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B3.3Spatial temporal sampling error plots are generated by progressively downgrading thespatial and temporal resolution of the high resolution radar images and comparing themto the original image. The spatial resolution is downgraded by averaging radarreflectivity, Z, in space in a manner physically analogous to increasing the samplingvolume (assuming reflectivity is uniform with height). Temporal resolution isdowngraded by sampling only every nth (for n = 2,3,…) radar frame when constructingthe 10 minute accumulation, simulating an increase in the sampling period of n timesthe original interval. The accumulation is then determined using an advection basedinterpolation scheme similar to that of Method 1 from Fabry et al. (1994). The rainfallaccumulation between consecutive radar scans is generated by translating the earlierframe in the direction of diagnosed echo motion for half of the sampling period whilstaccumulating at each one second step of the translation while the latter frame istranslated in the opposite direction for the second half of the period in the same manner.The comparison between the downgraded accumulation and the original high resolutionaccumulation is made byNMAE = 〈| − |〉 >⋁>〈〉 >Where L i and H i represent rainfall depth (in mm) in a single pixel from the downgradedand original resolution accumulations respectively, 〈… 〉 ⋁ indicates calculating themean over all pixels with intensities greater than T mm in either the downgraded ororiginal resolution accumulations. This gives the per pixel mean absolute error (MAE) asa fraction of the mean per pixel rainfall accumulation for all pixels with intensities over acertain threshold. This is termed normalized mean absolute error (NMAE) in thefollowing analysis.3. ResultsA typical spatial temporal sampling error diagram is shown in Figure 2. The diagramshows that the magnitude of the error due to sampling is very significant. We see thatfor 2000 m and 5 minute sampling, the sampling error is of a magnitude of 40% of themean rainfall accumulation.It is evident that when observing with lower spatial resolutions, the temporal resolutionis not as important as it is with higher spatial resolution observations. It is also the casethat with low temporal resolutions it is not very beneficial in terms of the error in rainfallaccumulation to increase the spatial resolution of the measurement.It is apparent that the sampling errors are greatest for rainfall with shorter length scales.This is because the overestimation in precipitation area seen with a low spatialresolution observation is more severe for rainfall with length scales closer to or smallerthan the size of the low resolution pixel. Also, small scale precipitation possesses muchshorter temporal evolution times (Venugopal et al., 1999) so that the radar samplingrate is much more important for capturing evolution of the rainfall field at these scales.-134-
B3.4Figure 2: Spatial temporal sampling error diagram for a mixed convective/stratiformevent on 4 th August 2009. Contours show normalized mean absolute error.Figure 3: Normalized mean absolute error, due to spatially averaging radar data to2000 m, as a function of an event’s mean spatial decorrelation length. . A curve has beenfitted returning an R 2 value of 0.92.-135-
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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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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
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Page 117 and 118: The Megha-Tropiques accumulated rai
Page 119 and 120: Figure 1 shows the flow chart of th
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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 and 150: form. The initial conditions of ens
Page 151 and 152: Comparative Study on QPE Methods of
Page 153: B3.2possible rain states that could
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
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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
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Page 181 and 182: NWP models’ Application in Flood
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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
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Page 201 and 202: Session DVerification of Precipitat
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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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