The Efficiency of the WRF Model for Simulating Typhoons

Open Access Discussions 5 erent physical and Sciences ff Natural Hazards Hazards Natural and Earth System System and Earth , and S. M. Barekati 4 ect the performance of the model. In this ff 287 288 , P. Ghafarian 2,3 , W. R. Ismail ciency of the WRF model for 1 ffi erent numerical experiments were conducted to determine the best combi- ff This discussion paper is/has beenSystem under Sciences review (NHESS). for Please the refer journal to Natural the Hazards corresponding and final Earth paper in NHESS if available. Center for Marine and Coastal Studies (CEMACS), Universiti Sains Malaysia,Section 11800 of Minden, Geography, School of Humanities, Universiti Sains Malaysia, 11800Centre Minden, for Pulau Global Sustainability Studies, Universiti Sains Malaysia, 11800 Minden,Iranian Pulau National Institute for OceanographyIran and Meteorological Atmospheric Organization, Science, Tehran, Tehran, Iran Iran of physical processes involved. For thisdynamical reason, schemes there which are can several be di utilized in simulations. However, there is controversy study is to havecomes attention showed that to the theliterature. suggested heat combinations fluxes are besides comparable with the the other ones parameters. in The the out- 1 Introduction Numerical weather forecasting modelsphysical and have dynamical several parameterization. The configuration more options complex model, relating the greater to variety statistical measurements. The resultsnation facilitated of the options determination suitable of forbest predicting the combinations each best were physics combi- parameter. examinedconfirmed. Then, for Finally, the the seven suggested best othernations combination typhoons for was and wind compared the speed with solutions prediction other were for introduced typhoon combi- Washi (2011). The contribution of this peratures, latent heat flux,characterized sensible typhoons. Through heat these flux, experiments, precipitationoptions several physics within rate, parameterization and the WRF windoriginated model speed in were that the exhaustively South tested Chinaone Sea for coarse in typhoon domain November and Noul, 2008. one30 which The km, nested model had and domain. domain that The of consisted resolution the of were of nested compared domain the with was coarse 10 the domain km. Climateparisons was In Forecast this between System study, predicted model Reanalysis simulation and (CFSR) results control data data set. were Com- made through the use of standard The Weather Research Forecast (WRF)related model to includes physics various parameters, configurationstudy, which di options can a nation of physics parameterization schemes for the simulation of sea surface tem- Abstract Nat. Hazards Earth Syst. Sci.www.nat-hazards-earth-syst-sci-discuss.net/2/287/2014/ Discuss., 2, 287–313, 2014 doi:10.5194/nhessd-2-287-2014 © Author(s) 2014. CC Attribution 3.0 License. simulating typhoons T. Haghroosta 1 Pulau Pinang, Malaysia 2 Pinang, Malaysia 3 Pinang, Malaysia 4 5 Received: 18 December 2013 – Accepted: 2Correspondence January to: 2014 T. – Haghroosta Published: ([email protected]) 14 JanuaryPublished 2014 by Copernicus Publications on behalf of the European Geosciences Union. The e 5 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . 1 − erent schemes ff , obtained from the Na- ◦ NCAR mesoscale model (MM5) / MMM), in collaboration with other institutes / 290 289 erent physical options for a boundary layer phe- ff erent experiments using the PSU ff A complete description of the physics options available in the WRF model was This paper is an attempt to use a variety of physics parameterization options from the Chandrasekar and Balaji (2012) also investigated the sensitivity of numerical sim- in Table 1. Herein, a total of six simulations were carried out. The first simulation used Cambodia (JTWC, 2008). 2 Materials and methods Final analysis six-hourly data setstional (FNL) Centres with for a Environmental resolution Predictionas of (NCEP), initial 1 were and inputted boundary to conditions. the All WRF schemes model utilized in this study are summarized subsequent 24 h. The systemdisturbance was on reclassified 14 to November. It aon was tropical 16 then depression November, reclassified and from as it a reached aa its tropical tropical maximum 993 storm point mbar at at 06:00 minimum 00:00 UTC UTCNoul on central was 17 pressure November, slightly with weakened anddle after maximum it of sustained made the winds landfall day in of on Vietnam, 74 17 almost kmh November, around and the mid- finally disappeared at the end of this day near et al. (2005). 