D. J. Stensrud, 2014: Ensemble Kalman filter analyses and forecasts of a severe mesoscale convective system using different choices of microphysics schemes. Mon. Wea. Rev., 142, 32433263, https://doi.org/10.1175/MWR-D-13-00260.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136, 463482, https://doi.org/10.1175/2007MWR2018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., S. B. Trier, D. W. Reif, R. D. Roberts, and T. M. Weckwerth, 2018: Nocturnal elevated convection initiation of the PECAN 4 July hailstorm. Mon. Wea. Rev., 146, 243262, https://doi.org/10.1175/MWR-D-17-0176.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wulfmeyer, V., H. Bauer, M. Grzeschik, A. Behrendt, F. Vandenberghe, E. V. Browell, S. Ismail, and R. A. Rerrare, 2006: Four-dimensional variational assimilation of water vapor differential absorption lidar data: The first case study within IHOP_2002. Mon. Wea. Rev., 134, 209230, https://doi.org/10.1175/MWR3070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621638, https://doi.org/10.1175/BAMS-D-14-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ziegler, C., M. Coniglio, M. Parker, and R. Schumacher, 2016: CSU/NCSU/NSSL MGAUS radiosonde data, version 3.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 June 2018, https://doi.org/10.5065/D6W66HXN.

    • Crossref
    • Export Citation
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An Evaluation of the Impact of Assimilating AERI Retrievals, Kinematic Profilers, Rawinsondes, and Surface Observations on a Forecast of a Nocturnal Convection Initiation Event during the PECAN Field Campaign

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  • 1 School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 2 Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania
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Abstract

Numerical weather prediction models often fail to correctly forecast convection initiation (CI) at night. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This study evaluates the impacts made to a nocturnal CI forecast on 26 June 2015 by assimilating a network of atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radio wind profilers, high-frequency rawinsondes, and mobile surface observations using an advanced, ensemble-based data assimilation system. Relative to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI event. Specifically, radio wind profilers and rawinsondes are shown to be the most impactful instrument by enhancing the moisture advection into the region of CI in the forecast. Assimilating thermodynamic profiles collected by the AERIs increases midlevel moisture and improves the ensemble probability of CI in the forecast. The impacts of assimilating the radio wind profilers, AERI retrievals, and rawinsondes remain large throughout forecasting the growth of the CI event into a mesoscale convective system. Assimilating Doppler lidar and surface data only slightly improves the CI forecast by enhancing the convergence along an outflow boundary that partially forces the nocturnal CI event. Our findings suggest that a mesoscale network of profiling and surface instruments has the potential to greatly improve short-term forecasts of nocturnal convection.

© 2019 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: Samuel K. Degelia, sdegelia@ou.edu

This article is included in the Plains Elevated Convection At Night (PECAN) Special Collection.

Abstract

Numerical weather prediction models often fail to correctly forecast convection initiation (CI) at night. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This study evaluates the impacts made to a nocturnal CI forecast on 26 June 2015 by assimilating a network of atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radio wind profilers, high-frequency rawinsondes, and mobile surface observations using an advanced, ensemble-based data assimilation system. Relative to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI event. Specifically, radio wind profilers and rawinsondes are shown to be the most impactful instrument by enhancing the moisture advection into the region of CI in the forecast. Assimilating thermodynamic profiles collected by the AERIs increases midlevel moisture and improves the ensemble probability of CI in the forecast. The impacts of assimilating the radio wind profilers, AERI retrievals, and rawinsondes remain large throughout forecasting the growth of the CI event into a mesoscale convective system. Assimilating Doppler lidar and surface data only slightly improves the CI forecast by enhancing the convergence along an outflow boundary that partially forces the nocturnal CI event. Our findings suggest that a mesoscale network of profiling and surface instruments has the potential to greatly improve short-term forecasts of nocturnal convection.

© 2019 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: Samuel K. Degelia, sdegelia@ou.edu

This article is included in the Plains Elevated Convection At Night (PECAN) Special Collection.

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