Earth Systems and Environment (2019) 3:101–112 https://doi.org/10.1007/s41748-019-00086-0

ORIGINAL ARTICLE

A High‑Resolution Mesoscale Model Approach to Reproduce Super Maysak (2015) Over Northwestern Pacifc Ocean

Gaurav Tiwari1 · Sushil Kumar2 · Ashish Routray3 · Jagabandhu Panda4 · Indu Jain5

Received: 5 June 2018 / Accepted: 3 January 2019 / Published online: 10 January 2019 © King Abdulaziz University and Springer Nature Switzerland AG 2019

Abstract In this study, an attempt is made to simulate super typhoon Maysak, which occurred over the northwest Pacifc Ocean in 2015 and made landfall on the coast. The aim of the present study is to assess the various atmospheric condi- tions during the life cycle of Maysak to explore the associated dynamics and behavior over the ocean. For this purpose, the advanced research core of the weather research and forecasting (WRF) mesoscale model is adopted. The model is simulated using 27-km horizontal grid resolution with National Centers for Environmental Prediction global Final analyses (FNL) initial conditions. The relevant parameters, namely storm track, intensity, wind–vorticity, rainfall, minimum sea level pres- sure, relative humidity, and maximum refectivity etc., were analyzed. The model is able to perform reasonably well when available observations over the region compared with the simulated values of these parameters. The present study is able to demonstrate the capability of WRF in simulating and predicting the relevant characteristic features of over the northwest Pacifc Ocean region through the case of Maysak.

Keywords Mesoscale model · ARW​ · Typhoon · Track of storm

1 Introduction region, but as much as 50% of total precipitation occurs over ocean basins (Jiang and Zipser 2010). The forecasting of the Typhoons (or tropical cyclones) are one of the most force- cyclonic storms and associated rainfall events can be done ful natural manifestations in the earth. Every year, a virtu- using numerical models or by adopting well-tested forecast ous number of typhoons occur in the western part of the methods. Probabilistic forecast methods have limitations of North Pacifc Ocean with high intensity. These typhoons subjectivity, whereas numerical models have limitations of generally move towards the east–north-westerly direction. the inadequacy of observations. But due to the development They induce heavy damages associated with strong winds of suitable methods, numerical models serve as a handy tool and storm surges (Maw et al. 2017; Rappaport 2000; Ema- for typhoon studies (Nguyen and Chen 2011; DeMaria et al. nuel 2005). Further, precipitation associated with storm 2007). events accounts for 6–9% of total rainfall over the tropical For last three decades, there is signifcant upgrading in numerical prediction of typhoons primarily focusing on * Gaurav Tiwari prediction of storm track and intensity (Pattanaik and Rao [email protected] 2009; Rogers et al. 2006; Tien et al. 2013) since they are quite important in operational point of view. Several pro- 1 Department of Earth and Environmental Sciences, Indian cesses in the planetary boundary layer (PBL) impact the Institute of Science Education and Research Bhopal, Bhopal, India dynamics of a severe typhoon, which can be seen by the output from numerical model simulations (e.g., Anthes and 2 Department of Applied Mathematics, Gautam Buddha University, Greater Noida, India Chang 1978). Continuous improvement in computer applica- tions allows numerical weather prediction models with fner 3 National Centre for Medium Range Weather Forecasting, A‑50, Sector‑62, Noida, India scales of resolution using higher order convergent numerical techniques and parameterizations (Tao et al. 2011). NWP 4 Department of Earth and Atmospheric Sciences, National Institute of Technology Rourkela, Rourkela, India models have numerous physical and dynamical parameteri- zation schemes with various options of physical processes 5 RMSI Pvt. Ltd., Noida, India