1.2 Case study: Typhoon Noul Typhoon Noul formed in November 2008 inical the disturbance South was China generated Sea in (Fig.Later, the 1). on At Philippines that first, (east a same of trop- day, Mindanao) thedisturbance on Joint recorded 12 Typhoon had Warning November. the Centre potential (JTWC) to estimated generate that a the significant tropical cyclone in the nomenon such as microphysics, longwaveterization, and surface shortwave layer, land radiation, surface, cumulus and parame- planetary boundary layer. developed by Wangand et the al. details (2010). of Each all schemes physics have option been contains comprehensively explained di by Skamarock WRF model. The model provides di and universities. Michalakes etexplained the al. equations, (2004) physics parameters, and and dynamic Skamarock parameters et available in al. the (2005) exhaustively model in predicting selectedthe parameters, South with China simulations Sea. relating to Typhoon Noul in 1.1 WRF model overview The WRF, a high resolutiona mesoscale next-generation model, numerical was utilized modelwas in for developed this weather by study. prediction the This Mesoscale of modelCentre and is mesoscale for Microscale processes. Atmospheric Meteorology It Research Division (NCAR of the National was investigated, with respectthe to north Indian the Ocean. tracking Thesuited and authors for simulating intensity identified cyclones the of over suite the tropical of Bay cyclones physics of options over Bengal. thatWeather is Research best Forecast (WRF) model to investigate the performance of this same carried out five di with variations in the physical(SST) parameterizations distributions. The used simulations and obtained invations. were sea then surface compared temperature with satellite obser- ulations of tropical cyclonesthe to best physics set parameterizations, with ofdian a Ocean. physics In view options another to study for determining by prediction Mandal et of al. cyclones (2004), the originating sensitivity in of the the MM5 north model In- it is critical thathave the been most conducted suitable around scheme theferent be world fields in selected of order for to study a find (Kwun study.Bhati, the et A 2011). best For al., variety scheme example, 2009; of Yang options et Jin studies for al.ations et dif- (2011) during al., studied typhoon wind 2010; speed Chanchu Ruiz and (2006), et precipitation which al., vari- occurred 2010; in Mohan the and South China Sea. They surrounding any perceived advantage of one particular scheme over others. Therefore, 5 5 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent statistical ff E. The South China Sea cient of correlation (CC), ◦ ffi erent experiments, to see which of ff N and 113 ◦ statistic should be used in conjunction indicates the better performance of the t t 292 291 The RMSE provides information on the short-term performance of a model by com- The physics options of the WRF were altered in di statistic, are being proposed in this case. The 3 Results and discussions The data used foris validation of available the on variables the wasdomain were derived related used from website for the purposes (Saha CFSR of et dataset analysis and al., and comparison. 2010). The results from the nested After selecting the best simulationsolutions for each were parameter evaluated in the byTropical case running Depression of the Typhoon Noul, 01W model the (2008),Songda for (2011), Kujira 7 and (2009), other Washi (2011). typhoons, Chan-Homcesses The for Peipah (2009), aim each (2007), was Nangka parameter. to Theby (2009), confirm typhoons Knapp the were et selected scheme al. selection from (2010). pro- all storm tracks dataset t with the MBE andand RMSE Kontoyiannis, 1995). errors The to smaller better valuemodel. of evaluate The a CC model’s as performancevalues a (Jacovides show statistical better performance parameter of was the used model. in this2.3 paper as Verification well. process Higher CC formance of a model.in A the estimated positive values, valueabsolute and gives value vice of the versa MBE average shows incompare amount all the the of of better case the model over-estimation parameters of performance.indicators. computed a The In by MBE, the order negative which model, to value. is one extensively evaluate The used, can and and smaller use the di RMSE, in combination with the better