12.3 REAL-TIME STORM-SCALE ENSEMBLE FORECAST EXPERIMENT ANALYSIS OF 2008 SPRING EXPERIMENT DATA

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1 Preprints, 24 th Conf. on Severe Locl Storm, Amer. Metor. Soc., Octoer 2008, Svnnh, 12.3 REAL-TIME STORM-SCALE ENSEMBLE FORECAST EXPERIMENT ANALYSIS OF 2008 SPRING EXPERIMENT DATA Fnyou Kong 1 *, Ming Xue 1,2, Kevin W. Thoms 1, Kelvin K. Droegemeier 1,2, Yunheng Wng 1, Keith Brewster 1, Jidong Go 1, Jck Kin 4, Steven J. Weiss 3, Dvid Bright 3, Michel C. Coniglio 3, nd Jun Du 5 1 Center for Anlysis nd Prediction of Storms, nd 2 School of Meteorology, University of Oklhom, Normn, OK NOAA/NMS/NCEP Storm Prediction Center 4 NOAA Ntionl Severe Storm Lortory, Normn, OK NOAA/NWS/NCEP, Cmp Springs, MD INTRODUCTION Funded prtly y NOAA CSTAR progrm nd in its second project yer, rel-time storm-scle ensemle forecsting experiment hs een conducted s prt of the NOAA Hzrdous Wether Tested (HWT) 2008 Spring Experiments (Xue et l. 2008; Kong et l. 2008). At 4-km horizontl grid spcing, the WRF-ARW-sed ensemle system, developed t the Center for Anlysis nd Prediction of Storms (CAPS), the University of Oklhom, runs dily for 30 hours from 14 April through 6 June, for domin covering most of the continentl U.S (Figure 1). This pilot system consists of ten hyrid perturtion memers tht consist of comintion of pertured initil conditions nd vrious microphysics nd PBL physics prmeteriztion schemes. Close collortions mong forecsters nd scientists from CAPS, the Storm Prediction Center (SPC), the Avition Wether Center (AWC), the Hydrometeorologicl Prediction Center (HPC), the Environmentl Modeling Center (EMC/NCEP), the Ntionl Severe Storms Lortory (NSSL), the NWS Normn Wether Forecst Office (WFO), the NWS Southern Region Hedqurters, nd the Pittsurgh Supercomputing Center (PSC) mke this unprecedented experiment hppen. Ensemle forecst products re creted in rel time through existing cpilities in the SPC version of the N- AWIPS system for evlution y reserchers nd opertionl forecsters during the experiment. The performnce of the ensemle forecsts, in terms of quntittive skill scores, is evluted to ssess the effectiveness of the EFs t storm-scle. This extended strct presents some post-seson nlysis results. Severl comprisons of 2008 nd 2007 ensemle dt re lso presented. More focus is given to evlution of post-processing techniques tht suit for deterministic QPF nd proilistic QPF (PQPF) derived from the storm-scle ensemle forecsts. 2. EXPERIMENT HIGHLIGHT As the second yer of the three-yer project, the 2008 Spring Progrm egn on 14 April 2008 nd *Corresponding Author Address: Dr. Fnyou Kong, Center for Anlysis nd Prediction of Storms, Univ. of Oklhom, Normn, OK 73072; e-mil: fkong@ou.edu ended on 6 June. All experimentl forecsts were generted with the Wether Reserch nd Forecst (WRF) Advnced Reserch WRF (ARW) model (V2.2), s in 2007 experiment (Kong et l. 2007; Xue et l. 2007). Severl mjor chnges from the 2007 experiment were mde for the 2008 seson: (1) The model domin ws enlrged (Figure 1); (2) Dily 30 h forecsts were initited t 0000 UTC, using NAM 12 km (218 grid) 00Z nlyses s ckground for initiliztion with the initil condition perturtions for the ensemle coming from the NCEP Short-Rnge Ensemle (SREF); (3) ville WSR-88D dt were ssimilted through ARPS 3DVAR nd cloud nlysis pckge into ll ut one memers; (4) Eight memers were constructed s hyrid with oth initil perturtions nd physics vritions. In 2007, only four memers were hyrid, nd no rdr dt ws ssimilted. The initil perturtions were extrcted from the 3 h forecsts of eight memers