Parameterized Tropical Cyclone Precipitation Model for Catastrophe Risk Assessment in China

Lu Yi aShanghai Typhoon Institute, China Meteorological Administration, Shanghai, China
bKey Laboratory of Typhoon Observations and Forecasting, Wenzhou, China
cKey Laboratory of Numerical Modeling for Tropical Cyclone of China Meteorological Administration, Shanghai, China
dEast China Normal University, Shanghai, China

Search for other papers by Lu Yi in
Current site
Google Scholar
PubMed
Close
,
Chen Peiyan aShanghai Typhoon Institute, China Meteorological Administration, Shanghai, China
bKey Laboratory of Typhoon Observations and Forecasting, Wenzhou, China
cKey Laboratory of Numerical Modeling for Tropical Cyclone of China Meteorological Administration, Shanghai, China

Search for other papers by Chen Peiyan in
Current site
Google Scholar
PubMed
Close
,
Yu Hui aShanghai Typhoon Institute, China Meteorological Administration, Shanghai, China
bKey Laboratory of Typhoon Observations and Forecasting, Wenzhou, China
cKey Laboratory of Numerical Modeling for Tropical Cyclone of China Meteorological Administration, Shanghai, China

Search for other papers by Yu Hui in
Current site
Google Scholar
PubMed
Close
,
Fang Pingzhi eChina Re Catastrophe Risk Management Company, Ltd., Beijing, China

Search for other papers by Fang Pingzhi in
Current site
Google Scholar
PubMed
Close
,
Gong Ting eChina Re Catastrophe Risk Management Company, Ltd., Beijing, China

Search for other papers by Gong Ting in
Current site
Google Scholar
PubMed
Close
,
Wang Xiaodong eChina Re Catastrophe Risk Management Company, Ltd., Beijing, China

Search for other papers by Wang Xiaodong in
Current site
Google Scholar
PubMed
Close
, and
Song Shengnan eChina Re Catastrophe Risk Management Company, Ltd., Beijing, China

Search for other papers by Song Shengnan in
Current site
Google Scholar
PubMed
Close
Free access

Abstract

Inland flooding and mudslides from tropical cyclone (TC) rainstorms are among the most destructive natural hazards in China, resulting in considerable direct economic losses and large numbers of fatalities. In this paper, a TC precipitation model (TCPM) is improved by incorporating the effects of complex terrain through a set of new parameters (e.g., slope, roughness, and attenuation distance) for a more accurate assessment of TC rainfall hazards in China. Moreover, by introducing parameterized spiral rainbands, the model could more accurately capture the intensity of extreme precipitation. The model comprehensively considers dynamic and thermodynamic precipitation factors and is adept at capturing the climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China. The model is verified by providing two comparisons. One is analysis including detailed results of three typical TC cases, and the other uses empirical cumulative distribution functions for extreme observations and simulations of historical landfalling TCs in China during the period 1960–2018. The comparisons reveal that the TCPM shows impressive performance for strong TCs with heavy precipitation within 200–300 km of the TC center. Moreover, both the modeled extreme hourly and total TC precipitation probability distributions are consistent with the observations. However, the model needs to be further improved for TCs with dispersive or long-distance precipitation.

Significance Statement

In this paper, an optimized and physics-based model for the simulation of tropical cyclone precipitation is described and used to estimate the risk of TC rainfall hazards in China. The work is innovative in that it considers the effect of complex terrain from three perspectives, including slope, roughness, and attenuation distance. The simulations demonstrated that the model is adept at capturing the main climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China, which is simple to run several hundred thousand times, with bright application prospects in catastrophe risk assessment.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chen Peiyan, chenpy76@hotmail.com

Abstract

Inland flooding and mudslides from tropical cyclone (TC) rainstorms are among the most destructive natural hazards in China, resulting in considerable direct economic losses and large numbers of fatalities. In this paper, a TC precipitation model (TCPM) is improved by incorporating the effects of complex terrain through a set of new parameters (e.g., slope, roughness, and attenuation distance) for a more accurate assessment of TC rainfall hazards in China. Moreover, by introducing parameterized spiral rainbands, the model could more accurately capture the intensity of extreme precipitation. The model comprehensively considers dynamic and thermodynamic precipitation factors and is adept at capturing the climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China. The model is verified by providing two comparisons. One is analysis including detailed results of three typical TC cases, and the other uses empirical cumulative distribution functions for extreme observations and simulations of historical landfalling TCs in China during the period 1960–2018. The comparisons reveal that the TCPM shows impressive performance for strong TCs with heavy precipitation within 200–300 km of the TC center. Moreover, both the modeled extreme hourly and total TC precipitation probability distributions are consistent with the observations. However, the model needs to be further improved for TCs with dispersive or long-distance precipitation.

Significance Statement

In this paper, an optimized and physics-based model for the simulation of tropical cyclone precipitation is described and used to estimate the risk of TC rainfall hazards in China. The work is innovative in that it considers the effect of complex terrain from three perspectives, including slope, roughness, and attenuation distance. The simulations demonstrated that the model is adept at capturing the main climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China, which is simple to run several hundred thousand times, with bright application prospects in catastrophe risk assessment.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chen Peiyan, chenpy76@hotmail.com

1. Introduction

Tropical cyclones (TCs), which are associated with extreme wind, precipitation, and storm surges, are responsible for significant loss of life and property damage in coastal areas (Smith and Katz 2013; Y. Lu et al. 2018). Often, a substantial number of losses are directly or indirectly related to TC rainfall, including wind-driven rain penetration and inland flooding (Czajkowski et al. 2017; Zhang et al. 2018; Yu and Chen 2019). TC rainfall may significantly increase in the future due to potential changes in TC activity (Knutson et al. 2010, 2013; Emanuel 2017). Long-term trend analyses have demonstrated that the severity of the TC impacts on the Chinese mainland has increased (Chen et al. 2013, 2019, 2021). Hence, it is essential to carry out TC precipitation risk assessments in China.