Vol.:(0123456789)1 3 102 G. Tiwari et al. involved with the complexity in the model (Haghroosta et al. by Cambodia. It intensifed rapidly and tracked westward 2014). Cumulus schemes play a vital role in the simulation across the Federated States of Micronesia. It traversed of typhoon track and intensity since a relationship between Chuuk and between March 29th and April 1st and these can be case dependent (Shin et al. 2010). The cus- brought caustic winds to a number of islands and reached tomization of numerical modeling systems takes place in Yap’s Ulithi Atoll and Fais Island with continual winds order to be tuned well for the prediction of diferent weather speed of 160 miles per hour. It intensifed explosively into events over a region (Das et al. 2015). Microphysics, PBL, a super typhoon of category 5 on March 31st. On April 1st, and cumulus physics-associated processes are signifcantly Maysak’s passed the Yap Island having winds speed up responsible for typhoon initiation and development (Chan- to 48 mph and eye widened to 40 km. On the same day, drasekar and Balaji 2012). An appreciable number of param- PAGASA (Philippine Atmospheric, Geophysical, and Astro- eterization schemes are available in the ARW (Advanced nomical Services Administration) started tracking typhoon Research WRF) model for use in order to get better pre- Maysak. On April 4th, it downgraded into a severe tropical dictions of these natural events. The precise prediction of cyclone and on April 5th, Maysak made landfall in in tropical storms’ structure and intensity changes is closely the form of a minimal tropical storm. It lowered to a tropical connected to its inner core structure and their development depression and fnally dissipated in the South Sea. (Dasari et al. 2017; Houze et al. 2006; Kossin and Eas- It is also known as Typhoon Chedeng in the Philippines tin 2001). The sensitivity to cloud microphysics and PBL and was one of the most powerful tropical cyclones in the schemes available in ARW is studied by (Li and Pu 2008) Northwestern Pacifc Ocean, which made huge damage in during the early rapid intensifcation of Hurricane Emily. the Philippines. Figure 1 illustrates some of the synoptic During a 72-h simulation period, diferent PBL schemes in meteorological features associated with the typhoon. Fig- the ARW model could lead to a diference up to 15 m/s in ure 1a shows a view of Typhoon Maysak at 06 UTC of April the maximum surface wind and 16 hPa in the central pres- 4th, 2015. Figure 1b, c is taken at 05 UTC and 0730 UTC on sure (Braun and Tao 2000). April 5th, 2015 respectively, from Regional and Mesoscale The objective of this study is to simulate the super Meteorology Branch (RAMMB) of NOAA Satellites and typhoon Maysak by adopting the appropriate combination of Information. Figure 1b depicts storm relative imagery from physical parameterizations, occurred over the northwestern Joint Polar Satellite System spacecraft. For this, Visible Pacifc Ocean during 1–7 April 2015, and validate the ARW Infrared Imaging Radiometer Suite (VIIRS) imagery is used, model results with observations. The country like the Philip- which is color enhanced to emphasize the coldest tempera- pines has very limited capability to run global or regional ture/highest clouds. Figure 1c shows the Passive Microwave models over the region day-to-day basis and it makes this Imagery (PMI)-based typhoon analysis and forecast and it study very signifcant to explore the dynamical and spatial provides information about location, liquid water, rainfall, variabilities of such typhoon for future assessments. This is etc. These satellite imageries provide an initial representa- an early modeling study using the mesoscale model ARW tion of the typhoon and its associated characteristics. Further over the region to simulate such intense super typhoon. Fur- demonstrations in this study are from the numerical model ther, accurate prediction of such events including landfall simulations. location, landfall time and associated damages is empha- sized. The Philippines receive the major force of the land- falls as compared to China and and it is very complex 3 Model Description and Numerical to understand the genesis and prediction of the typhoon. Experiments Synoptic features of typhoon Maysak are discussed in Sect. 2. Model description is given in Sect. 3, which also In this study, fully compressible non-hydrostatic ARW mes- includes the description of the data used and numerical oscale modeling system version 3.5.1 is used. ARW is devel- experiments conducted in the computational framework. oped by the Mesoscale and Microscale Meteorology Divi- Results and discussions are presented in Sect. 4. The last sion of National Center for Atmospheric Research (NCAR) section concludes about the outcomes of the study. in collaboration with other agencies. It is used to analyze spatial and dynamical features associated with typhoon May- sak occurred over the northwest Pacifc Ocean. For this pur- 2 Synoptic Features Associated pose, the horizontal grid resolution mesh of 27 km is con- with the Super Typhoon Maysak sidered and the vertical resolution of the model is defned by 38 sigma levels. The initial and boundary conditions for the The typhoon Maysak developed on March 26th, 2015 into a model are provided from NCEP (National Centers for Envi- tropical depression over west of Island and turned ronmental Prediction) FNL data with a resolution of 1° × 1°. into a typhoon gradually. The name Maysak was contributed The lateral boundary conditions are updated at every 6-hrs

