Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Sciences Discussions Earth System Hydrology and cient of 0.80. ffi 7238 7237 .bg.ac.rs) ff ´ c (curic@ ´ Curi ´ c and D. Janc ´ Curi This discussion paper is/has beenSystem under Sciences review for (HESS). the Please journal refer to Hydrology the and corresponding Earth final paper in HESS if available. Young et al., 1999).errors (Chumchean Many et al., studies, 2006). therefore, have been realized to minimize radar dictions. Meteorologists have two mainground: tools to estimate rain convective gauge precipitationaccumulated at and the precipitation meteorological accurately, the radarpoorly spatial data. represented pattern due of to Although the convectivetional rain limited precipitation network resolution gauges is (Barnolas and measure et spot-like coverage al.,little of 2010). basins the or Meteorological observa- in radars regions mayportant be where disadvantages convection a because is better they important. option areresults in an However, of radar indirect rainfall data measure depth have of in im- rainfall a and given with location worse compared to rain gauge data (Hunter, 1996; 1 Introduction Flash floods in small areascaused leading to by huge convective material precipitation. damagetemporal and distribution loss contributes The of to life the correct are improvements reproduction on mainly hydrological of analysis and both pre- its spatial and may be a useful tooldictions. to simulate The convective precipitation mainreproduces for objective well various of the analyses accumulated this and convective precipitation pre- papernetwork obtained data is from in the to the rain show gauge areaobservations with that and frequent model the split samples cloud-resolving storms. ofa model We the 15-yr perform areal period comparisons accumulated over between convective treatedStatistical precipitation area. for analyses Twenty-seven convective events reveal have that beenclosely the selected. match model their observed areal values accumulated with a convective correlation precipitation coe Convective clouds generate extremewith both rainfall large events spatial andtotal and accumulated flash temporal precipitation variability. floods fields For inical at this the radars small reason, surface has areas the with both rain monitoring strengths gauges of and and the weakness. meteorolog- Alternatively, a numerical cloud model Abstract Hydrol. Earth Syst. Sci.www.hydrol-earth-syst-sci-discuss.net/8/7237/2011/ Discuss., 8, 7237–7259, 2011 doi:10.5194/hessd-8-7237-2011 © Author(s) 2011. CC Attribution 3.0 License. Published by Copernicus Publications on behalf of the European Geosciences Union. Institute of Meteorology, University of , 11000 Belgrade, Received: 26 May 2011 – Accepted: 6Correspondence July to: 2011 M. – Published: 22 July 2011 Analysis of predicted and observed accumulated convective precipitation in the area with frequent split storms M. 5 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | E ◦ ´ c et ´ c and ´ Curi ´ and 21 Curi ◦ ´ c and Janc, ´ Curi 7240 7239 ´ c and Janc (1992). N and eastern longitude between 20 0 ´ Curi 16 ◦ N and 44 0 ´ c (1982) and erent times. However, convective storm splitting and subsequent ff 30 ◦ ´ Curi ective tool to estimate the convective precipitation in such a complex ect the most complex spatial convective precipitation pattern. Split right- ff ´ c et al., 1998; Cohen and McCaul, 2006). There are studies that use ff ´ Curi ected by convective events with flash floods that are often produced by the NW ff ected by the storm cell structure. Some convective storms contain essentially only Bearing the above considerations in mind, the main objective of this paper is to The complex feature of the accumulated convective precipitation in a small area is Various rainfall models are developed depending on the availability of radar data and ff plateau (west from the treatedin agreement area) with with a mean height of approximatively 1 km MSL storms occur frequently and may causelatitude flash between floods. 43 This area(Fig. is situated 1). at a The northern mostWest prominent Morava river region valley of whichvalley this floor is mountainous is roughly area flat oriented ishas and from the higher very centrally northwest mountains narrow, located to with especially southeast. peakis in values a the The over middle. 1.5 km Theair above mass southern the convective valley valley storms. floor. side These This storms valley were initialized frequently over the Zlatibor various meteorological and hydrological predictions and analyses. 