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Figure 5 : Increasing Trend of One Day Heavy Rainfall Events (1961-2008)The correct amount of rainfall and the correct onset of rain are very important for the many cropslike rice in Sri Lanka. Chandrapala, 1997 shows that Maha season (Co mbine of South westmonsoon and second inter monsoon) is most vulne rable fro m the variability of rainfall in theperiod 1961-1990 than 1931-1960. Also, Maha season is the highest cultivation season and m uchof the rice requirem ent is growing in th e dry zone in Sri Lanka. The sowing of rice crop is startwith the ons et of Second Inter Mons oon (SIM) during t he latter part of S eptember. Recentobservation shows that it did not receive SIM rainfall in correct tim e. Therefore Coefficient ofVariation (CV) or variabil ity has been calculated for different districts. Calculation of CV showsthat, it has in creased during the period 1980- 2008 than 1951-1979. The CV is high in the Dryzone and low in wet zone.2.2.1 Analysis of Rainfall VariationInterseasonal, interannual and spa tial variability i n r ainfall trend has been observed during thepast few decades all across Asia. Decreasing tre nds in annual mean rainfall are observed inRussia, North-East and North China, coastal belts a nd arid plains of Pakistan, parts of North-EastIndia, Indonesia, Philippines and so me areas in Japan. Annual mean rainfall exhibits increasingtrends in Western China, Changjiang Valley a nd the South-Eastern coast of China, ArabianPeninsula, Bangladesh and along the western coasts of the Philippines (IPCC AR4, 2007).Economy of Sri Lanka is very much depends on ag riculture and the high variability of rai nfallwithin the season will be highl y im pacted. Acco rding to t he rainfall records of MeteorologyDepartment, Second Interm onsoon was delay ed in 2007 an d 20 09 com paring the last 5 y ears.Therefore calculating variabilit y for different time period for different climate season willimportant for the policy makers to decide to adapt to the climate change.Analysis of CV during t he maha season shows that , it is hi gh in the dry zone and gradually it isdecreasing and it was not higher in wet zone. The comparison of CV for the periods 1951 – 1979and 1980 – 2 008 shows rem arkable increases dur ing the period 1980 – 200 8 (Figure 6a an d b).The highest changes are confined in the dry zone. Dry zone is the major area for growing rice and-266-
the rainfall v ariability will highly impacted for the crop during sowing ti me and also harvestingtime. Analysis of annual CV for different district s does not show the variability within the seasonor onset and withdrawal of the seaso n. Theref ore co mplex analy sis shoul d be done to findvariability as mentioned above.CV %6050403020100CV During the Maha Season (1951-1979 & 1980-2008)RatnapuraKalutaraColomboGampahaGalleNuwara EliyaMataraKegalleKandyMonaragalaBadullaKurunegalaAmparaHambantotaAnuradhapuraVavuniyaMullativuPolonnaruwaBaticaloaPuttalamTrincomaleeKillinochchiMannarJaffnaDistrictCV % 1951-1979 CV % 1980-2008(a)CV %6050403020100CV During the Yala Season (1951-1979 & 1980-2008)RatnapuraKalutaraColomboGampahaGalleNuwara EliyaMataraKegalleMataleKandyMonaragalaBadullaKurunegalaAmparaHambantotaAnuradhapurVavuniyaMullativuPolonnaruwaBaticaloaPuttalamTrincomaleeKillinochchiMannarJaffnaDistrictCV % 1951-1979 CV % 1980-2008(b)Figure 6 : (a) Rainfall Variability at different district during Maha season(b) Rainfall Variability at different district during Yala season3. Conclusion and DiscussionAverage temperature has been increase d and the in creasing trend is high during the recent past.With the increasing te mperature it co uld be seen that the cli mate of Sri La nka also cha ngedespecially during the recent past. According to the analysis of rainfall it could be seen that th eannual rainfall has significantly decreased. Primarily it will be impacted for the water resourcesand then secondly the decreasing of rainfall will impact for the sectors as agriculture and energy.Receiving rainfall at correct time is important for the economy in Sri Lanka. But analysis showedthat the variability has be en increased for the last few decades. Annual rainfall variability hasincreased almost all over the countr y, but variabili ty is high in Dry Zone than the Interm ediateZone and wet zone. The variability is higher in the recent past. Rainfall variability is much higherduring the maha season than yala season.-267-
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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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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
Page 253 and 254: 0200pressure (hPa)400600800spread a
Page 255 and 256: Impacts of multiple radar data assi
Page 257 and 258: High Resolution Radar Accumulation
Page 259 and 260: 3. MethodThe non-hydrostatic versio
Page 261 and 262: Figure 5: Accumulation in the Waipa
Page 263 and 264: J(X ) Jb Jo Jc1 T 1T X X ) B ( X
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Page 267 and 268: experiment, velocity assimilated ex
Page 269 and 270: Cycle Forecasting System in China.
Page 271 and 272: successfully forecast by AREM-RUC a
Page 273 and 274: Session FPrecipitation variability
Page 275 and 276: FRepresentation of Monsoon Systems
Page 277 and 278: 4 ResultsFigure 2: Monsoon mean pre
Page 279 and 280: explained by higher sea surface tem
Page 281 and 282: Variability of Rainfall, Increasing
Page 283 and 284: (a)(b)Figure 1 : (a) Change of Temp
Page 285: According the analy sis of rainfall
Page 289 and 290: Regional Impact of Climate Change i
Page 291 and 292: Figure 2. Map of the classified wea
Page 293 and 294: Table 3. The num ber of weather st
Page 295 and 296: such a mo del. Second, we ha ve o b
Page 297 and 298: Joint fuzzy an alysis o f SST , SAT
Page 299 and 300: Variability of Equatorial East Afri
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Page 303 and 304: Figure 4: (a) The first spatial mod
Page 305 and 306: Session GQuantitative Precipitation
Page 307 and 308: Ongoing developments on Limited Are
Page 309 and 310: een done but much i s s till needed
Page 311 and 312: Hacker, J., S.-Y. Ha, C. Sn yder, J
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Page 315 and 316: One advantage of ensemble forecasti
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Page 321 and 322: Term growth forecast s, th e error
Page 323 and 324: ponent of the Brier score is reduce
Page 325 and 326: in the combined field (Fig.3c). For
Page 327 and 328: Probabilistic QPF from a realtime m
Page 329 and 330: to help assessing impacts from diff
Page 331 and 332: abFigure 3.ETS scores of the 3-h ac
Page 333 and 334: Flow chart of multi-model rainfall
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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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