• Achatz, U., B. Ribstein, F. Senf, and R. Klein, 2017: The interaction between synoptic-scale balanced flow and a finite-amplitude mesoscale wave field throughout all atmospheric layers: Weak and moderately strong stratification. Quart. J. Roy. Meteor. Soc., 143, 342361, doi:10.1002/qj.2926.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adams-Selin, R. D., and R. H. Johnson, 2010: Mesoscale surface pressure and temperature features associated with bow echoes. Mon. Wea. Rev., 138, 212227, doi:10.1175/2009MWR2892.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adams-Selin, R. D., and R. H. Johnson, 2013: Examination of gravity waves associated with the 13 March 2003 bow echo. Mon. Wea. Rev., 141, 37353756, doi:10.1175/MWR-D-12-00343.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashley, W. S., and T. L. Mote, 2005: Derecho hazards in the United States. Bull. Amer. Meteor. Soc., 86, 15771592, doi:10.1175/BAMS-86-11-1577.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., J. M. Brown, G. Manikin, and G. Mann, 2007: The RTMA background—Hourly downscaling of RUC data to 5-km detail. 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., 4A.6. [Available online at http://ams.confex.com/ams/pdfpapers/124825.pdf.]

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bentley, M. L., T. L. Mote, and S. F. Byrd, 2000: A synoptic climatology of derecho producing mesoscale convective systems in the north-central Plains. Int. J. Climatol., 20, 13291349, doi:10.1002/1097-0088(200009)20:11<1329::AID-JOC537>3.0.CO;2-F.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosart, L. F., and A. Seimon, 1988: A case study of an unusually intense atmospheric gravity wave. Mon. Wea. Rev., 116, 18571886, doi:10.1175/1520-0493(1988)116<1857:ACSOAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosart, L. F., W. E. Bracken, and A. Seimon, 1998: A study of cyclone mesoscale structure with emphasis on a large-amplitude inertia–gravity wave. Mon. Wea. Rev., 126, 14971527, doi:10.1175/1520-0493(1998)126<1497:ASOCMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, R. A., and V. T. Wood, 2012: The tornadic vortex signature: An update. Wea. Forecasting, 27, 525530, doi:10.1175/WAF-D-11-00111.1.

  • Bullock, R., 2011: Development and implementation of MODE time domain object-based verification. 24th Conf. on Weather and Forecasting/20th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 96. [Available online at https://ams.confex.com/ams/91Annual/webprogram/Paper182677.html.]

  • Centurioni, L., A. Horányi, C. Cardinali, E. Charpentier, and R. Lumpkin, 2017: A global ocean observing system for measuring sea level atmospheric pressure: Effects and impacts on numerical weather prediction. Bull. Amer. Meteor. Soc., 98, 231238, doi:10.1175/BAMS-D-15-00080.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., R. G. Bullock, T. L. Jensen, M. Xue, and F. Kong, 2014: Application of object-based time-domain diagnostics for tracking precipitation systems in convection-allowing models. Wea. Forecasting, 29, 517542, doi:10.1175/WAF-D-13-00098.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coleman, T. A., and K. R. Knupp, 2009: Factors affecting surface wind speeds in gravity waves and wake lows. Wea. Forecasting, 24, 16641679, doi:10.1175/2009WAF2222248.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Compo, G. P., J. S. Whitaker, and P. D. Sardeshmukh, 2006: Feasibility of a 100-year reanalysis using only surface pressure data. Bull. Amer. Meteor. Soc., 87, 175190, doi:10.1175/BAMS-87-2-175.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 128, doi:10.1002/qj.776.

  • Coniglio, M. C., D. J. Stensrud, and M. B. Richman, 2004: An observational study of derecho-producing convective systems. Wea. Forecasting, 19, 320337, doi:10.1175/1520-0434(2004)019<0320:AOSODC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., S. F. Corfidi, and J. S. Kain, 2011: Environment and early evolution of the 8 May 2009 derecho-producing convective system. Mon. Wea. Rev., 139, 10831102, doi:10.1175/2010MWR3413.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crook, N. A., 1988: Trapping of low-level internal gravity waves. J. Atmos. Sci., 45, 15331541, doi:10.1175/1520-0469(1988)045<1533:TOLLIG>2.0.CO;2.