As compared with other TC hazards, such as severe winds and storm surges, the long-term TC precipitation risk has not been well quantified (Villarini et al. 2014). To gauge the risk of TC precipitation, a large number of TC events are needed, particularly for addressing extreme hazards induced by low-probability TCs. Due to the lack of sufficient measurements over a long period, statistical methods have been commonly used to generate large samples of synthetic TCs (Vickery et al. 2000; Fang and Shi 2012; Emanuel et al. 2008; Emanuel 2013). The basic idea of these models is that probability distributions of key TC parameters are obtained from the historical record. A Monte Carlo approach is applied on these distributions, and then large numbers of synthetic TCs can be efficiently generated. Based on synthetic TCs, the TC precipitation risk can be characterized by establishing a parameterized TC precipitation model (TCPM).

In reviewing existing studies, the methods on modeling TC precipitation mainly include numerical models and statistical parameterized models. However, the former models are more complex, which can hardly meet the requirement on computational efficiency for large samples (∼104) (Ren and Xiang 2017). In this case, statistical parameterized models are a better choice for TC precipitation risk assessment. To date, the widely accepted TC precipitation models mainly include the Hurricane Rainfall Rate and Distribution Estimator Model (HuRRDE; Rodgers et al. 1994; Peng et al. 1999), Rainfall Climate and Persistence Model (R-CLIPER; Tuleya et al. 2007), Risk Management Solution TC-Rain Model (RMS; Grieser and Jewson 2012), Modified-Smith-for-Rainfall Model (MSR; Langousis et al. 2008; Langousis and Veneziano 2009; Zhou 2017), and Physics-Based Tropical Cyclone Rainfall Model (PTCR; Emanuel 2017; P. Lu et al. 2018). Because of the ensemble-averaging nature, the dependence of precipitation on TC characteristics is coarse, and extremes are often not captured in R-CLIPER and HuRRDE. In comparison with R-CLIPER, MSR significantly improves azimuthal precipitation estimates by explicitly modeling the precipitation induced by horizontal wind convergence in the boundary layer. However, the interactions of TCs with their environment (wind shear, topography, etc.) are ignored. RMS and PTCR are physically based models accounting for major precipitation mechanisms, including temperature dependence, surface frictional convergence, vortex stretching, simple topography, and large-scale baroclinicity. However, the effect of complex topography, especially after a TC makes landfall, is not fully considered. In addition, the horizontal wind field in the boundary layer is too simple to comprehensively characterize the induced precipitation.

By compensating the deficiency of the RMS, an optimized model (TCPM) is established in this paper. The model innovatively introduces symmetric climate TC precipitation under the thermal conditions of the underlying surface, asymmetric TC precipitation under the effect of complex terrain, and a new parameterized scheme of spiral rain belt. To assess the performance of the model, the TCPM is applied to historical TCs that made landfall in China during the period from 1960 to 2018 and three typical TC cases, including Saomai (2006), Rammasun (2014), and Meranti (2016). The remainder of this paper is organized as follows. Section 2 describes the data and the model methods. Section 3 presents the simulation results and discusses the key findings. Section 4 provides the conclusions and offers suggestions for further research.

2. Data and method

a. Data

In this study, the track and intensity data of TCs that made landfall in China from 1980 to 2019 are obtained from the China Meteorological Administration (CMA) TC best track dataset (Ying et al. 2014; Lu et al. 2021). [These data are freely available online (https://tcdata.typhoon.org.cn/zjljsjj_zlhq.html).] The daily surface (land/ocean) temperature over the period from 1980 to 2019 is calculated from the National Centers for Environmental Prediction (NCEP) reanalysis with 0.25° × 0.25° grid scale (Chen et al. 2003). [The data are available online (https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/).]

The observed hourly precipitation data of TCs consist of satellite-estimated and meteorological station observation data. For satellite precipitation estimation, we use two datasets. One set is the TRMM data (https://disc.gsfc.nasa.gov/; Prat and Nelson 2013). The other set obtained from Shanghai Typhoon Institute (STI) is the landfall TC precipitation based on satellite data (STI-LTPreci-Sat; Yue et al. 2006a,b; Yu et al. 2009). For the hourly precipitation observed at stations, this dataset is obtained from tropical cyclone yearbooks published by the STI of the CMA.

Landform data (http://data.ess.tsinghua.edu.cn/) are divided into land cover and land use, whose resolution are 30 m (Wang et al. 2015; Gong et al. 2019). Moreover, the digital elevation model (DEM) data used to calculate slope are obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (GDEM), version 2 (V2), which was jointly developed and released by the Ministry of Economy, Trade, and Industry (METI) of Japan and the National Aeronautics and Space Administration (NASA) of the United States. (The access paths are http://www.jspacesystems.or.jp/ersdac/GDEM/E/1.html and http://www.gscloud.cn/sources/accessdata/421?pid=302.)

b. The TCPM model

The RMS, introduced by Grieser and Jewson (2012), is the basis of the TCPM. The RMS is based on climatological offshore rain rates parameterized by R-CLIPER (Tuleya et al. 2007), which takes into account temperature, simplified orography and landfall, rainbands, and the effects of extratropical transition and rain drift. Among these factors, the first two items are carefully weighed and the latter two items’ considerations are not adequate.