2 Study area and data For the purpose of this study we select the area in the west part of Serbia in which split show whether the cloud-resolvingan model area can with simulate frequent wellmodel storm convective with precipitation splitting substantially in episodes. improved microphysics2011) and We to reproduce initial use well conditions a the ( observed cloud-resolvingWe accumulated mesoscale convective compared precipitation in the this modeled area. precipitation and by observed using a amountscipitation statistical of analysis. in areal space Correct accumulated description and convective of time the in convective this pre- complex case would be essential with regard to tive precipitation herein is necessaryemphasize due that to the radar drainage datasity requirements. associated give We with must a the also splitting weakfind processes. estimation another For this e of reason, convective hydrometeorologistsscenario. must precipitation inten- in urban areas (Niyogy et al., 2011). The knowledge of the spatial pattern of convec- Hilgendorf, 2001). The importance of the storm splitting process is also recognized one kind of cell.multaneously or at Others di containsplit a motion mixture a of ordinaryand cells left-moving and storms supercells1999). are either cyclonic Each si- split and stormal., anticyclonic, also 2009). respectively contains one (Adlerman Split or stormsshows et more are that al., cells not the and mirror right-moving mayclockwise-turning images split storms hodographs. of again are each ( favored other.vective There in storms Observational are, the within evidence environment environments however, with characterized the the by long-lived same wind left-moving shear con- conditions (Grasso and lated convective precipitation fromresolving isolated model to storms match showJanc, observations 2011). the in mountainous capability and of flat the land areas cloud- ( a fields as inputs to2004b). the The hydrological improvement models of model (Droegemeiercipitation microphysics et is is al., essential very because 2000; sensitive2004a, convective Gilmore pre- to b). et the The al., choice uncertaintiesoutputs of in a ( cloud hydrometeor size microphysics distributionthe (Gilmore meteorological is models et critical to for al., simulate thewell the cloud convective as precipitation model to over compare a largeRecently, the comparison area modeled of as and the observed long-term datasets samples (e.g. of observed Amengual and et model al., areal 2007). accumu- the rain gauge network spatialthat, resolution the (Willems, best 2001; approach Sayedsimulating et is the al., to accumulated 2003). use convective precipitation Despite variousresolution with numerical a independently cloud higher of spatial models availability andThis that of temporal is are radar capable important data of to and predict rain and gauge reconstruct network the density. accumulated convective precipitation 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ´ c et al., 1998) ´ Curi ´ c and Janc (2011). ´ Curi . The analysis performed is 2 ´ c et al. (2011). The shapes of all ´ Curi 120 min. The wave radiating condition 20 km with a 500 m grid spacing in the = × t 7242 7241 85 km ´ c and Janc (2011). In our calculations the modified × ´ Curi ´ c et al. (2003, 2008). It represents a detailed portrait of the ´ Curi and a duration of less than 2 h, which only contributes to the daily accu- 1 − The advantage of the Khrgian-Mazin size distribution of cloud drops for prediction of For the purpose of this study, single-moment bulk microphysics parameterization The reference state is homogeneous in the horizontal direction with constant values The convective storms were simulated with a model that critically depends on the The model domain was 150 km The convective precipitation data were collected from the rain gauge network pre- hydrometeors were assumed to beform spherical, with except a for maximum snow, which diameterby is (Lin an exponential in et size a al., spectrum. hexagonal 1983). Cloud ice Hail spectrum and is snow assumed to are bethe each monodispersed. accumulated represented convective precipitation isFor demonstrated this with reason, itscribes is used both in the our100 cloud simulations. µm (also droplet The called Khrgian-Mazin and theMazin size raindrop unified size distribution Khrgian-Mazin spectra distribution de- size may that distribution). be are written The (Prupacher split unified and by Khrgian- Klett a ,1997; diameter of scheme is used as inCotton, other 2004; sensitivity Cohen studies of andwater, the McCaul, rain similar 2006). type and (Van Theequations den three model Heever and classes and microphysics parameterizations of represents are cloud ice based on (cloud ice, snow and hail). The microphysical model initial conditions. Initial conditionsThis are approach usually given has with many atotally single disadvantages unrepresentative sounding in because data. the the boundarytive used layer estimates by vertical of the the profiles time accumulated convective convection arereason, precipitation begins. are often we Quantita- therefore have limited. modified For this using the the temperature method and explained wind by Belgrade profiles midday in routine the soundings boundary were used. layer by the lower boundary is a free slip boundary. of temperature, humidity, pressure, windinitiated velocity by and using wind anvertical direction. ellipsoidal axis Convection centered warm was at cap the ground. with The a temperature 10 perturbation km used horizontal is axis 3 K. and a 1.5 km through the boundary. An upper boundary with a Rayleigh sponge layer is used, while area. The simulationsis were applied terminated to at the lateral boundaries to minimize the reflection of waves that pass freely dependent variables of thetion model potential are temperature the and Cartesianratios pressure; for wind the water components; turbulent vapor, theuses cloud kinetic perturba- a water energy; generalized and and terrain-following coordinate ice, the withas