Editorial Type:
Article
Article Type:
Research Article

Design and Implementation of a GSI-Based Convection-Allowing Ensemble-Based Data Assimilation and Forecast System for the PECAN Field Experiment. Part II: Overview and Evaluation of a Real-Time System

Aaron Johnson Cooperative Institute for Mesoscale Meteorological Studies, and School of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Aaron Johnson in
Current site
Google Scholar
PubMed Close
,
Xuguang Wang School of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Xuguang Wang in
Current site
Google Scholar
PubMed Close
, and
Samuel Degelia School of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Samuel Degelia in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jun 2017
Collections:
Plains Elevated Convection At Night (PECAN)
DOI:
https://doi.org/10.1175/WAF-D-16-0201.1
Page(s):
1227–1251
Restricted access

Abstract

Multiscale ensemble-based data assimilation and forecasts were performed in real time during the Plains Elevated Convection At Night (PECAN) field experiment. A 20-member ensemble of forecasts at 4-km grid spacing was initialized daily at both 1300 and 1900 UTC, together with a deterministic forecast at 1-km grid spacing initialized at 1300 UTC. The configuration of the GSI-based data assimilation and forecast system was guided by results presented in Part I of this two-part study. The present paper describes the implementation of the real-time system and the extensive forecast products that were generated to support the unique interests of PECAN researchers. Subjective and objective verification of the real-time forecasts from 1 June through 15 July 2015 is conducted, with an emphasis on nocturnal mesoscale convective systems (MCSs), nocturnal convective initiation (CI), nocturnal low-level jets (LLJs), and bores on the nocturnal stable layer. Verification of nocturnal precipitation during overnight hours, a proxy for MCSs, shows both greater skill and spread for the 1300 UTC forecasts than the 1900 UTC forecasts. Verification against observed soundings reveals that the forecast LLJs systematically peak, veer, and dissipate several hours before the observations. Comparisons with bores that passed over an Atmospheric Emitted Radiance Interferometer reveal an ability to predict borelike features that is greatly improved at 1-km, compared with 4-km, grid spacing. Objective verification of forecast CI timing reveals strong sensitivity to the PBL scheme but an overall unbiased ensemble.

© 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: Dr. Aaron Johnson, ajohns14@ou.edu

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

Abstract

Multiscale ensemble-based data assimilation and forecasts were performed in real time during the Plains Elevated Convection At Night (PECAN) field experiment. A 20-member ensemble of forecasts at 4-km grid spacing was initialized daily at both 1300 and 1900 UTC, together with a deterministic forecast at 1-km grid spacing initialized at 1300 UTC. The configuration of the GSI-based data assimilation and forecast system was guided by results presented in Part I of this two-part study. The present paper describes the implementation of the real-time system and the extensive forecast products that were generated to support the unique interests of PECAN researchers. Subjective and objective verification of the real-time forecasts from 1 June through 15 July 2015 is conducted, with an emphasis on nocturnal mesoscale convective systems (MCSs), nocturnal convective initiation (CI), nocturnal low-level jets (LLJs), and bores on the nocturnal stable layer. Verification of nocturnal precipitation during overnight hours, a proxy for MCSs, shows both greater skill and spread for the 1300 UTC forecasts than the 1900 UTC forecasts. Verification against observed soundings reveals that the forecast LLJs systematically peak, veer, and dissipate several hours before the observations. Comparisons with bores that passed over an Atmospheric Emitted Radiance Interferometer reveal an ability to predict borelike features that is greatly improved at 1-km, compared with 4-km, grid spacing. Objective verification of forecast CI timing reveals strong sensitivity to the PBL scheme but an overall unbiased ensemble.

© 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: Dr. Aaron Johnson, ajohns14@ou.edu

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

Save
Cover Weather and Forecasting
Weather and Forecasting

  • Aksoy, A., D. C. Dowell, and C. Snyder, 2010: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part II: Short-range ensemble forecasts. Mon. Wea. Rev., 138, 1273–1292, doi:10.1175/2009MWR3086.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bonner, W. D., 1966: Case study of thunderstorm activity in relation to the low-level jet. Mon. Wea. Rev., 94, 167–178, doi:10.1175/1520-0493(1966)094<0167:CSOTAI>2.3.CO;2.

    • 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, 2394–2416, doi:10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Clark, R., 2016: FP3 Ellis, KS radiosonde data, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 October 2016, http://doi.org/10.5065/D6GM85DZ.

    • Crossref
    • Export Citation

  • Coniglio, M. C., H. E. Brooks, S. J. Weiss, and S. F. Corfidi, 2007: Forecasting the maintenance of quasi-linear mesoscale convective systems. Wea. Forecasting, 22, 556–570, doi:10.1175/WAF1006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, 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, 1982–2005, doi: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. [Available online at https://ams.confex.com/ams/94Annual/webprogram/Paper239030.html.]