Therefore, the emphasis of the TCPM is to optimize the parametric scheme of the last two items, then systematically and comprehensively provide an optimized model. The first two parameterized schemes of the TCPM are both quoted from the RMS, whose formulas remain unchanged. However, the latter two parameterized schemes of the TCPM emphasize the key improvements of the RMS, including complex terrain and spiral rain belt. For the readability and comprehension of this paper, all essential formulas and instructions quoted from RMS are retained in the following explanation.

In brief, the components of the TCPM are symmetrical climatic precipitation, rain enhancement due to variations in thermal conditions on underlying surface, precipitation under the influence of complex terrain, and the spiral rain belt. Here, we elaborate the model in detail, explaining its formulation and parameters.

1) Symmetrical climatic precipitation

Using the precipitation retrieved from TRMM, the National Oceanic and Atmospheric Administration (NOAA) developed R-CLIPER by assuming an ideal symmetric distribution of precipitation (Tuleya et al. 2007). Using this ideal model for reference, the symmetric TC precipitation of the TCPM can be determined, the rain rate of which is represented as CR-CLI. The parameters in this function refer to the results corrected by Li (2014) on the basis of TRMM observations in the northwest Pacific Ocean during 1998–2011.

The TCPM is a horizontally distributed model in which symmetrical precipitation is estimated by water vapor. In the RMS model, Grieser and Jewson (2012) assume that the air column is saturated from the surface to a certain height; then, the negative variation rate in water vapor density S can be approximated as the TC condensation rate C of the air column. Thus, the instantaneous C caused by rising saturated air whose vertical velocity is w can be given as follows:
C=dSdt=dSdzdzdt=wdSdz,
S=S0exp(z/Hm),and
S0=0.622×6.1078R(T0+273)exp(aT0b+T),
where S0 is the surface saturation vapor density, Hm is the e-folding height of atmospheric moisture, and a = 17.67 and b = 243.5°C are the coefficients in the Magnus formula; T0 is the temperature at the surface level, and R = 287 J kg−1 K−1.
In a real TC, the air is saturated above a certain cloud-base level Zcb. Assuming a constant in-cloud updraft w and integrating Eq. (1) from Zcb upward provides an estimate of the column TC condensation rate C (Smith 1979; Roe 2005; Grieser and Jewson 2012):
C=wS0exp(Zcb/Hm).

2) Thermal condition on underlying surface

Sea surface temperature (SST) is an important factor affecting atmospheric saturation vapor pressure. According to the RMS model (Grieser and Jewson 2012), the symmetric rain rate dependent on the SST, or Ccl, can be considered as follows:
Ccl=CR-CLI[1+γ(T0T0¯)]and
γ=1CdCdT0=1T0+273+ab+T0aT0(b+T0)2,
where γ is the relative change in condensation rate with SST, T0 is the actual SST of a specific TC shortly before landfall, and T0¯ is set to the climatological SST, which is the average temperature for 24 h before TC landfall.

After a TC makes landfall, rain enhancement resulting from variations in thermal conditions of the underlying surface can be reflected from S0 in the basic formula.

3) Complex terrain

Previous studies have revealed that complex terrain plays an important role in causing the heavy precipitation of TCs making landfall (Chen and Ding 1979; Tao et al. 1980; Smith et al. 2009a,b; Yu and Cheng 2013; Xu et al. 2019). The influences can be subdivided into three aspects, including slope, roughness, and attenuation distance. Topographically induced convergence and upward motion is one of the most important mechanisms that may cause and strengthen storm rainfall. In the TCPM, these two aspects are mainly reflected by the wind field under complex terrain. In addition, for decay due to landfall over complex terrain, the thermodynamic and kinematic environment is affected, which in turn influences the precipitation processes that occur (DeHart 2017). This effect is mainly reflected by the sharp change in the distance-related boundary layer height in the TCPM. Therefore, the detailed description of this part includes the wind field under complex terrain and attenuation distance.

(i) Wind field

This paper decomposes complex terrain into the superposition of roughness (landform) and slope (topography) and then considers the influence of landform change and topography fluctuation on the wind field. The vertical updraft caused by the convergence and divergence of the horizontal wind field is an important factor driving TC precipitation (Dong et al. 2010). The horizontal wind field used in this paper is based on the gradient wind field model proposed by Georgiou (1985). This basic model without iterative calculation has high calculation efficiency, which meets the requirements of large sample acquisition for TC risk assessment. Moreover, due to the introduction of the Holland pressure field (Holland 1980), the characteristics of TCs in different sea areas can be considered.

This model (Georgiou 1985) expresses the balance of the forces generated by the horizontal pressure gradient, Coriolis acceleration and centrifugal acceleration in the presence of general storm translation. For the Northern Hemisphere and in polar coordinates, it is specified as follows:
Vg2(r,α)=rρP(r)r+Vg(r,α)(VTsinαfr)and
Ψg(r,α)=α+φ+90°,
where Vg(r, α) represents the gradient wind velocity at (r, α), which is 1 km above the ground; Ψg(r, α) is the wind direction corresponding to Vg(r, α); r is the radial distance from the TC center; α is the angle with clockwise positivity from the TC translation direction; ρ represents the air density; VT is the TC translation speed; f is the Coriolis parameter; φ is the TC translation direction with clockwise positivity from the north; and the pressure field P(r) can be given as follows (Holland 1980):
P(r)=Pc+(PwPc)exp[(Rmax/r)B],
where Pc is the central pressure, Pw is the ambient pressure, Rmax represents the radius of maximum winds, and B is the Holland parameter.

According to the above formula, two key parameters are essential, including Rmax and B. However, they have different characteristics in different oceans and vary with latitude in a given ocean (Fang et al. 2018). Thus, in this paper, these two parameters are determined by using the latest fitting results proposed by Fang et al. (2020) based on JTWC observations in the northwestern Pacific Ocean.

