Editorial Type:
Article
Article Type:
Research Article

Evaluation of Ensemble Configurations for the Analysis and Prediction of Heavy-Rain-Producing Mesoscale Convective Systems

Russ S. Schumacher Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Search for other papers by Russ S. Schumacher in
Current site
Google Scholar
PubMed Close
and
Adam J. Clark Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/ National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Adam J. Clark in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Nov 2014
DOI:
https://doi.org/10.1175/MWR-D-13-00357.1
Page(s):
4108–4138
Restricted access

Abstract

This study investigates probabilistic forecasts made using different convection-allowing ensemble configurations for a three-day period in June 2010 when numerous heavy-rain-producing mesoscale convective systems (MCSs) occurred in the United States. These MCSs developed both along a baroclinic zone in the Great Plains, and in association with a long-lived mesoscale convective vortex (MCV) in Texas and Arkansas. Four different ensemble configurations were developed using an ensemble-based data assimilation system. Two configurations used continuously cycled data assimilation, and two started the assimilation 24 h prior to the initialization of each forecast. Each configuration was run with both a single set of physical parameterizations and a mixture of physical parameterizations. These four ensemble forecasts were also compared with an ensemble run in real time by the Center for the Analysis and Prediction of Storms (CAPS). All five of these ensemble systems produced skillful probabilistic forecasts of the heavy-rain-producing MCSs, with the ensembles using mixed physics providing forecasts with greater skill and less overall bias compared to the single-physics ensembles. The forecasts using ensemble-based assimilation systems generally outperformed the real-time CAPS ensemble at lead times of 6–18 h, whereas the CAPS ensemble was the most skillful at forecast hours 24–30, though it also exhibited a wet bias. The differences between the ensemble precipitation forecasts were found to be related in part to differences in the analysis of the MCV and its environment, which in turn affected the evolution of errors in the forecasts of the MCSs. These results underscore the importance of representing model error in convection-allowing ensemble analysis and prediction systems.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/MWR-D-13-00357.s1.

Corresponding author address: Russ Schumacher, Department of Atmospheric Science, Colorado State University, 1371 Campus Delivery, Fort Collins, CO 80523. E-mail: russ.schumacher@colostate.edu

Abstract

This study investigates probabilistic forecasts made using different convection-allowing ensemble configurations for a three-day period in June 2010 when numerous heavy-rain-producing mesoscale convective systems (MCSs) occurred in the United States. These MCSs developed both along a baroclinic zone in the Great Plains, and in association with a long-lived mesoscale convective vortex (MCV) in Texas and Arkansas. Four different ensemble configurations were developed using an ensemble-based data assimilation system. Two configurations used continuously cycled data assimilation, and two started the assimilation 24 h prior to the initialization of each forecast. Each configuration was run with both a single set of physical parameterizations and a mixture of physical parameterizations. These four ensemble forecasts were also compared with an ensemble run in real time by the Center for the Analysis and Prediction of Storms (CAPS). All five of these ensemble systems produced skillful probabilistic forecasts of the heavy-rain-producing MCSs, with the ensembles using mixed physics providing forecasts with greater skill and less overall bias compared to the single-physics ensembles. The forecasts using ensemble-based assimilation systems generally outperformed the real-time CAPS ensemble at lead times of 6–18 h, whereas the CAPS ensemble was the most skillful at forecast hours 24–30, though it also exhibited a wet bias. The differences between the ensemble precipitation forecasts were found to be related in part to differences in the analysis of the MCV and its environment, which in turn affected the evolution of errors in the forecasts of the MCSs. These results underscore the importance of representing model error in convection-allowing ensemble analysis and prediction systems.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/MWR-D-13-00357.s1.

Corresponding author address: Russ Schumacher, Department of Atmospheric Science, Colorado State University, 1371 Campus Delivery, Fort Collins, CO 80523. E-mail: russ.schumacher@colostate.edu

Supplementary Materials

    • Supplemental Materials (GIF 6.09 MB)
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884–2903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72–83, doi:10.1111/j.1600-0870.2008.00361.x.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 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, 1283–1296, doi:10.1175/2009BAMS2618.1.

