• Bailey, S. C. C., C. A. Canter, M. P. Sama, A. L. Houston, and S. W. Smith, 2019: Unmanned aerial vehicles reveal the impact of a total solar eclipse on the atmospheric surface layer. Proc. Roy. Soc., A475, 20190212, https://doi.org/10.1098/RSPA.2019.0212.

    • Search Google Scholar
    • Export Citation
  • Banacos, P. C., and M. L. Ekster, 2010: The association of the elevated mixed layer with significant severe weather events in the northeastern United States. Wea. Forecasting, 25, 10821102, https://doi.org/10.1175/2010WAF2222363.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bolton, D., 1980: The computation of equivalent potential temperature. Mon. Wea. Rev., 108, 10461053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bunkers, M. J., J. R. Wetenkamp Jr., and J. J. Schild, 2010: Observations of the relationship between 700-mb temperaturese and severe weather reports across the contiguous United States. Wea. Forecasting, 25, 799814, https://doi.org/10.1175/2009WAF2222333.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carlson, T. N., and F. H. Ludlam, 1968: Conditions for the occurrence of severe local storms. Tellus, 20, 203226, https://doi.org/10.3402/tellusa.v20i2.10002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carlson, T. N., S. G. Benjamin, G. S. Forbes, and Y.-F. Li, 1983: Elevated mixed layers in the regional severe storm environment: Conceptual model and case studies. Mon. Wea. Rev., 111, 14531473, https://doi.org/10.1175/1520-0493(1983)111<1453:EMLITR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chilson, P. B., and et al. , 2019: Moving towards a network of autonomous UAS atmospheric profiling stations for observations in the Earth’s lower atmosphere: The 3D Mesonet concept. Sensors, 19, 2720, https://doi.org/10.3390/s19122720.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Boer, G., and et al. , 2020: Development of community, capabilities and understanding through unmanned aircraft-based atmospheric research: The LAPSE-RATE campaign. Bull. Amer. Meteor. Soc., 101, E684E699, https://doi.org/10.1175/BAMS-D-19-0050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fawbush, E. J., and R. C. Miller, 1954: The types of air masses in which North American tornadoes form. Bull. Amer. Meteor. Soc., 35, 154165, https://doi.org/10.1175/1520-0477-35.4.154.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gallo, B. T., and et al. , 2017: Breaking new ground in severe weather prediction: The 2015 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. Wea. Forecasting, 32, 15411568, https://doi.org/10.1175/WAF-D-16-0178.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greene, B. R., A. R. Segales, S. Waugh, S. Duthoit, and P. B. Chilson, 2018: Considerations for temperature sensor placement on rotary-wing unmanned aircraft systems. Atmos. Meas. Tech., 11, 55195530, https://doi.org/10.5194/amt-11-5519-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houston, A. L., and J. M. Keeler, 2018: The impact of sensor response and airspeed on the representation of the convective boundary layer and airmass boundaries by small unmanned aircraft systems. J. Atmos. Oceanic Technol., 35, 16871699, https://doi.org/10.1175/JTECH-D-18-0019.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houston, A. L., B. Argrow, J. Elston, J. Lahowetz, E. W. Frew, and P. C. Kennedy, 2012: The collaborative Colorado–Nebraska Unmanned Aircraft System Experiment. Bull. Amer. Meteor. Soc., 93, 3954, https://doi.org/10.1175/2011BAMS3073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Islam, A., A. L. Houston, A. Shankar, and C. Detweiler, 2019: Design and evaluation of sensor housing for boundary layer profiling using multirotors. Sensors, 19, 2481, https://doi.org/10.3390/s19112481.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacob, J. D., P. B. Chilson, A. L. Houston, and S. W. Smith, 2018: Considerations for atmospheric measurements with small unmanned aircraft systems. Atmosphere, 9, 252, https://doi.org/10.3390/atmos9070252.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johns, R. H., and C. A. Doswell III, 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
  • Kiefer, C. M., C. B. Clements, and B. E. Potter, 2012: Application of a mini unmanned aircraft system for in situ monitoring of fire plume thermodynamic properties. J. Atmos. Oceanic Technol., 29, 309315, https://doi.org/10.1175/JTECH-D-11-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knuth, S. L., and J. J. Cassano, 2014: Estimating sensible and latent heat fluxes using the integral method from in situ aircraft measurements. J. Atmos. Oceanic Technol., 31, 19641981, https://doi.org/10.1175/JTECH-D-14-00008.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koch, S. E., M. Fengler, P. B. Chilson, K. L. Elmore, B. Argrow, D. L. Andra Jr., and T. Lindley, 2018: On the use of unmanned aircraft for sampling mesoscale phenomena in the preconvective boundary layer. J. Atmos. Oceanic Technol., 35, 22652288, https://doi.org/10.1175/JTECH-D-18-0101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, T. R., M. Buban, E. Dumas, and C. B. Baker, 2019: On the use of rotary-wing aircraft to sample near-surface thermodynamic fields: Results from recent field campaigns. Sensors, 19, 10, https://doi.org/10.3390/s19010010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leuenberger, D., A. Haefele, N. Omanovic, M. Fengler, G. Martucci, B. Calpini, and O. Fuhrer, 2020: Improving high-impact numerical weather prediction with lidar and drone observations. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-19-0119.1, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moller, A. R., 2001: Severe local storms forecasting. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 433480, https://doi.org/10.1175/1520-0434(1992)007%3C0588:SLSF%3E2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murray, F. W., 1967: On the computation of saturation vapor pressure. J. Appl. Meteor., 6, 203204, https://doi.org/10.1175/1520-0450(1967)006<0203:OTCOSV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NOAA, 2010: National Weather Service manual 10-1401. NOAA Rep., 208 pp., https://www.nws.noaa.gov/directives/sym/pd01014001curr.pdf.