For the effect of roughness, the considerations over the ocean and land are different. For the open flat surface over the ocean, roughness is determined by waves related to wind speed (Fang et al. 2018, 2020). Therefore, a single wind speed reduction factor can be used to convert the gradient wind (1 km above) to the wind at 10 m above the ocean.

However, the underlying surface is complex for coastal areas in China, and not only is the terrain undulating, but the coastal roughness difference is also obvious due to natural and anthropogenic influences. In this situation, the single wind speed reduction factor mentioned above is not suitable for wind conversion after a TC makes landfall. Thus, Table 1 gives the wind speed ratios under four landform types to reflect the impact of landform changes on the wind field. The formula is as follows:
V10m(r,α)=R10mVg(r,α),
where R10m represent the wind speed ratios under four landform types, which can be calculated from the characteristic parameters (roughness, boundary layer height, and power-law exponent for the wind profile) in Table 1.
Table 1

Characteristic parameters and wind speed ratios of four landform types [load code for the design of building structures (GB50009-2012)].

Table 1
For the effect of slope, by introducing the terrain undulation coefficient Rtopography, which is associated with the slope β, the typhoon wind field formula under terrain undulation can be written as
vh=RtopographyV10m(r,α).
For windward slope:
Rtopography={1+2×0.3×tan(β)0<tan(β)<0.581+2×0.3×0.58tan(β)0.58.
For leeward slope:
Rtopography={12×0.3×tan(β)12tan(β)0.58<tan(β)<012×0.3×(0.58) 12×(0.58) tan(β)0.58.
Then, the complex-terrain-induced updraft Wtopography can be calculated by Wtopography=vhz, and the formula can be rewritten as
Wtopography=uzx+υzy,
where u and υ are the wind speeds in the x and y directions, respectively, forming the horizontal wind field vh, and z is the elevation of the terrain.

Through the above methods, the parameterized wind field model under complex terrain is completed, which provides horizontal and vertical wind fields (vh, Wtopography). Substituting the wind field into Eq. (4), the rain rate induced by the topographic slope and roughness Coro can be obtained.

(ii) Attenuation distance

As TCs make landfall, they are cut off from their oceanic energy sources and dissipate quickly. The effective height of the boundary layer is affected by the sharp change in the surface (Vickery et al. 2000). In this paper, the rain induced by landfall attenuation is characterized by the function of the effective height of the TC boundary layer Heff and vh.

Assuming incompressible flow, the equation of mass continuity may be written as follows:
wz=(ux+υy)=vh.
Moreover, w can be approximated as follows:
w(xt,z)0zvhdz(ux+υy)|z=10mHeff(xt),
where
Heff(xt)=Heff,[1exp(xt/xs)];
Heff, which is assumed to be zero at the coastline, increases gradually with distance inland from the coastline, and Heff,∞ is the maximum height of the TC boundary layer. The xt is the distance from the coastline, and xs is the distance when the height of the typhoon boundary layer decays. Substituting Eq. (16) into Eq. (4) yields the rain rate influenced by landfall attenuation Clf.

4) Spiral rain belt

The spiral rainband structure is one of the main characteristics of TCs. According to the observations of Guinn and Schubert (1993), the inner spiral is near the center of TCs, while the outer spiral is usually 500 km away from the center (Ren et al. 2011). The spiral rainbands of TCs are similar to equiangular spirals, and their intersection angles with isobars are between 10° and 20° (Chen and Ding 1979; Wang 2012; Yu et al. 2017; Li et al. 2017). Therefore, an equiangular spiral is used to describe the spiral angle θ in the TCPM (Sean et al. 1957; Willoughby 1978; Willoughby et al. 1984):
ln(rr0)=ln(r0)+tan αLθ,
where r0 is the initial radius of the spiral rainbands, which is set to 1.2 Rmax; αL is often 15° to represent the maximum intersection angle with isobar lines (Chen and Ding 1979).
Moreover, the TCPM incorporates an asymmetric precipitation distribution by adding an azimuthal Fourier decomposition of two waves. As the distance from the center of the TC increases, we gradually shift an increasingly symmetrical TC precipitation into asymmetric precipitation by applying a multiplicative weighting function to the rain rates calculated above. The specific formulas of the weight Casymmetry are defined as follows:
Casymmetry={1r<1.2Rmax1+r1.2Rmax3Rmax1.2Rmaxa0cos[2π(φφ0)]1.2Rmaxr3Rmax1+a0cos[2π(φφ0)]r>3Rmax,
where a0 is the relative amplitude of the asymmetry and φ0 includes a random variable and θ; a0, φ0, and θ represent the asymmetric strength and shape of a TC. The φ0 is based on the TC translation direction and takes a random value between −90° and 90°, which to a certain extent represents the combined effects of TC motion speed and vertical wind shear.
Based on the TC rain rates calculated above, the final hourly TC rain rate CTCPM is determined as follows:
CTCPM=(Ccl+Coro+Clf)Casymmetry.
In theory, the function above covers the symmetric climatic rain rate Ccl, rain rate induced by topographic slope and roughness Coro, and rain rate affected by attenuation distance Clf. At the same time, this function adjusts the spatial distribution of TC precipitation by a multiplicative weight Casymmetry.