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Fig. 1 a A view of Typhoon Maysak approaching Philippines at 06 April 5th, 2015 taken from Joint Polar Satellite System spacecraft; c UTC of April 4th, 2015. This image is taken from Philippine Atmos- Passive Microwave Imagery (PMI) of Typhoon Maysak at 0730 UTC pheric, Geophysical and Astronomical Services Administration of April 5th, 2015 taken from Regional and Mesoscale Meteorology (PAGASA); b relative imagery of Typhoon Maysak at 05 UTC of Branch (RAMMB) of NOAA period with a fxed throughout the variation of moisture fuxes and surface heating (Hong et al. model integration. The land surface boundary conditions are 2006). Kain–Fritsch scheme is used for cumulus convection taken from the United State Geological Survey (USGS) with (Kain and Fritsch 1993; Kain 2004) due to its wide use in a horizontal grid spacing of 5-min. The model is integrated simulating extreme weather events including cyclonic sys- for 186 h from 00 UTC April 1st to 18 UTC April 7th, 2015. tems (Panda et al. 2011, 2015) and heavy rainfall events (Igri Yonsei University (YSU) planetary boundary layer (PBL) et al. 2015, 2018; Kumar et al. 2014). Although ARW model scheme (Hong et al. 2006) is used in this study because it is treats Surface Layer and PBL parameterizations schemes a vertical difusion compendium with a non-turbulent mix- separately, they interact with each other strongly and hence ing in the PBL which is relevant for weather forecasting of the selection of PBL parameterization scheme decide the typhoons (Liu et al. 1997; Wang 2009). It provides a con- option of the Surface Layer parameterization scheme. Physi- vincing confguration of the PBL to an idealized daytime cal schemes YSU PBL and Noah surface parameterization

1 3 104 G. Tiwari et al. are a most appropriate combination for the simulation of a typhoon Maysak as signifed by the location of the MSLP typhoon (Carvalho et al. 2012). Dudhia Shortwave scheme center. The 06 hourly track errors (km) of the Typhoon at for shortwave radiation (Dudhia 1989) is considered for the diferent forecast time are shown in Fig. 3. For the frst 30 h study. Similarly, Rapid Radiation Transfer Model (RRTM) of the simulation model and NRL observation dataset, both scheme is applied for taking into account long-wave radia- followed a north-west track very closely. During these hours, tion (Mlawer et al. 1997) because of its versatility to main- the root mean square error is less than 50 km which is really tain the speed and accuracy level for a diverse range of tem- a signifcant output. After the next 6 h, model shows curve perature profles and molecular abundances. The interaction type structure and started following the observed track of these physical parameterizations holds the information more closely. The typhoon moved majorly in a northward fow from one type of physics to other during the model direction for next 12 h and then again followed westerly- simulations. A summarized confguration of ARW model north-westward up to landfall on April 5th. Both model is given in Table 1. and observation made the landfall in the north-east coast of the country more or less at the same time at a diference of above 50 km spatial distance and it is also a good result. 4 Results and Discussion Track errors are below 55 km up to the 06 UTC of April 5th except at 06 UTC of April 2nd. Track errors started gradu- The results from ARW simulations are discussed in this ally increasing after the landfall due to the not capturing of section. The discussion includes track prediction, track some local atmospheric conditions in the model. The overall errors, maximum sustainable surface wind at 10 m height, root mean square error is about 67 km. minimum sea level pressure (MSLP), precipitation, relative humidity (RH), relative vorticity and maximum refectivity 4.2 Wind, MSLP, SST and Relative Vorticity associated with the typhoon Maysak. The model simulated minimum sea level pressure (hPa) 4.1 Track and Track Errors valid for 00 UTC April 4th 2015 of Fig. 7a. Time series given in the Figs. 6 and 8 show the evolution of storm inten- Figure 2 illustrates the model simulated and the Naval sity in terms of maximum surface wind at 10 m (m/s) and Research Laboratory (NRL) observed (http://www.nrlmr​ MSLP (hPa). The accurate forecasting of cyclone wind is y.navy.mil/tcdat​/tc15/WPAC/04W.MAYSA​K/) tracks of an important concern because winds determine the level of

