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Chen, L. S., Y. Li, and Z. Q. Cheng, 2010: An overview of research and forecasting on rainfall associatedwith landfalling tropical cyclones. Adv. Atmos. Sci., 27(5), 967-976, doi: 0.1007/s00376-010-8171-y.Chen S. S., Knaf J. A., Marks JR. F. D., 2006, Effects of Vertical Wind Shear and Storm Motion onTropical Cyclone Rainfall Asymmetries Deduced from TRMM. Mon. Wea. Rev., 134, 3191-3208.Ebert, E., S. Kusselson and M. Turk, 2005: Validation of NESDIS operational tropical rainfall potential(TRaP) forecasts for Australian tropical cyclones. Aust. Meteorol. Mag., 54, 121-135.Ebert, E.E., Janowiak, J. and Kidd, C. 2007: Comparison of Near-Real-Time Precipitation Estimates fromSatellite Observations and Numerical Models. Bulletin of the American Meteorological Society,88,1,47-64.Ji, C. X., L. S. Chen, X. D. Xu, and F. Zhao, M. C. Wu , 2008: A study on variable quantitativeprecipitation estimation using Doppler radar data. Journal of Tropical Meteorology , 24(2), 147-155.(in Chinese)Kusselson, S. J., M. Turk, M. Seybold, E. E. Ebert, and S. Q. Kidder, 2010: Satellite derived ensembletropical rainfall potential (eTRaP): 2008-2009 results. 24th Conference on Hydrology, AMS 90thAnnual Meeting.Lee, C.-S., L.-R. Huang, H.-S. Shen, and S.-T. Wang, 2006: A climatology model for forecasting typhoonrainfall in Taiwan. Natural Hazards, 37, 87-105.L’Ecuyer T, Miller S, Mitrescu C, Haynes J, Kummerow C, Turk FJ (2007) A first look at the CloudSatprecipitation dataset. In: Proceedings 3rd IPWG Workshop. 23–27 October 2006, Melbourne[http://www.isac. cnr.it/~ipwg/]L’Ecuyer T, Turk J., Partrain P., Stephens G., 2008: Near-Real-Time Applications of CloudSat Data. J. Appl.Meteor., 47, 1982-1994.Liu, G.-R., C.-C. Chao, and C.-Y. Ho, 2008: Applying satellite-Estimated storm rotation speed to improvetyphoon rainfall potential technique. Wea. Forecasting, 22, 259-269.Lonfat, M., F. D. Marks, and S. Chen, 2004: Precipitation distribution in tropical cyclones using theTropical Rainfall Measuring Mission (TRMM) Microwave Imager: A global perspective. Mon. Wea.Rev., 132, 1645–1660.Lonfat, M., R. Rogers, T. Marchok, and F. D. Marks Jr., 2007: A parametric model for predicting hurricanerainfall. Mon. Wea. Rev., 135, 3086–3097.Marchok, T., R. Rogers, and R. Tuleya, 2007: Validation schemes for tropical cyclone quantitativeprecipitation forecasts: evaluation of operational models for U.S. landfalling cases. Wea. Forecasting,22, 726-746.Quilfen Y., Charpon B., and Trounadre J., 2009: Satellite Microwave Surface Observations in TropicalCyclones. Mon. Wea. Rev., 138, 421-437.Pu, Z., X. Li, and J. Sun, 2009: Impact of airborne Doppler radar data assimilation on the numericalsimulation of intensity changes of Hurricane Dennis near a landfall. J. Atmos. Sci., 66, 3351–3365.Robert, A., 1982: A semi-Lagrangian and semi-implicit numerical integration scheme for the primitivemeteorological equations. J. Meteorol. Soc. Japan, 60, 319-325.Stephens GL, Kummerow CD (2007) The remote sensing of clouds and precipitation from space: A review.J Atmos Sci 64:3742–3765Tuleya, R. E., M. DeMaria, and R. J. Kuligowski, 2007: Evalution of GFDL and simple statistical modelrainfall forecasts for U.S. landfalling tropical storms. Wea. Forecasting, 22, 56-69.Tuttle, J. D. and G. B. Foote, 1990: Determination of the boundary layer airflow from a single Dopplerradar. J. Atmos. Ocean. Technol., 7, 218-232.Wingo M. T., Cecil D. J., 2010: Effects of Vertical Wind Shear on Tropical Cyclone Precipitation. Mon.Wea. Rev., 2010, 138, 645-662.Xiao, Q., Y.-H. Guo, J. Sun, W.-C. Lee, D. M. Barker, and E. Lim, 2006: An Approach of RadarReflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) atLandfall. J. Appl. Meteoro. Clim., 46, 14-22.Yokoyama C., Takayabu Y. N., 2008: A Statistical Study on Rain Characteristics of Tropical CyclonesUsing TRMM Satellite Data. Mon. Wea. Rev., 136, 3848-3862.Zhao, K., and M. Xue, 2009: Assimilation of coastal Doppler radar data with the ARPS 3DVAR and cloudanalysis for the prediction of Hurricane Ike (2008), Geophys. Res. Lett., 36, L12803Zhao, Q., and Y. Jin, 2008: High-resolution radar data assimilation for Hurricane Isabel (2003) at landfall.Bull. Amer. Meteor. Soc., 89, 1355–1372.Zhong, Y., H. Yu, W. Teng, and P. Chen, 2009: A dynamic similitude scheme for tropical cyclonequantitative precipitation forecast. J. Appl. Meteoro. Sci., 20, 17-27. (In Chinese)-130-
Comparative Study on QPE Methods of X-band Polarimetric Radarin a Typhoon Event in South ChinaBin WANG, Yanjiao XIAOInstitute of Heavy Rain, China Meteorological Administration, WuhanIn this paper, several QPE methods based on X-band polarimetric radar measurementsare evaluated by comparing retrieved rainfall rate with rain gauges data in a typhoonevent in South China on 5 August, 2009. The first QPE approach is built based onGamma drop size distribution (DSD) from polarimetric radar measurements. Otheralgorithms are to compute precipitation amount by using relationships betweenrainfall rate and polarimetric radar measurements with three empirical equations R(Z H ), R (Z H , Z DR ) and R (K DP , Z DR , Z H ), respectively. Comparing retrieved resultsfrom these QPE methods with one hour rainfall amounts from rain gauges, theirperformances are evaluated and error effects on QPE are qualitatively analyzed. And,a preliminary discussion of Gamma DSD retrieval method is conducted.-131-
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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
Page 99 and 100: Observations of Precipitation Proce
Page 101 and 102: elevation angle. It grows and then
Page 103 and 104: Figure 8: Outflow (above) and inflo