    • 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, doi:10.1175/MWR3145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C. A., B. Brown, R. Bullock, and J. Halley-Gotway, 2009: The Method for Object-Based Diagnostic Evaluation (MODE) applied to numerical forecasts from the 2005 NSSL/SPC Spring Program. Wea. Forecasting, 24, 12521267, doi:10.1175/2009WAF2222241.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Groot-Hedlin, C. D., M. A. H. Hedlin, K. Walker, D. P. Drob, and M. Zumberge, 2008: Study of propagation from the shuttle Atlantis using a large seismic network. J. Acoust. Soc. Amer., 124, 14421451, doi:10.1121/1.2956475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Groot-Hedlin, C. D., M. A. H. Hedlin, and K. T. Walker, 2014: Detection of gravity waves across the USArray: A case study. Earth Planet. Sci. Lett., 402, 346352, doi:10.1016/j.epsl.2013.06.042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Pondeca, M., and Coauthors, 2011: The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current status and development. Wea. Forecasting, 26, 593612, doi:10.1175/WAF-D-10-05037.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Engerer, N. A., D. J. Stensrud, and M. C. Coniglio, 2008: Surface characteristics of observed cold pools. Mon. Wea. Rev., 136, 48394849, doi:10.1175/2008MWR2528.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, J. S., and C. A. Doswell III, 2001: Examination of derecho environments using proximity soundings. Wea. Forecasting, 16, 329342, doi:10.1175/1520-0434(2001)016<0329:EODEUP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Galarneau, T. J., Jr., L. F. Bosart, C. A. Davis, and R. McTaggart-Cowan, 2009: Baroclinic transition of a long-lived mesoscale convective vortex. Mon. Wea. Rev., 137, 562584, doi:10.1175/2008MWR2651.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grivet-Talocia, S., and F. Einaudi, 1998: Wavelet analysis of a microbarograph network. IEEE Trans. Geosci. Remote Sens., 36, 418433, doi:10.1109/36.662727.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grivet-Talocia, S., F. Einaudi, W. L. Clark, R. D. Dennett, G. D. Nastrom, and T. E. VanZandt, 1999: A 4-yr climatology of pressure disturbances using a barometer network in central Illinois. Mon. Wea. Rev., 127, 16131629, doi:10.1175/1520-0493(1999)127<1613:AYCOPD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guastini, C. T., and L. F. Bosart, 2016: Analysis of a progressive derecho climatology and associated formation environments. Mon. Wea. Rev., 144, 13631382, doi:10.1175/MWR-D-15-0256.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hedlin, M. A. H., D. Drob, K. Walker, and C. D. de Groot-Hedlin, 2010: A study of acoustic propagation from a large bolide in the atmosphere with a dense seismic network. J. Geophys. Res., 115, B11312, doi:10.1029/2010JB007669.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hedlin, M. A. H., C. D. de Groot-Hedlin, and D. P. Drob, 2012: A study of infrasound propagation using dense seismic network recordings of surface explosions. Bull. Seismol. Soc. Amer., 102, 19271937, doi:10.1785/0120110300.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., 1994: A general method for tracking analysis and its application to meteorological data. Mon. Wea. Rev., 122, 25732586, doi:10.1175/1520-0493(1994)122<2573:AGMFTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., B. J. Hoskins, J. Boyle, and C. Thorncroft, 2003: A comparison of recent reanalysis datasets using objective feature tracking: Storm tracks and tropical easterly waves. Mon. Wea. Rev., 131, 20122037, doi:10.1175/1520-0493(2003)131<2012:ACORRD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horel, J., and Coauthors, 2002: Mesowest: Cooperative mesonets in the western United States. Bull. Amer. Meteor. Soc., 83, 211225, doi:10.1175/1520-0477(2002)083<0211:MCMITW>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., and K. I. Hodges, 2002: New perspectives on the Northern Hemisphere winter storm tracks. J. Atmos. Sci., 59, 10411061, doi:10.1175/1520-0469(2002)059<1041:NPOTNH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ingleby, B., 2015: Global assimilation of air temperature, humidity, wind and pressure from surface stations. Quart. J. Roy. Meteor. Soc., 141, 504517, doi:10.1002/qj.2372.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacques, A. A., J. D. Horel, E. T. Crosman, F. L. Vernon, and J. Tytell, 2016: The Earthscope US Transportable Array 1 Hz surface pressure dataset. Geosci. Data J., 3, 2936, doi:10.1002/gdj3.37.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, J. T., P. L. MacKeen, A. Witt, E. D. Mitchell, G. J. Stumpf, M. D. Eilts, and K. W. Thomas, 1998: The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting, 13, 263276, doi:10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, S., and G. Lee, 2015: Radar-based cell tracking with fuzzy logic approach. Meteor. Appl., 22, 716730, doi:10.1002/met.1509.