the model’s performance. The MBE provides information on the long term per- paring the simulated values and the control data. The smaller the RMSE value, the 2.2 Evaluation of the model The most widely used statisticalestimation models indicators are in root the mean literatureMean square dealing Bias error with Error (RMSE), environmental (MBE), coe used and in t-static this (Jacovides study and for Kontoyiannis,selected assessing 1995). parameters, model These namely performance. were These SSTs,and values latent precipitation were rate calculated heat in for flux the (LHF), center of sensible typhoon. heat flux (SHF), domain, d02, withregion resolution under study of in this 10 km, analysis.Pacific Geographically, it Ocean. includes covers The the the west two side domains South ofis are the China centred tropical bounded at Sea, by 7 whichIndo-China South Peninsula is Ho China, et the Peninsular al. (2000). Malaysia, Borneo, the Philippines, and the be activated in the modellations. configuration, The in categories order were tolayer selected see and SST based on variations on surface during heat disturbances. all transfer simu- in the surface2.1 boundary Model domains Figure 2 indicates thewith defined spatial domains resolution of for 30 modelling. km, covers The a parent bigger region domain than called the d01, study area. The nested et al. (2010), referred to as control data in thisthose paper. is most suited forand accurate the analysis parameters of the mentionedwas interaction investigated earlier. between with The typhoon the intensity capability model. It of should predicting be typhoon noted that intensity the SST-update function must the default set of schemes. The outputs were compared with CFSR data by Saha 5 5 20 15 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 294 293 ected by local fluctuations, especially in highly un- ff values, and maximum amount of CC. t for these typhoons confirms that the suggested combinations show same t The simulated data from simulation 5 are compared with control data in Fig. 6. The The result of simulation 5, indicating its superior performance over others, is shown Figure 4 shows a comparison of simulated and control data for LHF in the case Figure 3 indicates the diurnal variation of control data and simulated SST in sim- stable conditions; thus, wind sensitivities tend to have more3.6 variation (Hu et Verification al., process 2010). Herein, to find whetherined the for best seven other combinations typhoons are namedMBE, in applicable and Sect. or 2.3. not, The calculated theyresults values were which of exam- are RMSE, CC, given in the Table 7. shows the bestmodel performance to for over-predict wind wind speedin speed was many noted prediction. earlier in studies A all (Hannaparison et simulations, general al., and between tendency 2010; was Ruiz simulated also for et observed windstudies, al., the wind 2010). speed speed Figure is and 7 significantly shows related a the control com- data. As noted in earlier results indicate that forecastsclose of to precipitation the control ratesvalues data. before for During and the the typhoon after period were the of lower than typhoon 14 the to are control3.5 17 data. November, the simulated Wind data speed Wind speed estimations duringTable 6. the In spite typhoon of were simulation 4 statisticallyin having evaluated, low comparison CC as with values, RMSE shown those and in MBE obtained values in are lower other simulations, and this simulation therefore In the case of precipitationconsistently rate, lowest simulation RMSE, 5 MBE, wasTable 5. the and best-performing t simulation, values, with and highest CC values, as shown in 3.4 Precipitation rate 3.3 Sensible heat flux As shown in Tablevalues 4, with of the highest the value six of CC, simulations, 0.93. number 4in Fig. can 5. strongly Almost predict all increasing the and SHF decreasing SHF values are predicted as well. of the best performingunder-prediction simulation. points, Although it there can are becontrol seen some that values. over-prediction most and simulated some values are very close to the ulation 6, with datalarge, there given is for a general everystronger, tendency and six-hour towards under-prediction over-prediction period when of the over SST typhoon when the is the weaker. study typhoon3.2 is duration. By and Latent heat flux As shown in Table 3,RMSE, simulation MBE, 1 and performs best for LHF prediction, with the minimum Statistical evaluation of SST islation presented is shown in in Table 2. bold.ature It The because can best be all result noted criteria of that arethan the simulation met, expected. 