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Ebert, E E, 2008. F uzzy verification o f h igh r esolution g ridded forecasts: A r eview and proposedframework. Meteorol. Appl., 15, 51-64.Fowler, T .L., T. Jensen, E. I. Tollerud, J. Halley Gotway, P. Oldenburg, a nd R. Bullock, 2010: NewModel E valuation Tools (MET) S oftware C apabilities for QPF V erification. Proceedings, Third WMOInternational Conference on QPE and QPF and Hydrology.Gilleland, E ., D . A hijevych, B .G. B rown, B . C asati, a nd E .E. E bert, 2009: I ntercomparison of s patialforecast verification methods. Wea. Forecasting, 24, 1416-1430.Gilleland, E., D.A. Ahijevych, B.G. Brown and E.E. Ebert, 2010a: Verifying Forecasts Spatially. Bull.Amer. Meteor. Soc., in press.Gilleland, E ., J. L indström, a nd F . L indgren, 2010 b: A nalyzing t he i mage warp f orecast v erificationmethod on precipitation fields from the ICP. Wea. Forecasting, 25, 1249-1262.Kain, J.S., M. Xue, M.C. Coniglio, S.J. Weiss, F. Kong, T.L. Jensen, B.G. Brown, J. Gao, K. Brewster,K.W. Thomas, Y. Wang, C.S. Schwartz, and J.J. Levit, 2010: Assessing advances in the assimilation ofradar data within a collaborative forecasting-research environment. Weather and Forecasting, in press.Lindström, J., E. Gilleland, and F. Lindgren, 2011: Forecast verification using image warping. Manuscriptin Preparation, available at: http://www.ral.ucar.edu/staff/ericg/LindstromEtAl2009.pdf).Mass, C.F., D. Ovens, K. Westrick, and B.A. Colle, 2002: Does increasing horizontal resolution producemore sk illful f orecasts? The results o f t wo y ears o f r eal-time num erical w eather pr ediction ov er thePacific Northwest. Bull. Amer. Meteorol. Soc., 83, 407-430.Mittermaier, M. P. an d N . R oberts, 2 010: Intercomparison of Spatial Forecast V erification M ethods:Identifying skillful spatial scales using the Fractions Skill Score. Wea. Forecasting, 25, 343-354.Table 1: Summary of the types of information that the general categories of methods can provide. Entries representgeneral characteristic of the method type, and understood that there are exceptions among individual methods; see textfor more information.Method categoryScaleswith skillLocation errorsIntensityerrorsStructureerrorsOccurrence (hits,misses and falsealarms)Neighborhood Yes No Yes No YesScale-separation Yes No Yes No YesFeatures-based No Yes Yes Yes YesDeformation No Yes Yes No No-186-
Figure 1: From Gilleland et al. (2010a, Fig. 3). Shows the 13 May 2005 ICP real test case, which is a 24-h accumulatedprecipitation forecast (in this case from the WRF 4-km NCEP model) along with the Stage II analysis field (panel a).Panel b depicts a simple neighborhood approach (upscaling) applied to the test case, panel c shows the scale separationwavelet decomposition to a binary difference field (i.e., part of the IS method) applied to the same test case. Panel d) is anexample of MODE (a feature-based method) and panel e is an example of an optical flow (a deformation approach) alsoapplied to this test case.-187-
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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
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
Page 201 and 202: Session DVerification of Precipitat
Page 203 and 204: Intercomparison of verification met
Page 205: intensity f or pr e- and pos t-defo
Page 209 and 210: New Model Evaluation Tools (MET) So
Page 211 and 212: During the HWT project, precipitati
Page 213 and 214: Figure 4: Example showing atmospher
Page 215 and 216: Performance Evaluation of the AREM
Page 217 and 218: 0-24h and 24-48h, the improved mode
Page 219 and 220: Fig.4 24h accumulative precipitatio
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Page 223 and 224: (panel c) and 20mm/24h (panel d) fo
Page 225 and 226: A new field verification score base
Page 227 and 228: still overlapping the observed feat
Page 229 and 230: Case Description of forecast featur
Page 231 and 232: Quantitative Precipitation Forecast
Page 233 and 234: GFS60 and NAM60 at the lowest thres
Page 235 and 236: difference between the two models i
Page 237 and 238: Scale-related Issues involving veri
Page 239 and 240: of scores for the GFS at heavier ra
Page 241 and 242: Fig. 4. Comparison of probability o
Page 243 and 244: From these equations one can see th
Page 245 and 246: Figure 2. Verification scores as a
Page 247 and 248: Session EData assimilation for prec
Page 249 and 250: Progress in data assimilation for N
Page 251 and 252: Here, k is the number of ensemble m
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Page 255 and 256: 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
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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
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-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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