rain, mixing equal well spacing snow as in and in x the hail and vertical. (graupel). y directions The model horizontal direction. Aboundary constant of vertical the grid model spacing area of is 200 displaced m to is the used. west with The respect western to that of the study 3 Model description The cloud-resolving mesoscalescribed model in which detail simulates by storm the dynamics convective with storm fullytegrates is interactive the hydrometeors. de- time-dependent, The nonhydrostatic model and used fully numerically compressible in- equations. The of 2.5 mm h mulated precipitation. The event isin observed the by study at area. leastmean The half observations convective of were the precipitation performed rain per after gaugerain event each stations gauge at convection networks event. rain have The gaugesafter Hellmann is each rain convection presented event. gauges. in A Table few The 2. stations observations have pluviographs. The were performed sented in Fig. 1.are also The presented geographical in coordinates Tableitation. 1. of We 54 For select our rain samples analysis, gauges from(1981–1995) we in with 27 use the convective their treated the precipitation altitudes area daily-accumulated eventsbased of over precip- approximately on a 6870 the 15-yr km period following criteria: the convective precipitation has an intensity in excess 5 5 25 20 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (1) (2) ˇ zice (west is the drop radius, M R ´ is the liquid water den- c and Janc (2011). W ort to reproduce observed ´ ρ ff Curi ´ c, 1989) were also used in our analy- 7244 7243 is the drop diameter. In our model scheme we D is the total liquid water mixing ratio; ´ c and Janc, 2011). This is due to the cloud splitting of the Q ´ c et al., 2003). The L2 track (north of the L1 one) is charac-  M ´ Curi 3 ´ R Curi 2 50 µm in agreement with the results of BD = = − B  ; M ected with the frequent front-side cell regeneration in presence of the ff 6 R M R exp ´ c, 1982; w ρQ 2 ρ 4 AD ´ Curi is the cloud air density; and 452 = . ρ ) 1 D Figure 2 demonstrates the spatial pattern of the mean convective precipitation in The storm splitting cases are frequent over the treated study area (23 cases from Hereafter we consider the convective precipitation from the individual convective = ( minimum. It follows the left-moving anticyclonic storm which is generally unfavored the study area.coincided Two with L1 the and tracks L2L1 of directions direction right- are (coincide and roughly especially left-moving withby analysed storms, the the because respectively. West alternate Morava they change As river of are noted,prevailing valley maxima axes) decrease the and is trend minima characterized of fromThis the the in convective northwest agreement precipitation with with to the the clonic the periodic storm precipitation southeast characteristics a as of is thevalley right-moving shown ( cy- in Fig.terized 3. by the non-periodic behavior of the convective precipitation with only one local the split storm caseshas than a for V-shape the form others.cyclonic ( (to The the spatial right) pattern andAACP of field anticyclonic the is (to in split the the left) storm form storms. cases of For a the large non-split continuous cases, area. the the convective precipitation from thearea split (Fig. storms 2). whose The tracks modelthe are used always model simulates in the and complete the observed storm study influenced life convective by cycle, precipitation splitting. but in we In compare the comparisonsconvective storms we study have in area, made real which an time. e are mainly 27 in total). Thestorm’s total propagation accumulated precipitation speed production (Gilmore is et highly al., dependent 2004b). on the The AACP values are greater for time was less thancipitation. 2 h The are selected treated.in convective storms agreement Hereafter, were with we all observations. analyzedevent initiated is the within The determined total the position convective by modelpass of pre- the domain through a first the warm radar growth bubble echo. phase for without each After significant convective initialization precipitation. the Our convective focus storms is given to the methodology of hail suppression. The convective storms whose observed duration analyzed convective storm wasfrom observed area in by Fig. S 1). band Unfortunately, only radar hail cells located were near monitored by U radar according to precipitation values (hereafter called AACP) for selected events over astorms 15-yr period. occurring atvations. a Besides scale data from that theoperational is rain “Hail gauge too Suppression network small Project” (Fig. (Radinovi sis. 1), to the This be available project data uses resolved from rocketsare by to the inject fired conventional seeding from obser- agent a particlesis very into 5 hail dense km). clouds. network Rockets Voluntary ofmulonimbus observers launching (Cb) report sites clouds the occurred (mean at days distance their when between launching convective sites sites. precipitation The from first Cu- radar echo of each N 4 Comparison with observations The objective of thisprecipitation that section produce is flash to floodsmodel version describe in used the reproduce the successfully study the general area observed areal characteristics and accumulated to convective of determine convective whether the as follows: where A In the above expression, which is used assity; a parameter and takesassumed arbitrary that values; 5 5 25 15 20 10 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cient ffi 0.09. + . This is in agreement erent precipitation pat- ` ff 9 10 × 80, while the bias is . 