Evaluating Smartphone Pressure Observations for Mesoscale Analyses and Forecasts

Luke E. Madaus Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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Clifford F. Mass Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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Abstract

Smartphone pressure observations have the potential to greatly increase surface observation density on convection-resolving scales. Currently available smartphone pressure observations are tested through assimilation in a mesoscale ensemble for a 3-day, convectively active period in the eastern United States. Both raw pressure (altimeter) observations and 1-h pressure (altimeter) tendency observations are considered. The available observation density closely follows population density, but observations are also available in rural areas. The smartphone observations are found to contain significant noise, which can limit their effectiveness. The assimilated smartphone observations contribute to small improvements in 1-h forecasts of surface pressure and 10-m wind, but produce larger errors in 2-m temperature forecasts. Short-term (0–4 h) precipitation forecasts are improved when smartphone pressure and pressure tendency observations are assimilated as compared with an ensemble that assimilates no observations. However, these improvements are limited to broad, mesoscale features with minimal skill provided at convective scales using the current smartphone observation density. A specific mesoscale convective system (MCS) is examined in detail, and smartphone pressure observations captured the expected dynamic structures associated with this feature. Possibilities for further development of smartphone observations are discussed.

© 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 e-mail: Luke E. Madaus, lmadaus@atmos.washington.edu

Abstract

Smartphone pressure observations have the potential to greatly increase surface observation density on convection-resolving scales. Currently available smartphone pressure observations are tested through assimilation in a mesoscale ensemble for a 3-day, convectively active period in the eastern United States. Both raw pressure (altimeter) observations and 1-h pressure (altimeter) tendency observations are considered. The available observation density closely follows population density, but observations are also available in rural areas. The smartphone observations are found to contain significant noise, which can limit their effectiveness. The assimilated smartphone observations contribute to small improvements in 1-h forecasts of surface pressure and 10-m wind, but produce larger errors in 2-m temperature forecasts. Short-term (0–4 h) precipitation forecasts are improved when smartphone pressure and pressure tendency observations are assimilated as compared with an ensemble that assimilates no observations. However, these improvements are limited to broad, mesoscale features with minimal skill provided at convective scales using the current smartphone observation density. A specific mesoscale convective system (MCS) is examined in detail, and smartphone pressure observations captured the expected dynamic structures associated with this feature. Possibilities for further development of smartphone observations are discussed.

© 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 e-mail: Luke E. Madaus, lmadaus@atmos.washington.edu
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  • Anderson, J., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J., 2012: Localization and sampling error correction in ensemble Kalman filter data assimilation. Mon. Wea. Rev., 140, 23592371, doi:10.1175/MWR-D-11-00013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 12831296, doi:10.1175/2009BAMS2618.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S., and Coauthors, 2015: 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
  • Berner, J., S. Ha, J. P. Hacker, A. Fournier, and C. Snyder, 2011: Model uncertainty in a mesoscale ensemble prediction system: Stochastic versus multiphysics representations. Mon. Wea. Rev., 139, 19721995, doi:10.1175/2010MWR3595.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 13, doi:10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burton, P., 2013: IFS documention–Cy40r1. Part I: Observations. ECMWF Tech. Rep., 76 pp. [Available online at http://www.ecmwf.int/sites/default/files/IFS_CY40R1_Part1.pdf.]

  • Dabberdt, W. F., and Coauthors, 2005: Multifunctional mesoscale observing networks. Bull. Amer. Meteor. Soc., 86, 961982, doi:10.1175/BAMS-86-7-961.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, J., M. Xue, and K. Droegemeier, 2011: The analysis and impact of simulated high-resolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter. Meteor. Atmos. Phys., 112, 4161, doi:10.1007/s00703-011-0130-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dubin, M., A. R. Hull, and K. S. W. Champion, 1976: U.S. Standard Atmosphere, 1976. NOAA/NASA Tech. Rep., U.S. Government Printing Office, 227 pp. [Available online at http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19770009539_1977009539.pdf.]