  • Reineman, B. D., L. Lenain, N. M. Statom, and W. K. Melville, 2013: Development and testing of instrumentation for UAV-based flux measurements within terrestrial and marine atmospheric boundary layers. J. Atmos. Oceanic Technol., 30, 12951319, https://doi.org/10.1175/JTECH-D-12-00176.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riganti, C. J., and A. L. Houston, 2017: Rear-flank outflow dynamics and thermodynamics in the 10 June 2010 Last Chance, Colorado, supercell. Mon. Wea. Rev., 145, 24872504, https://doi.org/10.1175/MWR-D-16-0128.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tetens, O., 1930: Uber einige meteorologische Begriffe. Z. Geophys., 6, 297309.

  • van den Kroonenberg, A. C., S. Martin, F. Beyrich, and J. Bange, 2012: Spatially-averaged temperature structure parameter over a heterogeneous surface measured by an unmanned aerial vehicle. Bound.-Layer Meteor., 142, 5577, https://doi.org/10.1007/s10546-011-9662-9.

    • 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, https://doi.org/10.1175/MWR3067.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Sounding Characteristics that Yield Significant Convective Inhibition Errors due to Ascent Rate and Sensor Response of In Situ Profiling Systems

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  • 1 Department of Earth and Atmospheric Sciences, University of Nebraska–Lincoln, Lincoln, Nebraska
  • | 2 Department of Earth and Atmospheric Sciences, Central Michigan University, Mount Pleasant, Michigan
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Abstract

Accurate measurements of the convective inhibition (CIN) associated with capping inversions are critical to forecasts of deep convection initiation. The goal of this work is to determine the sounding characteristics most vulnerable to CIN errors arising from hysteresis associated with sensor response and ascent rate of profiling systems. This examination uses 5058 steady-state analytic soundings prescribed using three free parameters that control inversion depth, static stability, and moisture content. A theoretical well-aspirated first-order sensor mounted on a platform that does not disturb its environment is “flown” in these soundings. Sounding characteristics that result in the largest relative CIN errors are also the characteristics that result in the smallest CIN. Because they are more likely to support deep convection initiation, it is particularly critical that environments with small CIN are represented accurately. The relationship between relative CIN error and CIN exists because sounding characteristics that contribute to large CIN do not proportionally increase the CIN error. Analysis also considers CIN intervals with (operationally important) CIN on the threshold between environments that will and will not support deep convection initiation. For these soundings, CIN error is found to be largest for deep, dry inversions characterized by small static stability.

Corresponding author: Adam L. Houston, ahouston2@unl.edu

Abstract

Accurate measurements of the convective inhibition (CIN) associated with capping inversions are critical to forecasts of deep convection initiation. The goal of this work is to determine the sounding characteristics most vulnerable to CIN errors arising from hysteresis associated with sensor response and ascent rate of profiling systems. This examination uses 5058 steady-state analytic soundings prescribed using three free parameters that control inversion depth, static stability, and moisture content. A theoretical well-aspirated first-order sensor mounted on a platform that does not disturb its environment is “flown” in these soundings. Sounding characteristics that result in the largest relative CIN errors are also the characteristics that result in the smallest CIN. Because they are more likely to support deep convection initiation, it is particularly critical that environments with small CIN are represented accurately. The relationship between relative CIN error and CIN exists because sounding characteristics that contribute to large CIN do not proportionally increase the CIN error. Analysis also considers CIN intervals with (operationally important) CIN on the threshold between environments that will and will not support deep convection initiation. For these soundings, CIN error is found to be largest for deep, dry inversions characterized by small static stability.

Corresponding author: Adam L. Houston, ahouston2@unl.edu
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