Table 1 WRF model Dynamics Non-hydrostatic confguration and physics used in the simulations ARW model confguration Model domain 115.5°E−144.5°E, 5.5°N–19.5°N Center of the domain 130.0°E, 12.5°N Horizontal grid resolution 27 km Map projection Mercator Initial and lateral boundary conditions NCEP/NCAR FNL data Horizontal grid system Arakawa C-grid Integration time step 150 s Topography USGS Vertical coordinates Terrain-following hydrostatic pressure vertical coordinate with 38 vertical levels Microphysics WSM 3-class scheme Radiation schemes RRTM for long wave Dudhia for short wave Planetary boundary layer physics Yonsei University Scheme (YSU) Cumulus scheme Kain-Fritsch scheme Land surface option Unifed Noah land surface model Surface layer option MM5 similarity scheme

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Fig. 2 Simulated and observed tracks of typhoon Maysak

Fig. 3 The track errors in km and RMSE of Typhoon Maysak with diferent forecast hours

devastation, moisture transport and eventually plays role model needs a minimum of 12 h to spin up. However, spin in the organization or propagation of the storm. Strength up time of a model be subject to the grid spacing and the and direction of surface winds of the typhoon are related time step (Routray et al. 2014). In the case of Maysak, to the gradient. When the isobars are observed 10-minute sustained wind was 195 km/h and closest together, winds are strongest and therefore near cold 1-minute sustained wind was 260 km/h while ARW model- fronts and low-pressure systems, strongest winds are usually simulated was 228 km/h at 00 experienced. UTC of April 1st. At the initial phase of the experiment, Root mean square error (RMSE) of the 10 m wind and there is enough diference in MSLP (hPa) values between MSLP is around 10 m/s and 14.47 hPa, respectively. It is simulated and observed values from 00 UTC of April 1st seen from the Fig. 8 that the absolute errors of the 10 m to 06 UTC of April 2nd but after 12 UTC of April 2nd to wind and MSLP in initial hours are larger due to the model 18 UTC of April 6th, the model is close to the observations spin-up time. Routray et al. (2014) found that the ARW and followed zigzag path. At 18 UTC of April 5th and 12

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Fig. 4 a, b Vector analysis of surface wind (m/s) at 10 m height 2015-04-05). The colour bar given in each plot signifes the magni- and c, d streamline of vorticity (× 10−5 ­s−1) valid for 1000 hPa and tude of the corresponding variables 850 hPa atmospheric pressure level at the time of landfall (03 UTC

