Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: American Meteorological Society
Languages: English
Types: Article
The development of NWP models with grid spacing down to 1 km should produce more realistic forecasts of convective storms. However, greater realism does not necessarily mean more accurate precipitation forecasts. The rapid growth of errors on small scales in conjunction with preexisting errors on larger scales may limit the usefulness of such models. The purpose of this paper is to examine whether improved model resolution alone is able to produce more skillful precipitation forecasts on useful scales, and how the skill varies with spatial scale. A verification method will be described in which skill is determined from a comparison of rainfall forecasts with radar using fractional coverage over different sized areas. The Met Office Unified Model was run with grid spacings of 12, 4, and 1 km for 10 days in which convection occurred during the summers of 2003 and 2004. All forecasts were run from 12-km initial states for a clean comparison. The results show that the 1-km model was the most skillful over all but the smallest scales (approximately <10–15 km). A measure of acceptable skill was defined; this was attained by the 1-km model at scales around 40–70 km, some 10–20 km less than that of the 12-km model. The biggest improvement occurred for heavier, more localized rain, despite it being more difficult to predict. The 4-km model did not improve much on the 12-km model because of the difficulties of representing convection at that resolution, which was accentuated by the spinup from 12-km fields.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Bousquet, O., C. A. Lin, and I. Zawadzki, 2006: Analysis of scale dependence of quantitative precipitation forecast verification: A case-study over the Mackenzie River basin. Quart. J. Roy. Meteor. Soc., 132, 2107-2125.
    • Briggs, W. M., and R. A. Levine, 1997: Wavelets and field forecast verification. Mon. Wea. Rev., 125, 1329-1341.
    • Casati, B., G. Ross, and D. B. Stephenson, 2004: A new intensityscale approach for the verification of spatial precipitation forecasts. Meteor. Appl., 11, 141-154.
    • Cullen, M. J. P., T. Davies, M. H. Mawson, J. A. James, S. C. Coulter, and A. Malcolm, 1997: An overview of numerical methods for the next generation UK NWP and climate model. Numerical Methods in Atmospheric and Ocean Modelling: The Andre J. Robert Memorial Volume, C. A. Lin, R. Laprise, and H. Ritchie, Eds., Canadian Meteorological and Oceanographic Society, 425-444.
    • Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and N. Wood, 2005: A new dynamical core for the Met Office's global and regional modelling of the atmosphere. Quart. J. Roy. Meteor. Soc., 131, 1759-1782.
    • 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, 1772-1784.
    • Deng, A., and D. R. Stauffer, 2006: On improving 4-km mesoscale model simulations. J. Appl. Meteor. Climatol., 45, 361-381.
    • Done, J., C. A. Davis, and M. Weisman, 2004: The next generation of NWP: Explicit forecasts of convection using the weather research and forecasting (WRF) model. Atmos. Sci. Lett., 5, 110-117.
    • Ebert, E. E., and J. L. McBride, 2000: Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol., 239, 179-202.
    • Essery, R., M. Best, and P. Cox, 2001: MOSES 2.2 technical documentation. Met Office, Hadley Centre Tech. Note 30, 30 pp.
    • Golding, B. W., 1998: Nimrod: A system for generating automated very short range forecasts. Meteor. Appl., 5, 1-16.
    • Gregory, D., and P. R. Rowntree, 1990: A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev., 118, 1483- 1506.
    • Harrison, D. L., S. J. Driscoll, and M. Kitchen, 2000: Improving precipitation estimates from weather radar using quality control and correction techniques. Meteor. Appl., 7, 135-144.
    • Jones, C. D., and B. Macpherson, 1997: A latent heat nudging scheme for the assimilation of precipitation data into an operational mesoscale model. Meteor. Appl., 4, 269-277.
    • Lean, H. W., and P. A. Clark, 2003: The effects of changing resolution on mesoscale modelling of line convection and slantwise circulations in FASTEX IOP 16. Quart. J. Roy. Meteor. Soc., 129, 2255-2278.
    • Lock, A. P., A. R. Brown, M. R. Bush, G. M. Martin, and R. N. B. Smith, 2000: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests. Mon. Wea. Rev., 128, 3187-3199.
    • Lorenc, A. C., and Coauthors, 2000: The Met Office global threedimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 126, 2991-3012.
    • Lorenz, E. N., 1969: Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci., 26, 636-646.
    • Macpherson, B., B. J. Wright, W. H. Hand, and A. J. Maycock, 1996: The impact of MOPS moisture data in the U.K. Meteorological Office mesoscale data assimilation scheme. Mon. Wea. Rev., 124, 1746-1766.
    • 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, 407-430.
    • Marzban, C., and S. Sandgathe, 2006: Cluster analysis for verification of precipitation fields. Wea. Forecasting, 21, 824-838.
    • --, and --, 2008: Cluster analysis for object-oriented verification of fields: A variation. Mon. Wea. Rev., in press.
    • Murphy, A. H., and E. S. Epstein, 1989: Skill scores and correlation coefficients in model verification. Mon. Wea. Rev., 117, 572-581.
    • Mylne, K. R., 2002: Decision-making from probability forecasts based on forecast value. Meteor. Appl., 9, 307-315.
    • Petch, J. C., 2006: Sensitivity studies of developing convection in a cloud-resolving model. Quart. J. Roy. Meteor. Soc., 132, 345- 358.
    • Potts, J. M., 2003: Basic concepts. Forecast Verification: A Practitioner's Guide in Atmospheric Science, I. T. Jolliffe and D. B. Stephenson, Eds., John Wiley & Sons, 13-36.
    • Richardson, D. S., 2000: Skill and relative economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 126, 649-667.
    • Roberts, N. M., 2003: The impact of a change to the use of the convection scheme in high-resolution simulations of convective events. Met Office Forecasting Research Tech. Rep. 407, 30 pp.
    • Romero, R., C. A. Doswell III, and R. Riosalido, 2001: Observations and fine-grid simulations of a convective outbreak in northeastern Spain: Importance of diurnal forcing and convective cold pools. Mon. Wea. Rev., 129, 2157-2182.
    • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 3019-3032.
    • Smith, R. N. B., E. M. Blyth, J. W. Finch, S. Goodchild, R. L. Hall, and S. Madry, 2006: Soil state and surface hydrology diagnosis based on MOSES in the Met Office Nimrod nowcasting system. Meteor. Appl., 13, 89-109.
    • Speer, M. S., and L. M. Leslie, 2002: The prediction of two cases of severe convection: Implications for forecast guidance. Meteor. Atmos. Phys., 80, 165-175.
    • Theis, S. E., A. Hense, and U. Damrath, 2005: Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach. Meteor. Appl., 12, 257-268.
    • Walser, A., D. Lüthi, and C. Schär, 2004: Predictability of precipitation in a cloud-resolving model. Mon. Wea. Rev., 132, 560-577.
    • Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527-548.
    • Wilson, D. R., and S. P. Ballard, 1999: A microphysically based precipitation scheme for the UK Meteorological Office Unified Model. Quart. J. Roy. Meteor. Soc., 125, 1607-1636.
    • Zepeda-Arce, J., E. Foufoula-Georgiou, and K. K. Droegemeier, 2000: Space-time rainfall organization and its role in validating quantitative precipitation forecasts. J. Geophys. Res., 105, 10 129-10 146.
    • Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 1173-1185.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article