3. Model validation

We develop a computational mesh with a 0.01° resolution, extending from the TC center to 500 km. The time resolution is 1 h. Model validation includes two parts: one is the detailed comparison of typical TC cases, and the other is the overall validation from 1960 to 2018. In the western North Pacific, TCs generally take three prevailing tracks, including the westward track, the northwestward track, and the northeastward recurving track (Wang et al. 2011). Therefore, we choose three extreme typhoons as representatives, namely, Saomai (2006), Rammasun (2014), and Meranti (2016).

a. Results of extreme TC cases

1) Introduction of extreme TC cases

Supertyphoon Saomai formed at 1200 UTC 5 August 2006 as a tropical depression and then rapidly strengthened into a supertyphoon by 1000 UTC 9 August 2006. It landed on the southern coast of Zhejiang Province at 0900 UTC 10 August, with a central pressure of 920 hPa and a maximum wind speed of 60 m s−1. Then Saomai weakened and dissipated inland approximately 1000 km west of the landfall point. The direct economic loss caused by Saomai reached CNY 19.7 billion (USD 3.07 billion) (Shanghai Typhoon Institute of Chinese Meteorological Administration 2008).

Supertyphoon Rammasun formed on 10 July 2014 as a tropical depression and then quickly gained strength and intensified to a supertyphoon at 0000 UTC 18 July 2014. Rammasun made landfall on Hainan Island and Guangdong Province successively, with maximum wind speeds of 72 and 70 m s−1, respectively. The direct economic losses reached CNY 44.89 billion (USD 7.0 billion), and 12.084 million people were affected (Shanghai Typhoon Institute of Chinese Meteorological Administration 2016).

Supertyphoon Meranti formed on 8 September 2016 as a tropical depression and then strengthened into a supertyphoon at 0300 UTC 12 September. After entering the Taiwan Strait, Meranti turned northwest and made landfall over Fujian Province at 1905 UTC 14 September, with a maximum wind speed of 52 m s−1. It gradually weakened across four provinces and ended up over the Yellow Sea. Meranti caused CNY 31.64 billion (USD 4.9 billion) in direct economic losses, and 3.755 million people were affected (Shanghai Typhoon Institute of Chinese Meteorological Administration 2018).

The information about these three TCs is given in Table 2. In addition, the tracks are shown in Fig. 1.

Fig. 1.
Fig. 1.

The tracks of Saomai (2006), Rammasun (2014), and Meranti (2016). [national standard (2006) on TC intensity (GBT 19201-2006)—tropical depression (TD): 10.8–17.1 m s−1; tropical storm (TS): 17.2–24.4 m s−1; strong tropical storm (STS): 24.5–32.6 m s−1; typhoon (TY): 32.7–41.4 m s−1; strong typhoon (STY): 41.5–50.9 m s−1; supertyphoon (SuperTY): ≥51.0 m s−1].

Citation: Journal of Applied Meteorology and Climatology 61, 9; 10.1175/JAMC-D-21-0157.1

Table 2

List of verified TC cases.

Table 2

2) Characteristics of precipitation distribution

Historical gauged and satellite-estimated TC precipitation of three typical TCs are used for this study. We simulate TC precipitation (i.e., total precipitation and hourly precipitation) by the TCPM.

Figure 2 displays the total precipitation of the three TCs. Overall, the comparisons indicate that the simulated TC precipitation, in terms of both distribution and intensity, mostly agree with the observations from stations and satellites, suggesting that the TCPM satisfactorily captures the dominant TC precipitation in China.

Fig. 2.
Fig. 2.

The simulated and observed total precipitation of three TCs, showing (left) simulations, (center) meteorological station observations, and (right) estimated precipitation by satellite for (a)–(c) Rammasun (2014), (d)–(f) Saomai (2006), and (g)–(i) Meranti (2016).

Citation: Journal of Applied Meteorology and Climatology 61, 9; 10.1175/JAMC-D-21-0157.1

Among all these cases, the simulations of Rammasun (2014) fit the gauge best, not only the distribution but also the extreme precipitation. According to the station observations, total precipitation of 50–150 mm was mainly distributed in southern Guangdong Province, central and western Guangxi Province, southwestern Guizhou Province, central and southern Yunnan Province, and central and eastern Sichuan Province. Precipitation of 150–300 mm mostly occurred in the south-central of Hainan Island, Zhanjiang in Guangdong Province, the southern coast of Guangxi Province, and the southern Yunnan Province. All of these characteristics can also be found in the simulated total precipitation. For total precipitation above 300 mm, both simulations and observations are concentrated from the northern Hainan Island to Fangchenggang in Guangxi Province. In particular, the locations of extreme precipitation are highly consistent. The simulated results for Saomai (2006) are also very impressive. The observed extreme total precipitation mainly appeared at the junction of Zhejiang and Fujian Provinces, as well as parts of north-central of Jiangxi Province. From the simulations, similar characteristics can be found, while the distribution is more extensive. At the periphery of the TC, the intensity of the simulated results is slightly higher than that of the observed results. According to the results of Meranti (2016), the distribution of simulated total precipitation is slightly different from the observation, and the result is not ideal, but the distribution of simulated results before the rapid weakening caused by the TC landfall is still considerable, especially on Taiwan Island and in southern Fujian Province.

In comparison with the historical satellite cloud images and inverse data (Fig. 2, right), the possible reasons for the above characteristics are analyzed. From the perspective of TC structure, Rammasun (2014) was more symmetric and tighter than the other two TCs. Thus, abundant heavy precipitation brought by this TC occurred near its center. Before making landfall, the structure of Saomai (2006) was similar to that of Rammasun (2014), with destructive precipitation at the landfall location. However, the overall circulation was small, which broke after landfall, and the intensity quickly dissipated, with TC precipitation declining at the same time. Different from the first two TCs, Meranti (2016) moved northward, with significant asymmetric structure. The northwest cloud system, which was far from the TC center, developed vigorously. The observations showed that the TC precipitation of Meranti (2016) gradually changed from precipitation near the TC center to precipitation at the TC periphery under interactions with other weather systems. Under the influence of the aftermath depression and peripheral circulations, Fujian, Zhejiang, and Jiangsu reported heavy or severe precipitation.