    • Search Google Scholar
    • Export Citation

  • Augustine, J. A., and F. Caracena, 1994: Lower-tropospheric precursors to nocturnal MCS development over the central United States. Wea. Forecasting, 9, 116–135, doi:10.1175/1520-0434(1994)009<0116:LTPTNM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ballish, B. A., and V. Krishna Kumar, 2008: Systematic differences in aircraft and radiosonde temperatures. Bull. Amer. Meteor. Soc., 89, 1689–1708, doi:10.1175/2008BAMS2332.1.

    • Search Google Scholar
    • Export Citation

  • Barker, D., and Coauthors, 2012: The Weather Research and Forecasting model’s community variational/ensemble data assimilation system: WRFDA. Bull. Amer. Meteor. Soc., 93, 831–843, doi:10.1175/BAMS-D-11-00167.1.

    • Search Google Scholar
    • Export Citation

  • Berner, J., S.-Y. 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, 1972–1995, doi:10.1175/2010MWR3595.1.

    • Search Google Scholar
    • Export Citation

  • Bonner, W. D., 1968: Climatology of the low level jet. Mon. Wea. Rev., 96, 833–850, doi:10.1175/1520-0493(1968)096<0833:COTLLJ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bouttier, F., B. Vié, O. Nuissier, and L. Raynaud, 2012: Impact of stochastic physics in a convection-permitting ensemble. Mon. Wea. Rev., 140, 3706–3721, doi:10.1175/MWR-D-12-00031.1.

    • Search Google Scholar
    • Export Citation

  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585, doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Clark, A. J., W. A. Gallus Jr., M. Xue, and F. Kong, 2009: A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles. Wea. Forecasting, 24, 1121–1140, doi:10.1175/2009WAF2222222.1.

    • Search Google Scholar
    • Export Citation

  • Clark, A. J., W. A. Gallus Jr., M. Xue, and F. Kong, 2010: Convection-allowing and convection-parameterizing ensemble forecasts of a mesoscale convective vortex and associated severe weather environment. Wea. Forecasting, 25, 1052–1081, doi:10.1175/2010WAF2222390.1.

    • Search Google Scholar
    • Export Citation

  • Clark, A. J., and Coauthors, 2011: Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble. Mon. Wea. Rev., 139, 1410–1418, doi:10.1175/2010MWR3624.1.

    • Search Google Scholar
    • Export Citation

  • Clark, A. J., and Coauthors, 2012: An overview of the 2010 Hazardous Weather Testbed Experimental Forecast Program spring experiment. Bull. Amer. Meteor. Soc., 93, 55–74, doi:10.1175/BAMS-D-11-00040.1.

    • Search Google Scholar
    • Export Citation

  • Clark, A. J., J. Gao, P. T. Marsh, T. Smith, J. S. Kain, J. Correia Jr., M. Xue, and F. Kong, 2013: Tornado pathlength forecasts from 2010 to 2011 using ensemble updraft helicity. Wea. Forecasting, 28, 387–407, doi:10.1175/WAF-D-12-00038.1.

    • Search Google Scholar
    • Export Citation

  • Davis, C. A., and S. B. Trier, 2002: Cloud-resolving simulations of mesoscale vortex intensification and its effect on a serial mesoscale convective system. Mon. Wea. Rev., 130, 2839–2858, doi:10.1175/1520-0493(2002)130<2839:CRSOMV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Davis, C. A., D. A. Ahijevych, and S. B. Trier, 2002: Detection and prediction of warm season midtropospheric vortices by the Rapid Update Cycle. Mon. Wea. Rev., 130, 24–42, doi:10.1175/1520-0493(2002)130<0024:DAPOWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Du, J., S. L. Mullen, and F. Sanders, 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125, 2427–2459, doi:10.1175/1520-0493(1997)125<2427:SREFOQ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., 2008: Fuzzy verification of high-resolution gridded forecasts: A review and proposed framework. Meteor. Appl., 15, 51–64, doi:10.1002/met.25.