  • Knupp, K. R., and Coauthors, 2014: Meteorological overview of the devastating 27 April 2011 tornado outbreak. Bull. Amer. Meteor. Soc., 95, 10411062, doi:10.1175/BAMS-D-11-00229.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koch, S. E., and C. O’Handley, 1997: Operational forecasting and detection of mesoscale gravity waves. Wea. Forecasting, 12, 253281, doi:10.1175/1520-0434(1997)012<0253:OFADOM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koch, S. E., and S. Saleeby, 2001: An automated system for the analysis of gravity waves and other mesoscale phenomena. Wea. Forecasting, 16, 661679, doi:10.1175/1520-0434(2001)016<0661:AASFTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • König, W., R. Sausen, and F. Sielmann, 1993: Objective identification of cyclones in GCM simulations. J. Climate, 6, 22172231, doi:10.1175/1520-0442(1993)006<2217:OIOCIG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koppel, L. L., L. F. Bosart, and D. Keyser, 2000: A 25-yr climatology of large-amplitude hourly surface pressure changes over the conterminous United States. Mon. Wea. Rev., 128, 5168, doi:10.1175/1520-0493(2000)128<0051:AYCOLA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kravtsov, S., I. Rudeva, and S. K. Gulev, 2015: Reconstructing sea level pressure variability via a feature tracking approach. J. Atmos. Sci., 72, 487506, doi:10.1175/JAS-D-14-0169.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lei, L., and J. L. Anderson, 2014: Impacts of frequent assimilation of surface pressure observations on atmospheric analyses. Mon. Wea. Rev., 142, 44774483, doi:10.1175/MWR-D-14-00097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindzen, R. S., and K. Tung, 1976: Banded convective activity and ducted gravity waves. Mon. Wea. Rev., 104, 16021617, doi:10.1175/1520-0493(1976)104<1602:BCAADG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., D.-G. Xi, Z.-L. Li, and C.-X. Shi, 2014: Automatic tracking and characterization of cumulonimbus clouds from FY-2C geostationary meteorological satellite images. Adv. Meteor., 2014, 478419, doi:10.1155/2014/478419.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loehrer, S. M., and R. H. Johnson, 1995: Surface pressure and precipitation life cycle characteristics of PRE-STORM mesoscale convective systems. Mon. Wea. Rev., 123, 600621, doi:10.1175/1520-0493(1995)123<0600:SPAPLC>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., 1993: Gregarious tropical convection. J. Atmos. Sci., 50, 20262037, doi:10.1175/1520-0469(1993)050<2026:GTC>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McMillen, J. D., and W. J. Steenburgh, 2015: Capabilities and limitations of convection-permitting WRF simulations of lake-effect systems over the Great Salt Lake. Wea. Forecasting, 30, 17111731, doi:10.1175/WAF-D-15-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Metz, N. D., and L. F. Bosart, 2010: Derecho and MCS development, evolution, and multiscale interactions during 3–5 July 2003. Mon. Wea. Rev., 138, 30483070, doi:10.1175/2010MWR3218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mittermaier, M. P., and R. Bullock, 2013: Using MODE to explore the spatial and temporal characteristics of cloud cover forecasts from high-resolution NWP models. Meteor. Appl., 20, 187196, doi:10.1002/met.1393.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Plougonven, R., and F. Zhang, 2014: Internal gravity waves from atmospheric jets and fronts. Rev. Geophys., 52, 3376, doi:10.1002/2012RG000419.