0 = r ´ c and Janc, 2011). The general sample ´ Curi 7246 7245 cients are lower than for the AACP values. Quan- ffi 74). The correlation coe . ` 0 ear and ) and one site near the L2 track () – = r ´ c et al., 2003). The cumulative relative frequency diagram (Fig. 5) shows the capabilities of our However, the good correlation between model and observed AACP datasets do not We now analyze the AACP value for each convective precipitation event. Simul- The reconstruction of AACP fields based on the punctual information is obtained ´ Curi physical scheme employed thecloud unified drop Khrgian-Mazin radius gamma of 50 size µm. distribution with a 5 Conclusions This study compares the arealand accumulated convective its precipitation model from observations valuesting during cases. a 15-yr The period maincloud-resolving in objective mesoscale of the model this study to research areative match is with the Precipitation to more observed demonstrate (AACPO) frequent Areal the split- values.accumulated Accumulated capability Convec- of convective We the precipitation compared eventsprecipitation the to events observed the for dataset study model of area counterparts 27 by of areal using the a same statistical analysis. The model micro- work. A correctcontribute to simulation improvements of in themodel hydrological has convective analysis the precipitation and capability in toradar predictions. data predict time nor convective In and rain precipitation addition, gauge in space data the the are would areas available. where neither reliable spatial distribution of convectiveare: precipitation. punctual Other information general modeltime derived limitations from and the space rain isferent gauge propagation not data; speeds fully a of coherentused rainstorm model etc. with initialization and the However, observed the correspondingcal cloud-resolving cloud; parameters modified model bulk within with sounding; microphysics the the dif- prediction scheme bulk carefully microphysical of adjusted scheme the microphysi- may AACP becomeavailable values (or a in strongly powerful the tool limited complex for in terrain the case, case where of radar a data storm are splitting) not with the rain gauge net- brdo ( titative estimates of theby accumulated deficiencies convective in precipitation themodel at microphysical to the scheme simulate point and precisely are byable the limited limitations small-scale in convective in space processes the and that cloud-resolving time. are highly The vari- given results show the model capability to simulate the these rain gauge stations. As noted, the best linear correlation is found for Bumbarevo precipitation simulated by the model (ACPM) versus their observed counterparts for estimates the observed values whichwith are the greater than nature 26 ofof smaller the and Khrgian-Mazin medium-range sizestatistics raindrops for distribution ( the that observed and generates model large AACP are amounts presented ingive Table us 3. the informationtion on of how the the accumulated modeltrack convective is (Ov precipitation. able to Wesee simulate Fig. selected reliable 1. two spatial Figure sites distribu- 6 near represents the the L1 scatter diagrams with the accumulated convective agram with the AACParea. model values The versus their regressionbetween observed the line counterparts observations (dash and for the line) the model is study values is also presented.model to The match correlation the coe observed values of the AACP. It is evident that the model over- launching sites. Such athat is method encircled is by limited anthe by isohyet precipitation due the amounts to uncertain from the determinationstorms spot-like the of rain is launching gauge observed the sites. networks after area 10:00 and The LT the first (local lack time). radar of echo oftaneously, convective the model simulationsWe are then compare performed the with sampletreated the of study model actual area AACP modified by values soundings. with using the a observed statistical values analysis. for the Figure 4 represents the scatter di- tern may also be atributed( to the less frequent front-cell regeneration outside the valley by using datadrawn from by the using rain the gauge kriging networks as and well the as launching data sites. about precipitation Isohyets occurrence are from the within the existing environmental condition. This substantial di 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , . This may ` 9 y, C., Foufoula- ff 10 × doi:10.1029/2007JD009483 ects of a river valley on an isolated ff cient of 0.80). This result demon- ffi ´ c, V.: The e 7248 7247 ` ekovi , 2010. ´ ´ c, V.: On the sensitivity of cloud microphysics under influence c, V.: Precipitation change from a Cumulonimbus cloud down- ´ c, V. : The influence of merging and individual storm splitting on ` ` ekovi ekovi ´ c, D., and Vu ` ekovi This research was supported by the Ministry of Science of Serbia. We ected by splitting process in a complex terrain. 