nd re scled to their initil perturtion mplitudes. All forecst output t hourly intervls were rchived t the Pittsurgh Supercomputing Center (PSC) Mss storge fcility nd will lter e mde ville to the ntionl wether community. Figure 1 shows the coverge re of the model domin, with terrin height info in color shding. Figure 1. Model domin coverge for 2008 seson, with terrin height in color shding. The dily 30 h ensemle forecsts, for the weekdys from Mondy through Fridy, strted t 0000 UTC nd ended t 0600 UTC of the next dy. Specil weekend runs were rrnged if it ws requested y SPC sed on the severe wether outlook. The ensemle configurtion includes ten hyrid memers, ll of which 1

2 were run on BIGBEN, NSF TerGrid resource (Cry XT3) t PSC. Model execution egn round 0230 UTC (21:30 locl time) nd finished in out 6-10 hours (depending on memers nd convection ctivities), using out 1000 CPUs, with results eing processed s they ecome ville. Tle 1. Ensemle memer configurtion memer IC LBC Rdr dt mp_phy sw-phy pl_phy cn 00Z ARPS 00Z NAMf yes Thompson Goddrd MYJ c0 00Z NAM 00Z NAMf no Thompson Goddrd MYJ n1 cn em_pert em_n1 yes Ferrier Goddrd YSU p1 cn + em_pert em_p1 yes WSM 6-clss Dudhi MYJ n2 cn nmm_pert nmm_n1 yes Thompson Goddrd MYJ p2 cn + nmm_pert nmm_p1 yes WSM 6-clss Dudhi YSU n3 cn etkf_pert etkf_n1 yes Thompson Dudhi YSU p3 cn + etkf_pert etkf_p1 yes Ferrier Dudhi MYJ n4 cn etbmj_pert etbmj_n1 yes WSM 6-clss Goddrd MYJ p4 cn + etbmj_pert etbmj_p1 yes Thompson Goddrd YSU Tle 1 outlines the sic configurtion for ech individul memers. cn refers to the control memer, with rdr dt nlysis, c0 is the sme s cn except no rdr dt. n1-n4 nd p1-p4 re memers with initil perturtion dded on top of cn initil condition, NAM nd NAMf refer to 12 km NAM nlysis nd forecst, respectively. ARPS refers to ARPS 3DVAR nlysis using NAM s ckground. For the eight pertured memers, the ensemle initil conditions consist of mixture of red perturtions coming from the 21Z SREF pertured memers (one pir ech from WRF-em, WRF-nmm, et-kf, nd et-bmj) nd physics vritions (grid-scle microphysics, shortwve rdition, lnd-surfce nd PBL physics). The lterl oundry conditions come from the corresponding forecsts directly for those pertured memers nd from the 00Z 12 km NAM forecst for the non-pertured memers (cn nd c0). For ll memers, the long-wve rdition schemes re RRTM, the surfce physics uses Noh scheme, nd no cumulus scheme is used (see WRF mnul for detil description for ll physics schemes). In ddition to the SPC s N-AWIPS system, CAPS lso mkes ville wepge demonstrting the EF products ( 3. VERIFICATION OF THE ENSEMBLE SYSTEM During the course of the experiment, there re totl of 36 dys with complete forecsts for ll ten memers. Post nlyses nd verifictions re crried out over these complete dtes to ssess the sttisticl feture nd the performnce of the ensemle system, unless specified in the text nd figures. Figure 2 shows the domin-men ensemle spred (defined s stndrd devition ginst ensemle men) of some fields, verged over 36 complete forecst dtes (covering most of the experiment period from April 16 through June 6).. It cn e seen tht the hyrid perturtion configurtion of the ensemle system exhiits resonle dispersion for the mss-relted fields such s se level pressure nd 500 hp geopotentil height. For QPF relted vriles such s hourly ccumulted precipittion nd reflectivity fields diurnl pttern is evident, reflecting the quiet morning hours (low round 10m) nd the ctive lte fternoon hours (high round 7pm) for the summer convective storm ctivity (Figure 2c). The rel-time storm-resolving (or storm-permitting, convection-permitting in some litertures) ensemle forecsting system offers unique cpility of producing quntittive precipittion forecst (QPF), oth deterministic nd proilistic, t very high temporl nd sptil resolution. For post-seson verifiction purpose, the experimentl fine grid (1 km) ntionl rdr mosic nd QPE products generted y the NSSL/NMQ project 1 re first interpolted to the 4 km grid model 1 2

