A Comparative Modeling Study of Supertyphoons Mangkhut and Yutu (2018) Past the Philippines with Ocean-Coupled HWRF

atmosphere

Article A Comparative Modeling Study of Supertyphoons Mangkhut and Yutu (2018) Past the Philippines with Ocean-Coupled HWRF

Thi-Chinh Nguyen and Ching-Yuang Huang *

Department of Atmospheric Sciences, National Central University, Taoyuan City 320317, Taiwan; [email protected] * Correspondence: [email protected]; Tel.: +886-3-4227151 (ext. 65532)

Abstract: The ocean-coupled Hurricane Weather Research and Forecasting (HWRF) system was used to investigate the evolution of Supertyphoons Mangkhut and Yutu (2018) over the Philippines Sea and near landfall in the northern Philippines. The simulation results indicate that Mangkhut at a deepening stage has a smaller track sensitivity to the use of different physics schemes but greater intensity sensitivity, which becomes reversed for Yutu at a weakening stage. When both upstream tracks are well simulated with some specific suite of physics schemes, sensitivity experiments indicate that both track deviations near the northern Philippines are only weakly modified by the air–sea interaction (ocean-coupled or uncoupled processes), the topographic effects of the Philippines terrain (retained or not), and the initial ocean temperature change along both typhoon tracks. The interactions between the internal typhoon vortex and the large-scale flow play an important role in the overall movement of both typhoons, which were explored for their structural and convective evolutions near the terrain. The wavenumber-one potential vorticity (PV) tendency budget of the typhoon vortex was analyzed to explain the induced typhoon translation from different physical processes. The west-northwestward translation for the stronger Mangkhut near the northern Philippines is primarily Citation: Nguyen, T.-C.; Huang, C.-Y. induced by both horizontal and vertical PV advection but with the latter further enhanced to dominate A Comparative Modeling Study of the northward deflection when closing in to the terrain. However, the northwestward translation Supertyphoons Mangkhut and Yutu and track deflection near landfall for the weaker Yutu are driven by the dominant horizontal PV (2018) Past the Philippines with advection. Differential diabatic heating is relatively less important for affecting the movement of Ocean-Coupled HWRF. Atmosphere both typhoons near landfall. 2021, 12, 1055. https://doi.org/ 10.3390/atmos12081055 Keywords: Mangkhut and Yutu; HWRF; track deflection; potential vorticity

Academic Editor: Chanh Kieu

Received: 1 July 2021 Accepted: 11 August 2021 1. Introduction Published: 17 August 2021 Tropical cyclones (TCs) are intense circulating storms characterized by low atmospheric pressure, strong wind, and heavy rainfall. A combination of strong wind and storm Publisher’s Note: MDPI stays neutral surges makes TCs seriously hazardous in coastal areas in the tropics and subtropics of the with regard to jurisdictional claims in world. Therefore, many previous studies have focused on investigating the evolution of published maps and institutional affil- the typhoon track and intensity prior to and near landfall (e.g., [1–12]). iations. It is well known that TC motions can be affected by external influences, such as air–sea interaction, ocean conditions, steep mountain terrain, and internal physical processes. The relationship between TC motions and sea surface temperature (SST) variations has been investigated in previous studies (e.g., [1,5,13,14]). The TC movement is affected by Copyright: © 2021 by the authors. asymmetric SST distributions, and as a result, TCs tend to move to the region of warmer Licensee MDPI, Basel, Switzerland. SST [6]. TC tracks may be deflected northward by warmer SST in the vicinity of TCs and This article is an open access article deflected southward by cooler SST [9]. By using a coupled hurricane–ocean model, it distributed under the terms and was found [1] that the TC track from the coupled model simulation moves to the south conditions of the Creative Commons (north) of the track simulated by the uncoupled model for stronger (weaker) TCs. In Attribution (CC BY) license (https:// addition, mountain terrain can also have influences on TC tracks and landfall. The TC creativecommons.org/licenses/by/ track becomes more complex as a TC moves closer to high topography (e.g., [15–19]). By 4.0/).

Atmosphere 2021, 12, 1055. https://doi.org/10.3390/atmos12081055 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, 1055 2 of 29

using an idealized simulation with a typhoon inserted at the east of the Philippines, Tang and Chan [16] showed that TCs tend to move with a rightward track deflection prior to landfall due to the effect of the Luzon terrain. A TC inserted at the eastern side of a mesoscale mountain range might experience an earlier northward deflection prior to landfall; afterward, it may take a sudden southward turn when closer to the mountain base, in response to the effects of a stronger northerly wind ahead and over the mountain due to flow channeling (e.g., [17–19]). The simulated typhoon track and intensity may be quite different depending on the physics parameterization schemes used by the numerical model (e.g., [20–25]). The microphysics, cumulus parameterization, and planetary boundary layer schemes have more or less influences on typhoon track and intensity predictions (e.g., [23,25,26]). By using the fifth-generation Penn State/NCAR Mesoscale Model (MM5), Srinivas et al. [21] indicated that some TC movements can be more dominantly controlled by convective processes, while planetary boundary layer turbulent diffusion can be important in determining the TC intensity. In addition, regarding the relative importance of cumulus parameterization, Kanase and Salvekar [25] found that the Kain–Fritsch convection scheme gives stronger vertical motions than other cumulus parameterization schemes and the simulated TC intensity thus is stronger than from the other schemes. TC motions may be modulated in a dynamic sense by the potential vorticity (PV) tendency budget through different physical processes, including horizontal PV advection, vertical PV advection, differential diabatic heating, and turbulent diffusion (e.g., [12,18,27–30]).Wu and Wang [31] showed that the movement of the vortex is only related to the wavenumber- one (WN-1) component of the PV tendency. Moreover, to better understand the roles of different physical processes in TC motions, Wu and Wang [31] also addressed a useful dy- namical framework based on PV tendency in which a TC tends to move to the region where the azimuthal WN-1 component of the PV tendency around the TC reaches a maximum. Chan et al. [28] showed that the asymmetric convection of a typhoon may be influenced by the typhoon track if it has a significant WN-1 component. TC translation is essentially dictated by the WN-1 horizontal PV advection but sometimes modified by differential latent heating associated with the convective clouds embedded in the TC (e.g., [17,28,32]). Hsu et al. [33] and Tang and Chan [16] found that the differential diabatic heating becomes more comparable to the horizontal PV advection when the TC is near and over a high mountain terrain. Li and Huang [18] showed that the southward deflection of Typhoon Megi (2016) near landfall may have occurred due to the southeastward translation induced by differential diabatic heating over the high mountain terrain of Taiwan. Thus, the effect of differential diabatic heating may become more important for affecting a TC motion near a higher mountain terrain. Typhoons Mangkhut (September 2018) and Yutu (October 2018) were supertyphoons that formed in the warm ocean in the western North Pacific (WNP). One note here is that they occurred near the same region within 45 days, underwent rapid intensification [34] before making landfall, and then made landfall in the northern Philippines. Both typhoons moved nearly westward toward the northern Philippines in the open ocean; however, Mangkhut and Yutu exhibited a northward and slightly southward track deflection, respectively, as they approached the northern Philippines. One note here is that when both typhoons moved closer to the northern Philippines, the typhoon intensity for Mangkhut was much stronger than that for Yutu (about 40 kt for 10 m maximum wind speed and 40 hPa for minimum sea-level pressure according to the United States Joint Typhoon Warn- ing Center (JTWC) best track data). The two supertyphoons embedded with different environmental conditions (including different SST warm eddies) occurring in one and half months indeed have undergone varying intensity development and movement. It is interesting to understand how the translation and intensity of the two typhoons were in response to the underlying conditions as well as induced track deflection near the Philippines. In this study, we focus on simulating the movement of both typhoons over the eastern offshore region of the northern Philippines and near landfall. First, sensitivity experiments Atmosphere 2021, 12, x FOR PEER REVIEW 3 of 32

deflection near the Philippines. In this study, we focus on simulating the movement of Atmosphere 2021, 12, 1055 both typhoons over the eastern offshore region of the northern Philippines and 3near of 29 landfall. First, sensitivity experiments with different physics schemes were performed to obtain an optimal scheme combination of cloud microphysics, cumulus parameterization, and the planetary boundary layer in simulating the typhoon track and intensity. Other sensitivitywith different experiments physics schemes with the were optimal performed scheme tocombination, obtain an optimal including scheme simulations combination with ocean-coupledof cloud microphysics, and uncoupled cumulus processes, parameterization, with and andwithout the planetarythe Philippines boundary terrain, layer and in withsimulating the modified the typhoon initial track ocean and temperature intensity. Other changes sensitivity along experiments both typhoon with paths the optimal were conductedscheme combination, to identify includingtheir relative simulations roles in th withe movement ocean-coupled of both and typhoons. uncoupled In this processes, study, thewith ocean-coupled and withoutthe model Philippines HWRF was terrain, applied and withto simulate the modified the evolution initial ocean of both temperature typhoons, whichchanges can along directly both simulate typhoon the paths effects were of conductedocean feedbacks to identify on typhoon their relative tracks rolesdue to in air– the seamovement interaction. of both Both typhoons. supertyphoons In this study,provide the a ocean-coupledgreat opportunity model to compare HWRF was the applied model predictabilityto simulate the and evolution sensitivity of both regarding typhoons, their which track can and directly intensity, simulate in particular the effects for of oceantrack deflection.feedbacks onThe typhoon numerical tracks model, due toobservat air–seaion interaction. data, and Both simulation supertyphoons methodology provide are a introducedgreat opportunity in Section to compare 2. The the simulated model predictability results of both and sensitivitytyphoons regardingare presented their trackand comparedand intensity, with in the particular observations for track in Section deflection. 3. In TheSection numerical 4, we focus model, on observationanalyses of data,both typhoonand simulation circulations, methodology with a discussion are introduced of their in Sectiontrack deviations2. The simulated offshore results and near of both the typhoons are presented and compared with the observations in Section3. In Section4, we northern Philippines. The potential vorticity budget is then used to identify the important focus on analyses of both typhoon circulations, with a discussion of their track deviations dynamic processes in the induced typhoon translation for explaining their track offshore and near the northern Philippines. The potential vorticity budget is then used to deviations, which is also presented in Section 4. Finally, conclusions are given in Section identify the important dynamic processes in the induced typhoon translation for explaining 5. their track deviations, which is also presented in Section4. Finally, conclusions are given in Section5. 2. Model Configuration and Numerical Experiments 2.1.2. Model Overview Configuration of Two Supertyphoons and Numerical Experiments 2.1. OverviewAccording of Twoto the Supertyphoons JTWC, Supertyphoon Mangkhut began as a tropical disturbance near theAccording International to the JTWC,Date Line Supertyphoon on 5 Septem Mangkhutber 2018 and began then as aintensified tropical disturbance into a tropical near stormthe International over the WNP Date at Line 1200 on UTC 5 September 7 Septembe 2018r. andAfter then that, intensified Mangkhut into underwent a tropical stormrapid intensificationover the WNP and at 1200 reached UTC its 7 September.strongest state After with that, a central Mangkhut sea-level underwent pressure rapid of 896 intensi- hPa andfication a maximum and reached wind its speed strongest of 155 state kt at with 0600 a UTC central 12 September. sea-level pressure Mangkhut’s of 896 movement hPa and a wasmaximum mainly windwestward speed over of 155 the kt open at 0600 ocean UTC but 12 was September. somewhat Mangkhut’s deflected northward movement when was closermainly to westward the northern over Philippines the open ocean (black but lin wase in somewhat Figure 1a deflected shown northwardlater). Mangkhut when closer then madeto the landfall northern at Philippines the coast of (black northeastern line in Figure Luzon1a shownIsland, later).Philippines, Mangkhut at 1800 then UTC made 14 September.landfall at the This coast made of northeasternMangkhut the Luzon strongest Island, storm Philippines, to strike atthe 1800 island UTC of 14 Luzon September. since TyphoonThis made Megi Mangkhut (2010). the Afterward, strongest stormthe typhoon to strike entered the island the ofSouth Luzon China since Sea Typhoon (SCS) Megi and finally(2010). Afterward,made landfall the typhoonat the enteredcoast of the western South ChinaGuangdong, Sea (SCS) China, and finally at 0900 made UTC landfall 16 September.at the coast of western Guangdong, China, at 0900 UTC 16 September.

(a) (b)

Figure 1. (a) The HWRF model triple nested domains and the ocean domain of the POM for Mangkhut at the initial time. The blue region and red and green boxes denote the parent ﬁxed domain and the two inner moving domains for the HWRF model, respectively. The black box shows the domain of the POM. The black line with pluses at an interval of 24 h indicates the best track from the JMA for Mangkhut from 1200 UTC 10 September to 1200 UTC 15 September 2018. (b) Same as in (a) but for Yutu, with the best track from 0000 UTC 26 October to 0000 UTC 31 October 2018. Atmosphere 2021, 12, 1055 4 of 29

Supertyphoon Yutu originated east of Guam and the Northern Mariana Islands on early 21 October 2018 and developed into a tropical storm several hours later. Favorable ocean conditions hastened Yutu’s development, and thus Yutu underwent rapid intensi- ﬁcation during this stage. Yutu reached the peak intensity at 1800 UTC 24 October, with the minimum sea-level pressure (MSLP) deepening to 904 hPa and Vmax increasing to 150 kt according to the JTWC. Yutu then moved mainly west-northwestward over the WNP (black line in Figure1b). On 28 October, Yutu took a slightly southwestward turn when approaching the northern Philippines. Yutu made landfall over the northern Philippines at around 2100 UTC 29 October. When entering the SCS, Yutu further weakened into a tropical depression and turned north-northwestward. Yutu dissipated on 2 November over the northern SCS.