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Onthey the are other worse hand, estimation the of radarnetworks the data may convective are precipitation be often intensity, unavailable coarse while or or themodel totally rain can gauge absent. give In reasonable lightcomputational estimation of resources, of these the unfavorable use AACP. occasions, Thanks oftional the to purposes the will cloud-resolving the soon mesoscale rapid be modelscontributors development possible. to for of In improvements opera- such in a hydrological way, analysis these and models predictions. mayAcknowledgements. become major able tool with which weexisting observed can values examine and the in convectiveagreement making precipitation, between predictions. both the in We observed reproducing musttics and emphasize should the that not model the be convective dis- precipitation alwaysinput attributed characteris- data to from the a uncertainty in single the sounding model are microphysics. often The unrepresentative, especially in areas far also presented tonoted, show the how model the AACP values modelbe are attributed somewhat data to overestimated the match over characteristics 26 of thea the significantly observed Khrgian-Mazin higher size counterparts. concentration distribution of thatanalysis generates small of As and the medium-size raindrops. accumulatedtions An convective shows additional precipitation that at theaccumulated model the precipitation. is selected able rain to gauge simulate sta- the reliable spatial distributions of the AACP values are wellstrates correlated the (correlation capability coe of thetive model precipitation a to reproduce successfullyprecipitation the pattern accumulated is convec- influenced withright- the and type left-moving and storms distribution of are the built. cells from The which cumulative relative frequency diagram is 5 5 30 25 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , J. 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N.: Precipitation uncertainty due to variations 5 5 30 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | EE 335 E 260 E 580 E 320 E 280 E 250 310 E 230 E 300 EE 350 295 EE 350 E 360 570 E 245 E 219 E 190 EE 860 1010 E 465 E 680 EE 460 990 EE 215 E 500 235 E 300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 47 53 43 12 19 02 29 49 38 52 06 14 25 35 41 47 01 04 14 19 09 51 48 57 54 59 ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ N 20 N 20 N 20 NN 20 N 20 20 N 20 N 20 N 20 NN 20 20 NN 20 N 20 20 N 20 N 20 N 20 NN 20 20 N 20 N 20 NN 20 20 N 20 N 20 N 20 N 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 44 53 53 57 54 56 54 50 53 50 54 46 45 46 43 43 44 41 36 34 35 36 01 33 47 37 34 ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ` eevska 43 ` eka Banja 23.4 ˇ sa 30.2 ` ` ´ eica 43 ˇ ci 30.1 eka Banja 43 za 18.9 ˇ sa 43 ´ ` c 20.4 ` ` ea 23.9 ˇ ear Banja 24.7 ` ˇ ´ zine Livadeeak 24.0 21.2 ˇ ci 43 zega 23.9 ekovica 19.0 za 43 ` ` ` ` ea 43 e 43 ˇ ear Banja 43 ˇ eak 43 zine Livade 43 zega 43 ` Ea ` Ea 7252 7251 EE 170E 580E Vukovica 420E Gru 360E Ad 130E Kni 270 E Ov 150 43 225 Po 43 43 E 320 43 E 340 Bumbarevo Brdo 43 EE 240 635 Goda Arilje 43 E 365 - E 260E Gu 165 43 E 470 Kraljevo 43 E 600 43 E 580 Bjelu EE 420 960 Kati 43 E 365 43 E 250 44 EE 330 190 Go 43 EE 530 340 Loboder Vrnja 43 E 400 Brezovica 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05 12 13 31 48 59 15 20 37 03 52 30 49 40 59 16 35 12 13 00 29 42 42 56 05 18 29 ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ Mean convective Mean convective N 20 NN 20 N 20 N 20 N 20 N 20 N 20 20 N 20 N 20 NN 20 20 N 20 N 20 N 20 N 20 N 20 N 20 NN 20 20 N 20 N 20 NN 20 20 NN 20 20 N 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 05 01 15 15 14 13 11 12 11 11 09 08 09 08 06 05 05 01 07 03 15 03 02 58 58 57 ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ´ ce 21.7 Vrba 21.6 ` ea 20.0 Brezovica 21.4 ˇ sice 20.4 Godaica 21.9 ´ ´ ci 26.8 Gru c 26.8 Bjelu ˇ Satornja 21.5 Zakuta 20.8 ´ c 21.9 Kaona-Dragaevska 30.5 ´ ce 44 ` ea 43 ˇ zde 30.7 Bumbarevo Brdo 18.7 ` eibare 31.8 Ivanjica 27.6 ` ˇ sice 44 ea 17.3 ˇ ˇ stuni stuni ` ˇ ´ ´ rain gaugestationsMionicaKo precipitation per rain gauge event (mm) stations 24.2 precipitation per Vu event (mm) 21.8 Ad VukosavciLjig 25.8 Kni Eumi Badnjevac 23.2Gornji BanjaniGornje Crnu Po 28.0 17.0 Kraljevo Vrdila 23.6 20.9 NatalinciRa 18.9 Ov Ko StaviceDonja Bre Donje Jaru RudnikStragari 26.6 Mojsinje 25.0 20.6 Arilje Gu 21.2 25.5 Pranjani 21.8Gornja Trep Kati Div Gornji MilanovacTopolaBareKragujevac 21.9Gornja DobrinjaGornja Gorevnica Osonica 25.4 18.2 21.8 18.6 19.4 Loboder Kamenica Vrnja Vitkovac Go 30.3 23.0 23.1 19.4 29,4 ci 44 c 44 ˇ Mean convective precipitation per event (in mm) at raingauge stations. Geographic coordinates and altitudes of rain gauge stations in the study area. Satornja 44 ´ c 44 ˇ zde 44 ` eibare 44 ` ea 44 ˇ ˇ stuni stuni ˇ ` MionicaKo PranjaniVukosavciNatalinci 44 Ra Ljig 44 44 Stavice 44 44 44 rain gaugestations latitude longitude (m) stations altitude rain gauge latitude longitude (m) altitude Donja Bre Donje Jaru Rudnik 44 StragariEumi Badnjevac 44 44 44 Gornje Crnu Ko PranjaniDiv 44 44 44 BareKragujevac 44 44 Gornja DobrinjaGornja Gorevnica 43 Gornja 43 Trep Table 2. Table 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