UTC of April 6th observations are similar to experimental are some factors which provide environments of enhanced values. Similarly, surface winds also followed the pattern cyclonic vorticity causing cyclonic phenomenon within like MSLP. ARW-simulated 10 m wind is looking quite favorable convection condition. At middle levels in the well after 06 UTC of April 3rd and also at landfall time (03 troposphere, developing tropical systems show signifcant UTC of April 5th) near Luzon, since it is much closer to the cyclonic vorticity. Typhoon spin-up is the result of the observations. Vectors (and magnitude) of horizontal wind at development of strong near-surface vorticity and the asso- 1000 hPa and 850 hPa atmospheric pressure level is show- ciated warm core (Raymond and Carrillo 2011). The strong ing in Fig. 4a, b. The clear circular pattern can be seen here. pressure drop of the typhoon is simulated by ARW model 850 hPa winds are relatively stronger than 1000 hPa one, and throughout the forecast hours (up to 06 UTC April 5th) as the typhoon eyes have crossed the coast in the Fig. 4b. There compared to the observations. Therefore, the intensity of is almost a diference of 10 m/s in the wind speed. Figure 4c, typhoon in terms of MSLP is over predicted by ARW simu- d depicts the streamlines of model-simulated relative vorti- lation. In few instants, the model has exactly simulated the city of typhoon Maysak at 03 UTC of April 5th, 2015 valid observed magnitude of 10 m wind and MSLP. Overall we for 1000 hPa and 850 hPa atmospheric pressure level and can say that the model has re-predicted the typhoon intensity describing well the local spinning motion of typhoon near in a realistic manner. the region of Luzon as typhoons form in oceanic regions Time-pressure cross-section of relative vorticity at the of heightened cyclonic vorticity. Monsoon troughs, tropical time of severe intensity and before the landfall is given in waves, topographical fows, and extra-tropical disturbances Fig. 5a, b while Fig. 5c, d show a vertical cross-section of

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Fig. 5 Time-pressure cross section of relative vorticity a at the time 5th 2015. Contours with coloured shades show the positive values of severe intense, b before the landfall and vertical cross section of while contours with the white background show negative values vertical velocity, c at the time of severe intense, d at 00 UTC of April vertical velocity at the same time. At 06 UTC 1st April, the the maximum vertical vorticity was centered near 133.0E relative vorticity is maximum from the surface to 875 hPa and 122.50E, respectively, for the given two time periods. and slightly lower up to 600 hPa. This was also the develop- The model simulated maximum sustained 10 m wind ment and intensifcation period of the typhoon. At 00UTC compared with that of the NRL observations. The error is 5th April, the maximum relative vorticity was from 800 to found to decrease with time until to 00UTC 5th April and 600 or 550 hPa and very negligible over 500 hPa because then slightly increases (Fig. 6). The overall RMSE is 10 m/s. this was the time of landfall. In the lower panel of the Fig. 5, The spatial distribution of sea level pressure is found to be

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Fig. 6 Model simulated 10 m 80 wind (m/s) and RMSE of Typhoon Maysak 70 Root Mean Square Error: 10 m/s

60 )

/s 50 (m

nd 40 wi 30 0m 1 20

OBS 10 Model 0

Time (dd_hh)

Fig. 7 a Simulated minimum sea level pressure (hPa) valid for 00 UTC April 4th 2015, b MSLP (in contours) and Sea surface temperature (Kel- vin) (in shaded) at 00 UTC April 5th 2015 reasonably well simulated by the model (Fig. 7a). Similar 4.3 Rainfall, Relative Humidity, Maximum to the 10 m maximum sustained wind, the RMSE values Refectivity and Moisture Convergence decrease with time for MSLP (Fig. 8) too. This is the quite interesting behavior of the model for both winds and MSLP. Rainfall is a quite important parameter while understand- The overall RMSE value for MSLP is 14.47 hPa. ing the meteorological characteristics of a cyclonic storm. Figure 7b shows model-simulated sea surface temperature The associated meteorological characteristics include (SST) at 00 UTC of 5th April. Relatively warmer SST provides relative humidity, moisture convergence, and maximum a more favorable condition for the progress and strengthening refectivity. At the time of landfall of typhoon Maysak, of cyclonic systems (Gabriel and Vecchi 2007; Singh et al. model-simulated total accumulated rainfall and relative 2018a, b). A well-established SST gradient is especially help- humidity are given in Fig. 9a, b. At the coast of the Phil- ful in the propagation and intensifcation of cyclonic systems ippines, model-simulated maximum rainfall is 100 mm. (Singh et al. 2018a) as observed in this case. The central and north-east coast of the country has experi- enced a huge amount of rainfall. As tropical storms evolve,