3 domin nd used s verifiction dtset to verify the predicted QPF quntities (1 h ccumulted precipittion nd composite reflectivity). nd 2008 sesons. Other thresholds exhiit similr pttern. In spite of the lrge reduction of BIAS score in 2008 seson, some memers (including cn nd c0) still hve mximum BIAS vlues of close to 2 during fternoon hours, pointing to over-prediction of precipittion y some memers.. Figure 2. Domin-men ensemle spred (stndrd devition), verged over 36 forecst dtes. () men se level pressure, ()) 500 hp geopotentil height, nd (c) 1 h ccumulted precipittion. 3.1 BIAS score In 2007 experiment seson, systemticlly high QPF ises (Figure 3) were oserved compred to severl other storm-scle deterministic forecsts produced during the sme period y NCAR nd NSSL (Kin, personl communiction). Prior to the 2008 seson, severl reruns of the 2007 dtes were crried out to identify the cuse of such high ises. Fctors considered include model strt time, dt for lterl oundry condition, numer of verticl levels, NAM dt type used (12 km 218 grid vs 40 km 212 grid). It turns out the mjor culprit is the use of 21Z NAM nlysis s the initil condition in 2007 seson, comined with the use of 18Z NAM forecst s LBCs (Kong et l. 2007). Figure 4 shows n exmple of BIAS score comprison from the reruns of My 6, Drk lck line refers to the condition of 2007 seson - 21Z NAM nlysis (NDAS) for IC nd 18Z NAM forecst for LBC with the highest BIAS score over the convection ctive hours. The forecst with 00Z NDAS nd 00Z LBC (solid red line) hs the lowest BIAS score. 18Z LBC lso contriutes to elevted BIAS (dsh red line). The chnge to initite ensemle forecsts t 0000 UTC using 00Z NAM nlysis nd forecst s IC ckground nd LBCs for the 2008 seson helps significntly ring down the QPF ises (Figure 3). Figure 3 shows the BIAS scores of 1 h ccumulted precipittion exceeding 0.1 in (2.54 mm) for individul memers nd ensemle men from oth 2007 c Figure 3. BIAS scores of 1 h ccumulted precipittion 0.1in verged over ll complete forecst dtes from () 2008 nd () 2007sesons. For 2007 seson, CN refers to control memer, PERT refer to four initil perturtion plus physics perturtion, nd PHYS refer to five physics perturtion only memers (Kong et l. 2007). Figure 4. BIAS scores of 1 h ccumulted precipittion 0.1in for the dt of My 6, IC nd LBC refer to times of NAM nlysis for initil condition nd NAM forecst for lterl oundry condition, respectively. 3

4 3.2 ETS scores The ETS scores re clculted for the criteri of 1 h ccumulted precipittion 0.1 in for ll memers nd the ensemle men (Figure 5). Compred to 2007 seson, the unique feture of 2008 experiment is the ddition of rdr dt for nine out of ten memers. Figure 5 indictes tht the inclusion of rdr dt helps oost the ETS scores for the initil hours nd the influence lsts for 12 h. c0 memer in 2008 nd ll memers in 2007 underscore the nine 2008 memers with rdr dt ssimilted for the initil 6 h. The rdr dt influence fde wy fter 12 h. The ensemle mens in Figure 5 show cler outscore to ll individul memers. However, it should e cutious to interpret the phenomen, s descried in next susection nd in Figure 6 indicting less usefulness of ensemle mens in high temporl (e.g., 1 h