    • Search Google Scholar
    • Export Citation

  • Eckel, F. A., and C. F. Mass, 2005: Aspects of effective mesoscale, short-range ensemble forecasting. Wea. Forecasting, 20, 328–350, doi:10.1175/WAF843.1.

    • Search Google Scholar
    • Export Citation

  • Fujita, T., D. J. Stensrud, and D. C. Dowell, 2007: Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties. Mon. Wea. Rev., 135, 1846–1868, doi:10.1175/MWR3391.1.

    • Search Google Scholar
    • Export Citation

  • Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723–757, doi:10.1002/qj.49712555417.

    • Search Google Scholar
    • Export Citation

  • Gilmour, I., L. A. Smith, and R. Buizza, 2001: Linear regime duration: Is 24 hours a long time in synoptic weather forecasting? J. Atmos. Sci., 58, 3525–3539, doi:10.1175/1520-0469(2001)058<3525:LRDIHA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ha, S.-Y., and C. Snyder, 2014: Influence of surface observations in mesoscale data assimilation using an ensemble Kalman filter. Mon. Wea. Rev., 142, 1489–1508, doi:10.1175/MWR-D-13-00108.1.

    • Search Google Scholar
    • Export Citation

  • Hakim, G. J., and R. D. Torn, 2008: Ensemble synoptic analysis. Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting: A Tribute to Fred Sanders, Meteor. Monogr., No. 55, Amer. Meteor. Soc., 147–161.

  • Hawblitzel, D. P., F. Zhang, Z. Meng, and C. A. Davis, 2007: Probabilistic evaluation of the dynamics and predictability of the mesoscale convective vortex of 10–13 June 2003. Mon. Wea. Rev., 135, 1544–1563, doi:10.1175/MWR3346.1.

    • Search Google Scholar
    • Export Citation

  • Haynes, P. H., and M. E. McIntyre, 1987: On the evolution of vorticity and potential vorticity in the presence of diabatic heating and frictional or other forces. J. Atmos. Sci., 44, 828–841, doi:10.1175/1520-0469(1987)044<0828:OTEOVA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Higgins, R. W., V. E. Kousky, and P. Xie, 2011: Extreme precipitation events in the south-central United States during May and June 2010: Historical perspective, role of ENSO, and trends. J. Hydrometeor., 12, 1056–1070, doi:10.1175/JHM-D-10-05039.1.

    • Search Google Scholar
    • Export Citation

  • Hohenegger, C., A. Walser, W. Langhans, and C. Schär, 2008: Cloud-resolving ensemble simulations of the August 2005 Alpine flood. Quart. J. Roy. Meteor. Soc., 134, 889–904, doi:10.1002/qj.252.

    • Search Google Scholar
    • Export Citation

  • 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., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945, doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Janjić, Z. I., 2000: Comments on “Development and evaluation of a convection scheme for use in climate models.” J. Atmos. Sci., 57, 3686–3686, doi:10.1175/1520-0469(2000)057<3686:CODAEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Janjić, Z. I., 2002: Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note 437, 61 pp.

  • 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.

    • Search Google Scholar
    • Export Citation

  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181, doi:10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Keyser, D., cited2014: PREPBUFR processing at NCEP. NOAA/NWS/NCEP/EMC. [Available online at http://www.emc.ncep.noaa.gov/mmb/data_processing/prepbufr.doc/document.htm.]

  • Kuo, Y.-H., T.-K. Wee, S. Sokolovskiy, C. Rocken, W. Schreiner, D. Hunt, and R. A. Anthes, 2004: Inversion and error estimation of GPS radio occultation data. J. Meteor. Soc. Japan, 82, 507–531, doi:10.2151/jmsj.2004.507.