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Fig. 8 Model simulated MSLP 1020 (hPa) and RMSE of Typhoon Root Mean Square Error: 14.47 hPa Maysak 1000

) 980 Pa (h 960 LP

MS 940

920 OBS Model 900

Time (dd_hh)

Fig. 9 Model simulated a 24 h accumulated rainfall (mm) and b relative humidity (%) at 03 UTC 2015-04-05

environmental relative humidity above the boundary layer However, the minimum value of ~ 35 dBz is seen at 06 decreases with time and near the surface approximately UTC of April 5th. The refectivity of typhoons determines it remains constant. After landfall, the simulated relative the rate of rainfall and the potential for severe hail. When humidity in coastal areas is about 80–100%. Figure 10 the refectivity is around 20 dBz, rain starts to fall. Simu- shows the model-simulated maximum refectivity (unit: lated value of refectivity at the coastal areas is around dBz) of typhoon Maysak at 03 UTC of April 5th. The 25-30 dBz which also indicates a good amount of rainfall spatial variability demonstrates the maximum refectivity along the coasts. The maximum value of just above 50 to be at the center and there is a gradual increase outward. dBz indicates a signifcant amount of rainfall on April 5th. A bar diagram for model simulated maximum refectivity Figure 12 shows the Moisture fux convergence (MFC) against the time series is given in Fig. 11 and it shows that in multiple of 10­ −1 (g kg−1 ­s−1) at 1000 and 850 hPa, there is not much variation between any two values as it respectively, where convergence is signified by posi- attained its highest value at 00 and 18 UTC of April 5th. tive values and used to forecast rainfall associated with

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indicates receipt of a signifcant amount of rainfall along the coastal and inland areas.

5 Conclusions

The aim of the present study is to analyze the meteorologi- cal characteristics associated with super typhoon Maysak using non-hydrostatic ARW mesoscale model. For this purpose, the model was integrated for 186 h with speci- fed periodic boundary conditions by considering a reason- able domain confguration. The comprehensive deductions can be drawn from the results and discussion section as ARW framework reasonably captured the relevant cyclonic parameters of typhoon Maysak. Model simulated maxi- mum surface wind, track intensity, and MSLP were found Fig. 10 Spatial variability of Model simulated maximum refectivity in very good agreement during the intensifcation of the (dBz) of Typhoon Maysak at 03 UTC 2015-04-05 storm. The track positions indicate northwestward moving typhoon with the divergent track. The simulated cyclone track is aligned to the south of the observed track in the synoptic scale and mesoscale systems. Near Luzon, its experiment. It is clear that the numerical simulation of value ranges between 0.5-3 which supports signifcant typhoon improves the understanding of their dynamic and rainfall over the region. At the surface, a strong conver- thermodynamic features. ARW model well predicted the gence is predicted by the model. Moisture transportation intensity of the typhoon and improvements are possible and convergence are quite important as far as determining with introducing satellite observations in the model initial the rainfall is concerned during a cyclonic storm (Panda conditions. The interesting aspect noticed while comput- et al. 2015). Here, the reasonable range qualitatively ing RMSE values is the decrease in error with time for

Fig. 11 Temporal variation of model simulated maximum refectivity (dBz) of Typhoon Maysak

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Fig. 12 The variation in mass fux convergence (× 10−1 g kg−1 ­s−1). Here, positive values represent convergence and negative ones are for diver- gence