ccumultion) precipittion forecst. ensemle men of 1 h ccumulted precipittion in comprison with oservtion (Figure 6d). Proility Mtching (PM, herefter) technique ws demonstrted y Eert (2001) nd Clrk et l. (2008) to e more useful y ssuming tht the est sptil representtion of rinfll is given y the ensemle men nd tht the est frequency distriution of rinfll is given y the ensemle memer QPFs. PM products for QPF re produced y first pooling QPF mounts of ll ensemle memers nd over ll grid points for given forecst led time nd sorted from the highest to the lowest to otin QPF distriution. The ensemle men QPF mounts re lso sorted from the highest to the lowest. Then the QPF vlues from the ensemle men re ressigned using vlues from the corresponding rnks of the QPF distriution. Given N ensemle memers nd M totl grid numers of model domin, there re MN elements in QPF distriution versus N in ensemle men. Eert (2001) picked every N sorted element from the QPF distriution pool. We denote it s PM Method 1. The prolem of this method is tht QPF vlues decrese drsticlly from higher rnks to lower rnks t the high end, especilly when very highresolution storm-permitting models re involved. Tht cn cuse rtificilly high pek QPF vlues if the first element of ech N segment is picked. Alterntely, rndom picking mong ech N element cn e used. We exercised new pproch y verging the N elements nd ssigning the men to the corresponding rnk of ensemle men (denoted PM Method 2). Figure 6 shows n exmple 1 h ccumulted precipittion using oth methods, with simple ensemle men nd oservtion side y side for comprison. Both methods demonstrte etter sptil coverge nd mplitudes thn the ensemle men, with PM Method 2 (111 mm) more close to oservtion (65 mm) thn PM Method 2 (132 mm) in mximum vlues. The ensemle men (Figure 6) hs mximum vlue of 28 mm. Figure 5. ETS of 1 h ccumulted precipittion 0.1in verged over ll complete forecst dtes from () 2008 nd () 2007sesons. 3.3 Deterministic QPF from ensemle For mny desired meteorologicl vriles such s se level pressure, 2 m temperture, 10 m wind etc. ensemle men is good deterministic quntity derived from ensemle forecsting products tht cn outscore forecsts using single model runs. However, owing to very high sptil nd temporl vrince ssocited with precipittion, especilly when produced with very highresolution model runs like in this cse, ensemle men of precipittion field tends to e excessively rod in re coverge nd too wek in mgnitude, nd thus is not useful QPF product (Eert 2001; Kong et l. 2006; Clrk et l. 2008). Figure 6 gives n exmple of c d Figure 6. 1-h ccumulted precipittion from 24 h ensemle forecst initited t 0000 UTC April 23, 2008 for () ensemle men, () PM in method 1, (c) PM in method 2, nd from (d) NSSL oservtion, vlid t 0000 UTC April 24,

5 Figure 7 gives more cler picture on how model predicted precipittion mximum vlues nd re coverge (represented y grid count) in the forms of ensemle men nd PM (oth Methods 1 nd 2) re compred to oservtions. In this single dy exmple (April 23, 2008), ensemle mens significntly underestimte precipittion intensity nd over-estimte precipittion re. Both PM methods improve the intensity (with Method 2 PM2 in the figure more close to oservtion) nd lower precipittion re (in Figure 7, PM1 nd PM2 re identicl). Figure 8 shows the sme curves (without PM1) s Figure 7 ut verged over ll ville 36 dys, confirmed the generl improvement of using PM ginst simple ensemle men. The improvement is especilly significnt for re coverge. generlly in the middle of individul memers nd perform much etter thn ensemle mens (Figure 9), with