  • Fowle, M. A., and P. J. Roebber, 2003: Short-range (0–48 h) numerical prediction of convective occurrence, mode, and location. Wea. Forecasting, 18, 782794, doi:10.1175/1520-0434(2003)018<0782:SHNPOC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gallus, W. A., Jr., J. Correia, and I. Jankov, 2005: The 4 June 1999 derecho event: A particularly difficult challenge for numerical weather prediction. Wea. Forecasting, 20, 705728, doi:10.1175/WAF883.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaspari, G., and S. Cohn, 2006: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757, doi:10.1002/qj.49712555417.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ha, S., J. Berner, and C. Snyder, 2015: A comparison of model error representations in mesoscale ensemble data assimilation. Mon. Wea. Rev., 143, 38933911, doi:10.1175/MWR-D-14-00395.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., 2004: Mesoscale convective systems. Rev. Geophys., 42, RG4003, doi:10.1029/2004RG000150.

  • Hu, M., H. Shao, D. Stark, and K. Newman, 2013: Gridpoint statistical interpolation (GSI) version 3.2 user’s guide. DTC Tech. Rep., Developmental Testbed Center, 181 pp. [Available online at http://www.dtcenter.org/com-GSI/users/docs/users_guide/GSIUserGuide_v3.2.pdf.]

  • Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]

  • Madaus, L. E., and G. J. Hakim, 2016: Observable surface anomalies preceding simulated isolated convective initiation. Mon. Wea. Rev., 144, 22652284, doi:10.1175/MWR-D-15-0332.1.

    • 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, doi:10.1175/MWR-D-13-00269.1.

    • 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
  • Mass, C. F., D. Ovens, K. Westrick, and B. A. Colle, 2002: Does increasing horizontal resolution produce more skillful forecasts? Bull. Amer. Meteor. Soc., 83, 407430, doi:10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPherson, R. A., and Coauthors, 2007: Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma Mesonet. J. Atmos. Oceanic Technol., 24, 301321, doi:10.1175/JTECH1976.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912, doi:10.2151/jmsj.87.895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olson, J. B., and J. M. Brown, 2012: Modifications to the MYNN PBL and surface layer scheme for WRF-ARW. Proc. 2012 WRF Users Workshop, Boulder, CO, NCAR, 3.3. [Available online at http://www2.mmm.ucar.edu/wrf/users/workshops/WS2012/abstracts/3.3.htm.]

  • Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 7897, doi:10.1175/2007MWR2123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., D. M. Schultz, and R. Romero, 2002: Synoptic regulation of the 3 May 1999 tornado outbreak. Wea. Forecasting, 17, 399429, doi:10.1175/1520-0434(2002)017<0399:SROTMT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., D. M. Schultz, B. A. Colle, and D. J. Stensrud, 2004: Toward improved prediction: High-resolution and ensemble modeling systems in operations. Wea. Forecasting, 19, 936949, doi:10.1175/1520-0434(2004)019<0936:TIPHAE>2.0.CO;2.

    • 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, doi:10.1175/2009WAF2222267.1.

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

    • Crossref
    • Export Citation
  • Smirnova, T., J. Brown, S. Benjamin, and J. Kenyon, 2015: Modifications to the Rapid Update Cycle land surface model (RUC LSM) available in the Weather Research and Forecast (WRF) Model. Mon. Wea. Rev., 144, 18511865, doi:10.1175/MWR-D-15-0198.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snook, N., M. Xue, and Y. Jung, 2015: Multiscale EnKF assimilation of radar and conventional observations and ensemble forecasting for a tornadic mesoscale convective system. Mon. Wea. Rev., 143, 10351057, doi:10.1175/MWR-D-13-00262.1.

    • 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, doi:10.1175/MWR-D-14-00126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and J. M. Fritsch, 1994: Mesoscale convective systems in weakly forced large-scale environments. Part III: Numerical simulations and implications for operational forecasting. Mon. Wea. Rev., 122, 20842104, doi:10.1175/1520-0493(1994)122<2084:MCSIWF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, J., and Coauthors, 2014: Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bull. Amer. Meteor. Soc., 95, 409426, doi:10.1175/BAMS-D-11-00263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, doi:10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheatley, D. M., and D. J. Stensrud, 2010: The impact of assimilating surface pressure observations on severe weather events in a WRF mesoscale ensemble system. Mon. Wea. Rev., 138, 16731694, doi:10.1175/2009MWR3042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., and W. J. Martin, 2006: A high-resolution modeling study of the 24 May 2002 dryline case during IHOP. Part I: Numerical simulation and general evolution of the dryline and convection. Mon. Wea. Rev., 134, 149171, doi:10.1175/MWR3071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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