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Wang, C.-C., G. T.-J. Chen, R. E. Carbone, 2004: A climatology of warm-seasoncloud patterns over East Asia based on GMS infrared brightnesstemperature observations. Mon. Wea. Rev., 132, 1606–1629.Yu, R., T. Zhou, A. Xiong, Y. Zhu, and J. Li (2007), Diurnal variations of summerprecipitation over contiguous China, Geophys. Res. Lett., 34, L01704,doi:10.1029/2006GL028129.-10-
Convection over the Taunus Mountains: Distribution, Tracks and LifeCycleChristoph Brendel and Bodo AhrensInstitute for Atmospheric and Environmental Sciences, University of Frankfurtchristoph.brendel@iau.uni-frankfurt.de1. IntroductionDeep convection belongs to one of the most intensive atmospheric processes on earth. Deepconvection is often associated with heavy rainfalls, wind gusts, hail and lightning. In some cases,strongly depending on favourable meteorological key parameters, the development of tornados ispossible (DOSWELL 2001). These natural hazards can result in high damages, insurance costs andsometimes loss of human lives. Convective processes occur on the local- and mesoscale, and arehighly variable in space and time in their occurrence and intensity. For this reason forecasting ofconvection in detail is difficult. Especially the areas of formation, the tracks of single convective cellsand their behaviour are not predictable. Rain gauge networks are also not able to detect convectiveprecipitation in its totality and therefore a high-resolution long-term climatology of deep convectiondoes not exist. Regarding these problems there was a motivation to find out how convective cellsoccur and trace over complex terrain, e.g. low mountain ranges. There are some examinations aboutthe influence of mountainous areas on convection done in the USA. On the one hand the aim was toidentify regions, which initiate convection depending on different stream directions (BANTA andSCHAAF 1987) and on the other hand the suggestion was to investigate the influence of the ColoradoRocky Mountain Range on the diurnal cycle of convective precipitation for two summer seasons(KARR and WOOTEN 1976).2. Area of InvestigationThe area of investigation is located in the German low mountain ranges with focus on the TaunusMountains (Fig. 1). The Taunus Mountains consist of three parts: a main ridge (a) that is orientatedfrom southwest to northeast; a northern part with some side ridges orientated from southeast tonorthwest, separated by a large valley/basin (b) into a western (c) and an eastern part (d); and asouthern narrow zone (e) dropping off steeply to the Rhein-Main-Basin.Figure 1: Area ofinvestigation,digital elevationmodel (shaded),area borders ofunspoiled nature(grey lines),dimension of thewhole area (80kmnorth-south and100km west-east)-11-
Page 1: World Meteorological OrganizationWW
Page 5 and 6: ForewordWeather disasters often ari
Page 7 and 8: CONFERENCE SCHEDULEThird WMO Intern
Page 9 and 10: Conference program for the third WM
Page 11 and 12: Conference program:A. High impact p
Page 13: B3. 13:45-15:40 Session Chair: Rich
Page 17 and 18: P. 19:15-20:30 19-20 Oct. 2010 Post
Page 19 and 20: H. Conference summaryConvener: Chun
Page 21 and 22: Session AHigh impact Precipitation
Page 23 and 24: 1. IntroductionSynoptic and Mesosca
Page 25 and 26: and PS each accounting for about 20
Page 27 and 28: ain-producing storms (Schumacher an
Page 29: Houze, R. A., Jr., S. A. Rutledge,
Page 33 and 34: in the southeast of the investigate
Page 35 and 36: Spatial and Temporal Variations of
Page 37 and 38: were normalized to sum 1 to give th
Page 39 and 40: during the continuous one day (Rx1d
Page 41 and 42: The Weather Research and Forecastin
Page 43 and 44: Convective rainfall in wet runs is
Page 45 and 46: On the Study of Tropical Cyclone Ra
Page 47 and 48: Fig.2 Severe tropical storm Bilis (
Page 49 and 50: Numerical simulation (Zhu H.Y, et a
Page 51 and 52: Influence of Typhoon Songda (2004)
Page 53 and 54: southeastern part of typhoon circul
Page 55 and 56: the thermal structure is asymmetric
Page 57 and 58: The estimation of TC quantitative p
Page 59 and 60: from 0000 UTC on 8 August 2009 to 0
Page 61 and 62: and 1.23:1 in 24h, indicates that t
Page 63 and 64: in the latter 24-h, on average, the
Page 65 and 66: 4. Sensitivity experiment results1)
Page 67 and 68: ReferencesChen, L., and Y. Ding, 19
Page 69 and 70: of the documented intensity and tra
Page 71 and 72: Figure 1b shows the distribution of
Page 73 and 74: influence.In order to explain the v
Page 75 and 76: torrential rain days in 2009. The d
Page 77 and 78: over China mainland for the 32 typh
Page 79 and 80: 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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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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