2.2. The Ocean-Coupled Model The Hurricane Weather Research and Forecasting (HWRF) model version 3.7 (https: //dtcenter.org/HurrWRF/users/index.php accessed on 10 August 2021; [35]) was applied to simulate the TC movement in this study. HWRF is a non-hydrostatic, primitive-equation, ocean-coupled model developed at the National Centers for Environmental Prediction (NCEP). Two components of this model are the Non-hydrostatic Mesoscale Model (NMM) dynamic core of the WRF model (WRF-NMM; https://dtcenter.org/wrf-nmm/users/ index.php accessed on 10 August 2021) for the atmospheric component and the Princeton Ocean Model (POM) for Tropical Cyclones (MPIPOM-TC; [36]) for the ocean component. Figure1 shows the domains of the WRF-NMM and the ocean domain of the POM for both typhoons at the initial time. In the WRF-NMM, the triple nested domains are set for the simulation of both typhoons. The parent domain (18 km resolution) covers an area of 80◦ × 80◦ and is fixed with time, while the two inner domains (6 and 2 km resolution, respectively) cover 12◦ × 12◦ and 7.1◦ × 7.1◦, respectively, and both are moving domains following the simulated TC. The model has 61 vertical levels, with the model top set to 2 hPa for the three domains. Mangkhut was simulated from 1200 UTC 10 September to 1200 UTC 15 September 2018, while Yutu was simulated from 0000 UTC 26 October to 0000 UTC 31 October 2018, the total simulation time for both typhoons was 120 h. The oceanic component of the HWRF model is a version of the POM adapted for TCs developed at Princeton University, called MPIPOM-TC. The MPIPOM-TC is run with approximately 9 km horizontal grid spacing and 23 vertical levels. In the part of interaction fluxes, the exchange of information between the atmospheric and oceanic components through the coupler with the frequency of information exchange of 9 min is considered for all of the forecast time. The WRF provides the POM with surface fluxes, including sensible heat flux, latent heat flux, and short-wave radiation, at the sea–air interface, while the POM provides the WRF with sea surface temperature during the simulations.

2.3. Data Description Due to the difference in the grids of the atmosphere and ocean components of the HWRF model, a generalized bilinear interpolation was used in this study. We used the NCEP Global Data Assimilation System (GDAS) Final Analysis for the initial and boundary conditions of the atmosphere model. The data have 0.25◦ × 0.25◦ spatial resolution, 31 vertical levels, and a 6 h time resolution. The ocean surface and ocean depth are initialized by the daily ﬁner Hybrid Coordinate Ocean Model (HYCOM) analysis with 1/12◦ resolution. The ocean temperature is then predicted by the MPIPOM-TC with the initial conditions from HYCOM and the boundary condition from EN4.2.0 monthly temperature. Note that monthly EN4.2.0 data with 1◦ resolution are linearly interpolated to daily data. Moreover, the spin-up time for adjusting the ocean temperature to properly simulate the typhoon in this study is 2 days. We used the typhoon observation data from the JTWC including the best track, the MSLP at the TC center, and the 10 m maximum wind speed (Vmax) of the TC (one-minute average maximum wind speed) to determine the track and intensity for both typhoons. Atmosphere 2021, 12, 1055 5 of 29

Moreover, similar to JTWC, the observation data were also taken from Japan Meteorological Agency (JMA), including the best track and Vmax (the average maximum wind speed in 10 minutes) for reference. In addition, the precipitation data used for veriﬁcation on the simulated rainfall near the northern Philippines were obtained from the Tropical Rainfall Measuring Mission (TRMM). We used the daily TRMM 3B42 rainfall with 0.25◦ × 0.25◦ spatial resolution. European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data with 0.25◦ × 0.25◦ spatial resolution were used to verify the simulated inner-core structure for Yutu.

2.4. Numerical Experiments To examine the influences of model physics on the prediction of the typhoon track and intensity, a number of physics sensitivity experiments were conducted and are shown in Table1. To investigate the sensitivity of typhoon simulation to microphysics schemes, the Ferrier–Aligo (FA) [37], WRF Single-Moment 6-class (WSM6) [38], new Thompson et al. (Thom) [39], Eta HWRF (EtaH), and Tropical-Ferrier (TF) schemes were used. The cumulus parameterization schemes, including the simplified Arakawa–Schubert (SAS) [40] and the Kain–Fritsch (KF) [41] schemes, were used to investigate the sensitivity of model simulations to cumulus parameterization. Both cumulus parameterization schemes have been widely applied in typhoon predictions. The Yonsei University (YSU) [42] and NCEP Global Forecast System (GFS) [43] schemes were chosen to investigate the sensitivity of model simulations to planetary boundary layer parameterization. The other model physics schemes were applied to simulations of both typhoons, including the Monin–Obukhov scheme [44] for surface flux calculations and the RRTMG scheme [45] for radiation effect, which includes diurnal variations and interactive effects of clouds. The details of physics schemes are in [35]. Note that this study is not oriented to offer a complete assessment of physics schemes currently available for simulations of TCs over the WNP. Sensitivities and thus predictability related to TC motions and structural changes are rather limited as only some popular physics schemes are selected in this study.

Table 1. Physics-sensitivity experiments for both supertyphoons approaching the northern Philippines.

Cloud Cumulus Experiments PBL Scheme Microphysics Parameterization FA Ferrier-Aligo Kain-Fritsch GFS WSM6 WSM6 Kain-Fritsch GFS KF–GFS Thom Thompson Kain-Fritsch GFS EtaH Eta HWRF Kain-Fritsch GFS TF Tropical-Ferrier Kain-Fritsch GFS FA Ferrier-Aligo Kain-Fritsch YSU WSM6 WSM6 Kain-Fritsch YSU KF–YSU Thom Thompson Kain-Fritsch YSU EtaH Eta HWRF Kain-Fritsch YSU TF Tropical-Ferrier Kain-Fritsch YSU FA Ferrier-Aligo SAS GFS WSM6 WSM6 SAS GFS SAS–GFS Thom Thompson SAS GFS EtaH Eta HWRF SAS GFS TF Tropical-Ferrier SAS GFS

Moreover, to examine the impacts of air–sea interactions, topographical effects, and initial ocean temperature changes, three sets of numerical experiments were conducted and are described in Table2. To compare their simulation differences resulting from the ocean- coupled effects, both coupled (CTL) and uncoupled (UN) experiments were performed. In the second set of numerical experiments for exploring the inﬂuence of the mountain terrain, the whole Philippines (5–19◦ N, 115–127◦ E) and Luzon Island (14–19◦ N, 119–123◦ E) are reset to plain in noPhi and noLu experiments, respectively, for comparison with the normal Atmosphere 2021, 12, 1055 6 of 29

terrain (CTL). The regions of the reset terrain are indicated by the red and blue boxes for noPhi and noLu experiments, respectively, in Figure2. In the third set of numerical experiments for investigating the inﬂuences of the initial ocean temperature change along the track for both typhoons, two sensitivity experiments were conducted. The regions of the ocean temperature change are indicated by the dashed rectangular box shown later in Figure 3a,b. Over these regions, the initial ocean temperature is modiﬁed by +1 ◦C (Tp1) and −1 ◦C (Tm1); the temperature within the whole depth of the ocean is also adjusted by the same amount.

Table 2. Numerical experiments for Supertyphoons Mangkhut and Yutu approaching the northern Philippines.

Ocean Temperature Experiments Include Terrain Air–Sea Coupled Changes CTL Yes No Yes UN Yes No No Tp1 Yes Increase by 1 ◦C Yes ◦ Atmosphere 2021, 12, x FOR PEER REVIEW Tm1 Yes Decrease by 1 C Yes 7 of 32 noPhi No (the Philippines) No Yes noLu No (Luzon) No Yes

FigureFigure 2. 2.PhilippinesPhilippines terrain terrain heights heights (shaded (shaded in inmeters) meters) and and the the surrounding surrounding areas. areas. Red Red and and blue blue boxesboxes indicate indicate the the regions regions where where the the associated associated terrain terrain heights heights are are reset reset to toplain plain in inthe the terrain terrain sensitivitysensitivity experiments. experiments. 2.5. PV Tendency Budget 2.5. PV Tendency Budget To analyze the dynamic process in the track change, Ertel’s PV tendency budget To analyze the dynamic process in the tracωa k change, Ertel’s PV tendency budget was was used. For Ertel’s PV deﬁned by q = ρ ·∇θv, the prognostic equation of PV can be used. For Ertel’s PV defined by 𝑞 = ∙𝛻𝜃 the prognostic equation of PV can be derived derived as , as ∂q ωa dθv ∇ρ × ∇p 1 = −U·∇q + ·∇ + ∇θv· + ∇θv·(∇ × Fr), (1) 𝜕𝑞∂t 𝝎𝒂ρ 𝑑𝜃dt𝑣 𝛻𝜌 ×𝛻𝑝ρ3 1 ρ = −𝑼 ∙𝛻𝑞+ ∙𝛻 +𝛻𝜃 ∙ + 𝛻𝜃 ∙ 𝛻 × 𝑭 ， (1) 𝜕𝑡 𝜌 𝑑𝑡 𝑣 𝜌3 𝜌 𝑣 𝒓

where 𝝎𝒂 =𝝎+2𝛀 (earth’s rotation vector 𝛀 and the relative vorticity vector 𝝎 = 𝛻 ×𝑼); 𝑼 is the 3D wind vector (u, v, w); ρ, p, and 𝜃𝑣 are density, pressure, and virtual potential temperature, respectively; and 𝑭𝒓 = 𝑭𝒓/𝜌 (𝑭𝒓 is the turbulent diffusion per unit mass in the momentum equation). The PV tendency budget terms (right-hand side) include the advection (both horizontal and vertical), differential diabatic heating, solenoidal effects, and turbulent diffusion effects. Wu and Wang [31] addressed a regression method to compute the east–west and north–south components of the typhoon translation speed given by 𝜕𝑞 𝜕𝑞 𝜕𝑞 𝜕𝑞 ∑𝑁 0 1 ∑𝑁 0 1 𝑖=1 𝜕𝑥 𝜕𝑡 𝑖=1 𝜕𝑦 𝜕𝑡 𝑖 𝑖 𝑖 𝑖 𝐶 = − ; 𝐶 = − , (2) 𝑥 𝜕𝑞 2 𝑦 𝜕𝑞 2 ∑𝑁 0 ∑𝑁 0 𝑖=1 𝜕𝑥 𝑖=1 𝜕𝑦 𝑖 𝑖 where q denotes PV and indices 0 and 1 represent the symmetric and WN-1 components, respectively, for any grid point i within a specified radius of the typhoon center. In this study, we chose a radius of 150 km (see [18]).

Atmosphere 2021, 12, 1055 7 of 29

where ωa = ω + 2Ω (earth’s rotation vector Ω and the relative vorticity vector ω = ∇ × U); U is the 3D wind vector (u, v, w); ρ, p, and θv are density, pressure, and virtual potential temperature, respectively; and Fr = Fer/ρ (Fer is the turbulent diffusion per unit mass in the momentum equation). The PV tendency budget terms (right-hand side) include the advection (both horizontal and vertical), differential diabatic heating, solenoidal effects, and turbulent diffusion effects. Wu and Wang [31] addressed a regression method to compute the east–west and north–south components of the typhoon translation speed given by N ∂q0 ∂q1 N ∂q0 ∂q1 ∑i=1 ∂x ∂t ∑i=1 ∂y ∂t C = − i i ; C = − i i , (2) x 2 y 2 N ∂q0 N ∂q0 ∑i= ∑i= 1 ∂x i 1 ∂y i where q denotes PV and indices 0 and 1 represent the symmetric and WN-1 components, respectively, for any grid point i within a speciﬁed radius of the typhoon center. In this study, we chose a radius of 150 km (see [18]).