E (Fig. 1). o 21 and and of Serbia in which of Serbia in which o area. We compared compared area. We situated at a northern 20 ) ` 9 0.60 10 − × precipitation in this precipitation ) AACPM ( ` 9 1.06 area is the centrally located West Morava area is the centrally located West 10 h floods. This area is h floods. This mulated convective precipitation by using a mulated − × ogical and hydrologicalrd to various meteorol

5 7254 7253 the convective precipitation in space and time in in time and precipitation in space the convective 120 min for the observed (AACPO) and model values = N and eastern longitude between t ′ o after 44 16 ` 9 Fig.1. Study area with rain gauge network. N and Sample characteristicsSample size AACPO ( MeanGeometric meanMedianVarianceStd. dev.MinimumMaximum 28.58 27RangeLower quartileUpper 30.59 quartileQuartile range 118.18 29.8Skewness 29.97 Kurtosis 10.87 27 11.8 49.7 30.68 21.4 44.17 38.8 37.9 31.8 17.4 6.64 18.1 0.09 43.3 26.0 34.2 25.2 8.2 0.04 ′ o 43 30 General sample statistics for areal accumulated convective precipitation at the surface Study area with rain gauge network. 5 mm) expressed in 10

> Fig. 1. Table 3. ( (AACPM). split storms occur frequently and may cause flas split storms latitude between region of this mountainous prominent The most