  • Duda, J. D., and W. A. Gallus Jr., 2013: The impact of large-scale forcing on skill of simulated convective initiation and upscale evolution with convection-allowing grid spacings in the WRF. Wea. Forecasting, 28, 994–1018, doi:10.1175/WAF-D-13-00005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • French, A. J., and M. D. Parker, 2010: The response of simulated nocturnal convective systems to a developing low-level jet. J. Atmos. Sci., 67, 3384–3408, doi:10.1175/2010JAS3329.1.

    • 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, 767–786, doi:10.1175/BAMS-D-15-00257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Haase, S. P., and R. K. Smith, 1989: The numerical simulation of atmospheric gravity currents. Part II. Environments with stable layers. Geophys. Astrophys. Fluid Dyn., 46, 1–2, 35–51, doi:10.1080/03091928908208903.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Haghi, K., and D. Parson, 2014: A systematic approach to identifying and characterizing atmospheric bores and other fine-line features during IHOP_2002. World Weather Open Science Conf., Montreal, QC, Canada, WMO, SCI-PS228.02. [Available online at http://wwosc2014.org/pdf/20140825-WWOSC-FinalBookofAbstracts.pdf.]

  • Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129–151.

  • Janjić, Z. I., 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271–285, doi:10.1007/s00703-001-0587-6.

  • 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. Wea. Forecasting, 32, 289–315, doi:10.1175/WAF-D-16-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Johnson, A., X. Wang, M. Xue, and F. Kong, 2011: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part II: Ensemble clustering over the whole experiment period. Mon. Wea. Rev., 139, 3694–3710, doi:10.1175/MWR-D-11-00016.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Johnson, A., X. Wang, F. Kong, and M. Xue, 2013: Object-based evaluation of the impact of horizontal grid spacing on convection-allowing forecasts. Mon. Wea. Rev., 141, 3413–3425, doi:10.1175/MWR-D-13-00027.1.

    • 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, 3087–3108, doi:10.1175/MWR-D-14-00345.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jones, T. A., and D. J. Stensrud, 2012: Assimilating AIRS temperature and mixing ratio profiles using an ensemble Kalman filter approach for convective-scale forecasts. Wea. Forecasting, 27, 541–564, doi:10.1175/WAF-D-11-00090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jung, Y., M. Xue, and M. Tong, 2012: Ensemble Kalman filter analyses of the 29–30 May 2004 Oklahoma tornadic thunderstorm using one- and two-moment bulk microphysics schemes, with verification against polarimetric radar data. Mon. Wea. Rev., 140, 1457–1475, doi:10.1175/MWR-D-11-00032.1.

    • 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, 1213–1225, doi:10.1175/BAMS-D-11-00264.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Karyampudi, V. M., S. E. Koch, C. Chen, J. W. Rottman, and M. L. Kaplan, 1995: The influence of the Rocky Mountains on the 13–14 April 1986 severe weather outbreak. Part II: Evolution of a prefrontal bore and its role in triggering a squall line. Mon. Wea. Rev., 123, 1423–1446, doi:10.1175/1520-0493(1995)123<1423:TIOTRM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lutzak, P. A., 2013: A proposal for analyzing and forecasting lower-atmospheric undular bores in the western Gulf of Mexico region. Wea. Forecasting, 28, 55–76, doi:10.1175/WAF-D-12-00051.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Marsham, J. H., S. B. Trier, T. M. Weckwerth, and J. W. Wilson, 2011: Observations of elevated convection initiation leading to a surface-based squall line during 13 June IHOP_2012. Mon. Wea. Rev., 139, 247–271, doi:10.1175/2010MWR3422.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Melhauser, C., and F. Zhang, 2012: Practical and intrinsic predictability of severe convective weather at the mesoscales. J. Atmos. Sci., 69, 3350–3371, doi:10.1175/JAS-D-11-0315.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Parker, M., 2008: Response of simulated squall lines to low-level cooling. J. Atmos. Sci., 65, 1323–1341, doi:10.1175/2007JAS2507.1.

  • Parsons, D. B., B. Geerts, and T. Weckwerth, 2013: Plains Elevated Convection at Night (PECAN) scientific program overview. Dept. of Energy Rep. DOE/SC-ARM-14-035, 15 pp. [Available online at https://www.arm.gov/publications/programdocs/doe-sc-arm-14-035.pdf.]