3. Simulation Results of Sensitivity Experiments 3.1. Sensitivity to Physics Schemes The use of physical parameterization also affects the results of simulated a tropical- like cyclone in the Mediterranean Sea that have been shown in previous studies with an ocean-coupled model (e.g., [46–48]). Therefore, to investigate the impacts of different physics schemes on the prediction of the typhoon track and intensity for both typhoons moving toward the northern Philippines, a total of 15 ocean-coupled experiments with different cloud-microphysics, cumulus parameterization, and planetary boundary layer parameterization schemes were conducted. The simulated figures for these physics sensitivity experiments are shown in AppendixA. The simulation results indicate that all the experiments can capture the northward track deflection of Mangkhut near the northern Philippines; however, the simulated northward deflection appears somewhat earlier than the best track data from the JTWC. Mangkhut has a relatively greater sensitivity for simulation of typhoon intensity in the physics sensitivity experiments. For Yutu, the simulated tracks become largely diverged about 1 day after the model initial time, while the simulated typhoon intensities, in general, show similar development trends consistent with the best track intensity from the JTWC. The thing worth noting here is that all the sensitivity experiments fail to capture the slightly westward track of Yutu near the northern Philippines, regardless of the combination of physics schemes. In general, Mangkhut at a deepening stage has a smaller track sensitivity to the use of different physics schemes but a greater intensity sensitivity, which becomes reversed for Yutu at a weakening stage. As a result, the combination of SAS cumulus parameterization and GFS planetary boundary layer parameterization schemes performs better in the track simulation of Mangkhut, and the TF cloud-microphysics scheme performed better in the typhoon intensity simulation. However, the combination of SAS–GFS and TF cloud-microphysics schemes show a significantly northward track deflection at the later forecast and cannot capture a landfalling typhoon. The combination of the SAS–GFS and EtaH cloud-microphysics schemes not only captures a landfalling typhoon but also simulates a relatively strong maximum wind speed that lies between the best track maximum wind speeds of the JMA and the JTWC. Therefore, in the following section, we analyze the results from the simulation with the combination of SAS–GFS and the EtaH cloud-microphysics schemes for Mangkhut, which is denoted as the control experiment (CTL). For Yutu, the combination of TF cloud-microphysics, KF cumulus parameterization, and GFS planetary boundary layer parameterization schemes appear to better reproduce both the typhoon track and the intensity, which is thus used as the control experiment (CTL) for later analyses. Atmosphere 2021, 12, 1055 8 of 29

3.2. Ocean Condition and SST Cooling Ocean conditions such as temperature changes in the ocean surface and upper mixed layer are important for affecting TC translation and intensity development (e.g., [13,14]). The initial SST from HYCOM analysis and the SST differences between 96 h and the initial time for Mangkhut and Yutu are shown in Figure3. Both typhoons are generated over the warm ocean at about 29.5 ◦C at the initial time. The SST east of the typhoon center for Yutu is about 3 ◦C cooler than for Mangkhut (Figure3a,b). The SST rightward of the typhoon track (above 20◦ N north of the typhoon) is warmer for Mangkhut (about 28.5 ◦C) than for Yutu (about 27.5 ◦C) because Mangkhut formed at an earlier time as compared to Yutu. One note here is that on the second and third days, Yutu will enter into slightly cooler regions (27.5 ◦C) from 136◦ E. That is one of the reasons for the decreasing trend of typhoon intensity at the later forecast for Yutu. The evolved SST at 96 h is cooler over the Atmosphere 2021, 12, x FOR PEER REVIEW 9 of 32 regions rightward of the typhoon track for both typhoons. The maximum SST cooling for Mangkhut only reaches about 1.5 ◦C, while it increases to about 4.5 ◦C for Yutu. The SST cooling for Yutu is considerably stronger than for Mangkhut since Yutu moves at a slower translation speedtranslation compared speed to compared Mangkhut. to Mangkhut.Therefore, Yutu Therefore, also has Yutu more also residence has more time residence time to to enhance theenhance cooling the of coolingthe ocean of thetemperat oceanure temperature due to upwelling due to upwelling and turbulent and turbulentmixing mixing in in the ocean mixedthe ocean layer. mixed layer.

Figure 3. InitialFigure sea surface3. Initial temperature sea surface temperature for (a) MK-CTL for (a at) MK-CTL 1200 UTC at 101200 September UTC 10 September 2018 and (2018b) YT-CTL and (b) atYT- 0000 UTC 26 October 2018.CTL SST at 0000 difference UTC (shaded26 October colors 2018. in SST◦C) betweendifference 96 (shade h andd the colors initial in time °C) between for (c) MK-CTL 96 h and and the (d initial) YT-CTL. The dashed and solidtime lines for (c show) MK-CTL the simulated and (d) YT-CTL. tracks of The the CTLdashed and and the solid best tracklines fromshow thetheJTWC, simulated respectively. tracks of Solidthe circles in the tracks indicateCTL and the the typhoon best track centers from the at an JTWC, interval respectively. of 24 h. The Solid dashed circles box in the indicates tracks theindicate oceanic the regiontyphoon where the associated temperaturecenters at changedan interval in theof ocean–temperature24 h. The dashed box sensitivity indicates experiments. the oceanic region where the associated temperature changed in the ocean–temperature sensitivity experiments. 3.3. Sensitivity to Air–Sea Coupling 3.3. Sensitivity to Air–Sea Coupling Typhoon motion and intensity may vary under different air–sea interaction processes Typhoon motion and intensity may vary under different air–sea interaction processes and energy supplies from the ocean. The simulated typhoon track and intensity (Vmax) and energy suppliesobtained from from the coupled ocean. (CTL) The simulated and uncoupled typhoon (UN) track experiments, and intensity the (V bestmax) tracks from obtained fromboth coupled the JTWC (CTL) and and the uncoupled JMA, and (UN) the typhoon experiments, tracks the from best several tracks operational from both forecasts for the JTWC andMangkhut the JMA, and and Yutu the are typhoon presented tracks in Figure from4 .several For Mangkhut, operational both forecasts experiments for can capture Mangkhut andthe northwardYutu are presented track deﬂection in Figure when 4. the For typhoon Mangkhut, moves both closer experiments to the northern can Philippines; capture the northwardhowever, the track time deflection of track deviation when the is somewhattyphoon moves earlier closer as compared to the tonorthern the best tracks from Philippines; however, the time of track deviation is somewhat earlier as compared to the best tracks from the JTWC. A comparison between CTL and UN shows that Mangkhut is slightly sensitive to the ocean temperature variation during the simulation (Figure 4a). Further comparison on the simulated tracks for Mangkhut shows that the uncoupled (MK-UN) experiment indeed gives better performance than the coupled (MK-CTL) experiment near offshore of the northern Philippines and successfully captures the landfall position compared to the best track from the JTWC. For Yutu, the results show that the track deviation is not significant between YT-CTL and YT-UN near landfall. Both simulated tracks for Yutu moves northwestward after 72 h, while the observed typhoon mainly moves westward. There are noted track deviations from the best track after 72 h for both YT-CTL and YT-UN. It is clearly seen that all of the operational forecasts tend to produce northward-biased tracks without landfall for Mangkhut (Figure 4e). Moreover, most operational forecasts show a northward track deflection near offshore of the northern Philippines without landfall for Yutu (Figure 4f). The track performances of HWRF for both typhoons thus are reasonable and acceptable.

Atmosphere 2021, 12, 1055 9 of 29

Atmosphere 2021, 12, x FOR PEER REVIEW 10 of 32 the JTWC. A comparison between CTL and UN shows that Mangkhut is slightly sensitive to the ocean temperature variation during the simulation (Figure4a). Further comparison on the simulated tracks for Mangkhut shows that the uncoupled (MK-UN) experiment indeedFor gives typhoon better intensity, performance it clearly than shows the coupled that during (MK-CTL) the northward experiment track near deflection offshore ofstage the (48–96 northern h) of Philippines Mangkhut, and Vmax successfully is maintained captures at about the 110–125 landfall kt, positionwhile it is compared only about to the80–90 best kt trackfor Yutu. from Therefore, the JTWC. Mangkhut For Yutu, evolve the resultss as a showstronger that cyclone the track when deviation closing isin notto signiﬁcantthe northern between Philippines YT-CTL as and compared YT-UN nearto Yutu. landfall. For Boththe simulatedcoupled and tracks uncoupled for Yutu movesexperiments, northwestward the simulated after 72intensity h, while for the UN observed is stronger typhoon than mainly that for moves CTL westward.for both Theretyphoons are noted(Figure track 4c,d). deviations This is mainly from the due best to the track TC–ocean after 72 hinteraction for both YT-CTL that tends and to YT-UN. cool Itdown is clearly the SST seen along that the all typhoon of the operational track in both forecasts CTL experiments tend to produce through northward-biased the processes of tracksupwelling without and landfallvertical mixing for Mangkhut in the oceanic (Figure mixing4e). Moreover, layer (e.g., most [8,49–51]). operational However, forecasts the showsimulated a northward typhoon intensity track deﬂection for MK-UN near is offshore closer to of the the best northern track intensity Philippines (Vmax without) from landfallthe JTWC for than Yutu that (Figure for 4CTL.f). The Near track landfall performances after 90 ofh HWRFfor Yutu, for the both simulated typhoons typhoon thus are reasonableintensity for and UN, acceptable. however, is more over-predicted than for CTL.

FigureFigure 4. 4.( a()a) Simulated Simulated tracks tracks ofof MangkhutMangkhut forfor thethe coupledcoupled (CTL)(CTL) (red line) and uncoupled (UN) (UN) (blue (blue line) line) experiments experiments andand the the best best tracks tracks ofof thethe JTWCJTWC (solid bl blackack line) line) and and the the JMA JMA (dashed (dashed black black line). line). (b) ( bis) as is asin in(a) (buta) but for forYutu. Yutu. Circles Circles in in(a ()a )and and (b ()b mark) mark the the typhoon typhoon centers centers every every 24 24h, associat h, associateded with with the thenumbers numbers indicating indicating the UTC the UTC time. time. (c) and (c) ( andd) are (d )the are thesame same as in as ( ina) and (a) and (b) but (b) for but the for time the timeevolution evolution of 10 m of maximum 10 m maximum wind speed wind (kt), speed respectively. (kt), respectively. Track forecasts Track forecasts at several at severaloperational operational centers centers (from http (froms://www.typhoon2000.ph/multi/ https://www.typhoon2000.ph/multi/ acessed onaccessed 14 August on 10 2021) August for ( 2021)e) Mangkhut for (e) Mangkhut and (f) Yutu. and The simulated time is for Mangkhut from 1200 UTC 10 September to 1200 UTC 15 September 2018, while it is for Yutu (f) Yutu. The simulated time is for Mangkhut from 1200 UTC 10 September to 1200 UTC 15 September 2018, while it is for from 0000 UTC 26 October to 0000 UTC 31 October. Yutu from 0000 UTC 26 October to 0000 UTC 31 October.

Atmosphere 2021, 12, 1055 10 of 29

For typhoon intensity, it clearly shows that during the northward track deﬂection stage (48–96 h) of Mangkhut, Vmax is maintained at about 110–125 kt, while it is only about 80–90 kt for Yutu. Therefore, Mangkhut evolves as a stronger cyclone when closing in to the northern Philippines as compared to Yutu. For the coupled and uncoupled experiments, the simulated intensity for UN is stronger than that for CTL for both typhoons (Figure4c,d) . This is mainly due to the TC–ocean interaction that tends to cool down the SST along the typhoon track in both CTL experiments through the processes of upwelling and vertical mixing in the oceanic mixing layer (e.g., [8,49–51]). However, the simulated typhoon intensity for MK-UN is closer to the best track intensity (Vmax) from the JTWC than that for CTL. Near landfall after 90 h for Yutu, the simulated typhoon intensity for UN, however, is more over-predicted than for CTL.

3.4. Sensitivity to Initial Ocean Temperature and Philippines Terrain To explore the influences of the initial ocean temperature and the Philippines and Luzon terrain on the evolution of both typhoons, several sensitivity experiments were conducted and are listed in Table2. The regions with the Philippines and Luzon terrain reset and ocean temperature change are shown in Figures2 and3, respectively. Figure5 shows the simulated track for CTL, the track in response to the change in initial ocean temperature in the experiments, Tp1 (1 ◦C increase) and Tm1 (1 ◦C decrease), and the tracks for the experiments with the mountain reset to plain, noPhi (without Philippines terrain), and noLu (without Luzon terrain) for both typhoons. All CTL, Tp1, and Tm1 closely simulate the track on the first two days (for Mangkhut) or the first three days (for Yutu) and then exhibit northward track deviations for both typhoons (Figure5a,b). The biased tracks near landfall in the northern Philippines are only slightly modified by the initial ocean temperature change along with the typhoon track for Mangkhut, where MK-Tm1 (Tm1 experiment for Mangkhut) slightly reduces the northward deviation near landfall. However, the northward track deviation after 48 h is still evident for both the initial ocean temperature change experiments, MK-Tp1 and MK-Tm1. Similar to Mangkhut, the simulated track for YT-Tm1 (Tm1 experiment for Yutu) slightly reduces the northward deviation, while it shows a more northward deviation for YT-Tp1 as compared to YT-CTL. Note that the northward track deflection after 72 h is still significant but closer to YT-CTL for YT-Tm1. The simulated typhoon intensity in terms of the MSLP and Vmax for Yutu is consistent with the modified initial ocean temperature so that the typhoon intensity for YT-Tp1 is stronger than for YT-CTL and the typhoon intensity for YT-Tm1 is weaker than for YT-CTL (Figure5d). However, it is mixed and not clear for Mangkhut (Figure5c). It can be due to the reason that Yutu moves at a slower translation speed compared to Mangkhut to undergo more exchanges with the ocean surface. When the Philippines and Luzon terrains are reset to plains, the northward track deflection slightly improves, in particular for MK-noLu (Figure5a). Compared to MK-CTL, the experiment without the Luzon terrain (MK-noLu) slightly reduces the northward deviation near offshore of the northern Philippines. In addition, MK-noPhi (noPhi experiment for Mangkhut) gives a somewhat more northward deviation near offshore of the northern Philippines compared to MK-CTL. As a result, the Philippines terrain and the Luzon terrain are not as important for simulating the track of Mangkhut. Similar no-terrain experiments for Yutu also relatively improve the track prediction by reducing the northward deviation near offshore of northern Philippines after 72 h. For YT-noPhi (noPhi experiment for Yutu), the simulated track shifts more southward, with a reduced northward deflection near landfall than that for YT-noLu (noLu experiment for Yutu). Thus, the track deflection near landfall for Mangkhut is only slightly sensitive to the Philippines terrain and the Luzon terrain, but it is relatively more sensitive for Yutu. Note that the topography of the Philippines generally consists of lowlands and low mountains (lower than 1000 m), except for the highest mountain at the western part of Luzon (about 2000 m) (Figure2). The simulated and observed tracks are closer to the northern end of the Philippines for Mangkhut, in better agreement than those for Yutu; thus the latter tracks are relatively Atmosphere 2021, 12, x FOR PEER REVIEW 11 of 32