6 SST with works simu- the satisfactorily exception for of temper- the CC value, which was lower 3.1 Sea surface temperature 5 5 20 15 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (last access: 1 September er, D., Deng, A., McQueen, J., erent physics options available ff ff 296 295 erent schemes defined in this paper, SST, LHF, SHF, ff http://www.usno.navy.mil/JTWC/ erent variables, when considering typhoon existence in the ff This study was supported by a fellowship of Centre for Marine and Coastal Overall, the performance ofprediction of the a WRF number model of important is parameters acceptable related and to typhoon satisfactory intensity for over The recommended combinations of physics optionswere for confirmed the mentioned with parameters seven other typhoons. Comparing the presentedsuggested best groups simulations can be with applicable other in predicting studies issues. showed that the the South China Sea region. in the WRF model,in for the prediction South of China surface Sea. parameters under stormy conditions This case study analysed the performance of di – – – – These two suggested simulations for best wind predicting were conductedAccording to for the ty- Fig. 8, the best suggested physics options for predicting typhoon tion computing models, Agr. Water Manage., 27, 365–371, 1995. schemes in the WRF model, J. Appl. Meteorol. Clim., 49, 1831–1844, 2010. Tsidulko, M., Janjic, Z., and Jovic,simulated, D.: and Comparison HPAC-assumed boundary-layer of meteorological observed, variables MM5 forIHOP 3 and days field WRF-NMM during experiment, model- the Bound.-Lay. Meteorol., 134, 285–306, 2010. height in the SouthRes., China 105, Sea 981–990, observed 2000. with TOPEX/Poseidon altimeter data, J. Geophys. rameterizations, J. Earth Syst. Sci., 121, 923–946, 2012. 2013), 2008. forecasts for California, 19th2010. Symposium on Boundary Layers and Turbulence, Colorado, gests suitable options for di South China Sea. According to di precipitation rate and windrespectively. speed, Therefore, are the best model estimated configurationof by should the simulations be objective 6, of chosen 1, the fromas 5, study follows: the 5, being viewpoint and undertaken. 4 The main conclusions of this study are 4 Conclusions From the results obtained,options it that is performs evident best for that all there desired is parameters. no However, the single present combination study sug- of physics indicated in Table 8. phoon Washi (2011). The wind speed predicteddata by set, WRF and model (best these simulation), new CFSR simulations are compared (Fig. 8). intensity through this studynearly (WRF) in and the also range of byin CFSR other’s some data studies set. points. (Sim Of course 7 the and simulation Sim 8 showed 8) higher are values In this part, twoChandrasekar sets and of Balaji simulations were (2012),best defined and physics according Angevine options to for (2010) wind the which prediction.of previous The were studies simulations sim7, considered are by and as indicated by the sim8, abbreviations respectively. The details of their represented physics options are 3.7 Comparison with other studies for the wind speed prediction issue Jacovides, C. and Kontoyiannis, H.: Statistical procedures for the evaluation of evapotranspira- Hu, X.-M., Nielsen-Gammon, J. W., and Zhang, F.: Evaluation of three planetary boundary layer Ho, C. R., Zheng, Q., Soong, Y. S., Kuo, N. J., and Hu, J. H.: Seasonal variability of sea surface Hanna, S. R., Reen, B., Hendrick, E., Santos, L., Stau Chandrasekar, R. and Balaji, C.: Sensitivity of tropical cyclone Jal simulations to physics pa- Best Track Data Set: available at: Angevine, W. M.: The Total Energy-Mass Flux PBL scheme in WRF: Experience in real-time References Acknowledgements. Studies (CEMACS), Universiti Sains Malaysia. 