3) Comparison with other models

To intuitively understand the effect of the improved parameterized schemes, we ran the TCPM, R-CLIPER, and RMS models for the three TCs. Figure 3 shows the simulated total precipitation. It can be found that the simulated total precipitation of three models is relatively consistent over the ocean. However, when complex terrain is present, different from the underestimation of R-CLIPER and the overestimation of RMS, the simulation results of the TCPM are closer to reality.

Fig. 3.
Fig. 3.

The simulated total precipitation of three TCs by using different models, showing simulations modeled by (left) TCPM, (center) R-CLIPER, and (right) RMS for (a)–(c) Rammasun (2014), (d)–(f) Saomai (2006), and (g)–(i) Meranti (2016).

Citation: Journal of Applied Meteorology and Climatology 61, 9; 10.1175/JAMC-D-21-0157.1

The R-CLIPER model is a statistical model for the climate persistence of TC precipitation. It is based on the ideal assumption that TC precipitation is distributed in an isotropic symmetry and describes the precipitation structure based on data statistics. Rough parameterized schemes lose many important factors, such as topography. The ideal symmetrical distribution leads to the failure of precipitation distribution and extremes. Although the RMS considers the impact of the simplified terrain on the wind field, the model adopts the simplified wind field parameterized by the Willoughby wind profile (Willoughby et al. 2006). The wind field does not consider the landing attenuation and topographic effects, resulting in a serious overestimation of the total precipitation induced by terrain.

For the simulation capability of extreme precipitation, Table 3 shows the maximum total precipitation simulated by the TCPM, R-CLIPER, and RMS. It can be seen that the extreme total precipitation of R-CLIPER is far weaker than the TCPM result, while the extreme total precipitation of RMS is much stronger.

Table 3

The simulated maximum total precipitation of three TCs by using TCPM, R-CLIPER, and RMS, respectively.

Table 3

According to Figs. 1 and 2 and Table 2, we can obtain the following conclusions. 1) Not only the TCPM but also R-CLIPER and RMS can capture the overall distribution pattern of TC total precipitation. 2) For extreme total precipitation induced by terrain, compared with the severe underestimation of R-CLIPER and the severe overestimation of RMS, the TCPM considers more comprehensive parameterized schemes, an approach that is closer to reality, reflecting its advantages of improvements.

4) Analysis of extreme precipitation

To quantitatively evaluate the performance of the extreme simulation of the TCPM, Tables 4 and 5 show the error comparison of the simulated and observed maximum total precipitation and hourly precipitation of the three TCs.

Table 4

The simulated and observed maximum total precipitation of three TCs. The percentage of errors is given in parentheses.

Table 4
Table 5

The simulated and observed maximum hourly precipitation of three historical TCs. The percentage of errors is given in parentheses.

Table 5

In terms of the extreme total precipitation, the optimal simulation is from Rammasun (2014), whose error with station observations is only 29.2 mm. However, the errors between the simulated and observed extreme total precipitation of Saomai (2006) and Meranti (2016) are remarkable. Both simulations are higher than the observations, with differences of 433.3 and 250.3 mm, respectively. However, from the aspect of extreme hourly precipitation intensity, the simulated effect of Rammasun (2014) is inferior to the others (Table 3). Specifically, the simulated extreme hourly precipitation of Rammasun (2014) is weaker than the observed value, with an error of 64.6 mm. For Saomai (2006), the intensities of simulated and observed extreme hourly precipitation are consistent, with an error of only 4 mm, which is nearly the same. Similarly, the simulated and observed hourly precipitation of Meranti (2016) are also very close, with an error of 1.2 mm.

To perform a preliminary analysis for the above performance, we choose station matched points from the simulation grid. Figure 4 compares the gauged and simulated total precipitation of matching points. The simulated extreme total precipitation of Saomai (2006) and Meranti (2016) obviously falls back, which is more consistent with the observations. For instance, at all selected points of Saomai (2006), the simulated extreme total precipitation is 297.5 mm, which is slightly higher than the observed 265 mm. In addition, the location of heavy total precipitation basically matches the observations.

Fig. 4.
Fig. 4.

The simulated and gauged total precipitation of matching points of the three historical TCs, showing (left) modeled and (right) meteorological station observations for (a),(b) Rammasun (2014); (c),(d) Saomai (2006); and (e),(f) Meranti (2016).

Citation: Journal of Applied Meteorology and Climatology 61, 9; 10.1175/JAMC-D-21-0157.1

However, as described in section 3a(2), the asymmetric structure of Meranti (2016) is so significant that the center of outer heavy precipitation can hardly be simulated. For Rammasun (2014), when only grid points matching stations are selected, the whole distribution pattern of simulated total precipitation is consistent with the observed pattern, but the simulated intensity decreases slightly, which may be related to the sparse distribution of meteorological stations and the failure to match the grid with extreme precipitation.

b. Application to extended period

The ultimate goal of this model is to capture the possible extreme TC precipitation and to provide a scientific basis for catastrophic risk assessment. Therefore, as a further test, the model is applied to northwestern Pacific TCs making landfall in China from 1960 to 2018. As a reference, observed hourly precipitation data are available from the Yearbook of Tropical Cyclone in China, with 1401 stations in mainland China, as shown in Fig. 1.

From the best-track dataset of historical TCs, the applicability of the model is revealed by comparing the empirical cumulative distribution function (ecdf) for historical simulations and observations. For each TC, two variables are considered. First, the maximum hourly precipitation with a resolution of 0.01°. Second, the maximum total precipitation during the track of a TC 800 km before making landfall. To obtain a better view of the performance of the TCPM, when analyzing the extreme total TC precipitations, we select those grid points matching the observation stations to carry out point-to-point comparisons.