MSLP and 10 m maximum sustained wind. In general, Das MK, Chowdhury Md AM, Das S (2015) Sensitivity study with the presentation of the ARW model is acceptable and physical parameterization schemes for simulation of mesoscale convective systems associated with squall events. Int J Earth highly recommended for the real-time prediction of future Atmos Sci 2:20–36 typhoon over this region. Dasari HP, Brahmananda RV, Ramakrishna SSVS, Paparao G, Nanaji RN, Ramesh KP (2017) On the movement of Acknowledgements The frst author is thankful to the Department of LEHAR. Earth Syst Environ 1:1–14 Science and Technology, Govt. of India for providing research fellow- DeMaria M, Knaf JA, Sampson C (2007) Evaluation of long-term ship. The authors sincerely acknowledge departmental computational trends in tropical cyclone intensity forecasts. Meteorol Atmos lab at Gautam Buddha University for numerical simulation, United Phys 97:19–28 States Naval Research Laboratory for afording observational data and Dudhia J (1989) Numerical study of convection observed during winter thankful to NCEP/NCAR for using 1­ 0 × ­10 Final Analysis input data. monsoon experiment using a mesoscale two-dimensional model. We would like to express our gratefulness to the WRF working group to J Atmos Sci 46:3077–3107 develop a mesoscale community model. The valuable feedbacks from Emanuel K (2005) Increasing destructiveness of tropical cyclones over anonymous reviewers are highly appreciated and acknowledged, which the past 30 years. Nature 436:636–638 helped in overall improvement of the manuscript. Gabriel A, Vecchi BJS (2007) Effect of remote sea surface tem- perature change on tropical cyclone potential intensity. Nature Compliance with Ethical Standards 450:1066–1070 Haghroosta T, Ismail WR, Ghafarian P, Barekati SM (2014) The ef- ciency of the weather research and forecasting (WRF) model for Conflict of Interest On behalf of all authors, the corresponding author simulating typhoons. Nat Hazards Earth Syst Sci 14:2179–2187 states that there is no confict of interest. Hong SY, Noh Y, Dudhia J (2006) A new vertical difusion package with explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341 References Houze RA et al (2006) The Hurricane and intensity change experiment: observations and modeling of Hurricanes Katrina, Ophelia, and Rita. Bull Am Meteorol Soc 87:1503–1521 Anthes RA, Chang SW (1978) Response of the hurricane boundary Igri PM, Tanessong RS, Vondou DA, Mkankam FK, Panda J (2015) layer to changes of sea surface tempera-ture in a numerical model. Added-value of 3DVAR data assimilation in the Simulation of J Atmos Sci 35:1240–1255 heavy rainfall events over Western and Central Africa. Pure Appl Braun SA, Tao WK (2000) Sensitivity of high-resolution simulations of Geophys 172:2751–2776 Hurricane Bob (1991) to planetary boundary layer parameteriza- Igri PM, Tanessong RS, Vondou DA, Panda J, Garba A, Mkankam FK, tions. Mon Weather Rev 128:3941–3961 Kamga A (2018) Assessing the performance of WRF model in Carvalho D, Rocha A, Gomez-Gesteira M, Santos C (2012) A sensitiv- predicting high-impact weather conditions over central and west- ity study of the WRF model in wind simulation for an area of high ern Africa: an ensemble-based approach. Nat Haz 93:1565–1587 wind energy. Environ Model Softw 33:23–34 Jiang H, Zipser EJ (2010) Contribution of tropical cyclones to the Chandrasekar R, Balaji C (2012) Sensitivity of tropical cyclone global precipitation from eight seasons of TRMM data: regional, Jal simulations to physics parameterizations. J Earth Syst Sci seasonal, and interannual variations. J Clim 23:1526–1543 121:923–946

