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case, implying the import ance of determi ning appropriate microphysical scheme toachieve comparable results with observations before detailed studies.ObservationLinThompsonMilbrandt-YauMorrisonFig. 4 Comparison of 24- h accumulated precipitation at Beiji ng area from 00:00 UTC, 31 May to00:00, 1 June 2005 using different microphysical sc hemes with observatio ns from Wang et al.(2009).4. SummaryThe ef fects of p arameterization of cloud microphysics on precipit ation for a deepconvective system have bee n investigated using the WRF model. Dif ferentparameterization schemes for microphysica l proces ses of cloud and precipit ationincluded in the WRF model are tested. The results showed that the Lin and Thompsonschemes could reproduce the precipit ation p attern more consistent with themeasurements than the other schemes, but both of these single-moment schemesoverestimated the precipitation amount, especially for the Lin scheme. It is suggestedthat the microphysical schemes should be tested before the model is used forprecipitation prediction for a specific region.Acknowledgements:This study was partially funded by the Public Meteorology Special F oundation of-504-
MOST (Grant No. GYHY20070 6036), the National Natural Sciences Foundation ofChina (Grant No. 41030962), and the “Excellent Creative Research Team in Jiangs uUniversities” program.ReferencesKhain, A. and B. Lynn, 2 009: Simulation of a supercell storm in clean and dirty atmosphere usingweather research and forecast model with spectral bin microphysics. J. Geophys. Res., 114,D19209.Li, G., Y. Wang, K.-H. Lee, et al., 2009: Impacts of aerosols on the development and precipitationof a mesoscale squall line. J. Geophys. Res., 114, D17205.Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk Parameterization of the Snow Field in a CloudModel. J. Appl. Meteorol., 22, 1065-1092.Lord, S. J., H. E. Willou ghby, and J. M. Pio trowicz, 1984: R ole of a Parameterized Ice-PhaseMicrophysics in an Axisymmetric, Nonhydrostatic Tropical Cyclone Model. J. Atmos. Sci., 41,2836-2848.Milbrandt, J. A. and M. K. Yau, 2005: A Multimom ent Bulk Microphysics Parameterization. Part I:Analysis of the Role of the Spectral Shape Parameter. J. Atmos. Sci., 62, 3051-3064.Morrison, H., G. Thompson, and V. Tatarsk ii, 2009: Impact of Cloud Microphysics on theDevelopment of Trailing Stratiform Pr ecipitation in a Simulated Squall Line: Comparison ofOne- and Two-Moment Schemes. Mon. Wea. Rev., 137, 991-1007.Rutledge, S . A. and P. V. Hobbs, 1984: The Mesoscale and Microscale Structure an dOrganization of Clouds and Precipitation in Mi dlatitude Cyclones. XII: A Diagnostic ModelingStudy of Precipitation Developme nt in Narrow C old-Frontal Ra inbands. J. Atmos. Sci., 41,2949-2972.Tao, W.-K., J. Simpson, and M. McCumber, 1989: An Ice-Water Saturation Adjustment. Mon. Wea.Rev., 117, 231-235.Thompson, G., P. R. Field, R. M. Rasmussen, et al., 2008: Explicit Forecasts o f WinterPrecipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a NewSnow Parameterization. Mon. Wea. Rev., 136, 5095-5115.Wang, X.-M, Q. Zhong, S.-Y. Han, 2009: A Numerical Case Study on the Evolution of Hail Cloudand the Thr ee-Dimensional Structure of Supercell. Plateau Meteorology (in Chinese ), 28,352-365.-505-
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World Meteorological OrganizationWW
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ForewordWeather disasters often ari
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CONFERENCE SCHEDULEThird WMO Intern
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Conference program for the third WM
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Conference program:A. High impact p
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B3. 13:45-15:40 Session Chair: Rich
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P. 19:15-20:30 19-20 Oct. 2010 Post
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H. Conference summaryConvener: Chun
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Session AHigh impact Precipitation
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1. IntroductionSynoptic and Mesosca
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and PS each accounting for about 20
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ain-producing storms (Schumacher an
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Houze, R. A., Jr., S. A. Rutledge,
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Convection over the Taunus Mountain
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in the southeast of the investigate
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Spatial and Temporal Variations of
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were normalized to sum 1 to give th
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during the continuous one day (Rx1d
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The Weather Research and Forecastin
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Convective rainfall in wet runs is
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On the Study of Tropical Cyclone Ra
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Fig.2 Severe tropical storm Bilis (
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Numerical simulation (Zhu H.Y, et a
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Influence of Typhoon Songda (2004)
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southeastern part of typhoon circul
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the thermal structure is asymmetric
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The estimation of TC quantitative p
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from 0000 UTC on 8 August 2009 to 0
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and 1.23:1 in 24h, indicates that t
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in the latter 24-h, on average, the
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4. Sensitivity experiment results1)
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ReferencesChen, L., and Y. Ding, 19
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of the documented intensity and tra
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Figure 1b shows the distribution of
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influence.In order to explain the v
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torrential rain days in 2009. The d
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over China mainland for the 32 typh
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Advances in Understanding the “Pe
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3. Contributions of the east-west o
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Fig. 3. Hovmoller diagram of the ra
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convective band over the Taiwan Str
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The mechanism analysis of cloud and
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The simulations were performed with
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different sub-regions to interpret
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An Idealized Numerical Study on the
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The Effect of ENSO on the Summer Mo
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Session BQuantitative Precipitation
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Observations of Precipitation Proce