Page 105 and 106: Figure 10: Model forecasts of vario
Page 107 and 108: ReferencesPaul Joe, Chris Doyle, Al
Page 109 and 110: produce the ‘predicted’ analysi
Page 111 and 112: asis. First, co-located daily CMORP
Page 113 and 114: used in this paper were obtained fr
Page 115 and 116: with thresholds of 0.1mm (including
Page 117 and 118: The Megha-Tropiques accumulated rai
Page 119 and 120: Figure 1 shows the flow chart of th
Page 121 and 122: Over the Ouémé site, the evolutio
Page 123 and 124: Starting from June 15 , 2 001, t he
Page 125 and 126: 5. Year and Season-wise Comparison
Page 127 and 128: QPE and QPF of Japan Meteorological
Page 129 and 130: By multiply ing calibrated echo int
Page 131 and 132: 4.3 ACCURACY OF VSRFCritical Succes
Page 133 and 134: Verification of Tropical Cyclones-R
Page 135 and 136: To co mpare t he ab ilities of 3B 4
Page 137 and 138: AllcasesLandfallTCsTable 3. TS, ETS
Page 139 and 140: The representation of the rain gaug
Page 141 and 142: 3 Results16Percentage(%)14121086200
Page 143 and 144: 403530Related Error(%)25201510504 C
Page 145 and 146: Recent Progresseson TC Precipitatio
Page 147 and 148: fields, particularly taking account
Page 149: form. The initial conditions of ens
Page 153 and 154: B3.2possible rain states that could
Page 155 and 156: B3.4Figure 2: Spatial temporal samp
Page 157 and 158: A Radar-based Technique for the Ide
Page 159 and 160: et al. (2007), the HEM obtained exa
Page 161 and 162: these calibration algorithms.7. Con
Page 163 and 164: CINRAD Warning Indexof Local Extrem
Page 165 and 166: Research of Rainfall Estimation usi
Page 167 and 168: Figure 1 has shown the relationship
Page 169 and 170: 4. Summary and discussion(1) T he T
Page 171 and 172: Session CHydrologic prediction and
Page 173 and 174: Hydrological Perspective on QPE/QPF
Page 175 and 176: the river system in the basin. The
Page 177 and 178: (2) As input of the hydrological di
Page 179 and 180: satellite estimates) and the foreca
Page 181 and 182: NWP models’ Application in Flood
Page 183 and 184: Deltares’ Flood Early Warning Sys
Page 185 and 186: A further challenge for hydrologic
Page 187 and 188: 2.2 Test areaHuaihe Basin locates i
Page 189 and 190: Fig. 4 The same as Fig.3, but at Wa
Page 191 and 192: Third WMO International Conference
Page 193 and 194: There is a unimodal pattern in the
Page 195 and 196: Number of days with 1mm and above95
Page 197 and 198: homogeneous (Fig. 1). Thi s area is
Page 199 and 200: 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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QPF verification using object-orien
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measure the S and A components resp
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5. ConclusionsSeveral problems aris
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2.1 MethodologyIn the EOF analysis,
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calculated respectively, the compar
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IntroductionProbabilistic predictio
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Once the models are obtained, they
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REFERENCES- Charles Mutai: 2000, Di
Page 491 and 492:
This paper selects the process of w
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(b1)cntl (b2)snd (b3)cmwFig.5the cl
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(2) There is no much difference in
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Analysis of Convective Precipitatio
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F(per/6min)30025020015010050F(per/6
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Research on the Relationship betwee
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Fig.1 The k-Z relationship for smal
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esearch on the correction for atten
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22224diction of the heavy precipita
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Study on Mechanism of an Extra Rain
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which promoted the shear line devel
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References[1] SUN Shu-Qing and ZHOU
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the main p oints in our estimation
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QC test to reject the bogus faulty
Page 519 and 520:
-499-
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The Impact of Parameterization of C
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LinThompsonMilbrandt-YauMorrisonFig
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MOST (Grant No. GYHY20070 6036), th
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National Meteorological Center (NMC
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Fig. 4 Comparison of percentile are
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Impact of Different Boundary Layer
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(a)observed rainfall,(b)simulated r
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In te rms of ener getics, the hea v
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The Estimation of Rainstorm Forecas
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Fig. 2 TS Score of accumulated 24h
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Study on the 3D Structure of Typhoo
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abFig.1 (a) Total precipitation (mm
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satellite images at 12:00BST on 18
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