For the purpose of this study we select the area in the west part predictions and analyses.

the modeled and observed amounts of areal accu and observed amounts the modeled of Correct description statistical analysis. rega with would be essential case this complex 2. Study area and data reproduce well the observed accumulated convective reproduce well the observed accumulated using

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ipitation event.

ching sites. Such a regression line (dash diagram with the AACP the with diagram convective storms pass pass storms convective ved after 10 LT (local time). ved after 10 LT (local time). Isohyets are drawn by ways in the study area (Fig. area in the study ways ll initiated within the model the model within ll initiated Our focus is given to the is given to the Our focus the precipitation amounts from the from the precipitation amounts of the area that is encircled by an isohyet uent tracks of the splitted storms from the from storms the splitted uent tracks of is represented by isohyets with an icrement is represented by isohyets with an icrement ted convective precipitation (in mm) at the precipitation (in mm) ted convective for the study area. The es with the observed values for the treated the treated observed values for es with the med with the actual modified soundings. We soundings. We with the actual modified med represents the scatter orm life cycle, but we compare the model and and model the compare but we cycle, life orm After initialization the After initialization oduce observed convective storms in real time. time. real in storms convective observed oduce position of a warm bubble for each convective bubble convective for each a warm of position 11 13 on occurrence from the laun on occurrence from 7256 7255 nd the launching sites. NW direction. NW significant precipitation. precipitation. significant cted convective storms were a cted convective storms split storms whose tracks are al tracks whose storms split ho of convective storms is obser ho of convective storms on in the study area, which are mainly influenced by splitting. splitting. by influenced mainly are which area, study the in on networks and the lack of Spatial distribution of the mean accumulated convective precipitation (in mm) at the Mean accumulated convective precipitation (ACP, in mm) along the L1 and L2 tracks. Fig. 3. Fig. 2. surface for the studyof area 1 in mm. Fig. The 1. L1direction. and Precipitation L2 is represent the represented most by frequent isohyets tracks with of an the icrement splitted storms from the NW We now analyze the AACP value for each convective prec of 1 mm. The L1 and L2 represent the most freq most of 1 mm. The L1 and L2 represent the Fig.2. Spatial distribution of the mean accumula of the mean Fig.2. Spatial distribution surface for the study area in Fig. 1. Precipitation Precipitation surface for the study area in Fig. 1. domain in agreement with observations. The with observations. in agreement domain without the growth phase through the from precipitation convective st complete the simulates used model The 2). precipitati convective observed repr to effort an made have we comparisons In event is determined by the first radar echo. by the first determined event is whose observed duration time was less we analyzed the total h are treated. Hereafter, than 2 time whose observed duration sele The convective precipitation. (ACP, in mm) along the L1 and L2 tracks. (ACP, in mm) convective precipitation Fig.3. Mean accumulated model values versus their observed counterparts then compare the sample of model AACP valu model of the sample then compare study area by using a statistical analysis. Fig. 4 is obtained by of AACP fields based on the punctual information The reconstruction a the rain gauge networks using data from are perfor simulations the model Simultaneously, the kriging as well as data about precipitati launching sites. The first radar ec due to the spot-like rain gauge method is limited by the uncertain determination by the uncertain determination is limited method Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

), correlation n

l 9 statistics for the observed and 27 8.2 34.2 0.04 26.0 43.3 25.2 6.64 18.1 31.8 -0.60 29.97 30.68 × 44.17 (10) AACPM the treated study area. The solid line is capabilities of our (Fig. 5) shows the capabilities . This is in agreement with the nature of with the nature of . This is in agreement erved values. The linear regression is l

9 upper part of the Figure. Sample size (n), upper part of the Figure. Sample l nd bias are also shown. 9 ted convective precipitation (AACPM) values ted convective precipitation