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Kain JS (2004) The Kain-Fritsch convective parameterization: an Rappaport EN (2000) Loss of life in the United States associated update. J Appl Meteorol 43:170–181 with recent Atlantic Tropical cyclones. Bull Am Meteorol Soc Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale 81:2065–2074 models: the Kain-Fritcsh scheme. In: Emanuel KA, Raymond DJ Raymond DJ, Carrillo CL (2011) The vorticity budget of developing (eds) The representation of cumulus convection in numerical mod- typhoon Nuri (2008). Atmos Chem Phys 11:147–163 els. American Meteorological Society, p 246 Rogers R, Aberson S, Black M, Black P, Cione J, Dodge J, Gamache Kossin JP, Eastin MD (2001) Two distinct regimes in the kinematics J, Kaplan J, Powell M, Dunion J, Uhlhorn E, Shay N, Surgi N and thermodynamic structure of the hurricane eye and eyewall. J (2006) The intensity forecasting experiment: a NOAA multiyear Atmos Sci 58:1079–1090 feld program for improving tropical cyclone intensity forecasts. Kumar S, Chauhan R, Routray A, Panda J (2014) Impact of parameteri- Bull Am Meterol Soc 87:1523–1537 zation schemes and 3DVAR data assimilation for simulation of Routray A, Kar SC, Mali P, Sowjanya K (2014) Simulation of mon- heavy rainfall events along west coast of India with WRF mod- soon depressions using WRF-VAR: impact of diferent back- eling system. Int J Earth Atmos Sci 01:18–34 ground error statistics and lateral boundary conditions. Mon Li X, Pu Z (2008) Sensitivity of numerical simulation of early rapid Weather Rev 142:p3586 intensifcation of hurricane Emily (2005) to cloud microphysical Shin HH, Hong SY, Dudhia J, Kim YJ (2010) Orography-induced and planetary boundary layer parameterization. Mon Weather Rev gravity wave drag parameterization in the global WRF: imple- 136:4819–4838 mentation and sensitivity to shortwave radiation schemes. Liu Y, Zhang DL, Yau MK (1997) A multiscale numerical study of Advances in Meteorology Hurricane Andrew (1992). Part I: explicit simulation and verifca- Singh K, Panda J, Rath SS (2018a) Variability in landfalling trends tion. Mon Weather Rev 125:3073–3093 of cyclonic disturbances over North Indian Ocean region during Maw KW, Towfqul IARM, Sein ZMM, Jinzhong M, Argete JC (2017) current and pre-warming climate. Theor Appl Climatol. https://​ Simulation of in Myanmar Coast. Earth Syst Environ doi.org/10.1007/s0070​4-018-2605-3 1:1–12 Singh K, Panda J, Sahoo M, Mohapatra M (2018b) Variability in Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) tropical cyclone climatology over North Indian Ocean during Radiative transfer for inhomogeneous atmosphere: RRTM, a the period 1891 to 2015. Asia-Pacifc J Atmos Sci. https​://doi. validated correlated-k model for the longwave. J Geophys Res org/10.1007/s1314​3-018-0069-0 102(D14):16663–16682 Tao WK, Shi JJ, Lin P, Chen J, Lang S, Chang MY, Yang MJ, Wu CC, Nguyen VH, Chen YL (2011) High-resolution initialization and Lidard CP, Sui CH, Jou BJD (2011) High-resolution numerical simulations of typhoon morakot (2009). Mon Weather Rev simulation of the extreme rainfall associated with Typhoon Mora- 139:1463–1491 kot. Part I: comparing the impact of microphysics and PBL param- Panda J, Giri RK, Patel KH, Sharma AK, Sharma RK (2011) Impact eterizations with observations. Terr Atmos Ocean Sci 22:673–696 of satellite derived winds and cumulus physics during the Tien DD, Ngo-Duc T, Mai TH, Kieu C (2013) A study of the connec- occurrence of the tropical cyclone Phyan. Indian J Sci Technol tion between tropical cyclone track and intensity errors in the 04:859–875 WRF model. Meteorol Atmos Phys 122:55–64 Panda J, Singh H, Wang PK, Giri RK, Routray A (2015) A quali- Wang Y (2009) How do outer spiral rain bands afect tropical cyclone tative study of some meteorological features during tropical structure and intensity? J Atmos Sci 66:1250–1273 cyclone PHET using satellite observations and WRF modeling system. J Indian Soc Rem Sens 43:45–56 Pattanaik DR, Rao YVR (2009) Track prediction of very severe cyclone ‘Nargis’ using high resolution weather research fore- casting (WRF) model. J Earth Syst Sci 118:309

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