    • Search Google Scholar
    • Export Citation

  • Kursinski, E. R., G. A. Hajj, J. T. Schofield, R. P. Linfield, and K. R. Hardy, 1997: Observing Earth’s atmosphere with radio occultation measurements using the Global Positioning System. J. Geophys. Res., 102, 23 429–23 465, doi:10.1029/97JD01569.

    • Search Google Scholar
    • Export Citation

  • Leoncini, G., R. S. Plant, S. L. Gray, and P. A. Clark, 2013: Ensemble forecasts of a flood-producing storm: Comparison of the influence of model-state perturbations and parameter modifications. Quart. J. Roy. Meteor. Soc., 139, 198–211, doi:10.1002/qj.1951.

    • Search Google Scholar
    • Export Citation

  • 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 http://ams.confex.com/ams/pdfpapers/83847.pdf.]

  • Mason, S. J., and N. E. Graham, 2002: Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Quart. J. Roy. Meteor. Soc., 128, 2145–2166, doi:10.1256/003590002320603584.

    • Search Google Scholar
    • Export Citation

  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys., 20, 851–875, doi:10.1029/RG020i004p00851.

    • Search Google Scholar
    • Export Citation

  • Meng, Z., and F. Zhang, 2011: Limited-area ensemble-based data assimilation. Mon. Wea. Rev., 139, 2025–2045, doi:10.1175/2011MWR3418.1.

    • Search Google Scholar
    • Export Citation

  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360, doi:10.1175/BAMS-87-3-343.

    • Search Google Scholar
    • Export Citation

  • Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62, 1665–1677, doi:10.1175/JAS3446.1.

    • Search Google Scholar
    • Export Citation

  • National Research Council, 2010: When Weather Matters: Science and Services to Meet Critical Societal Needs. Board on Atmospheric Sciences and Climate, National Academies Press, 181 pp.

    • Search Google Scholar
    • Export Citation

  • Nielsen-Gammon, J. W., X.-M. Hu, F. Zhang, and J. E. Pleim, 2010: Evaluation of planetary boundary layer scheme sensitivities for the purpose of parameter estimation. Mon. Wea. Rev., 138, 3400–3417, doi:10.1175/2010MWR3292.1.

    • Search Google Scholar
    • Export Citation

  • NOAA, cited2013: Storm events database. [Available online at http://www.ncdc.noaa.gov/stormevents/.]

  • Noh, Y., W. G. Cheon, S. Y. Hong, and S. Raasch, 2003: Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107, 401–427, doi:10.1023/A:1022146015946.

    • Search Google Scholar
    • Export Citation

  • 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, 78–97, doi:10.1175/2007MWR2123.1.

    • Search Google Scholar
    • Export Citation

  • Romine, G. S., C. S. Schwartz, C. Snyder, J. L. Anderson, and M. L. Weisman, 2013: Model bias in a continuously cycled assimilation system and its influence on convection-permitting forecasts. Mon. Wea. Rev., 141, 1263–1284, doi:10.1175/MWR-D-12-00112.1.

    • Search Google Scholar
    • Export Citation

  • Schumacher, R. S., A. J. Clark, M. Xue, and F. Kong, 2013: Factors influencing the development and maintenance of nocturnal heavy-rain-producing convective systems in a storm-scale ensemble. Mon. Wea. Rev., 141, 2778–2801, doi:10.1175/MWR-D-12-00239.1.

    • 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 variationalensemble data assimilation systems. Mon. Wea. Rev., 142, 716–738, doi:10.1175/MWR-D-13-00100.1.

    • 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, 263–280, doi:10.1175/2009WAF2222267.1.

    • Search Google Scholar
    • 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. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3_bw.pdf].

  • Smirnova, T. G., J. M. Brown, S. G. Benjamin, and D. Kim, 2000: Parameterization of cold-season processes in the MAPS land-surface scheme. J. Geophys. Res., 105, 4077–4086, doi:10.1029/1999JD901047.

