• Ahijevych, D., , E. Gilleland, , B. G. Brown, , and E. E. Ebert, 2009: Application of spatial verification methods to idealized and NWP-gridded precipitation forecasts. Wea. Forecasting, 24, 14851497, doi:10.1175/2009WAF2222298.1.

    • Search Google Scholar
    • Export Citation
  • Alhamed, A., , S. Lakshmivarahan, , and D. J. Stensrud, 2002: Cluster analysis of multimodel ensemble data from SAMEX. Mon. Wea. Rev., 130, 226256, doi:10.1175/1520-0493(2002)130<0226:CAOMED>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bala, G., and et al. , 2008: Evaluation of a CCSM3 simulation with a finite volume dynamical core for the atmosphere at 1° latitude × 1.25° longitude resolution. J. Climate, 21, 14671486, doi:10.1175/2007JCLI2060.1.

    • Search Google Scholar
    • Export Citation
  • Collins, W. D., and et al. , 2006: The Community Climate System Model version 3 (CCSM3). J. Climate, 19, 21222143, doi:10.1175/JCLI3761.1.

    • Search Google Scholar
    • Export Citation
  • Davis, C., , 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.

    • Search Google Scholar
    • Export Citation
  • Douglass, B. P., 2000: Real-time UML: Developing Efficient Objects for Embedded Systems. Addison Wesley, 328 pp.

  • Ebert, E. E., , and J. L. McBride, 2000: Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol., 239, 179202, doi:10.1016/S0022-1694(00)00343-7.

    • Search Google Scholar
    • Export Citation
  • Eitzen, Z. A., , K. M. Xu, , and T. Wong, 2008: Statistical analyses of satellite cloud object data from CERES. Part V: Relationships between physical properties of marine boundary layer clouds. J. Climate, 21, 66686688, doi:10.1175/2008JCLI2307.1.

    • Search Google Scholar
    • Export Citation
  • Everitt, B. S., , S. Landau, , M. Leese, , and D. Stahl, 2011: Cluster Analysis. Wiley, 346 pp.

  • Gilleland, E., 2013: Testing competing precipitation forecasts accurately and efficiently: The spatial prediction comparison test. Mon. Wea. Rev., 141, 340355, doi:10.1175/MWR-D-12-00155.1.

    • Search Google Scholar
    • Export Citation
  • Huth, R., , I. Nemesova, , and N. Klimperova, 1993: Weather categorization based on the average linkage clustering technique—An application to European midlatitudes. Int. J. Climatol., 13, 817835, doi:10.1002/joc.3370130802.

    • Search Google Scholar
    • Export Citation
  • Isaaks, E. H., , and R. M. Srivastava, 1989: Applied Geostatistics. Oxford University Press, 561 pp.

  • Jakob, C., , and G. Tselioudis, 2003: Objective identification of cloud regimes in the tropical western Pacific. Geophys. Res. Lett., 30, 2082, doi:10.1029/2003GL018367.

    • Search Google Scholar
    • Export Citation
  • Johnson, A., , X. G. Wang, , F. Y. Kong, , and M. Xue, 2011: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 spring experiment. Part I: Development of the object-oriented cluster analysis method for precipitation fields. Mon. Wea. Rev., 139, 36733693, doi:10.1175/MWR-D-11-00015.1.

    • Search Google Scholar
    • Export Citation
  • Kent, J., , J. P. Whitehead, , C. Jablonowski, , and R. B. Rood, 2014: Determining the effective resolution of advection schemes. Part I: Dispersion analysis. J. Comput. Phys., 278, 485–496, doi:10.1016/j.jcp.2014.01.043.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., , R. Rabin, , and V. DeBrunner, 2003: Multiscale storm identification and forecast. Atmos. Res., 67–68, 367380, doi:10.1016/S0169-8095(03)00068-1.

    • Search Google Scholar
    • Export Citation
  • Lin, S. J., 2004: A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Wea. Rev., 132, 22932307, doi:10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Littmann, T., 2000: An empirical classification of weather types in the Mediterranean basin and their interrelation with rainfall. Theor. Appl. Climatol., 66, 161171, doi:10.1007/s007040070022.