exception of 0.1in threshold. ETS scores of PM re close to ensemle men for light rin (Figure 10), with smll outscore fter 15 h, nd clerly outscore ensemle men for 0.5in rin threshold (Figure 10), oth outscore individul memers. Such improvements re not reflected in RMSE scores with PM curve just in etween individul memers (figure not shown). Figure 7. Mximum 1 h ccumulted precipittion () nd grid counts (re coverge) () for the 30 h period strting t 0000 UTC April 23, PM1 nd PM2 refer to PM method 1 nd 2, respectively. c d Figure 8. Mximum 1 h ccumulted precipittion () nd grid counts (re coverge) (), verged over 36 dys. PM refers to PM method 2. In next severl figures, trditionl verifiction scores re compred mong PM (method 2), ensemle men, nd individul memers. BIAS scores of PM re Figure 9. Averged BIAS scores of 1 h precipittion 0.01in (), 0.1in (), 0.5in (c), nd 1.0in (d). 3.3 Proilistic QPF from ensemle The ility to generte proilistic QPF (PQPF) from ensemle memers is one of selling point for ensemle forecsting. However, otining skillful nd 5

6 relile PQPF from high-resolution nd storm-permitting ensemle forecsts remins huge chllenging tsk, especilly for high temporl PQPF such s 1 h ccumulted precipittion s compred to 12 h, 24 h or even longer period of ccumultion. Figure 11. Proility of 1 h ccumulted precipittion 0.1in for the 24 h forecst vlid t 0000 UTC April 24, Figure 10. ETS scores of 1 h precipittion 0.01in ( nd 0.5in (). During the course of Spring Experiment, vrious proilistic QPF forecst vriles were derived y using simple reltive frequency mong ensemle memers for specified thresholds. Figure 11 shows n exmple proility mp produced in qusi rel-time of the 24 h forecst of 1 h ccumulted precipittion exceeding 0.1 in, vlid t 0000 UTC April 24, 2008 (Figure 6d is the corresponding oservtion). Post-seson verifiction of these simple PQPF re conducted y mens of verifiction rnk histogrm (Hmill nd Colucci 1997) nd reliility digrms. Figure 12 presents 24 h nd 30 h verifiction rnk histogrm chrts for the 1 h ccumulted precipittion, verged over the complete 36 dy dtset. Smll righttilting nd U-shpe cn e seen, indicting some degree of overprediction of precipittion nd underdispersion. But in generl, the verifiction rnk histogrms re quite flt. Figure 13 shows reliility digrms, with oth 2007 nd 2008 dt presented, of two forecst led-time nd thresholds of 1 h ccumulted precipittion. Even though 2008 ensemle dt show improvement over 2007, oth yers feture reliility lines quite wy from (elow) the digonl line (perfect reliility), indicting much less idel PQPF for 1 h ccumulted precipittion. Verifiction of longer period ccumulted precipittion PQPFs my show different performnce nd re the next set of nlysis tsks. Bis-correction nd vrious clirtion procedures re lso clled into ttention for post-processing of ensemle forecsts to improve skills nd reliility/resolution of PQPFs. Figure 12. Verifiction rnk histogrm of 1 h ccumulted precipittion for the forecst hours of () 24 h nd () 30 h. 3.3 PQPF nd is correction Mny reserches demonstrted the merit nd success in post-processing ensemle forecsting dt through is-correction nd vrious clirtion techniques to produce PQPF with improved skills nd reliilities for 24 h ccumulted period nd longer nd over very corse grids (e.g., Hmill nd Colucci 1997; Eckel nd Wlters 1998). Post-processing high-resolution PQPF for periods shorter thn 24 h hve not een exmined until recent (Stensrud nd Yussouf 2007; Yussouf nd Stensrud 2008). In the two journl ppers lst cited, Stensrud nd Yussouf (2007, 2008) employed simple inning technique to remove is of 3 h ccumulted precipittion from multimodel short-rnge ensemle forecsting system, using pst 12 dys s trining 6