5 5 25 20 15 10 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | PSU / Janjic Cumulus parameterization Tiedtke Kain Fritsch Betts Miller Janjic New Simplified Arakawa Schubert ACM2 Kain Fritsch Planetary boundary layer Mellor Yamada Janjic University Yamada Janjic Yonsei University , 2010. usion usion ff ff Pleim Xiu Land surface thermal di thermal di 297 298 Xiu Surface layer Eta 5-layer 10.1155/2010/167436 Shortwave radiation New Goddard radiation RRTM Dudhia MM5RRTM Noah GoddardNew Yonsei Goddard MM5 5-layer erent simulations conducted in the study, using various combinations of schemes. ff Di Moment 3-class Thompson University ization over South America:3342–3355, validation 2010. against surface variables, Mon. Weather Rev.,ers, 138, J. G.: A description of the advanced researchSea WRF version and 2, Bay NCAR 101, ofDynamics, 2005. and Bengal Societal during Impacts, edited Monsoon2011. by: Season, Lupo, P. Recent A., InTech, Hurricane Croatia and Research China, – 616 Climate, pp., Delhi, India, Adv. Met., 2011, 621235.621231-621213, 2011. parameterizations, Nat. Hazards, 51, 63–77, 2009. ical processes on predictionmesoscale model, of Nat. tropical Hazards, cyclones 31, 391–414, over 2004. the Bayweather of research Bengal and with forecast NCAR model:of software the architecture 11th and ECMWF performance, Workshop156–168, on Proceedings 2004. the Use of High Performance Computing In Meteorology, model, Adv. Met., 2010, 1–11, doi: Am. Meteorol. Soc., 91, 363–376, 2010. mesoscale model predictions of surface winds in a typhoon to planetary boundary layer tional best track archive for climate stewardship (IBTrACS) unifying tropical cyclone data, B. 6 Lin et al. RRTMG RRTMG TEMF RUC TEMF Betts Miller 5 Lin et al. RRTM Goddard Pleim 1 WRF single Sim Microphysics Longwave 2 Eta3 New 4 GFDL Stony Brook GFDL Eta Noah Mellor Table 1. Skamarock, W. C., Klemp, J. B., Dudhia,Yang, J., L., Gill, Li, D. W.-W., O., Wang, Barker, D., D. and M., Li, Wang, Y.: W., Analysis and of Pow- Tropical Cyclones in the South China Ruiz, J. J., Saulo, C., and Nogués-Paegle, J.: WRF model sensitivity to choice of parameter- Mohan, M. and Bhati, S.: Analysis of WRF Model Performance over Subtropical Region of Michalakes, J., Dudhia, J., Gill, D., Henderson, T., Klemp, J., Skamarock, W., and Wang, W.: The Mandal, M., Mohanty, U., and Raman, S.: A study on the impact of parameterization of phys- Jin, J., Miller, N. L., and Schlegel, N.: Sensitivity study of four land surface schemes in the WRF Kwun, J. H., Kim, Y.-K., Seo, J.-W., Jeong, J. H., and You, S. H.: Sensitivity of MM5 and WRF Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The interna- 5 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 16.28 − 0.16 0.016 0.65 − − 0.06 49.65 0.12 0.10 − − − 0.16 − 0.10.25 0.156 22.67 31.563 − − 300 299 − 0.28 0.31 − erent simulations for LHF. erent simulations for SST. ff ff 115.20.4987 112.65 0.6105 140.83 0.2132 143.95 0.514 168.91 0.5312 13.39 0.5967 1.0475 1.1727 1.8735 0.4938 0.065 − Sim 1 Sim 2 Sim 3 Sim 4 Sim 5 Sim 6 2.96 95.758 0.6901 − Sim 1 Sim 2 Sim 30.1577 Sim 4 Sim 5 Sim 6 statistic 0.38 0.84 0.7 0.57 0.35 CC MBEt 0.11 RMSE 0.716 0.86 0.91 0.717 0.725 statistic RMSE CC MBE t Statistical evaluation of di Statistical evaluation of di Table 3. Table 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0.00026 0.30098 0.00016 3.94333 14.384 54.621 0.524 − 1.392 0.00025 0.40514 0.00015 3.69970 23.694 0.9337 0.483 0.0382 0.029 7.777 − − 22.38 302 301 − erent simulations for precipitation rate. erent simulations for SHF. 9.254 ff ff − 12.85 − Sim 1 Sim 2 Sim 3 Sim 4 Sim 5 Sim 6 Sim 1 Sim 2 Sim 3 Sim 4 Sim 5 Sim 6 statistic 1.1505 1.6007 2.0224 1.7716 CCMBE t 0.6069 0.8885 0.7291 RMSE 58.375 30.899 60.715 64.481 statistic 4.08518 4.06251 3.84105 4.41405 CCMBEt 0.32941 0.00017 0.10501 0.00017 0.26422 0.00016 0.36922 0.00018 RMSE 0.00027 0.00028 0.00026 0.00028 Statistical evaluation of di Statistical evaluation of di Table 5. Table 4. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2.187 0.709 − statistic t 3.097 4.390 3.151 − 5.075 4.916 0.080.13 1.21 0.07 1.8 0.06 1.07 0.66 5.49 0.4 127.3912.02 6.16 0.83 17.433.97 4.27 2.87 1.54 21.61 1.24 8.65 1.57 2.13 2.011.95 2.54 2.6 − − − − − − − − − − − − − − 1.643 3.110 − 3.172 2.932 304 303 − 5.205 RMSE CC MBE 2.013 erent simulations for wind speed. ff − 3.103 Sim 1Sim 4 0.62Sim 5 0.68Sim 6 0.63 0.85 0.81 0.82 Sim 1 0.84 Sim 4 129.49 0.87 Sim 5 156.11 0.82 Sim 6 233.01 0.76 137.84 0.75 39.6Sim 1 0.81 Sim 4 42.29Sim 2.47 5 24.65Sim 0.55 6 22.03 0.47 31.97Sim 0.68 1Sim 0.67 4 0.0014Sim 5 0.00142 0.68Sim 6 0.00135 0.72 0.00063 0.00141 0.73 0.00066 4.77 Sim 1 0.67 0.00061 4.89 Sim 7.17 4 0.00063 4.76 Sim 5 6.9 4.73 Sim 6 7.79 0.68 7.38 0.72 0.63 1.24 0.67 1.73 2.697 − Sim 1 Sim 2 Sim 3 Sim 4 Sim 5 Sim 6 SST LHF SHF Prate Wind speed statistic 4.133 CC 0.427 0.372 0.496 0.415 0.574 RMSE 4.283MBE t 3.873 4.104 The values of statistic parameters for confirming the best combinations suggested for Statistical evaluation of di Table 7. the selected parameters. Table 6. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 19