    • Search Google Scholar
    • Export Citation
  • Liu, Z., , and R. George, 2005: Mining weather data using fuzzy cluster analysis. Fuzzy Modeling with Spatial Information for Geographic Problems, F. E. Petry, V. B. Robinson, and M. A. Cobb, Eds., Springer Berlin Heidelberg, 105–119.

  • Marzban, C., , and S. Sandgathe, 2006: Cluster analysis for verification of precipitation fields. Wea. Forecasting, 21, 824838, doi:10.1175/WAF948.1.

    • Search Google Scholar
    • Export Citation
  • Marzban, C., , and S. Sandgathe, 2009: Verification with variograms. Wea. Forecasting, 24, 11021120, doi:10.1175/2009WAF2222122.1.

  • Matheron, G., 1963: Principles of geostatistics. Econ. Geol., 58, 12461266, doi:10.2113/gsecongeo.58.8.1246.

  • Micheas, A. C., , I. F. Neil, , S. A. Lack, , and C. K. Wikle, 2007: Cell identification and verification of QPF ensembles using shape analysis techniques. J. Hydrol., 343, 105116, doi:10.1016/j.jhydrol.2007.05.036.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., and et al. , 2010: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, 289 pp.

  • Peak, J. E., , and P. M. Tag, 1994: Segmentation of satellite imagery using hierarchical thresholding and neural networks. J. Appl. Meteor., 33, 605616, doi:10.1175/1520-0450(1994)033<0605:SOSIUH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Posselt, D. J., , A. R. Jongeward, , C. Y. Hsu, , and G. L. Potter, 2012: Object-based evaluation of MERRA cloud physical properties and radiative fluxes during the 1998 El Nino–La Nina transition. J. Climate, 25, 73137327, doi:10.1175/JCLI-D-11-00724.1.

    • Search Google Scholar
    • Export Citation
  • Reed, K. A., , and C. Jablonowski, 2012: Idealized tropical cyclone simulations of intermediate complexity: A test case for AGCMs. J. Adv. Model. Earth Syst.,4, M04001, doi:10.1029/2011MS000099.

  • Rudolf, B., , C. Beck, , J. Grieser, , and U. Schneider, cited 2005: Global precipitation analysis products. DWD Global Precipitation Climatology Centre (GPCC) Publ., 8 pp.

  • Schneider, U., , A. Becker, , P. Finger, , A. Meyer-Christoffer, , M. Ziese, , and B. Rudolf, 2013: GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol.,115, 15–40, doi:10.1007/s00704-013-0860-x.

  • Skok, G., , J. Bacmeister, , and J. Tribbia, 2013: Analysis of tropical cyclone precipitation using an object-based algorithm. J. Climate, 26, 25632579, doi:10.1175/JCLI-D-12-00135.1.

    • Search Google Scholar
    • Export Citation
  • Spath, H., 1985: Cluster Dissection and Analysis: Theory, FORTRAN Programs, Examples. Horwood, 226 pp.

  • Wernli, H., , M. Paulat, , M. Hagen, , and C. Frei, 2008: SAL—A novel quality measure for the verification of quantitative precipitation forecasts. Mon. Wea. Rev., 136, 44704487, doi:10.1175/2008MWR2415.1.

    • Search Google Scholar
    • Export Citation
  • Williamson, D. L., 2007: The evolution of dynamical cores for global atmospheric models. J. Meteor. Soc. Japan, 85B, 241269, doi:10.2151/jmsj.85B.241.

    • Search Google Scholar
    • Export Citation
  • Williamson, D. L., 2008: Equivalent finite volume and Eulerian spectral transform horizontal resolutions established from aqua-planet simulations. Tellus,60A, 839–847, doi:10.1111/j.1600-0870.2008.00340.x.

  • Xu, K. M., 2009: Evaluation of cloud physical properties of ECMWF analysis and re-analysis (ERA) against CERES tropical deep convective cloud object observations. Mon. Wea. Rev., 137, 207223, doi:10.1175/2008MWR2633.1.