  • Raible, C. C., P. M. Della-Marta, C. Schwierz, H. Wernli, and R. Blender, 2008: Northern Hemisphere extratropical cyclones: A comparison of detection and tracking methods and different reanalyses. Mon. Wea. Rev., 136, 880897, doi:10.1175/2007MWR2143.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramamurthy, M. K., R. M. Rauber, B. P. Collins, and N. K. Malhotra, 1993: A comparative study of large-amplitude gravity-wave events. Mon. Wea. Rev., 121, 29512974, doi:10.1175/1520-0493(1993)121<2951:ACSOLA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruppert, J. H., and L. F. Bosart, 2014: A case study of the interaction of a mesoscale gravity wave with a mesoscale convective system. Mon. Wea. Rev., 142, 14031429, doi:10.1175/MWR-D-13-00274.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, R. S., 1990: Large-amplitude mesoscale wave disturbances within the intense Midwest extratropical cyclone of 15 December 1987. Wea. Forecasting, 5, 533558, doi:10.1175/1520-0434(1990)005<0533:LAMWDW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, B. T., T. E. Castellanos, A. C. Winters, C. M. Mead, A. R. Dean, and R. L. Thompson, 2013: Measured severe convective wind climatology and associated convective modes of thunderstorms in the contiguous United States, 2003–09. Wea. Forecasting, 28, 229236, doi:10.1175/WAF-D-12-00096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., D. M. Wheatley, N. T. Atkins, R. W. Przybylinski, and R. Wolf, 2006: Buyer beware: Some words of caution on the use of severe wind reports in postevent assessment and research. Wea. Forecasting, 21, 408415, doi:10.1175/WAF925.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tyndall, D., and J. Horel, 2013: Impacts of mesonet observations on meteorological surface analyses. Wea. Forecasting, 28, 254269, doi:10.1175/WAF-D-12-00027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tytell, J., F. Vernon, M. Hedlin, C. de Groot Hedlin, J. Reyes, B. Busby, K. Hafner, and J. Eakins, 2016: The USArray Transportable Array as a platform for weather observation and research. Bull. Amer. Meteor. Soc., 97, 603619, doi:10.1175/BAMS-D-14-00204.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., G. P. Compo, X. Wei, and T. M. Hamill, 2004: Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev., 132, 11901200, doi:10.1175/1520-0493(2004)132<1190:RWRUED>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yussouf, N., D. C. Dowell, L. J. Wicker, K. H. Knopfmeier, and D. M. Wheatley, 2015: Storm-scale data assimilation and ensemble forecasts for the 27 April 2011 severe weather outbreak in Alabama. Mon. Wea. Rev., 143, 30443066, doi:10.1175/MWR-D-14-00268.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., S. E. Koch, C. A. Davis, and M. L. Kaplan, 2001: Wavelet analysis and the governing dynamics of a large-amplitude mesoscale gravity-wave event along the East Coast of the United States. Quart. J. Roy. Meteor. Soc., 127, 22092245, doi:10.1002/qj.49712757702.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Tracking Mesoscale Pressure Perturbations Using the USArray Transportable Array

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  • 1 Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah
  • 2 Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
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Abstract

Mesoscale convective phenomena induce pressure perturbations that can alter the strength and magnitude of surface winds, precipitation, and other sensible weather, which, in some cases, can inflict injuries and damage to property. This work extends prior research to identify and characterize mesoscale pressure features using a unique resource of 1-Hz pressure observations available from the USArray Transportable Array (TA) seismic field campaign.

A two-dimensional variational technique is used to obtain 5-km surface pressure analysis grids every 5 min from 1 March to 31 August 2011 from the TA observations and gridded surface pressure from the Real-Time Mesoscale Analysis over a swath of the central United States. Bandpass-filtering and feature-tracking algorithms are employed to isolate, identify, and assess prominent mesoscale pressure perturbations and their properties. Two case studies, the first involving mesoscale convective systems and the second using a solitary gravity wave, are analyzed using additional surface observation and gridded data resources. Summary statistics for tracked features during the period reviewed indicate a majority of perturbations last less than 3 h, produce maximum perturbation magnitudes between 2 and 5 hPa, and move at speeds ranging from 15 to 35 m s−1. The results of this study combined with improvements nationwide in real-time access to pressure observations at subhourly reporting intervals highlight the potential for improved detection and nowcasting of high-impact mesoscale weather features.

© 2017 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: Alexander A. Jacques, alexander.jacques@utah.edu

Abstract

Mesoscale convective phenomena induce pressure perturbations that can alter the strength and magnitude of surface winds, precipitation, and other sensible weather, which, in some cases, can inflict injuries and damage to property. This work extends prior research to identify and characterize mesoscale pressure features using a unique resource of 1-Hz pressure observations available from the USArray Transportable Array (TA) seismic field campaign.

A two-dimensional variational technique is used to obtain 5-km surface pressure analysis grids every 5 min from 1 March to 31 August 2011 from the TA observations and gridded surface pressure from the Real-Time Mesoscale Analysis over a swath of the central United States. Bandpass-filtering and feature-tracking algorithms are employed to isolate, identify, and assess prominent mesoscale pressure perturbations and their properties. Two case studies, the first involving mesoscale convective systems and the second using a solitary gravity wave, are analyzed using additional surface observation and gridded data resources. Summary statistics for tracked features during the period reviewed indicate a majority of perturbations last less than 3 h, produce maximum perturbation magnitudes between 2 and 5 hPa, and move at speeds ranging from 15 to 35 m s−1. The results of this study combined with improvements nationwide in real-time access to pressure observations at subhourly reporting intervals highlight the potential for improved detection and nowcasting of high-impact mesoscale weather features.

© 2017 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: Alexander A. Jacques, alexander.jacques@utah.edu
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