3.4. Sensitivity to Initial Ocean Temperature and Philippines Terrain To explore the influences of the initial ocean temperature and the Philippines and Luzon terrain on the evolution of both typhoons, several sensitivity experiments were conducted and are listed in Table 2. The regions with the Philippines and Luzon terrain reset and ocean temperature change are shown in Figures 2 and 3, respectively. Figure 5 shows the simulated track for CTL, the track in response to the change in initial ocean temperature in the experiments, Tp1 (1 °C increase) and Tm1 (1 °C decrease), and the tracks for the experiments with the mountain reset to plain, noPhi (without Philippines terrain), and noLu (without Luzon terrain) for both typhoons. All CTL, Tp1, and Tm1 closely simulate the track on the first two days (for Mangkhut) or the first three days (for Yutu) and then exhibit northward track deviations for both typhoons (Figure 5a,b). The biased tracks near landfall in the northern Philippines are only slightly modified by the Atmosphere 2021, 12, 1055 initial ocean temperature change along with the typhoon track for Mangkhut, where11 MK- of 29 Tm1 (Tm1 experiment for Mangkhut) slightly reduces the northward deviation near landfall. However, the northward track deviation after 48 h is still evident for both the initial ocean temperature change experiments, MK-Tp1 and MK-Tm1. Similar to more inﬂuenced by the topographic effects. It may also be partially due to the fact that Mangkhut, the simulated track for YT-Tm1 (Tm1 experiment for Yutu) slightly reduces the simulated Yutu near landfall is considerably weaker than Mangkhut. Nevertheless, the northward deviation, while it shows a more northward deviation for YT-Tp1 as the topographic effects of both the Philippines and Luzon on the track simulations of both compared to YT-CTL. Note that the northward track deflection after 72 h is still significant typhoons are not signiﬁcant but can produce about a 200 km difference in the landfall but closer to YT-CTL for YT-Tm1. The simulated typhoon intensity in terms of the MSLP positions for Yutu. In contrast, the simulated typhoon intensity in terms of the MSLP and and Vmax for Yutu is consistent with the modified initial ocean temperature so that the Vmax for noPhi and noLu is similar to that of CTL before making landfall for both typhoons typhoon intensity for YT-Tp1 is stronger than for YT-CTL and the typhoon intensity for as shown in Figure5c,d. The weakening degree of the typhoon intensity near and after YT-Tm1landfall byis weaker the topographic than for effectsYT-CTL is less(Figure than 5d). that However, by 1 ◦C decrease it is mixed in SST and as not seen clear in Tm1. for

MangkhutAll the experiments (Figure 5c). in thisIt can study be due cannot to the reproducereason that theYutu observed moves intensity at a slower spike translation near 84 h speedat that compared time because to Mangkhut the simulated to undergo tracks take more an exchanges increasing with deviation the ocean from surface. the best track.

Figure 5. Simulated tracks for (a) Mangkhut from 1200 UTC 10 September to 1200 UTC 15 September and ( b) Yutu from 0000 UTC 26 October to 0000 UTC 31 October. ( (cc)) and and ( (d)) are the same as in ( a) and (b), respectively, but for thethe timetime evolution for 10 m maximum wind speed (kt). The best track (b (blacklack line) is from the JTWC. The simulated tracks for CTL, Tp1, Tm1, noPhi, and noLunoLu areare shownshown byby red,red, green,green, magenta,magenta, cyan,cyan, andand yellowyellow lines,lines, respectively.respectively.

Figure6 shows the along-track vertical cross section of the ocean temperature averaged in a 200 km radius of the typhoon center at the initial time and the sea surface temperature at 96 h for MK-CTL, MK-Tp1, and MK-Tm1. The ocean temperature along the TC track ◦ ◦ from 125 E to 135 E is warmer (or colder) than MK-CTL in MK-Tp1 (or MK-Tm1) due to the modified initial ocean temperature at the initial time. Moreover, the ocean depth ◦ of 26 C isotherm (Z26) is deeper for MK-Tp1 than that for MK-CTL in the region with changing ocean temperature, indicating that MK-Tp1 is associated with a thicker ocean layer in this region, which becomes reversed for MK-Tm1. After 96 h, the reduction in ◦ SST for MK-Tp1 is about 1 C weaker than that for MK-CTL as a result of deeper Z26 of MK-Tp1 at the initial time, while it is an about 1 ◦C stronger SST cooling for MK-Tm1. The results for Yutu are consistent with those for Mangkhut, while the reduction in SST for YT-CTL, YT-Tp1, and YT-Tm1 is stronger than that for Mangkhut (figures not shown). As a result, the simulated typhoon intensity for Yutu is consistent with the modified initial ocean temperature. The typhoon intensity for YT-Tp1 is stronger than that for YT-CTL, but the typhoon intensity for Tm1 is weaker than that for YT-CTL. However, it is mixed and not clear for Mangkhut. AtmosphereAtmosphere2021 2021,,12 12,, 1055x FOR PEER REVIEW 1312 of 2932

FigureFigure 6.6. Along-track vertical crosscross sectionsection ofof oceanocean temperaturetemperature averagedaveraged inin aa 200200 kmkm radiusradius ofof thethe typhoontyphoon centercenter atat thethe initialinitial timetime (shaded(shaded colorscolors inin ◦°C)C) for (a)) MK-CTL,MK-CTL, ((c) MK-Tp1,MK-Tp1, and (e) MK-Tm1. ( b), ( d), and (f) are the same as in (a), (c), andand (e),), respectively, but for for the the sea sea surface surface temper temperatureature at at 96 96 h h(1200 (1200 UTC UTC 14 14September). September). In ( Ina), ( (ac),), (andc), and (e), (thee), theblack black line linerepresents represents the the26 °C 26 ◦isotherm.C isotherm. The The dashed dashed and and solid solid lines lines in in(b (),b ),(d (),d ),and and (f ()f )show show the the best best track track from from the JTWCJTWC andand simulated track, respectively. simulated track, respectively.

3.5. Sensitivity to Initial Time The simulated typhoon tracks in previousprevious experimentsexperiments exhibitexhibit increasingincreasing deﬂectiondeflection after 48 h for MangkhutMangkhut and 7272 hh forfor Yutu,Yutu, whichwhich causescauses largerlarger tracktrack deviationsdeviations fromfrom thethe observed tracks.tracks. ToTo understand understand whether whether such such track track deviations deviations for Mangkhutfor Mangkhut and Yutuand Yutu may alsomay bealso related be related to the modelto the initializationmodel initialization time, we conductedtime, we conducte experimentsd experiments for sensitivity for tosensitivity the model to initializationthe model initialization time. The experimentstime. The experiments were initialized were initialized at 12, 24 and at 12, 36 h24 after and the36 h initial after timethe initial of MK-CTL time of for MK-CTL Mangkhut for Mangkhut and at 12, 24,and 36 at and 12, 4824, h 36 after and the 48 initialh after time the ofinitial YT-CTL time forof YT-CTL Yutu. The for simulated Yutu. The tracks simula fromted tracks these experimentsfrom these experiments are shown inareFigure shown7 . Forin Figure Mangkhut, 7. For all Mangkhut, the experiments all the fail experime to producents fail large to northward-biased produce large northward-biased tracks after 48 h (1200tracks UTC after 12 48 September) h (1200 UTC before 12 September) 84 h (0000 before UTC 14 84 September) h (0000 UTC compared 14 September) to the bestcompared tracks fromto the the best JMA tracks and from the JTWCthe JMA (Figure and 7thea). JTWC Similar (Figure to Mangkhut, 7a). Similar all theto Mangkhut, experiments all forthe Yutuexperiments also fail for to capture Yutu also the westwardfail to capture track the after westward 72 h (0000 track UTC after 29 October). 72 h (0000 For theUTC later 29 threeOctober). initial For times, the later the experiments three initial eventimes, show the northwestwardexperiments even tracks show without northwestward a landfall fortracks Yutu without at later a landfall forecast for times. YutuThus, at later the forecast track times. simulation Thus, of the Yutu track is simulation more sensitive of Yutu to

Atmosphere 2021Atmosphere, 12, x FOR 2021PEER, 12 REVIEW, 1055 14 of 32 13 of 29

is more sensitivethe modelto the model initialization initialization time. Thetime. track The deviationtrack deviation and sensitivity and sensitivity also beginalso to increase begin to increaseat about at about the time the time when when the CTL the CTL track track begins begins to deviate to deviate from from the best the track.best It remains track. It remainschallenging challenging to accurately to accurately capturing capturing the trackthe track deﬂection deflection of Yutu of Yutu given given a long a path in the long path in thecomplicated complicated environment. environment.

Figure 7. (a) SimulatedFigure 7. ( tracksa) Simulated for Mangkhut tracks for at 1200Mangkhut UTC 10 at September 1200 UTC (CTL,10 September red), 0000 (CTL, UTC red), 11 September 0000 UTC (1100, 11 blue), 1200 UTC 11 SeptemberSeptember (1112,(1100, green),blue), 1200 and UTC 0000 11 UTC September 12 on September (1112, green), (1200, and magenta), 0000 UTC as well12 on as September the best track (1200, of the JMA (solid black) andmagenta), the JTWC as well (dashed as the black). best (trackb) Simulated of the JMA tracks (solid for Yutublack) at and 0000 the UTC JTWC 26 October (dashed (CTL, black). red), (b 1200) UTC 26 October (2612,Simulated blue), 0000tracks UTC for 27 Yutu October at 0000 (2700, UTC green), 26 Octobe 1200 UTCr (CTL, 27 Octoberred), 1200 (2712, UTC magenta), 26 October and (2612, 0000 UTC blue), 28 October 0000 UTC 27 October (2700, green), 1200 UTC 27 October (2712, magenta), and 0000 UTC 28 October (2800, cyan), as well as the best track of the JMA (solid black) and the JTWC (dashed black). Circles mark typhoon centers (2800, cyan), as well as the best track of the JMA (solid black) and the JTWC (dashed black). Circles every 12 h. mark typhoon centers every 12 h. 4. Coupled Simulations and Track Deflection 4. Coupled Simulations4.1. Simulated and Precipitation Track Deflection 4.1. Simulated PrecipitationAlthough all sensitivity experiments did not completely capture the entire tracks Althoughof all Mangkhut sensitivity and experiments Yutu after did 48 and not 72completely h, respectively, capture it the is of entire great tracks interest of to show the Mangkhut andsimulated Yutu after rainfall 48 and of both 72 h, typhoons respectively, with theit is northwardof great interest track deflection to show the that reveals the simulated rainfallactivity of ofboth cloud typhoons convection. with The the couplednorthward simulations track deflection of CTL were that usedreveals for the presentation activity of cloudof both convection. typhoons. The coupled simulations of CTL were used for the presentation of bothFigure typhoons.8 shows the simulated 48 h accumulated rainfall of CTL for Mangkhut (from Figure 80000 shows UTC the 13 simulated to 0000 UTC 48 h 15accumulated September rainfall 2018) and of CTL Yutu for (from Mangkhut 0000 UTC (from 29 to 0000 UTC 0000 UTC 13 31to October0000 UTC 2018). 15 September The rainfall 2018) observation and Yutu (from for verification 0000 UTC is29 the to daily0000 UTC Tropical Rainfall 31 October 2018).Measuring The rainfall Mission observation (TRMM) 3B42 for rainfallverification data duringis the daily the same Tropical simulation Rainfall time. For both Measuring Missionsimulation (TRMM) and 3B42 observation, rainfall data major during rainfall the is same mainly simulation distributed time. over For theboth mountainous simulation andregions observation, of the northern major rainfall Philippines. is mainly However, distributed differences over the in precipitationmountainous between the regions of thesimulation northern resultsPhilippines. and the However, TRMM aredifferences still noted, in particularlyprecipitation for between Yutu with the a northward-

Atmosphere 2021, 12, x FOR PEER REVIEW 15 of 32

Atmosphere 2021, 12, 1055 14 of 29

simulation results and the TRMM are still noted, particularly for Yutu with a northward- biasedbiased track. track. The The observation observation rainfall rainfall covers covers an an area area north north of of 12° 12 ◦NN for for Mangkhut Mangkhut and and northnorth of of 14° 14 ◦NN for for Yutu, Yutu, which which is isconsiderably considerably different different from from the the simulated simulated rainfall rainfall mainly mainly concentratedconcentrated north north of of 15° 15 ◦NN for for Mangkhut Mangkhut and and north north of of 16° 16 ◦NN for for Yutu. Yutu. The The agreement agreement withwith the the observation observation appearsappears toto be be better better for for Mangkhut Mangkhut with with a less a less northward-biased northward-biased track. track.Moreover, Moreover, maximum maximum precipitation precipitation from thefrom simulations the simulations is stronger is stronger than that than observed that observedfor both for typhoons. both typhoons. The maximum The maximum simulated simulated precipitation precipitation is larger is than larger 450 than mm 450 for mm both fortyphoons, both typhoons, while thewhile maximum the maximum observations observations are about are about 200–300 200–300 mm mm for Mangkhutfor Mangkhut and and150–200 150–200 mm mm for for Yutu. Yutu. We We note note that that the the simulated simulated typhoontyphoon forfor Yutu moves at at a a slower slower translationtranslation speed speed compared compared to to the the observed observed typhoon. typhoon. Thus, Thus, the the simulated simulated Yutu Yutu has has a a longerlonger residence residence time time to to accumulate accumulate precipitation precipitation over over the the Philippines. Philippines. The The simulated simulated accumulatedaccumulated rainfall rainfall also also indicates indicates the themajor major region region of latent of latent heating heating associated associated with both with typhoonsboth typhoons near and near during and during landfall landfall in the in thenorthern northern Philippines. Philippines. Except Except for for the the local local topographicallytopographically enhanced enhanced rainfall rainfall over over the the hi highergher northwestern northwestern Luzon Luzon (see (see Figure Figure 2),2), the the generalgeneral features features of of main main rainfall rainfall near the northern PhilippinesPhilippines areare only only slightly slightly modiﬁed modified in inthe the terrain terrain sensitivity sensitivity experiments experiments for for both both typhoons typhoons (ﬁgures (figures not not shown). shown).

