Advances in Geosciences www.adv-geosci.net 1680-7340 1680-7359 10 Observation, Prediction and Verification of Precipitation (EGU Session 2006) 2007 10.5194/adgeo-10-3-2007 http://www.adv-geosci.net/10/3/2007/ http://www.adv-geosci.net/10/3/2007/adgeo-10-3-2007.html http://www.adv-geosci.net/10/3/2007/adgeo-10-3-2007.pdf 3 8 2007-04-26 Fine-scale precipitation structure of a cold front and the problem of the representativeness error S. Ivanov svvivo@te.net.ua J. Palamarchuk Odessa State Environmental University, Ukraine The proper knowledge of spatial precipitation structure is important as much as that of microphysics processes for reliable quantitative precipitation forecasts. This study considers some aspects of how precipitation is organized on meso- and fine-scales, within a cold front line and how moisture transport is driven by these structures. Also, a spectral space vision of the representativeness error is proposed, which highlights uncertainties arising on the scales lying between resolutions of different networks. This approach is used to explain an improper simulation of humidity and precipitation fields in models whose resolution is coarser than the scales being considered. Austin, P. M.: Relation between measured radar reflectivity and surface rainfall, Mon. Wea. Rev., 115, 1053–1070, 1987. Bormann, N., Saarinen, S., Kelly, G., and Thépaut, J.-N.: The spatial structure of observation errors in atmospheric motion vectors from geostationary satellite data, Mon. Wea. Rev., 131, 706–718, 2003. Browning, K. A.: Mesoscale structure of rain system in the British Isles, J. Meteorol. Soc. Japan, 11, 52, 314–327, 1974. Claussen, M.: Estimation of areally averaged surface fluxes, Boundary-Layer Meteorol., 54, 387–410, 1991. Chen, Q. and Kuo, Y.: A harmonic-sine series expansion and its application to partitioning and reconstruction problems in a limited area, Mon. Wea. Rev., 120, 91–112, 1992. Collier, C. G.: Accuracy of radar estimates by radar. Part I: Calibration by telemetring raingauges, J. Hydrol., 83, 207–223, 1986. Crum, T. D., Saffle, R. E., and Wilson, J. W.: An update on the NEXRAD program and future WSR-88D support to operations, Wea. Forecast., 13, 253–262, 1998. Evans, K. F. and Wiscombe, W. J.: An algorithm for generating stochastic cloud fields from radar profile statistics, Atmos. Res., 72, 263–289, 2004. Eyre, J. P.: Variational assimilation of remotely-sensed observations of the atmosphere, J. Meteorol. Soc. Japan, 75, 331–338, 1997. Fraedrich, K. and Ziehmann-Schlumbohm, C.: Predictability experiments with persistence forecasts in a red-noise atmosphere, Q. J. R. Meteorol. Soc., 120, 387–428, 1994. Gilmore, M. S., Straka, J. M., and Rasmussen, E. N.: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme, Mon. Wea. Rev., 132, 2610–2627, 2004. Hobbs, P. V., Matejka, T. J., Herzegh, P. H., Locatelly, J. D., and Houze, R. A.: The meso-scale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. Part I. A case study of a cold front, J. Atmos. Sci., 37, 568–596, 1980. Hollingsworth, A. and Lonnberg, P.: The statistical structure of short-range forecast errors as determined from radiosonde data, Tellus, 38A, 111–136, 1986. Houze, R. A. and Hobbs, P. V.: Organization and structure of precipitating cloud systems, Adv. Geophys., 24, 225–315, 1982. Jones, C. D. and Macpherson, B.: A latent heat nudging scheme for the assimilation of precipitation data into an operational Mesoscale model, Meteorol. Appl,, 4, 269–277, 1997. Jung, T.: Systematic errors of the atmospheric circulation in the ECMWF forecasting system, Q. J. R. Meteorol. Soc., 131, 1045–1073, 2005. Kreitzberg, C. W. and Brown H. A.: Mesoscale weather systems within an occlusion, J. Appl. Meteorol., 9, 417–432, 1970. Mahfouf, J.-F. and Noilhan, J.: Comparative study of various formulations of evaporation from bare soil using in-situ data, J. Appl. Meteorol., 30, 1354–1365, 1991. Mandelbrot, B. B.: The Fractal Geometry of Nature, W H Freeman, New York, USA, 460 p., 1982. Palmer, T. N.: A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parameterization in weather and climate prediction models, Q. J. R. Meteorol. Soc., 127, 279–304, 2001. Reisner, J., Rasmussen R. J., and Bruintjes R. T.: Explicit forecasting of supercooled liquid water in winter stroms using the MM5 mesoscale model, Quart. J. Roy. Meteor. Soc., 124B, 1071–1107, 1998. Roache, P. J.: Quantification of Uncertainty in Computational Fluid Dynamics, J. Fluid Mech., 29, 123–160, 1997. Roache, P. J.: Verification and Validation in Computational Science and Engineering, Hermosa Publishers, Albuquerque, New Mexico, 1998. Scheirer, R. and Schmidt, S.: CLABAUTAIR: a new algorithm for retrieving three dimensional cloud structure from airborne microphysical measurements, Atmos. Chem. Phys., 5, 2333–2340, 2005. Schultz, P.: An explicit cloud physics parameterization for operational numerical weather prediction, Mon. Wea. Rev., 123, 3331–3343, 1995. Tao, W.-K. and Simpson, J.: Goddard Cumulus Ensemble Model. Part I: Model Description. Terrestrial, Atmos. Oceanic Sci., 4, 35–72, 1993. Tanaka, H. L. and Kimura, K.: Time series analysis of natural variability in barotropic energy of the atmosphere with ECMWF global analysis, J. Meteorol. Soc. Japan, 76, 267–274, 1998. Turcotte, D. L.: Fractals and Chaos in Geology and Geophysics, Cambridge University Press, U.K., 412p., 1997. Tustison, B., Harris, D., and Foufoula-Georgiou, E.: Scale issues in verification of precipitation forecasts, J. Geophys. Res., 106, 11 775–11 784, 2001. Venema, V., Meyer, S., Gimeno García, S., Kniffka, A., Simmer, C., Crewell, S., Löhnert, U., Trautmann, U., and Macke, A.: Surrogate cloud fields generated with the Iterative Amplitude Adapted Fourier Transform algorithm, Tellus 58A, 104–120, 2006. Zehnder, J. A.: A comparison of convergence- and surface-flux-based convective parameterizations with applications to tropical cyclogenesis, J. Atmos. Sci., 58, 283–301, 2001.