    • Search Google Scholar
    • Export Citation

  • Stensrud, D. J., J.-W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128, 2077–2107, doi:10.1175/1520-0493(2000)128<2077:UICAMP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Stratman, D. R., M. C. Coniglio, S. E. Koch, and M. Xue, 2013: Use of multiple verification methods to evaluate forecasts of convection from hot- and cold-start convection-allowing models. Wea. Forecasting, 28, 119–138, doi:10.1175/WAF-D-12-00022.1.

    • Search Google Scholar
    • Export Citation

  • Tao, W.-K., J. Simpson, and M. McCumber, 1989: An ice-water saturation adjustment. Mon. Wea. Rev., 117, 231–235, doi:10.1175/1520-0493(1989)117<0231:AIWSA>2.0.CO;2.

    • 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, 5095–5115, doi:10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation

  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800, doi:10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Torn, R. D., 2010: Diagnosis of the downstream ridging associated with extratropical transition using short-term ensemble forecasts. J. Atmos. Sci., 67, 817–833, doi:10.1175/2009JAS3093.1.

    • Search Google Scholar
    • Export Citation

  • Torn, R. D., and G. J. Hakim, 2008: Performance characteristics of a pseudo-operational ensemble Kalman filter. Mon. Wea. Rev., 136, 3947–3963, doi:10.1175/2008MWR2443.1.

    • Search Google Scholar
    • Export Citation

  • Torn, R. D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 2490–2502, doi:10.1175/MWR3187.1.

    • Search Google Scholar
    • Export Citation

  • Tuttle, J. D., and C. A. Davis, 2006: Corridors of warm season precipitation in the central United States. Mon. Wea. Rev., 134, 2297–2317, doi:10.1175/MWR3188.1.

    • Search Google Scholar
    • Export Citation

  • Velden, C., and Coauthors, 2005: Recent innovations in deriving tropospheric winds from meteorological satellites. Bull. Amer. Meteor. Soc., 86, 205–223, doi:10.1175/BAMS-86-2-205.

    • Search Google Scholar
    • Export Citation

  • Vié, B., O. Nussier, and V. Ducrocq, 2011: Cloud-resolving ensemble simulations of Mediterranean heavy precipitating events: Uncertainty on initial conditions and lateral boundary conditions. Mon. Wea. Rev., 139, 403–423, doi:10.1175/2010MWR3487.1.

    • 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, 1673–1694, doi:10.1175/2009MWR3042.1.

    • Search Google Scholar
    • Export Citation

  • Wheatley, D. M., D. J. Stensrud, D. C. Dowell, and N. Yussouf, 2012: Application of a WRF mesoscale data assimilation system to springtime severe weather events 2007–09. Mon. Wea. Rev., 140, 1539–1557, doi:10.1175/MWR-D-11-00106.1.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Academic Press, 704 pp.

  • Xue, M., and Coauthors, 2007: CAPS realtime storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2007 spring experiment. 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., 3B.1. [Available online at http://ams.confex.com/ams/pdfpapers/124587.pdf.]

  • Xue, M., and Coauthors, 2011: Realtime convection-permitting ensemble and convection-resolving deterministic forecasts of CAPS for the Hazardous Weather Testbed 2010 spring experiment. 24th Conf. on Weather Analysis and Forecasting/20th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 9A.2. [Available online at https://ams.confex.com/ams/91Annual/webprogram/Manuscript/Paper183227/Xue_CAPS_2011_SpringExperiment_24thWAF20thNWP_ExtendedAbstract.pdf.]

  • Zhang, C., Y. Wang, and K. Hamilton, 2011: Improved representation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme. Mon. Wea. Rev., 139, 3489–3513, doi:10.1175/MWR-D-10-05091.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 1173–1185, doi:10.1175/1520-0469(2003)060<1173:EOMCOM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 353 112 12
PDF Downloads 279 68 4