× vations and the model model the and vations obser the between cient 27 14 17.4 37.9 0.09 21.4 49.7 11.8 29.8 equency (%) for data shown in Fig. 4. 38.8 accumulated convective precipitation at the precipitation convective accumulated -1.06 10.87 30.59 28.58 26 10 × 118.18

(10) AACPO 15 7258 7257 after t=120 min for the observed (AACPO) and after t=120 min l 9 es of the AACP. It is evident that the model overestimates overestimates model the the AACP. It is evident that of es 10 ounts of smaller and medium- bution that generates large amounts of smaller Kurtosis Median Variance Std. dev. Minimum Maximum Range Lower quartile Upper quartile Quartile range Skewness Sample Sample characteristics size Sample Mean mean Geometric correlation coefficient (r) a and Janc, 2011). The general sample ć uri Ć ) and bias are also shown. r 5 mm) expressed in > ) vs. their observed counterparts (AACPO) for ) vs. their observed counterparts (AACPO) Scatter diagram of model areal accumulated convective precipitation (AACPM) values Diagram of relative cumulative frequency (%) for data shown in Fig. 4. ℓ ) vs. their observed counterparts (AACPO) for the treated study area. The solid line is the the 1:1 correspondence between model and obs the 1:1 correspondence between model 9 cient ( ` ffi 9 Fig.5. Diagram of relative cumulative fr cumulative of relative Fig.5. Diagram presented with the dash line and the equation at presented with the dash line and the equation (10 Fig.4. Scatter diagram of model areal accumula of model Fig.4. Scatter diagram the Khrgian-Mazin size distri the observed values which are greater than model to match the observed valu diagram frequency relative The cumulative line) is also presented. The correlation coeffi correlation The presented. also is line) values is r = 0.80, while the bias is +0.09. +0.09. is bias the while 0.80, r = is values Fig. 5. 1:1 correspondence between modelwith and observed the values. dash line The linear and regression the is equation presented at upper part of the Figure. Sample size ( Fig. 4. (10 coe surface ( model values (AACPM).

Table 3. General sample statistics for areal Table 3. General sample range raindrops ( 3. model AACP are presented in Table Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

ar banja č Kraljevo and (b) ar banja, b) Kraljevo ns. As noted, the best ns. As noted, the best č rrelation coefficients are ` ear banja, Ov (a) to simulate reliable spatial distribution of the of spatial distribution reliable to simulate model and observed AACP datasets do not model nvective precipitation, ACPM (mm), vs their vs their precipitation, ACPM (mm), nvective encies in the microphysical scheme and by scheme encies in the microphysical and time. The given results show the model The given results show the model and time.

ected two sites near the L1 track (Ov track ected two sites near the L1 fully coherent with the corresponding modified with the corresponding fully coherent 16 onvective precipitation simulated by the model model the by precipitation simulated onvective distribution of convective precipitation. Other precipitation. of convective distribution (Bumbarevo brdo) – see Fig. 1. Fig. 6 represents brdo) – see (Bumbarevo 7259 for rain gauge stations a) Ov and c) Bumbarevo brdo. mbarevo brdo (r=0.74). The co for the AACP values. Quantitative estimates of the accumulated convective of the accumulated convective estimates for the AACP values. Quantitative

Scatter diagrams of model accumulated convective precipitation, ACPM (mm), vs. their observed counterparts, ACPO (mm), Bumbarevo brdo. Fig.6. Scatter diagrams of model accumulated co accumulated of model Fig.6. Scatter diagrams

the model is able on how give us the information good correlation between However, the lower than by defici precipitation at the point are limited convective the small-scale model to simulate precisely in the cloud-resolving limitations are highly variable in space processes that spatial the reliable capability to simulate accumulated convective precipitation. We sel We precipitation. convective accumulated and Kraljevo) and one site near the L2 track linear correlation is found for Bu the rain gauge data; a derived from information are: punctual limitations general model not and space is initialization time rainstorm with the accumulated c the scatter diagrams for these rain gauge statio counterparts observed (ACPM) versus their Fig. 6. observed counterparts, ACPO (mm), for rain gauge stations (c)