FigureFigure 8. 8. TheThe 48 48h accumulated h accumulated rainfall rainfall (color (color shaded shaded in mm) in mm) (from (from 0000 0000 UTC UTC 13 to 13 0000 to 0000 UTC UTC 15 September15 September 2018) 2018) for ( fora) MK-CTL (a) MK-CTL and and (c) (observationc) observation rainfall rainfall from from TRMM_3B42_Daily. TRMM_3B42_Daily. (b ()b and) and (d (d) ) areare the the same same as as in in ( a) and ((cc),), respectively,respectively, but but for for Yutu Yutu from from 0000 0000 UTC UTC 29 to29 0000 to 0000 UTC UTC 31 October 31 October 2018. 2018.

Atmosphere 2021, 12, 1055 15 of 29

Atmosphere 2021, 12, x FOR PEER REVIEW 16 of 32

4.2. Typhoon Circulation and Convection

4.2. TyphoonThe typhoon Circulation translation and Convection and convection can be further illustrated through the evolution of the simulated horizontal wind and vertical velocity. Simulated typhoon circulations The typhoon translation and convection can be further illustrated through the evolutionaveraged of in the 1–8 simulated km at 48, horizontal 60 and 93 wind h for and Mangkhut vertical velocity. are shown Simulated in Figure typhoon9. As the simu- circulationslated typhoon averaged track in starts 1–8 km to exhibitat 48, 60 aand northward 93 h for Mangkhut deviation are compared shown in to Figure the JTWC’s 9. best Astrack the atsimulated 48 h, a strongertyphoon track southeasterly starts to ex windhibit appearsa northward north-northeast deviation compared of the typhoonto the center JTWC’s(Figure best9a) associatedtrack at 48 h, with a stronger the relatively southeasterly more symmetricalwind appears vertical north-northeast motions of (Figure the 9b) in typhoonconsistency center with (Figure the west-northwestward 9a) associated with the translation relatively of more the typhoon symmetrical at this vertical time. Note that motionsthe strong (Figure updrafts 9b) in are consistency associated with with the intense west-northwestward latent heating (ﬁgurestranslation not of shown). the When typhoonthe typhoon at this moves time. Note closer that to the strong east coast updrafts of the are Philippines associated with (at 60 intense h), the latent southeasterly heatingwind northeast (figures not of theshown). typhoon When center the istyphoon about 10moves m s− closer1 stronger to the than east that coast at 48of hthe(Figure 9c) . PhilippinesStronger upward (at 60 h), motions the southeasterly are mainly wind induced northeast in the of southeastthe typhoon quadrant center is ofabout the inner10 vortex −1 m(Figure s stronger9d).The than induced that at 48 ﬂow h (Figure asymmetry 9c). Stronger dominated upward by motions the enhanced are mainly southeasterly induced wind in the southeast quadrant of the inner vortex (Figure 9d). The induced flow asymmetry at this time tends to drive the typhoon more northward. When closing in to landfall, the dominated by the enhanced southeasterly wind at this time tends to drive the typhoon morehigh-wind-speed northward. When zones closing remain in nearto landfall, north-northeast the high-wind-speed of the inner zones vortex remain to inducenear a more north-northeastwestward but of slightly the inner northward vortex to componentinduce a more in westward typhoon but translation slightly northward at 93 h (Figure 9e), componentbut now with in typhoon stronger translation upward motionsat 93 h (F mainlyigure 9e), shifted but now to the with south stronger of the upward typhoon center motions(Figure mainly9f). shifted to the south of the typhoon center (Figure 9f).

Figure 9. The simulated horizontal wind speed (shaded colors in m s−1) averaged in 1–8 km height for MK-CTL at (a) 48 h, (c) 60 h, and (e) 93 h. (b), (d), and (f) as in (a), (c), and (e), respectively, but − − for vertical velocity (shaded colors in 10 1 m s 1). The black line shows the simulated track with solid cycles at an interval of 6 h. Atmosphere 2021, 12, 1055 16 of 29

Figure 10 shows the simulated horizontal wind and vertical velocity averaged in 1–8 km height at 72, 84 and 96 h for YT-CTL. It is clearly shown that both horizontal wind speed and upward motions for Yutu are considerably weaker than Mangkhut as shown in Figure 10. At 72 h, Yutu continues to move west-southwestward but later with a signiﬁcantly northward-biased track compared to the JTWC’s best track. At this time, the stronger northeasterly wind northwest of the typhoon center (Figure 10a) and the relatively symmetric strong upward motions (Figure 10b) are consistent with the west-southwestward movement of Yutu. When Yutu moves closer to the northern Philippines (at 84 h), the high-wind-speed region of the vortex core has rotated clockwise to the northeast of the typhoon center (Figure 10c) to give a stronger southeasterly wind and upward motions (Figure 10d). As a result, Yutu at this time tends to move west-northwestward. Near landfall, the high-wind-speed regions continue to prevail north-northeast of the inner vortex at 96 h, and the wind speed is slightly stronger (about 5 m s−1) than that at 84 h (Figure 10e). The stronger upward motions at this time are mainly present at the northern quadrant of the typhoon (Figure 10f). Note that the CTL tracks for both typhoons are quite similar prior to landfall at nearly the same location. However, the inner core with a more westward movement for Mangkhut is stronger at a smaller size when compared to Atmosphere 2021, 12, x FOR PEER REVIEW 18 of 32 the weaker Yutu with a west-northwestward movement. Consequently, their associated circulations and upward motions exhibit quite different features when closer to landfall.

FigureFigure 10. 10. TheThe simulated simulated horizontal horizontal wind wind speed speed (shaded (shaded colors in colors m s−1) in averaged m s−1) in averaged 1–8 km height in 1–8 km forheight YT-CTL for YT-CTLat (a) 72at h,( a(c)) 7284 h,h, (cand) 84 (e h,) 96 and h. (e(b)), 96 (d h.), (andb), ((df)), are and the (f )same are the as samein (a), as(c), in and (a), ( (ec),), and −1 −1 respectively,(e), respectively, but for but vertical for vertical velocity velocity (shaded (shaded colors in colors10 m s in). 10 The−1 blackm s− 1line). The shows black the linesimulated shows the track with solid cycles at an interval of 6 h. simulated track with solid cycles at an interval of 6 h. To further understand the relation between the typhoon circulation and track change for both typhoons, the time evolutions of the simulated horizontal wind speed, vertical velocity, and diabatic heating in the inner core with respect to azimuthal variations during the entire simulations are shown in Figure 11. For Mangkhut, the stronger wind speed is present mainly north of the typhoon center (Figure 11a) before northward deflection, with the stronger upward motion and diabatic heating to the south. As the typhoon moves closer to the east coast, the speedy core region rotates clockwise to the northeast of the typhoon center and the wind speed becomes much stronger (about 60–70 m s−1) (Figure 11a). Note that stronger upward motion and diabatic heating also rotate clockwise roughly to the southwest quadrant (Figure 11b,c). For Yutu, horizontal wind speed, vertical velocity, and diabatic heating all are much weaker than for Mangkhut in the deflection period (enclosed by the two dashed lines). In response to the northward deflection, horizontal wind speed, upward motion, and diabatic heating for Yutu are in phase and stronger at the northeast quadrant (also see Figure 10). The induced flow asymmetry dominated by the enhanced southeasterly wind tends to drive the vortex more northwestward for Yutu. The Hovmöller plots have identified large differences in the flow asymmetry and convection of both typhoons over the open ocean and near the northern Philippines.

Atmosphere 2021, 12, 1055 17 of 29

To further understand the relation between the typhoon circulation and track change for both typhoons, the time evolutions of the simulated horizontal wind speed, vertical velocity, and diabatic heating in the inner core with respect to azimuthal variations during the entire simulations are shown in Figure 11. For Mangkhut, the stronger wind speed is present mainly north of the typhoon center (Figure 11a) before northward deflection, with the stronger upward motion and diabatic heating to the south. As the typhoon moves closer to the east coast, the speedy core region rotates clockwise to the northeast of the typhoon center and the wind speed becomes much stronger (about 60–70 m s−1) (Figure 11a). Note that stronger upward motion and diabatic heating also rotate clockwise roughly to the southwest quadrant (Figure 11b,c). For Yutu, horizontal wind speed, vertical velocity, and diabatic heating all are much weaker than for Mangkhut in the deflection period (enclosed by the two dashed lines). In response to the northward deflection, horizontal wind speed, upward motion, and diabatic heating for Yutu are in phase and stronger at the northeast quadrant (also see Figure 10). The induced flow asymmetry dominated by the enhanced southeasterly wind tends to drive the vortex more northwestward for Yutu. The Atmosphere 2021,Hovmöller 12, x FOR PEER REVIEW plotshave identified large differences in the flow asymmetry and convection19 of 32 of

both typhoons over the open ocean and near the northern Philippines.

Figure 11.Figure Hovmöller 11. plotsHovmöller of (a) horizontal plots ofwind (a) speed horizontal (in m s−1 wind), (b) vertical speed velocity (in m (in s− m1), s− (1b), )and vertical (c) diabatic velocity heating (in m s−1), −1 (in W h ) averaged in 1–8 km height and radii of− 0.5°1 –1.5° with respect to azimuth for Mangkhut. (d–f) Same◦ as◦ in (a–c), respectively,and but (c )for diabatic Yutu. The heating two black (in dashed W h lines) averaged indicate the in time 1–8 period km height of track anddeflection radii and of landfall. 0.5 –1.5 with respect to azimuth for Mangkhut. (d–f) Same as in (a–c), respectively, but for Yutu. The two black dashed lines indicate the time periodERA5 ofis trackthe fifth-generation deflection and ECMWF landfall. atmospheric reanalysis of the global climate (January 1950 to present) that provides hourly estimates of a large number of atmospheric ERA5 isvariables. the fifth-generation ERA5 reanalysis ECMWF data are used atmospheric to compare reanalysiswith simulation of the results global in many climate previous studies (e.g., [52,53]). In this study, to verify the simulated inner-core structure (January 1950for to Supertyphoon present) that Yutu provides when the hourly simulated estimates typhoon of track a large starts numberto exhibit ofa northward atmospheric variables. ERA5deviation reanalysis compared data to the are JTWC’s used best to comparetrack, ERA5 with reanalysis simulation is also used. results Figure in 12 many previous studiesshows (e.g., the simulated [52,53]). horizontal In this study,wind and to vertical verify velocity the simulated compared to inner-core ERA5. The inner- structure for Supertyphooncore structure Yutu whenfor YT-CTL the at simulated 72 h (0000 UTC typhoon 29 October) track and starts at 84 h to (1200 exhibit UTC 29 a October) northward in this figure is rather consistent with the typhoon structure shown in Figure 10. However, deviation compared to the JTWC’s best track, ERA5 reanalysis is also used. Figure 12 shows there are some differences when comparing the simulated results with ERA5. The the simulatedsimulated horizontal typhoon wind for andYT-CTL vertical has a consider velocityably compared stronger horizontal to ERA5. wind Theand upward inner-core structure for YT-CTLmotions to at the 72 northeast h (0000 UTCof the 29typhoon October) center and compared at 84 hto (1200 ERA5.UTC It can 29be a October) reason why in this figure is ratherYT-CTL consistent with the with stronger the asymmetric typhoon structurewind at the shown northeast in quadrant Figure takes10. However, a northward there are some differencesdeflection when closing comparing in to the northern the simulated Philippines results compared with to the ERA5. best track. The We simulated note that the typhoon structure from ERA5 is consistent with the cloud convection structure (exhibited through precipitation) from TRMM-3B42RT and the cloud top temperature structure from satellite-observed infrared cloud top brightness temperature images (figures not shown). In general, the stronger asymmetric wind structure at the northeast quadrant induces the northward deflection for Yutu after 72 h. As discussed in Section 3, the typhoon circulation and convection may also be somewhat influenced by other effects (from varying terrain heights and nearby SSTs), but the primary control of the track is the development of the typhoon circulation that is primarily in response to internal physical processes.

Atmosphere 2021, 12, 1055 18 of 29

typhoon for YT-CTL has a considerably stronger horizontal wind and upward motions to the northeast of the typhoon center compared to ERA5. It can be a reason why YT-CTL with the stronger asymmetric wind at the northeast quadrant takes a northward deflection when closing in to the northern Philippines compared to the best track. We note that the typhoon structure from ERA5 is consistent with the cloud convection structure (exhibited through precipitation) from TRMM-3B42RT and the cloud top temperature structure from satellite-observed infrared cloud top brightness temperature images (figures not shown). In general, the stronger asymmetric wind structure at the northeast quadrant induces the northward deflection for Yutu after 72 h. As discussed in Section3, the typhoon circulation and convection may also be somewhat influenced by other effects (from varying terrain Atmosphere 2021, 12, x FOR PEER REVIEW 20 of 32 heights and nearby SSTs), but the primary control of the track is the development of the typhoon circulation that is primarily in response to internal physical processes.

FigureFigure 12. 12.SimulatedSimulated horizontal horizontal wind (wind wind vectors; (wind m vectors;s−1) and vertical m s−1 velocity) and vertical (shaded velocitycolors in (shaded colors −1 min s m) at s −7001) hPa at 700 for hPa(a) ERA5 for (anda) ERA5 (b) YT-CTL and ( atb) 0000 YT-CTL UTC 29 at October. 0000 UTC (c) and 29 October. (d) are the ( csame) and as ( d) are the same in (a) and (b), respectively, but at 1200 UTC 29 October. (e) and (f) are the same as in (a) and (b), respectively,as in (a) and but (atb ),1800 respectively, UTC 29 October. but at 1200 UTC 29 October. (e) and (f) are the same as in (a) and (b), respectively, but at 1800 UTC 29 October. 4.3. PV Tendency Budget The asymmetric structure of the PV tendency budget is used to examine the dynamic processes in the track change. For representing the hourly average, the PV tendency budget terms at domain 3 are calculated at 10 min intervals from 47 to 48, 59 to 60 and 92 to 93 h for MK-CTL and from 71 to 72, 83 to 84 and 95 to 96 h for YT-CTL.