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elevation angle. It grows and then
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Figure 8: Outflow (above) and inflo
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Figure 10: Model forecasts of vario
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ReferencesPaul Joe, Chris Doyle, Al
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produce the ‘predicted’ analysi
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asis. First, co-located daily CMORP
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used in this paper were obtained fr
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with thresholds of 0.1mm (including
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The Megha-Tropiques accumulated rai
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Figure 1 shows the flow chart of th
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Over the Ouémé site, the evolutio
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Starting from June 15 , 2 001, t he
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5. Year and Season-wise Comparison
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QPE and QPF of Japan Meteorological
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By multiply ing calibrated echo int
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4.3 ACCURACY OF VSRFCritical Succes
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Verification of Tropical Cyclones-R
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To co mpare t he ab ilities of 3B 4
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AllcasesLandfallTCsTable 3. TS, ETS
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The representation of the rain gaug
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3 Results16Percentage(%)14121086200
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403530Related Error(%)25201510504 C
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Recent Progresseson TC Precipitatio
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fields, particularly taking account
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form. The initial conditions of ens
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Comparative Study on QPE Methods of
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B3.2possible rain states that could
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B3.4Figure 2: Spatial temporal samp
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A Radar-based Technique for the Ide
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et al. (2007), the HEM obtained exa
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these calibration algorithms.7. Con
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CINRAD Warning Indexof Local Extrem
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Research of Rainfall Estimation usi
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Figure 1 has shown the relationship
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4. Summary and discussion(1) T he T
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Session CHydrologic prediction and
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Hydrological Perspective on QPE/QPF
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the river system in the basin. The
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(2) As input of the hydrological di
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satellite estimates) and the foreca
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NWP models’ Application in Flood
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Deltares’ Flood Early Warning Sys
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A further challenge for hydrologic
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2.2 Test areaHuaihe Basin locates i
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Fig. 4 The same as Fig.3, but at Wa
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Third WMO International Conference
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There is a unimodal pattern in the
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Number of days with 1mm and above95
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homogeneous (Fig. 1). Thi s area is
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a, without precipitation in future
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Session DVerification of Precipitat
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Intercomparison of verification met
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intensity f or pr e- and pos t-defo
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Figure 1: From Gilleland et al. (20
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New Model Evaluation Tools (MET) So
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During the HWT project, precipitati
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Figure 4: Example showing atmospher
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Performance Evaluation of the AREM
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0-24h and 24-48h, the improved mode
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Fig.4 24h accumulative precipitatio
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simple upscaling technique whereby
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(panel c) and 20mm/24h (panel d) fo
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A new field verification score base
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still overlapping the observed feat
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Case Description of forecast featur
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Quantitative Precipitation Forecast
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GFS60 and NAM60 at the lowest thres
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difference between the two models i
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Scale-related Issues involving veri
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of scores for the GFS at heavier ra
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Fig. 4. Comparison of probability o
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From these equations one can see th
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Figure 2. Verification scores as a
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Session EData assimilation for prec
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Progress in data assimilation for N
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Here, k is the number of ensemble m
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0200pressure (hPa)400600800spread a
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Impacts of multiple radar data assi
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High Resolution Radar Accumulation
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3. MethodThe non-hydrostatic versio
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Figure 5: Accumulation in the Waipa
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J(X ) Jb Jo Jc1 T 1T X X ) B ( X
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prediction, and so forth, to observ
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experiment, velocity assimilated ex
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Cycle Forecasting System in China.