Figure 5 shows these cumulative distributions. The ecdfs of the modeled results are similar to the observations. Thus, the TCPM can capture the historical extreme hourly and total precipitations. For extreme hourly TC precipitation below 35 mm, the modeled results are underestimated. In contrast, this model overestimates the extreme hourly precipitations of 35–100 mm. When the extreme hourly precipitation reaches more than 100 mm, the ecdfs for the modeled results are basically consistent with the historical ones. For the extreme total precipitation, the ecdfs are more consistent. As shown in Fig. 5b, when the precipitation is above 300 mm, the two ecdfs almost coincide, which means that the performance of the TCPM is very good. Similar to the extreme hourly precipitation, the extreme total precipitation below 300 mm is underestimated, which may be related to the insufficient consideration of long-distance and peripheral precipitation in this model.

Fig. 5.
Fig. 5.

The ecdf for (a) extreme hourly TC precipitation and (b) extreme total TC precipitation of observed (black lines) and modeled (red lines) TCs landing in China during 1960–2018. The red shading represents the 95% confidence band.

Citation: Journal of Applied Meteorology and Climatology 61, 9; 10.1175/JAMC-D-21-0157.1

4. Summary and discussion

Through introducing several new parameterized schemes, the basic model (RMS) of this paper is improved in several aspects. By comprehensively considering the simplified thermal and dynamic effects, we revise the symmetric climate TC precipitation under the thermal condition of the underlying surface, optimize asymmetric TC precipitation under the effect of complex terrain, and improve the distribution of spiral rain belts. This optimized TCPM is computationally cheaper and physically based and largely captures the dominant processes that control the typhoon-induced rain rate. To evaluate the performance of this model, we apply it to three extreme TC cases representing typical TC tracks that influence China, including Saomai (2006), Rammasun (2014), and Meranti (2016), and historical TCs making landfall in China during the period from 1960 to 2018.

In general, the model shows impressive performances for strong TCs with heavy precipitation within 200–300 km of the TC center, especially for extreme TCs. For Rammasun (2014), both the pattern and maximum value of total precipitation are reproduced well by the model, but the extreme hourly precipitation is underestimated. In contrast, for Saomai (2006), whose intensity quickly dissipated, the simulated extreme hourly precipitation was very close to the observed value, but the extreme total precipitation was overestimated. Meranti (2016), with strong asymmetry, shows similar results as Saomai (2006). This similarity is closely related to the physical basis of this model. The model mainly reflects TC precipitation for strong TCs but hardly captures dispersive or long-distance precipitation. Moreover, both modeled extreme hourly and total precipitation probability distributions are basically consistent with the observations.

The historical simulations and comparison of three models illustrate that the TCPM has the advantage of considering the impact of complex terrain on precipitation. Different from the severe underestimation of R-CLIPER and the severe overestimation of RMS, the TCPM can capture the reasonable extreme precipitation, which is essential for risk assessment. Considering the computing performance of the TCPM, the model has strong portability, which can be widely used in TC rainstorm catastrophe risk assessment, which usually requires 10 000 samples. However, this model still has some shortcomings, such as insufficient consideration of thermal conditions, and the basic model is based on satellite data, whose precipitation has a certain underestimate. In addition, the TCPM is not suitable for refined TC precipitation simulation and forecasting, whose parameterized scheme needs further improvement.

To conclude, given the degree of simplification, we consider the simulated results of the TCPM to be surprisingly good. Further comparison of modeled and observed TC precipitation would undoubtedly identify weaknesses in this model and would help to improve the parameterizations.

Acknowledgments.

This study was sponsored by Shanghai Sailing Program (Grant 21YF1456900), the National Key R&D Program of China (Grant 2018YFB1501104), Shanghai Science and Technology Research Program (Grant 19dz1200101), fundamental research funds of the Shanghai Typhoon Institute of the China Meteorological Administration (Grant 2021JB06), and the Key Laboratory of Typhoon Observations and Forecasting, Wenzhou.

Data availability statement.

Datasets derived from public resources are shown in section 2, with the available locations. Because of confidentiality agreements, the STI-LTPreci-Sat data and hourly precipitation observed at stations can only be made available to bona fide researchers subject to a nondisclosure agreement. Details of the data and how to request access are available from the Shanghai Typhoon Institute, CMA.

REFERENCES

  • Chen, J., C. Chang, and T. Li, 2003: Annual cycle of the South China sea surface temperature using the NCEP/NCAR reanalysis. J. Meteor. Soc. Japan, 81, 879884, https://doi.org/10.2151/jmsj.81.879.

    • Search Google Scholar
    • Export Citation
  • Chen, L., and Y. Ding, 1979: An Introduction to West Pacific Typhoons (in Chinese). Chinese Science Press, 491 pp.

  • Chen, P., X. Lei, and M. Ying, 2013: Introduction and application of a new comprehensive assessment index for damage caused by tropical cyclones. Trop. Cyclone Res. Rev., 2, 176183, https://doi.org/10.6057/2013TCRR03.05.

    • Search Google Scholar
    • Export Citation
  • Chen, P., H. Yu, M. Xu, X. T. Lei, and F. Zeng, 2019: A simplified index to assess the combined impact of tropical cyclone precipitation and wind on China. Front Earth Sci., 13, 672681, https://doi.org/10.1007/s11707-019-0793-5.

    • Search Google Scholar
    • Export Citation
  • Chen, P., H. Yu, K. Cheung, J. Xin, and Y. Lu, 2021: A potential risk index dataset for landfalling tropical cyclones in the Chinese mainland (PRITC dataset V1.0). Adv. Atmos. Sci., 38, 17911802, https://doi.org/10.1007/s00376-021-0365-y.