Atmosphere 2021, 12, 1055 19 of 29

4.3. PV Tendency Budget The asymmetric structure of the PV tendency budget is used to examine the dynamic processes in the track change. For representing the hourly average, the PV tendency budget terms at domain 3 are calculated at 10 min intervals from 47 to 48, 59 to 60 and 92 to 93 h for MK-CTL and from 71 to 72, 83 to 84 and 95 to 96 h for YT-CTL. To identify the relative importance of different physical processes contributing to vortex translation, the hourly mean WN-1 flow and PV tendency budget averaged in 1–8 km height at 48, 60 and 93 h for MK-CTL are shown in Figure 13. When the typhoon starts to take a northward-biased track, it is horizontal PV advection that drives the vortex mainly west-northwestward at a speed of 3.77 m s−1 (Figure 13d). However, the translation induced by differential diabatic heating is mainly southward, with a much smaller magnitude of 1.04 m s−1 (Figure 13j). The induced translation by rather symmetric vertical PV advection (Figure 13g) is also small with an induced speed of 0.67 m s−1 compared to other terms. Consequently, the WN-1 net PV tendency of the typhoon supports a west-northwestward translation at a speed of 3.37 m s−1 (Figure 13a), wherein horizontal PV advection contributes to the northward deflection at this time. At 60 h, vertical PV advection changes the induced translation toward the west at a high speed of 7.30 m s−1 (Figure 13h), much faster than at 48 h. The translation induced by differential diabatic heating at this time becomes much weaker at a small magnitude of 0.15 m s−1 (Figure 13k). Horizontal PV advection tends to contribute to the northward translation at a speed of 3.52 m s−1 at 60 h (Figure 13e), which makes the net translation move northwestward at 7.61 m s−1 with a slightly westward component at this time (Figure 13b). As the typhoon is near landfall, horizontal and vertical PV advection at 93 h tends to contribute to the same translation direction (northwestward) with a speed of 5.8 and 1.46 m s−1, respectively. The net translation at a speed of 7.96 m s−1 moves in the same direction as at 60 h (Figure 13c). The typhoon movement at this time affected by differential diabatic heating is southwestward at an increased speed of 2.22 m s−1 (Figure 13l), well corresponding to the stronger upward motions and diabatic heating mainly to the south- southwest quadrant of Mangkhut in Figure 11. Figure 14 shows the WN-1 PV tendency budget at 72, 84, and 96 h for YT-CTL. At 72 h, horizontal PV advection induces a west-northwestward translation at a speed of 5.21 m s−1 (Figure 14d). The induced translation by vertical PV advection and differential diabatic heating is relatively weaker (1.03 and 1.24 m s−1, respectively), with southward and southeastward translation, respectively (Figure 14g,j). The net PV tendency budget at this time leads to induce a west-southwestward translation at a speed of 3.80 m s−1 (Figure 14a), wherein vertical PV advection and differential diabatic heating contribute to a slight reduction of the northward deflection of Yutu. At 84 h, vertical PV advection and differential diabatic heating now decrease the induced translation to 0.65 and 0.94 m s−1, respectively (Figure 14h,k). Horizontal PV advection gives a west-northwestward translation at a speed of 3.65 m s−1 (Figure 14e) and dominates the net PV tendency budget. The net PV tendency budget drives the typhoon roughly west-northwestward at a speed of 2.06 m s−1 (Figure 14b). Near landfall, the translation induced by horizontal PV advection is mainly northwestward, with an increased magnitude of 5.29 m s−1 at 96 h (Figure 14f), which dominates the net PV budget with a northwestward translation at a speed of 4.09 m s−1 (Figure 14c). Note that the translation induced by vertical PV advection (0.27 m s−1) is reduced at this time, while it becomes slightly stronger at a speed of 1.30 m s−1 for differential diabatic heating compared to that (0.94 m s−1) at 84 h (Figure 14i,l). It can be seen that the net PV tendency budget at these times (Figure 14a–c) is dominated by the horizontal PV advection (Figure 14d–f). Differential diabatic heating has a relatively increasing influence on typhoon motion as the typhoon is closing in to landfall. Atmosphere 2021, 12, x FOR PEER REVIEW 22 of 32 Atmosphere 2021, 12, 1055 20 of 29

−5 −1 FigureFigure 13. The13. WN-1The horizontalWN-1 horizontal ﬂow and PV flow tendency and budgetPV te (ndency shaded colors, budget 10 ( PVUshaded s ) averagedcolors, in10 1–8−5 PVU km height s−1) foraveraged MK-CTL at in 48 1–8 h for km (a) netheight PV budget, for MK-CTL (d) horizontal at 48 PV h advection,for (a) net (g) PV vertical budget, PV advection, (d) horizontal and (j) differential PV advection, diabatic heating. (b), (e), (h), and (k) are the same as in (a), (d), (g), and (j), respectively, but at 60 h. (c), (f), (i), and (l) are the same (g) vertical PV advection, and (j) differential diabatic heating. (b), (e), (h), and (k) are the same as in as in (a), (d), (g), and (j), respectively, but at 93 h. The bold reference wind vector (in m s−1) indicates the induced vortex (a), (d), (g), and (j), respectively, but at 60 h. (c), (f), (i), and (l) are the same as in (a), (d), (g), and (j), translation velocity. The budget results are averaged within 1 h ahead of the analysis time. respectively, but at 93 h. The bold reference wind vector (in m s−1) indicates the induced vortex translation velocity. The budget results are averaged within 1 h ahead of the analysis time.

Figure 14 shows the WN-1 PV tendency budget at 72, 84, and 96 h for YT-CTL. At 72 h, horizontal PV advection induces a west-northwestward translation at a speed of 5.21 m s−1 (Figure 14d). The induced translation by vertical PV advection and differential diabatic heating is relatively weaker (1.03 and 1.24 m s−1, respectively), with southward and southeastward translation, respectively (Figure 14g,j). The net PV tendency budget at this time leads to induce a west-southwestward translation at a speed of 3.80 m s−1 (Figure 14a), wherein vertical PV advection and differential diabatic heating contribute to a slight reduction of the northward deflection of Yutu. At 84 h, vertical PV advection and differential diabatic heating now decrease the induced translation to 0.65 and 0.94 m s−1, respectively (Figure 14h,k). Horizontal PV advection gives a west-northwestward translation at a speed of 3.65 m s−1 (Figure 14e) and dominates the net PV tendency budget. The net PV tendency budget drives the typhoon roughly west-northwestward at a speed of 2.06 m s−1 (Figure 14b). Near landfall, the translation induced by horizontal PV advection is mainly northwestward, with an increased magnitude of 5.29 m s−1 at 96 h (Figure 14f), which dominates the net PV budget with a northwestward translation at a

Atmosphere 2021, 12, x FOR PEER REVIEW 23 of 32

speed of 4.09 m s−1 (Figure 14c). Note that the translation induced by vertical PV advection (0.27 m s−1) is reduced at this time, while it becomes slightly stronger at a speed of 1.30 m s−1 for differential diabatic heating compared to that (0.94 m s−1) at 84 h (Figure 14i,l). It can be seen that the net PV tendency budget at these times (Figure 14a–c) is dominated by Atmosphere 2021the, 12 ,horizontal 1055 PV advection (Figure 14d–f). Differential diabatic heating has a relatively 21 of 29 increasing influence on typhoon motion as the typhoon is closing in to landfall.

−5 −1 Figure 14.FigureThe WN-114. The horizontal WN-1 ﬂowhorizontal and PV tendencyflow and budget PV tendency (shaded colors, budget 10 (shadedPVU s colors,) averaged 10− in5 PVU 1–8 km s− height1) for YT-CTLaveraged at 72 h in for 1–8 (a) netkm PV height budget, for ( dYT-CTL) horizontal at 72 PV h advection, for (a) net (g )PV vertical budget, PV advection, (d) horizontal and (j )PV differential advection, diabatic heating.(g (b) ),vertical (e), (h), PV and advection, (k) are the sameand ( asj) differential in (a), (d), (g ),diabatic and (j), heating. respectively, (b), but (e), at (h 84), h.and (c), (k (f)), are (i), the and same (l) are as the in same −1 as in (a),(a (),d ),(d (g),), (g and), and (j), respectively,(j), respectively, but at but 96 h.at The84 h. bold (c), reference (f), (i), and wind (l) vectorare the (in same m s as) indicatesin (a), (d the), (g induced), and (j vortex), translationrespectively, velocity. The but budget at 96 results h. The are bo averagedld reference within wind 1 h ahead vector of the(in analysism s−1) indicates time. the induced vortex translation velocity. The budget results are averaged within 1 h ahead of the analysis time. To further understand different contributions to the vortex translation from different physical processes in the entire tracks, the time evolutions of their induced translations To further understandfor both typhoons different during contribution their simulations to the times vortex are translation shown in Figure from different15. These induced physical processestranslations, in the entire as inferred tracks, from the thetime net evolutions WN-1 PV tendency of their budget, induced agree translations well with the actual for both typhoonsmovement during their of both simulation typhoons showntimes are earlier. shown For Mangkhut,in Figure 15. the These horizontal induced PV advection translations, as inferredroughly dominatesfrom the thenet PVWN-1 tendency PV tendency throughout budget, most of agree the simulation well with period. the When actual movementthe of simulated both typhoons typhoon shown starts toea takerlier. a northward-biasedFor Mangkhut, the track horizontal after 48 h, PV vertical PV advection roughlyadvection dominates becomes the PV more tendency important throughout but mainly most hastens of the the westward simulation movement. period. Later on, When the simulatedthe west-northwestward typhoon starts to take track a with northward-biased a more northward track component after 48 is h, mainly vertical contributed by the strong northward vertical PV advection. The vertical PV advection is the main PV advection becomes more important but mainly hastens the westward movement. Later reason for the increased northward deﬂection near landfall around 72 h. Near and during landfall, differential diabatic heating helps with some southward effects in response to the stronger convection at the southern quadrant of the typhoon, as seen in Figure9.

Atmosphere 2021, 12, x FOR PEER REVIEW 24 of 32

on, the west-northwestward track with a more northward component is mainly contributed by the strong northward vertical PV advection. The vertical PV advection is Atmosphere 2021, 12, 1055 22 of 29 the main reason for the increased northward deflection near landfall around 72 h. Near and during landfall, differential diabatic heating helps with some southward effects in response to the stronger convection at the southern quadrant of the typhoon, as seen in ForFigure Yutu, 9. For the Yutu, horizontal the horizontal PV advection PV advection is also theis also more the dominantmore dominant term term controlling controlling the PVthe tendencyPV tendency for mostfor most of the of simulationthe simulation time, time, except except from from the stagethe stage of 24–42 of 24–42 h when h when the inducedthe induced southward southward tendency tendency is almost is almost offset offset by by the the strong strong northward northward tendency tendency from from thethe differentialdifferential diabaticdiabatic heating. Note Note that that this this stage stage is is the the period period when when Yutu Yutu is isabout about to toreach reach the the maximum maximum intensity intensity so that so that the theconvec convectivetive cloud cloud heating heating is more is more vigorous. vigorous. With Withthe weakening the weakening Yutu, Yutu, the northwestward the northwestward translat translationion after after 72 h 72 for hfor Yutu Yutu (see(see Figure Figure 4)4 )is isalmost almost completely completely controlled controlled by by the the stronges strongestt horizontalhorizontal PVPV advection.advection. DifferentialDifferential diabaticdiabatic heatingheating nearnear landfalllandfall aroundaround 9696 hh exhibitsexhibits somesome weakweak southward-southeastwardsouthward-southeastward tendency,tendency, whichwhich isis insigniﬁcantinsignificant inin affectingaffecting thethe typhoon typhoon movement. movement. InIn summary,summary, thethe simulatedsimulated increasing increasing northward northwar trackd track bias isbias essentially is essentially related related to the vertical to the PV vertical advection PV afteradvection 48 h for after Mangkhut 48 h for andMangkhut to the horizontal and to the PV horizontal advection PV after advection 72 h for after Yutu. 72 Differential h for Yutu. diabaticDifferential heating diabatic is relatively heating lessis relatively important less than important horizontal than PV horizontal advection PV for advection affecting the for movementaffecting the of movement both typhoons, of both while typhoons, the former while may the somewhat former may modulate somewhat the modulate effect of the the lattereffect asof boththe latter typhoons as both are typhoons near landfall. are near landfall.