  • Romine, G. S., C. S. Schwartz, J. Berner, K. R. Fossell, C. Snyder, J. L. Anderson, and M. L. Weisman, 2014: Representing forecast error in a convection-permitting ensemble system. Mon. Wea. Rev., 142, 4519–4541, doi:10.1175/MWR-D-14-00100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rottman, J. W., and J. E. Simpson, 1989: The formation of internal bores in the atmosphere: A laboratory model. Quart. J. Roy. Meteor. Soc., 115, 941–963, doi:10.1002/qj.49711548809.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schumacher, R. S., and A. J. Clark, 2014: Evaluation of ensemble configurations for the analysis and prediction of heavy-rain-producing mesoscale convective systems. Mon. Wea. Rev., 142, 4108–4138, doi:10.1175/MWR-D-13-00357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schwartz, C. S., and Z. Liu, 2014: Convection-permitting forecasts initialized with continuously cycling limited-area 3DVAR, ensemble Kalman filter, and “hybrid” variational–ensemble data assimilation systems. Mon. Wea. Rev., 142, 716–738, doi:10.1175/MWR-D-13-00100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schwartz, C. S., G. S. Romine, K. R. Smith, and M. L. Weisman, 2014: Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter. Wea. Forecasting, 29, 1295–1318, doi:10.1175/WAF-D-13-00145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schwartz, C. S., G. S. Romine, R. A. Sobash, K. R. Fossell, and M. L. Weisman, 2015a: NCAR’s experimental real-time convection-allowing ensemble prediction system. Wea. Forecasting, 30, 1645–1654, doi:10.1175/WAF-D-15-0103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schwartz, C. S., G. S. Romine, M. L. Weisman, R. A. Sobash, K. R. Fossell, K. W. Manning, and S. B. Trier, 2015b: A real-time convection-allowing ensemble prediction system initialized by mesoscale ensemble Kalman filter analyses. Wea. Forecasting, 30, 1158–1181, doi:10.1175/WAF-D-15-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 3019–3032, doi:10.1175/MWR2830.1.

  • Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663–1677, doi:10.1175//2555.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sukoriansky, S., B. Galperin, and V. Perov, 2005: Application of a new spectral theory of stably stratified turbulence to atmospheric boundary layers over sea ice. Bound.-Layer Meteor., 117, 231–257, doi:10.1007/s10546-004-6848-4.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Toth, Z., Y. Zhu, and R. Wobus, 2004: March 2004 upgrades of the NCEP global ensemble forecast system. NOAA/NCEP/EMC. [Available online at http://www.emc.ncep.noaa.gov/gmb/ens/ens_imp_news.html.]

  • Turner, D., 2016: FP3 AERIoe thermodynamic profile retrieval data, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 October 2016, doi:10.5065/D6Z31WV0.

    • 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, 752–771, doi:10.1175/JAMC-D-13-0126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • UCAR/NCAR–Earth Observing Laboratory, 2016a: FP4 NCAR/EOL QC soundings, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 October 2016, doi:10.5065/D63776XH.

    • Crossref
    • Export Citation

  • UCAR/NCAR–Earth Observing Laboratory, 2016b: FP5 NCAR/EOL QC soundings, version 2.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 October 2016, doi:10.5065/D6ZG6QF7.

    • Crossref
    • Export Citation

  • Vanderwende, B. J., J. K. Lundquist, M. E. Rhodes, E. S. Takle, and S. L. Irvin, 2015: Observing and simulating the summertime low-level jet in central Iowa. Mon. Wea. Rev., 143, 2319–2336, doi:10.1175/MWR-D-14-00325.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Vermeesch, K. 2015. FP2 Greensburg, KS radiosonde data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 1 October 2016, doi:10.5065/D6FQ9TPH.

    • Crossref
    • 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, 1447–1471, doi:10.1175/MWR-D-16-0231.1.

    • 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, 5–22, doi:10.1175/MWR3067.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Weckwerth, T. M., and Coauthors, 2004: An overview of the International H2O Project IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253–277, doi:10.1175/BAMS-85-2-253.

    • Crossref
    • Search Google Scholar
    • 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, 62–79, doi:10.1111/j.1600-0870.2007.00273.x.

    • 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, 3243–3263, doi:10.1175/MWR-D-13-00260.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wheatley, D. M., K. H. Knopfmeier, T. A. Jones, and G. J. Creager, 2015: Storm-scale data assimilation and ensemble forecasting with the NSSL Experimental Warn-on-forecast System. Part I: Radar data experiments. Wea. Forecasting, 30, 1795–1817, doi:10.1175/WAF-D-15-0043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wilson, J. W., and R. D. Roberts, 2006: Summary of convective storm initiation and evolution during IHOP: Observational and modeling perspective. Mon. Wea. Rev., 134, 23–47, doi:10.1175/MWR3069.1.

    • 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, 517–538, doi:10.1175/2009MWR2925.1.

    • Crossref
    • Search Google Scholar
    • 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, 1238–1253, doi:10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 1321–1338, doi:10.1175/2011BAMS-D-11-00047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 4024 3494 1763
PDF Downloads 282 62 3