    • Search Google Scholar
    • Export Citation
  • Xu, K. M., , T. M. Wong, , B. A. Wielicki, , L. Parker, , and Z. A. Eitzen, 2005: Statistical analyses of satellite cloud object data from CERES. Part I: Methodology and preliminary results of the 1998 El Niño/2000 La Niña. J. Climate, 18, 24972514, doi:10.1175/JCLI3418.1.

    • Search Google Scholar
    • Export Citation
  • Yorgun, M. S., , and R. B. Rood, 2014: An object-based approach for quantification of GCM biases of the simulation of orographic precipitation. Part I: Idealized simulations. J. Climate, 27, 91399154, doi:10.1175/JCLI-D-14-00051.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 23 23 2
PDF Downloads 8 8 0

An Object-Based Approach for Quantification of GCM Biases of the Simulation of Orographic Precipitation. Part II: Quantitative Analysis

View More View Less
  • 1 Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan
© Get Permissions
Restricted access

Abstract

An object-based evaluation method is applied to the simulated orographic precipitation for the idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and Eulerian spectral transform dynamical cores with varying resolutions. The method consists of the application of k-means cluster analysis to the precipitation features to determine their spatial boundaries and the calculation of the semivariograms (SVs) for the isolated features for evaluation.

The quantitative analysis revealed differences between the simulated precipitation by the FV and Eulerian spectral transform models that are not visually apparent. The simulated large-scale precipitation features of the idealized test cases provide analogs to orographic precipitation features observed in simulations of Atmospheric Model Intercomparison Project (AMIP) models. The spatial boundaries of these features (determined by k-means clustering) for Eulerian spectral T85 and T170 resolutions revealed the level of merger between the two large-scale features simulated because of each peak in the double mountain idealized setup. Both FV 1° and 0.5° resolutions were able to simulate the dryer region between the two mountains. The SVs of precipitation for the single and double mountain setups show close agreement between FV 1°, FV 0.5°, and Eulerian spectral T170 resolutions; however, Eulerian spectral T85 simulated the precipitation in lower intensity, indicating the qualitative difference in resolutions previously determined to be equivalent. Such close agreement was not observed in the more realistic idealized setup.

Publisher’s Note: The online version of this article does not match the print volume due to a late addition to the acknowledgements section.

Corresponding author address: M. Soner Yorgun, Space Research Building, 2455 Hayward Street, Ann Arbor, MI 48109-2143. E-mail: yorgun@umich.edu

Abstract

An object-based evaluation method is applied to the simulated orographic precipitation for the idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and Eulerian spectral transform dynamical cores with varying resolutions. The method consists of the application of k-means cluster analysis to the precipitation features to determine their spatial boundaries and the calculation of the semivariograms (SVs) for the isolated features for evaluation.

The quantitative analysis revealed differences between the simulated precipitation by the FV and Eulerian spectral transform models that are not visually apparent. The simulated large-scale precipitation features of the idealized test cases provide analogs to orographic precipitation features observed in simulations of Atmospheric Model Intercomparison Project (AMIP) models. The spatial boundaries of these features (determined by k-means clustering) for Eulerian spectral T85 and T170 resolutions revealed the level of merger between the two large-scale features simulated because of each peak in the double mountain idealized setup. Both FV 1° and 0.5° resolutions were able to simulate the dryer region between the two mountains. The SVs of precipitation for the single and double mountain setups show close agreement between FV 1°, FV 0.5°, and Eulerian spectral T170 resolutions; however, Eulerian spectral T85 simulated the precipitation in lower intensity, indicating the qualitative difference in resolutions previously determined to be equivalent. Such close agreement was not observed in the more realistic idealized setup.

Publisher’s Note: The online version of this article does not match the print volume due to a late addition to the acknowledgements section.

Corresponding author address: M. Soner Yorgun, Space Research Building, 2455 Hayward Street, Ann Arbor, MI 48109-2143. E-mail: yorgun@umich.edu
Save