Figure 15. Time evolution of the translation velocity (vector) induced by different PV budget terms during the simulation time of 120 h for (a) MK-CTL and (b) YT-CTL. HDIA, VADV, HADV, and SUM denote differential diabatic heating, vertical PV advection, horizontal PV advection, and the sum of the former three terms, respectively. Atmosphere 2021, 12, 1055 23 of 29

5. Conclusions Two supertyphoons, Mangkhut and Yutu, occurred consecutively in 2018 over the western North Pacific. Both typhoons underwent rapid intensification and headed westward toward the northern Philippines, but Mangkhut deflected northward in the open ocean and Yutu deflected southward and then moved westward near the northern Philip- pines. In this study, the air–sea-coupled model HWRF at 2 km resolution was used to simulate the evolution of both typhoons approaching the eastern coast of the northern Philippines and near landfall. Sensitivity experiments were performed using different physics schemes consisting of varying cloud microphysics, cumulus parameterizations, and planetary boundary layer parameterizations. The 5-day simulation results indicate that Mangkhut has relatively less track sensitivity to the use of different physics parameterizations but greater sensitivity to the prediction of typhoon intensity. All the experiments can capture the observed northward track deflection of Mangkhut near the northern Philippines; however, the simulated northward deflection appears somewhat earlier than the best track. For Yutu, the results show reversed responses in that the simulated tracks become largely diverged about 1 day after the model initial time in the physics sensitivity experiments, while the simulated typhoon intensities, in general, show similar development trends consistent with the best track intensity. One note here is that all the sensitivity experiments fail to capture the westward track of Yutu near the northern Philippines, regardless of the combination of physics schemes. The simulation results from the terrain sensitivity experiments indicate that the earlier northward deflection of Mangkhut and the later northward deflection of Yutu are not sensitive to the topographic effects of the whole Philippines and Luzon terrain where the highest mountain is only about 2 km height. Moreover, influences of air–sea interaction during the simulations and the initial ocean temperature change along the typhoon tracks have slightly impacted the typhoon intensity but without producing noted changes in the tracks for both typhoons, as indicated from their ocean-coupled and uncoupled simulations. Thus, the Philippines terrain and air–sea interaction are not key factors vital to the track simulation of both typhoons. The northward track deflection of both typhoons was analyzed with the coupled simulations. The interactions between the internal typhoon vortex and the large-scale flow play an important role in the overall movement of both typhoons, which are explored for their structural and convective evolutions near the terrain. The stronger-wind-speed regions appear mainly to the north-northeast of both typhoons, which can be part of the reason why both typhoons move westward to northwestward when closing in to the northern Philippines. The stronger upward motion and diabatic heating for stronger Mangkhut concentrate roughly to the south-southwest quadrant, while they are in phase with the horizontal wind speed to the north for weaker Yutu. The track deflections of Mangkhut and Yutu are explained by the wavenumber-one PV tendency budget from different physical processes. In general, the horizontal PV advection dominates the PV tendency throughout most of the simulation time for both typhoons. However, the vertical PV advection becomes more important after 48 h for Mangkhut when the northward track deviation is ensuing. The west-northwestward translation for the stronger Mangkhut near the northern Philippines is primarily induced by both horizontal and vertical PV advection. The northwestward translation with track deflection near landfall for the weaker Yutu is mainly driven by the dominant horizontal PV advection. Compared to horizontal PV advection and vertical PV advection, differential diabatic heating is relatively less important for affecting the movement of both typhoons near landfall.

Author Contributions: Conceptualization, C.-Y.H.; Methodology, C.-Y.H.; Software, Formal analysis, and Data curation, C.-Y.H. and T.-C.N.; Writing—original draft preparation, T.-C.N.; Writing—review and editing, C.-Y.H.; Project administration, C.-Y.H. Both authors have read and agreed to the published version of the manuscript. Atmosphere 2021, 12, 1055 24 of 29

Funding: This study was supported by the Ministry of Science and Technology (MOST) (grant no. MOST 110-2111-M-008-014) in Taiwan. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The best track data are obtained from the JMA, and the model forecasts are available from the workstation of the typhoon laboratory at the Department of Atmospheric Sciences, National Central University from 140.115.35.103. Acknowledgments: The authors would like to thank J.-S. Hong at the Central Weather Bureau (CWB) and C.-Y. Liu at the National Central University (NCU) for valuable comments on the research work for the manuscript. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Appendix A Sensitivity to Physics Schemes Figure A1 shows the simulated tracks for the physics sensitivity experiments and the best track from the JTWC for both typhoons. It is evident that the physics schemes play a more dominant role in track simulations for Yutu, while having relatively less influence on Mangkhut. It is clearly seen that most of the experiments obtain well-simulated tracks in the first two days (1200 UTC 10 September to 1200 UTC 12 September) for Mangkhut but only in the tracks of the first day (0000 UTC 26 October to 0000 UTC 27 October) for Yutu that become significantly diversified later on. The observed movement of Mangkhut, in general, is relatively better captured by all the sensitivity experiments compared to those for Yutu. The combined SAS–GFS schemes replicate the Mangkhut track well in all the forecast times (Figure A1e), while the tracks associated with the other scheme combinations deviate with larger northward deviation in the later forecasts (Figure A1a,c). For the combined SAS–GFS and five different microphysics schemes, the experiment using the EtaH scheme obtains a relatively well-simulated track (in magenta) in the first two days (1200 UTC 10 September to 1200 UTC 12 September), with later landfall in the northern Philippines. The simulated track with WSM6 shows landfall but farther north than the best track in the first two days. The track simulations using the FA, Thom, and TF microphysics schemes show a larger northward deviation in the last three days. For the other combinations, the track deflection was far greater than that of the combined SAS–GFS, except for the combined KF–YSU using WSM6 and Thom cloud-microphysics schemes that also show relatively good track simulations. For Yutu, it was found that all simulation experiments have a slight deviation from the JTWC’s best track during the first day of the forecast (26 October) and tend to produce significantly northward deflection as the typhoon moves closer to the northern Philippines. With the fixed set of cumulus parameterization and planetary boundary layer parameterization, the combined KF–GFS better simulates the TC track than the other combinations (but it becomes reversed for Mangkhut). For the cloud-microphysics schemes, it is evident that the track performances are divided into three groups, with small track deflection for the TF cloud-microphysics scheme, large track deflection for the FA and EtaH cloud-microphysics scheme, and very large track deflection for the WSM6 and Thom microphysics schemes. However, such results are not clearly identifiable for Mangkhut. As a result, the WSM6 and Thom cloud-microphysics schemes have degraded track performances with larger errors. In general, the KF–GFS with the TF could-microphysics scheme is an optimal combination for track simulation of Yutu. Atmosphere 2021, 12, 1055 25 of 29 Atmosphere 2021, 12, x FOR PEER REVIEW 28 of 32

Figure A1. SimulatedSimulated tracks tracks from from th thee cloud-microphysics sensitivity expe experimentsriments for for Mangkhut from 1200 UTC 10 September to 1200 UTC 15 September with the set of cumuluscumulus parameterization and planetary boundary layer schemes, (a) KF–GFS,KF–GFS, ((cc)) KF–YSU,KF–YSU, and and ( e()e) SAS–GFS. SAS–GFS. (b (),b), (d (),d and), and (f )( aref) are the the same same as inas (ina), ( (ac),), ( andc), and (e), respectively,(e), respectively, but for but Yutu for Yutu from 0000from UTC0000 26UTC October 26 October to 0000 to UTC 0000 31 UTC October. 31 October. The red, blue,The red, green, blue, magenta, green, andmagenta, cyan lines and denote cyan lines the cloud-microphysics denote the cloud- schemesmicrophysics FA, WSM6,schemes Thom, FA, WSM6, EtaH, Thom, and TF, EtaH, respectively. and TF, respectively The black line. The denotes black line the denotes JTWC’s the best JTWC’s track. best Circles track. mark Circles the typhoonmark the centerstyphoon every centers 24 h,every associated 24 h, associat with theed numbers with the indicatingnumbers indicating UTC time. UTC time.

Opposite to track simulations, the intensity simulations for Mangkhut show greater dependence on physics schemes (Figure A2A2).). For the cloud-microphysics schemes, most of the the simulations simulations resulted resulted in in relatively relatively weak weak intensity intensity simulations simulations in both in both MSLP MSLP and V andmax comparedVmax compared with the with best the track best intensity track intensity from the from JTWC. the JTWC. Specifically, Speciﬁcally, the experiments the experiments using WSM6using WSM6 and Thom and give Thom a relatively give a relatively weaker intensity weaker intensity for both the for MSLP both the and MSLP Vmax compared and Vmax withcompared the best with track the intensity best track from intensity the JT fromWC. theThe JTWC. experiments The experiments using the FA using and the EtaH FA schemesand EtaH show schemes stronger show MSLPs stronger and MSLPs Vmax compared and Vmax tocompared that of the toexperiments that of the using experiments WSM6 andusing Thom WSM6 schemes; and Thom however, schemes; they are however, also we theyaker than are also the best weaker track than intensity the best from track the JTWC.intensity Only from the the TF JTWC. scheme Only gives the reasonable TF scheme magnitudes gives reasonable of the magnitudes MSLP and V ofmax the that MSLP are similarand Vmax tothat the arebest similar track to intensity the best trackfrom intensity the JTWC; from however, the JTWC; the however, simulated the simulated typhoon intensitytyphoon intensityon the last on day the is last over-predicted day is over-predicted due to its due most to offshore its most offshoretrack without track withoutlandfall (seelandfall Figure (see A1a). Figure Regarding A1a). Regarding cumulus cumulus parame parameterizationterization and planetary and planetary boundary boundary layer parameterization,layer parameterization, the combined the combined SAS–GFS SAS–GFS again performs again performs slightly better slightly in betterthe intensity in the

Atmosphere 2021, Atmosphere12, x FOR PEER2021 ,REVIEW12, 1055 29 of 32 26 of 29

simulation thanintensity the other simulation scheme combinations. than the other For scheme four combinations.experiments with For landfalling four experiments with tracks in the landfallingnorthern Philippines tracks in the (as northern shown Philippines in Figure (asA1), shown the typhoon in Figure A1intensity), the typhoon is intensity underpredictedis underpredictedin terms of both in V termsmax and of MSLP both V formax theand combination MSLP for the of combination the SAS–GFS of and the SAS–GFS and the WSM6 cloud-microphysicsthe WSM6 cloud-microphysics schemes, the combination schemes, the of combination the KF–YSU, of and the KF–YSU,the WSM6 and the WSM6 and Thom cloud-microphysicsand Thom cloud-microphysics schemes. Note that schemes. only the Note combination that only of the SAS–GFS combination and of SAS–GFS EtaH shows a andlandfall EtaH typhoon shows awith landfall a relative typhoonly stronger with arelatively intensity strongerof both the intensity MSLP and of both the MSLP Vmax. and Vmax.

Figure A2. TimeFigure evolution A2. Time of evolution simulated of minimum simulated sea-level minimum pressure sea-level (hPa) pressure for Mangkhut (hPa) for from Mangkhut 1200 UTC from 10 1200 September to 1200 UTC 15UTC September 10 September 2018 for to the 1200 scheme UTC combination15 September of 2018 (a) KF–GFS, for the scheme (c) KF–YSU, combination and (e) SAS–GFS. of (a) KF–GFS, (b), (d (),c) and (f) are the same asKF–YSU, in (a), (c), and and ( (ee)), SAS–GFS. respectively, (b), but(d), forand 10 (f m) are maximum the same wind as in speed (a), (c (kt)), and for (e a), forecast respectively, of total but 120 for h. The red, 10 m maximum wind speed (kt) for a forecast of total 120 h. The red, blue, green, magenta, and cyan blue, green, magenta, and cyan lines denote the cloud-microphysics schemes FA, WSM6, Thom, EtaH, and TF, respectively. lines denote the cloud-microphysics schemes FA, WSM6, Thom, EtaH, and TF, respectively. The The best track intensity is from the JTWC (black line). best track intensity is from the JTWC (black line). For Yutu, different combinations of the physics schemes produced different trends of For Yutu, different combinations of the physics schemes produced different trends typhoon track but obtained comparable development of the typhoon intensity in terms of typhoon track but obtained comparable development of the typhoon intensity in terms of the MSLP and Vmax (Figure A3). The time evolution of the typhoon intensity for Yutu of the MSLP and Vmax (Figure A3). The time evolution of the typhoon intensity for Yutu shows that all experiments can capture the weakening tendency of the MSLP. However, the shows that all experiments can capture the weakening tendency of the MSLP. However, value of Vmax for most of the experiments shows signiﬁcantly weaker magnitudes than the the value of Vmax for most of the experiments shows significantly weaker magnitudes than

Atmosphere 2021, 12, x FOR PEER REVIEW 30 of 32

Atmosphere 2021, 12, 1055 27 of 29

the best track value before 96 h. Similar to the track simulations, these intensity simulations are bestalso track divided value into before three 96 h. grou Similarps but to the with track relatively simulations, smaller these intensitydifferences simulations are also divided into three groups but with relatively smaller differences within only within only about 5–10 hPa (MSLP) and 5–10 kt (Vmax). For typhoon intensity, the cloud- about 5–10 hPa (MSLP) and 5–10 kt (V ). For typhoon intensity, the cloud-microphysics microphysics scheme TF, in general, performs maxbest, which provides a reasonable scheme TF, in general, performs best, which provides a reasonable prediction of both the prediction of both the MSLP and Vmax. Some combinations of specific physics schemes MSLP and Vmax. Some combinations of speciﬁc physics schemes tend to produce earlier tend to producerecurved earlier tracksrecurved that passtracks over that colder pass SST over regions colder at higher SST latitudes,regions whichat higher thus leads to latitudes, whichthe thus development leads to the of development weakening typhoons. of weakening typhoons.

Figure A3.FigureTime A3. evolution Time evolution of simulated of minimum simulated sea-level minimum pressure sea- (hPa)level forpressure Yutu from (hPa) 0000 for UTC Yutu 26 Octoberfrom 0000 to0000 UTC 31 OctoberUTC 2018 26 forOctober the scheme to 0000 combination UTC 31 October of (a) KF–GFS, 2018 for (c) the KF–YSU, scheme and combination (e) SAS–GFS. of (b ),(a ()d KF–GFS,), and (f) are(c) theKF– same as in (a), (cYSU,), and and (e), respectively,(e) SAS–GFS. but (b for), ( 10d), m and maximum (f) are the wind same speed as (kt)in (a for), ( ac), forecast and (e of), respectively, total 120 h. The but red, for blue, 10 m green, magenta,maximum and cyan lineswind denote speed the (kt) cloud-microphysics for a forecast of total schemes 120 h. FA, The WSM6, red, Thom,blue, green, EtaH, andmagenta, TF, respectively. and cyan The best track intensitylines denote is from the the cloud-microphysics JTWC (black line). schemes FA, WSM6, Thom, EtaH, and TF, respectively. The best track intensity is from the JTWC (black line).