usion ﬀ

(NOAA, 2008) (NOAA, 306 305

Sim7 Sim8 Two simulations introduced by other studies.

Fig1. Typhoon Noul trace in November in November Noul trace Typhoon Fig1. Typhoon Noul trace in November (NOAA, 2008).

MicrophysicsLongwave radiationShortwave radiationSurface layerLand surface RRTMPlanetary boundary layer RRTMG WRFCumulus single parameterization Moment 3-class Mellor Yamada Grell-Devenyi Janjic Eta MM5 Pleim Xiu TEMF Kain RRTM Fritsch Dudhia 5-layer thermal TEMF di 1 2 3 Fig. 1. Table 8.

21 hourly hourly -

308 307

yphoon Noul yphoon

Comparison Comparison of best model performance (simulation 6) with control data, for ediction during t during ediction

Model Domains

Comparison of best model performance (simulation 6) with control data, for six-hourly

Fig.2 Model domains.

Fig. Fig. 3 SST pr

1 2 3 1 2 3 4 Fig. 3. SST prediction during Typhoon Noul. Fig. 2.

310 309

yphoon Noul yphoon yphoon Noul yphoon t Comparison Comparison of best model performance (simulation 1) with control data, for Comparison Comparison of best model performance (simulation 5) with control data, for

Fig. Fig. 4 t prediction during LHF Comparison of best model performance (simulation 5) with control data, for six-hourly Comparison of best model performance (simulation 1) with control data, for six-hourly

Fig. Fig. 5 during prediction SHF

1 2 3 4 1 2 3 4 Fig. 5. SHF prediction during Typhoon Noul. Fig. 4. LHF prediction during Typhoon Noul.

25 hourly hourly -

24 hourly hourly -

six

312 311 yphoon Noul yphoon t

yphoon Noul yphoon Comparison Comparison of best model performance (simulation 4) with control data, for Comparison Comparison of best model performance (simulation 5) with control data, for

Comparison of best model performance (simulation 4) with control data, for six-hourly Comparison of best model performance (simulation 5) with control data, for six-hourly

precipitation rate prediction rate during precipitation Fig. Fig. 6

wind prediction during t wind prediction Fig. Fig. 7

1 2 3 4 3 4 1 2 Fig. 7. wind prediction during Typhoon Noul. Fig. 6. precipitation rate prediction during typhoon Noul. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

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WRF CFSR Sim Sim

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00 00 00 00 00 00 00 ...... Comparison Comparison wind speed prediction for typhoon Washi by different simulations and data

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Wind speed (m/s) speed Wind Comparison wind speed prediction for typhoon Washi by di

Fig. Fig. 8 sets

1 2 3 4 sets. Fig. 8.