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3 rd Conference on QPE /QPF and Hydrology. World Meteorological Organization, Nanjing, China, Oct.18-‐22,2010. Radar-based Quantitative Precipitation Estimation for the Cool Season in MountainousRegionsYoucun Qi *1,2 , Jinzhong Min 2 , Jian Zhang 1 , David Kingsmill 3 , Kenneth Howard 11 NOAA/National Severe Storms Lab2 Nanjing University of Information Science and Technology3 NOAA/Earth System Research Lab1 IntroductionThis paper presents a case study of radar-derivedquantitative precipitation estimation (QPE) for the cool seasonin the complex terrain of California as shown in Fig.1, with afocus on developing a VPR correction algorithm fororographically enhanced precipitation processes. It wascommonly observed that precipitation amounts increase withdistance up a mountain slope when a deep, moist layer of airrises over the mountain barrier. The increase is moresignificant with higher freezing levels since condensedmoisture requires time to accumulate on particles before theyreach the ground. The case to be discussed occurred on 13-14October 2009 in the central and northern California. At thebeginning of 13 th of October 2009, a very wet and windystorm struck central and northern California. This storm wasfueled by unusually strong upper jet energy across the Pacificand a surge of tropical moisture from former super typhoonMelor, which struck Japan earlier on the 8 th of October 2009.It brought intense precipitation to the central and northernCalifornia. Rainfall estimations are computed from WSR-88Dobservations with the developed VPR correction algorithm.For this winter storm, the rainfall estimation skill wasincreased using the new VPR correction algorithm, and theroot mean square errors of 48hr rainfall accumulationcompared to gauge was reduced from 2.09 inch (before VPRcorrection) to 1.55 inch (after VPR correction). The new VPRcorrection algorithm is described in the next section, anddetailed case study results are presented in section 3. The lastsection, section 4, provides a summary.Fig.1 Map of the study domain. .* Corresponding author : Youcun Qi, 120 David L. Boren Blvd., Suite 2100 Norman, OK 73072-‐7304; E-‐mail: youcun.qi@noaa.gov -446-
3 rd Conference on QPE /QPF and Hydrology. World Meteorological Organization, Nanjing, China, Oct.18-‐22,2010. 2. MethodologyIn order to obtain an accurate radar QPE in the currentstudy was generated with the following 5 steps:1), Minimizing ground clutter contaminations in the QPEthrough a reflectivity quality control (QC);2), Reducing QPE errors due to orographic process,bright band, and radar beam sampling in the ice regionsthrough a VPR correction;3), Mitigating discontinuities in the QPE due to beamblockages through a new way of deriving the hybrid scanreflectivity;4), Mosaicing HSR fields from multiple radars on to acommon QPE analysis grid;5), Reducing QPE errors by applying spatially varying Z-R relationships representing different microphysical processesacross the analysis domain.The current study, we mainly focused on step2developing a robust and effective VPR correction algorithm.This module is an extension of the technique developed byZhang and Qi 2010 (hereafter, ZQ10), in which radar QPEerrors associated with a bright band were corrected usingsingle tilt apparent VPRs. The detailed VPR correctionscheme is discussed in the rest of this section.2.1 Generate apparent vertical reflectivity profile (AVPR)The first step of the new VPR correction involescomputing apparent VPRs by taking azimuthal mean ofreflectivities from predefined precipitation areas with potentialbright band contamination (ZQ10) and with radar sampling inthe ice region. Each AVPR is computed for an individual tilt,and the areas are delineated according to the followingcriteria: 1) VIL (vertically integrated liquid, Greene and Clark1972) =15dBZ and composite reflectivity > 30dBZ (tocapture bright band peak) for bright band areas; 4) Reflectivity> 0 dBZ or composite reflectivity >30 dBZ for ice regions.Fig.2 The conceptual VPR model used in the current study. The blue dots represent the observed VPR, and the red line is theidealized VPR. The blue lines are bright band top (upper) and bottom (below). The solid green and dashed black lines arebright band peak and the 0 o C height, respectively.The orographic enhancement effect (e.g., Kitchen et al.1994) is shown as an increase of areal mean reflectivity withdecreasing height in the mean radar AVPR below the brightband bottom (Fig.2). Vertical pointing precipitation profilerradar data are used to derive the VPR slope for the orographiceffect in the current study. The profiler data were obtainedfrom two S-band precipitation profiler radars deployed duringthe National Oceanic and Atmospheric Administration(NOAA) Hydro-meteorological Test-bed (HMT,http://hmt.noaa.gov) in Nov. 2005 to Apr. 2006. The radarswere located in Cazadero (CZC) near the west coast ofCalifornia and in Alta (ATA) on the California Sierras asshown in Fig.1. These profiler radars measure time evolutionsof the reflectivity structure along a vertical column with high-447-
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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
Page 415 and 416: Comparison of calibration technique
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Page 419 and 420: 24-h rainfall forecast. Nevertheles
Page 421 and 422: ANALYSIS FOR TURBULENCE AND WIND GU
Page 423 and 424: Research and Application of Typhoon
Page 425 and 426: [3] TIAN H, MA K Y,LIN Z S. Analysi
Page 427 and 428: What’s more, th e tw o curves als
Page 429 and 430: precipitation in southern areas of
Page 431 and 432: Chen Huai, Wang Yongbo, Shi N eng,
Page 433 and 434: An hourly updated convection-allowi
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Page 437 and 438: Downscaling precipitation for weath
Page 439 and 440: abFig.2. The 24h accumulated precip
Page 441 and 442: A Kalman Filter based QPF calibrati
Page 443 and 444: Scale Parameter10090807060504030rai
Page 445 and 446: It is clear that the values of scal
Page 447 and 448: THE IMPACT OF NORTH PACIFIC SUBTROP
Page 449 and 450: negative in the northern hemisphere
Page 451 and 452: Establishment and Verification of M
Page 453 and 454: Multi-model Ensemble QPF for Southe
Page 455 and 456: (4)andcan be estimated for all mode
Page 457 and 458: Table 4 The number of observations
Page 459 and 460: gauge coverage is definitely not sa
Page 461 and 462: It would be an attractive result if
Page 463 and 464: Analysis on a permanent elongate co
Page 465: PECS grow. The evolution of meso-sc
Page 469 and 470: 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
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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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