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successfully forecast by AREM-RUC a
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Session FPrecipitation variability
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FRepresentation of Monsoon Systems
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4 ResultsFigure 2: Monsoon mean pre
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explained by higher sea surface tem
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Variability of Rainfall, Increasing
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(a)(b)Figure 1 : (a) Change of Temp
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According the analy sis of rainfall
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the rainfall v ariability will high
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Regional Impact of Climate Change i
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Figure 2. Map of the classified wea
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Table 3. The num ber of weather st
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such a mo del. Second, we ha ve o b
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Joint fuzzy an alysis o f SST , SAT
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Variability of Equatorial East Afri
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(d)2.5 4.0 5.5(c)-10 0 10(b)-10 0 5
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Figure 4: (a) The first spatial mod
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Session GQuantitative Precipitation
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Ongoing developments on Limited Are
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een done but much i s s till needed
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Hacker, J., S.-Y. Ha, C. Sn yder, J
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the initial field as possible based
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One advantage of ensemble forecasti
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southern hemisphere through the equ
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model results predict rate moves in
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Term growth forecast s, th e error
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ponent of the Brier score is reduce
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in the combined field (Fig.3c). For
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Probabilistic QPF from a realtime m
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to help assessing impacts from diff
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abFigure 3.ETS scores of the 3-h ac
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Flow chart of multi-model rainfall
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GermanJapan NCEP Ens Forecaster(a)E
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methodology. However, the operation
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Research and Operation on Quantitat
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A numerical study of aerosol effect
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maximum cloud water mixing ratio in
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an increase in concentration of clo
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perturbations. Blending combines th
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Figure 2. Comparison of CRPSSof 12h
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Global QPF and QPE: Where do we hav
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countries, the 1 o 1 o GPCC rainfa
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determine the origin of these diffe
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Fig. 2: The global distribution of
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Fig. 4: Scatter plots showing the c
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Multiscale Spectral Structures of T
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mountain ridges and are anticipated
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triggered convection conditions pre
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Rainfall forecasts from the Met Off
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Figure 2: (a) Domain-averaged rainf
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4. Summary and discussionWe have de
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G3itself it is incapable of providi
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G3Precipitation for February 2nd, 2
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Can Multi-model ensemble forecasts
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amounts over thresholds, 1, 3, 5, 1
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In addition to combination of all t
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-Carry out the verification for oth
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How sensitive are probabilistic pre
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CMORPH (Climate Prediction Center M
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Among the alternatives that have be
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Based on the daily mean temperature
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the individual models and the EMN t
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The Development of Ensemble Predict
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70(a)70(b)60AREM-SY AREM-CTL AREM-E
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Poster Presentations-379-
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Coupling Ensemble weather predictio
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sub-catchments to be used as inputs
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This work was supported by the Rese
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3. DataFor the investigation the we
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Flash flood forecasting by coupling
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gauges. Fig. 1 shows the event accu
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The floods were sim ulated with bot
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Comparison of calibration technique
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10.10.010.00110.10.010.0010 0.2 0.4
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24-h rainfall forecast. Nevertheles
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ANALYSIS FOR TURBULENCE AND WIND GU
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Research and Application of Typhoon
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[3] TIAN H, MA K Y,LIN Z S. Analysi
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What’s more, th e tw o curves als
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precipitation in southern areas of
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Chen Huai, Wang Yongbo, Shi N eng,
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An hourly updated convection-allowi
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GFS dataset or any other reason, th
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Downscaling precipitation for weath
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abFig.2. The 24h accumulated precip
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A Kalman Filter based QPF calibrati
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Scale Parameter10090807060504030rai
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It is clear that the values of scal
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THE IMPACT OF NORTH PACIFIC SUBTROP
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negative in the northern hemisphere
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Establishment and Verification of M
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Multi-model Ensemble QPF for Southe
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(4)andcan be estimated for all mode
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Table 4 The number of observations
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gauge coverage is definitely not sa
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It would be an attractive result if
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Analysis on a permanent elongate co
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PECS grow. The evolution of meso-sc
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3 rd Conference on QPE /QPF an
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Page 473 and 474: 3 rd Conference on QPE /QPF an
Page 475 and 476: QPF verification using object-orien
Page 477 and 478: measure the S and A components resp
Page 479 and 480: 5. ConclusionsSeveral problems aris
Page 481 and 482: 2.1 MethodologyIn the EOF analysis,
Page 483 and 484: calculated respectively, the compar
Page 485 and 486: IntroductionProbabilistic predictio
Page 487 and 488: Once the models are obtained, they
Page 489 and 490: REFERENCES- Charles Mutai: 2000, Di
Page 491 and 492: This paper selects the process of w
Page 493 and 494: (b1)cntl (b2)snd (b3)cmwFig.5the cl
Page 495 and 496: (2) There is no much difference in
Page 497 and 498: Analysis of Convective Precipitatio
Page 499 and 500: F(per/6min)30025020015010050F(per/6
Page 501 and 502: Research on the Relationship betwee
Page 503 and 504: Fig.1 The k-Z relationship for smal
Page 505 and 506: esearch on the correction for atten
Page 507 and 508: 22224diction of the heavy precipita
Page 509 and 510: Study on Mechanism of an Extra Rain
Page 511 and 512: which promoted the shear line devel
Page 513 and 514: References[1] SUN Shu-Qing and ZHOU
Page 515 and 516: the main p oints in our estimation
Page 517 and 518: QC test to reject the bogus faulty
Page 519 and 520: -499-
Page 521 and 522: The Impact of Parameterization of C
Page 523: LinThompsonMilbrandt-YauMorrisonFig
Page 527 and 528: National Meteorological Center (NMC
Page 529 and 530: Fig. 4 Comparison of percentile are
Page 531 and 532: Impact of Different Boundary Layer
Page 533 and 534: (a)observed rainfall,(b)simulated r
Page 535 and 536: In te rms of ener getics, the hea v
Page 537 and 538: The Estimation of Rainstorm Forecas
Page 539 and 540: Fig. 2 TS Score of accumulated 24h
Page 541 and 542: Study on the 3D Structure of Typhoo
Page 543 and 544: abFig.1 (a) Total precipitation (mm
Page 545 and 546: satellite images at 12:00BST on 18
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