7 period, nd produced significntly more skillful nd relile PQPFs thn the rw forecst proilities. However, the inclusion of zero oservtion in populting QPF ins leds to somewht noisy precipittion field fter djustment, which in turn leds to noisy proility mps nd irregulr (noisy?) verifiction rnk histogrm chrts with excessively high mxim (see Figure 8). Trditionl is correction concept of removing men is sed on short trining period prior to forecst time doesn t ddress such excessive pek vlue issue when pplying to QPF, since men is for QPF, when verged over domin, is often very smll mount. Hving the correction of excessive precipittion in mind, new pproch is exercised y correcting precipittion ises sed on rnks. For ech memer t ech forecst hour, the 1 h ccumulted precipittion field is sorted from the highest to the lowest, nd the sme rnks re verged over trining period (12 dys in this exercise). The oservtions over the sme 12 dys period re lso sorted nd verged. Figure 14 is exmple rnking results for the 24 h forecst, verged over 12 dy period from April 16 through My 7, Figure 13. Reliility digrms for the 1 h ccumulted precipittion 0.01in t 24 h () nd 0.1in t 12 h. Solid lines re from 2008 seson dt, nd thick dsh lines re from 2007 seson dt. Recently, Clrk et l. (2008) demonstrted tht pplying is correction pproch similr to proility mtching (PM) (Eert 2001), except using oservtion s QPF distriution, to ll individul memers cn result in more flt rnk histogrms. This pproch, however, is not is correction technique to produce PQPFs ecuse current oservtion is required. Another chllenge of QPF is correction lies in the use of high-resolution storm-permitting model forecsts which produce very high vrince precipittion fields Figure 14. () Sorted 1 h ccumulted precipittion, nd () differences etween memers nd oservtion (is) for the 24 h forecst, verged over 12-dy period from April 16 to My 7, In Figure 14, e wre of the use of logrithm coordinte for rnk (from 1 to M, M is totl horizontl grid points), nd only first third of rnks re shown. It cn e seen tht individul memers vry widely round oservtion for the highest severl hundred rnks, with 7

8 pek ises (difference etween memers nd oservtions for the first rnk Figure 14) swing 20 mm on either side. This specific trining period shows high positive ises for cn, co, nd n2, nd high negtive is for p4. The men ises in Figure 14 re used to correct ech memer forecst of the following dte y pplying the men ises to corresponding rnks of sorted precipittion field of ech memer t ech forecst hour. A totl 15 dys of is-corrections re mde, from My 8 through My 26, Ensemle mens nd PMs re generted from the corrected dtset. BIAS scores re presented in Figure 15, using the corrected 15 dys dtset. Compred with Figure 9, significnt improvement cn e seen, especilly for the two lower thresholds, cross ll forecst hours. Figure 16 shows rnk histogrm chrts of two forecst hours, 18 h nd 24 h, oth with more flt distriutions compred to rw ensemle. The difference is very smll for the 6 h nd 30 h forecst times (figure not shown). Figure 16. Verifiction rnk histogrm of 1 h ccumulted precipittion for the forecst hours of () 18 h, nd () 24 h, verged over 15 dys of is corrected dtset. c d In spite of ig improvement in BIAS score nd some improvement in rnk histogrm, no improvement is shown in reliility digrms (Figure not shown). 