References 1. Wu, L.; Wang, B.; Braun, S.A. Impacts of air–sea interaction on tropical cyclone track and intensity. Mon. Weather Rev. 2005, 133, 3299–3314.

Atmosphere 2021, 12, 1055 28 of 29

References 1. Wu, L.; Wang, B.; Braun, S.A. Impacts of air–sea interaction on tropical cyclone track and intensity. Mon. Weather Rev. 2005, 133, 3299–3314. [CrossRef] 2. Davis, C.; Wang, W.; Chen, S.S.; Chen, Y.; Corbosiero, K.; DeMaria, M.; Dudhia, J.; Holland, G.; Klemp, J.; Michalakes, J.; et al. Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Weather Rev. 2008, 136, 1990–2005. [CrossRef] 3. Huang, Y.H.; Wu, C.C.; Wang, Y. The influence of island topography on typhoon track deflection. Mon. Weather Rev. 2011, 139, 1708–1727. [CrossRef] 4. Kieu, Q.C.; Nguyen, M.T.; Hoang, T.M.; Ngo, D.T. Sensitivity of the track and intensity forecasts of Typhoon Megi (2010) to satellite-derived atmospheric motion vectors with the ensemble kalman filter. J. Atmos. Ocean. Technol. 2012, 29, 1794–1810. [CrossRef] 5. Yun, K.S.; Chan, J.C.L.; Ha, K.J. Effects of SST magnitude and gradient on typhoon tracks around East Asia: A case study for Typhoon Maemi (2003). Atmos. Res. 2012, 109, 36–51. [CrossRef] 6. Choi, Y.; Yun, K.S.; Ha, K.J.; Kim, K.Y.; Yoon, S.J.; Chan, J.C.L. Effects of asymmetric SST distribution on straight-moving Typhoon Ewiniar (2006) and recurving Typhoon Maemi (2003). Mon. Weather Rev. 2013, 141, 3950–3967. [CrossRef] 7. Li, X. Sensitivity of WRF simulated typhoon track and intensity over the Northwest Pacific Ocean to cumulus schemes. Sci. China Earth Sci. 2013, 56, 270–281. [CrossRef] 8. Wu, C.-C.; Tu, W.-T.; Pun, I.-F.; Lin, I.-I.; Peng, M.S. Tropical cyclone-ocean interaction in Typhoon Megi (2010)—A synergy study based on ITOP observations and atmosphere-ocean coupled model simulations. J. Geophys. Res. Atmos. 2015, 121, 153–167. [CrossRef] 9. Katsube, K.; Inatsu, M. Response of tropical cyclone tracks to sea surface temperature in the western North Pacific. J. Clim. 2016, 29, 1955–1975. [CrossRef] 10. Srinivas, C.V.; Mohan, G.M.; Naidu, C.V.; Baskaran, R.; Venkatraman, B. Impact of air-sea coupling on the simulation of tropical cyclones in the North Indian Ocean using a simple 3-D ocean model coupled to ARW. J. Geophys. Res. Atmos. 2016, 121, 9400–9421. [CrossRef] 11. Bender, M.A.; Marchok, T.P.; Sampson, C.R.; Knaff, J.A.; Morin, M.J. Impact of storm size on prediction of storm track and intensity using the 2016 operational GFDL hurricane model. Weather Forecast. 2017, 32, 1491–1508. [CrossRef] 12. Hsu, L.-H.; Su, S.-H.; Fovell, R.G.; Kuo, H.-C. On typhoon track deflections near the east coast of Taiwan. Mon. Weather Rev. 2018, 146, 1495–1510. [CrossRef] 13. Chang, S.W.; Madala, R.V. Numerical simulation of the influence of sea surface temperature on translating tropical cyclones. J. Atmos. Sci. 1980, 37, 2617–2630. [CrossRef] 14. Sun, J.; Oey, L.Y.; Chang, R.; Xu, F.; Huang, S.-M. Ocean response to typhoon Nuri (2008) in western Pacific and South China Sea. Ocean Dyn. 2015, 65, 735–749. [CrossRef] 15. Lin, Y.L.; Chen, S.Y.; Hill, C.M.; Huang, C.Y. Control parameters for track continuity and deflection associated with tropical cyclones over a mesoscale mountain. J. Atmos. Sci. 2005, 62, 1849–1866. [CrossRef] 16. Tang, C.K.; Chan, J.C.L. Idealized simulations of the effect of Taiwan and Philippines topographies on tropical cyclone tracks. Q. J. R. Meteorol. Soc. 2014, 140, 1578–1589. [CrossRef] 17. Huang, C.Y.; Chen, C.A.; Chen, S.H.; Nolan, D.S. On the upstream track deflection of tropical cyclones past a mountain range: Idealized experiments. J. Atmos. Sci. 2016, 73, 3157–3180. [CrossRef] 18. Li, D.-Y.; Huang, C.-Y. The influences of orography and ocean on track of Typhoon Megi (2016) past Taiwan as identified by HWRF. J. Geophys. Res. Atmos. 2018, 123, 11492–11517. [CrossRef] 19. Huang, C.-Y.; Juan, T.-C.; Kuo, H.-C.; Chen, J.-H. Track deflection of Typhoon Maria (2018) during a westbound passage offshore of northern Taiwan: Topographic influence. Mon. Weather Rev. 2020, 148, 4519–4544. [CrossRef] 20. Mandal, M.; Mohanty, U.C.; Raman, S. A Study on the impact of parameterization of physical processes on prediction of tropical cyclones over the Bay of Bengal with NCAR/PSU mesoscale model. Nat. Hazards 2004, 31, 391–414. [CrossRef] 21. Srinivas, C.V.; Venkatesan, R.; Bhaskar Rao, D.V.; Prasad, D.H. Numerical simulation of Andhra severe cyclone (2003): Model sensitivity to boundary layer and convection parameterization. Pure Appl. Geophys. 2007, 164, 1465–1487. [CrossRef] 22. Raju, P.V.S.; Potty, J.; Mohanty, U.C. Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model. Meteorol. Atmos. Phys. 2011, 113, 125–137. [CrossRef] 23. Chandrasekar, R.; Balaji, C. Sensitivity of tropical cyclone Jal simulations to physics parameterizations. J. Earth Syst. Sci. 2012, 121, 923–946. [CrossRef] 24. Nasrollahi, N.; Aghakouchak, A.; Li, J.; Gao, X.; Hsu, K.; Sorooshian, S. Assessing the impacts of different WRF precipitation physics in hurricane simulations. Weather Forecast. 2012, 27, 1003–1016. [CrossRef] 25. Kanase, R.D.; Salvekar, P.S. Impact of physical parameterization schemes on track and intensity of severe cyclonic storms in Bay of Bengal. Meteorol. Atmos. Phys. 2015, 127, 537–559. [CrossRef] 26. Trivedi, D.K.; Mukhopadhyay, P.; Vaidya, S.S. Impact of physical parameterization schemes on the numerical simulation of Orissa super cyclone (1999). Mausam 2006, 57, 97–110. 27. Chan, J.C.L.; Williams, R.T. Analytical and numerical studies of the beta-effect in tropical cyclone motion. Part I: Zero mean flow. J. Atmos. Sci. 1987, 44, 1257–1265. [CrossRef] Atmosphere 2021, 12, 1055 29 of 29

28. Chan, J.C.L.; Ko, F.M.F.; Lei, Y.M. Relationship between potential vorticity tendency and tropical cyclone motion. J. Atmos. Sci. 2002, 59, 1317–1336. [CrossRef] 29. Wu, C.C.; Huang, T.-S.; Huang, W.-P.; Chou, K.-H. A new look at the binary interaction: Potential vorticity diagnosis of the unusual southward movement of tropical storm Bopha (2000) and its interaction with Supertyphoon Saomai (2000). Mon. Weather Rev. 2003, 131, 1289–1300. [CrossRef] 30. Wu, C.C.; Huang, T.-S.; Chou, K.-H. Potential vorticity diagnosis of the key factors affecting the motion of Typhoon Sinlaku (2002). Mon. Weather Rev. 2004, 132, 2084–2093. [CrossRef] 31. Wu, L.; Wang, B. A potential vorticity tendency diagnostic approach for tropical cyclone motion. Mon. Weather Rev. 2000, 128, 1899–1911. [CrossRef] 32. Yu, H.; Huang, W.; Duan, Y.H.; Chan, J.C.L.; Chen, P.Y.; Yu, R.L. A simulation study on pre-landfall erratic track of Typhoon Haitang (2005). Meteorol. Atmos. Phys. 2007, 97, 189–206. [CrossRef] 33. Hsu, L.-H.; Kuo, H.-C.; Fovell, R.G. On the geographic asymmetry of typhoon translation speed across the mountainous island of Taiwan. J. Atmos. Sci. 2013, 70, 1006–1022. [CrossRef] 34. Kaplan, J.; DeMaria, M. Large-scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic basin. Weather Forecast. 2003, 18, 1093–1108. [CrossRef] 35. Tallapragada, V.; Bernardet, L.; Biswas, M.; Ginis, I.; Kwon, Y.; Liu, Q.; Zhang, X. Hurricane Weather Research and Forecasting (HWRF) Model: 2015 scientific documentation. NCAR Tech. Notes 2016.[CrossRef] 36. Yablonsky, R.M.; Ginis, I.; Thomas, B.; Tallapragada, V.; Sheinin, D.; Bernardet, L. Description and analysis of the ocean component of NOAA’s operational Hurricane Weather Research and Forecasting Model (HWRF). J. Atmos. Ocean. Technol. 2015, 32, 144–163. [CrossRef] 37. Aligo, E.; Ferrier, B.S.; Carley, J.; Rodgers, E.; Pyle, M.; Weiss, S.J.; Jirak, I.L. Modified microphysics for use in high resolution NAM forecasts. In Proceedings of the 27th AMS Conference on Severe Local Storms, Madison, WI, USA, 3–7 November 2014. 38. Hong, S.-Y.; Kim, J.-H.; Lim, J.-O.; Dudhia, J. The WRF single–moment 6–class microphysics scheme (WSM6). J. Korean Meteor. Soc. 2006, 42, 129–151. 39. Thompson, G.; Field, P.R.; Rasmussen, R.M.; Hall, W.D. Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization. Mon. Weather Rev. 2008, 136, 5095–5115. [CrossRef] 40. Han, J.; Pan, H.-L. Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Weather Forecast. 2011, 26, 520–533. [CrossRef] 41. Kain, J.S. The Kain–Fritsch convective parameterization: An update. J. Appl. Meteorol. 2004, 43, 170–181. [CrossRef] 42. Hong, S.-Y.; Noh, Y.; Dudhia, J. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev. 2006, 134, 2318–2341. [CrossRef] 43. Hong, S.Y.; Pan, H.L. Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Weather Rev. 1996, 124, 2322–2339. [CrossRef] 44. Monin, A.S.; Obukhov, A.M. Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib. Geophys. Inst. Acad. Sci. 1954, 151, 163–187. 45. Iacono, M.J.; Delamere, J.S.; Mlawer, E.J.; Shephard, M.W.; Clough, S.A.; Collins, W.D. Radiative forcing by long–lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res. 2008, 113, D13103. [CrossRef] 46. Ricchi, A.; Miglietta, M.M.; Bonaldo, D.; Cioni, G.; Rizza, U.; Carniel, S. Multi-physics ensemble versus atmosphere-ocean coupled model simulations for a Tropical-Like Cyclone in the Mediterranean Sea, Special Issue Mediterranean Tropical-like cyclones (Medicanes). Atmosphere 2019, 10, 202. [CrossRef] 47. Ricchi, A.; Miglietta, M.M.; Barbariol, F.; Benetazzo, A.; Bergamasco, A.; Bonaldo, D.; Cassardo, C.F.; Falcieri, M.; Modugno, G.; Russo, A.; et al. Sensitivity of a Mediterranean tropical-like cyclone to different model configurations and coupling strategies. Atmosphere 2017, 8, 92. [CrossRef] 48. Miglietta, M.M.; Mastrangelo, D.; Conte, D. Influence of physics parameterization schemes on the simulation of a tropical-like cyclone in the Mediterranean Sea. Atmos. Res. 2015, 153, 360–375. [CrossRef] 49. Bender, M.A.; Ginis, I. Real-case simulations of hurricane-ocean interaction using a high-resolution coupled model: Effects on hurricane intensity. Mon. Weather Rev. 2000, 128, 917–946. [CrossRef] 50. Lee, C.-Y.; Chen, S.S. Symmetric and asymmetric structures of hurricane boundary layer in coupled atmosphere–wave–ocean models and observations. J. Atmos. Sci. 2012, 69, 3576–3594. [CrossRef] 51. Sun, J.; Oey, L.Y. The influence of the ocean on Typhoon Nuri (2008). Mon. Weather Rev. 2015, 143, 4493–4513. [CrossRef] 52. Wang, X.; Chen, D.; Pang, G.; Anwar, S.A.; Ou, T.; Yang, M.X. Effects of cumulus parameterization and land-surface hydrology schemes on Tibetan Plateau climate simulation during the wet season: Insights from the RegCM4 model. Clim. Dyn. 2021, 1–27. [CrossRef] 53. Steptoe, H.; Savage, N.H.; Sadri, S.; Salmon, K.; Maalick, Z.; Webster, S. Tropical cyclone simulations over Bangladesh at convection permitting 4.4 km & 1.5 km resolution. Sci. Data 2021, 8, 62. [PubMed]