4. DISCUSSION The extremely high vrince nture of 1 h ccumulted precipittion from high-resolution storm-permitting WRF ensemle forecst my intrinsiclly discourge post-clirtion effort to e le to produce relile nd high-skill PQPFs mesured y conventionl verifiction metrics. New verifiction pproches such s oject oriented nd neighorhood methods (Schwrtz et l. 2008) my e solution. As future focus, we will further exmine vrious post-processing techniques including refining the is correction pproch exercised in this pper, nd pply to 3 h ccumultion or longer period. Comprisons of QPF/PQPFs, in terms of quntittive skill scores, from the high-resolution ensemle forecst in the Spring Experiment with the NCEP opertionl NAM 12 km nd SREF forecsts over the sme domin nd period will e nother vlule effort. Figure 15. Sme s Figure 9, except is corrected nd verged over 15 dys. 5. ACKNOWLEDGMENTS 8

9 This work is minly funded y grnt to CAPS from the NOAA CSTAR progrm. Supplementry support is lso provided y the NSF ITR project LEAD (ATM ), nd y NSSL. The numericl forecsting is performed t the Pittsurgh Supercomputing Center. Dvid O Nel of PSC provided enormous technicl nd logistic support for the experiment. The uthors lso thnk Drs. Jimy Dudhi, Morris Weismn, Greg Thompson nd Wei Wng for their very helpful suggestions nd input on the WRF model configurtions. REFERENCES Clrk, A. J., W. A. Gllus, M. Xue, nd F. Kong, 2008: A comprison of precipittion forecst skill etween smll ner-convection-permitting nd lrge convection-prmeterizing ensemles. Sumitted to Wether nd Forecsting. Eert, E. E., 2001: Aility of poor mn s ensemle to predict the proility nd distriution of precipittion. Mon. We. Rev., 129, Eckel, F. A., nd M. K. Wlters, 1998: Clirted proilistic quntittive precipittion forecsts sed on the MRF ensemle. Wether nd Forecsting, 13, Hmill, T. M., nd S. J. Colucci, 1997: Verifiction of Et-RSM short-rnge ensemle forecst. Mon. We. Rev., 125, Kong, F., K. K. Droegemeier, nd N. L. Hickmon, 2006: Multiresolution ensemle forecsts of n oserved torndic thunderstorm system. Prt I: Comprison of corse- nd fine-grid experiments. Mon. We. Rev., 134, Kong, F., nd co-uthors, 2007: Preliminry nlysis on the rel-time storm-scle ensemle forecsts produced s prt of the NOAA Hzrdous Wether Tested 2007 Spring Experiment. Preprints, 22th Conf. on Wether Anlysis nd Forecsting nd 18th Conf. on Numericl Wether Prediction Amer. Meteor. Soc., Prk City, UT, 3B.2. Kong, F., nd co-uthors, 2008: Rel-time storm-scle ensemle forecst experiment. 9th WRF User s Workshop, NCAR Center Green Cmpus, June 2008, Pper 7.3. Schwrtz, C. S., nd couthors, 2008: Towrd improved convection-llowing ensemles: model physics sensitivities nd optimizing proilistic guidnce with smll ensemle memership. Preprints, 24th Conf. on Severe Locl Storm, Amer. Meteor. Soc., Svnnh, GA, Pper 13A.6. Stensrud, D. J., nd N. Yussouf, 2007: Relile proilistic quntittive precipittion forecsts from short-rnge ensemle forecsting system. Wether nd Forecsting, 22, Yussouf, N., nd D. J. Stensrud, 2008: Relile proilistic quntittive precipittion forecsts from short-rnge ensemle forecsting system during the 2005/06 cool seson. Mon. We. Rev., 136, Xue, M., nd co-uthors, 2007: CAPS reltime stormscle ensemle nd high-resolution forecsts s prt of the NOAA hzrdous wether tested 2007 spring experiment. 22nd Conf. We. Anl. Forecsting/18th Conf. Num. We. Prediction., Amer. Meteor. Soc., Prk City, UT, Pper 3B.1. Xue, M., nd co-uthors, 2008: CAPS Reltime Stormscle Ensemle nd High-resolution Forecsts s Prt of the NOAA Hzrdous Wether Tested 2008 Spring Experiment. Preprints, 24th Conf. on Severe Locl Storm, Amer. Meteor. Soc., Svnnh, GA, Pper