    • Search Google Scholar
    • Export Citation
  • Czajkowski, J., G. Villarini, M. Montgomery, E. Michel-Kerjan, and R. Goska, 2017: Assessing current and future freshwater flood risk from North Atlantic tropical cyclones via insurance claims. Sci. Rep., 7, 41609, https://doi.org/10.1038/srep41609.

    • Search Google Scholar
    • Export Citation
  • DeHart, J. C., 2017: Orographic modification of precipitation processes in a tropical cyclone moving over a continental mountain range. Ph.D. thesis, University of Washington, 119 pp.

  • Dong, M., L. Chen, Y. Li, and C. Lu, 2010: Rainfall reinforcement associated with landfalling tropical cyclones. J. Atmos. Sci., 67, 35413558, https://doi.org/10.1175/2010JAS3268.1.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2013: Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc. Natl. Acad. Sci. USA, 110, 12 21912 224, https://doi.org/10.1073/pnas.1301293110.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2017: Assessing the present and future probability of hurricane Harvey’s rainfall. Proc. Natl. Acad. Sci. USA, 114, 12 68112 684, https://doi.org/10.1073/pnas.1716222114.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., R. Sundararajan, and J. Williams, 2008: Hurricanes and global warming: Results from downscaling IPCC AR4 simulations. Bull. Amer. Meteor. Soc., 89, 347368, https://doi.org/10.1175/BAMS-89-3-347.

    • Search Google Scholar
    • Export Citation
  • Fang, P., B. Zhao, Z. Zeng, H. Yu, X. Lei, and J. Tan, 2018: Effects of wind direction on variations in friction velocity with wind speed under conditions of strong onshore wind. J. Geophys. Res. Atmos., 123, 73407353, https://doi.org/10.1029/2017JD028010.

    • Search Google Scholar
    • Export Citation
  • Fang, P., G. Ye, and H. Yu, 2020: A parametric wind field model and its application in simulating historical typhoons in the western North Pacific Ocean. J. Wind Eng. Ind. Aerodyn., 199, 104131, https://doi.org/10.1016/j.jweia.2020.104131.

    • Search Google Scholar
    • Export Citation
  • Fang, W., and X. Shi, 2012: A review of stochastic modeling of tropical cyclone track and intensity for disaster risk assessment. Adv. Earth Sci., 27, 866875.

    • Search Google Scholar
    • Export Citation
  • Georgiou, P. N., 1985: Design wind speeds in tropical cyclone-prone regions. Ph.D. thesis, Faculty of Engineering Science, University of Western Ontario, 292 pp.

  • Gong, P., and Coauthors, 2019: Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Sci. Bull., 64, 370373, https://doi.org/10.1016/j.scib.2019.03.002.

    • Search Google Scholar
    • Export Citation
  • Grieser, J., and S. Jewson, 2012: The RMS TC-rain model. Meteor. Z., 21, 7988, https://doi.org/10.1127/0941-2948/2012/0265.

  • Guinn, T. A., and W. H. Schubert, 1993: Hurricane spiral bands. J. Atmos. Sci., 50, 33803380, https://doi.org/10.1175/1520-0469(1993)050<3380:HSB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Holland, G. J., 1980: An analytic model of the wind and pressure profiles in hurricanes. Mon. Wea. Rev., 108, 12121218, https://doi.org/10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157163, https://doi.org/10.1038/ngeo779.

  • Knutson, T. R., and Coauthors, 2013: Dynamical downscaling projections of twenty-first-century Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. J. Climate, 26, 65916617, https://doi.org/10.1175/JCLI-D-12-00539.1.

    • Search Google Scholar
    • Export Citation
  • Langousis, A., and D. Veneziano, 2009: Theoretical model of rainfall in tropical cyclones for the assessment of long-term risk. J. Geophys. Res., 114, D02106, https://doi.org/10.1029/2008JD010080.

    • Search Google Scholar
    • Export Citation
  • Langousis, A., D. Veneziano, and S. Chen, 2008: Boundary layer model for moving tropical cyclones. Hurricanes and Climate Change, Springer, 265286.

    • Search Google Scholar
    • Export Citation
  • Li, Q., Y. Wang, and Y. Duan, 2017: A numerical study of outer rainband formation in a sheared tropical cyclone. J. Atmos. Sci., 74, 203227, https://doi.org/10.1175/JAS-D-16-0123.1.

    • Search Google Scholar
    • Export Citation
  • Li, Y., 2014: Parametric modeling on tropical cyclone rainfall based on structure analysis: A case study in northwest Pacific basin with China as focus (in Chinese). Ph.D. thesis, Dept. of Disaster Reduction and Emergency Management, Beijing Normal University, 187 pp.

  • Lu, P., N. Lin, K. Emanuel, D. Chavas, and J. Smith, 2018: Assessing hurricane rainfall mechanisms using a physics-based model: Hurricanes Isabel (2003) and Irene (2011). J. Atmos. Sci., 75, 23372358, https://doi.org/10.1175/JAS-D-17-0264.1.

    • Search Google Scholar
    • Export Citation
  • Lu, X. Q., H. Yu, M. Ying, B. K. Zhao, S. Zhang, L. M. Lin, L. N. Bai, and R. J. Wan, 2021: Western North Pacific tropical cyclone database created by the China Meteorological Administration. Adv. Atmos. Sci., 38, 690699, https://doi.org/10.1007/s00376-020-0211-7.

    • Search Google Scholar
    • Export Citation
  • Lu, Y., F. M. Ren, and W. J. Zhu, 2018: Risk zoning of typhoon disasters in Zhejiang Province, China. Nat. Hazards Earth Syst. Sci., 18, 29212932, https://doi.org/10.5194/nhess-18-2921-2018.

    • Search Google Scholar
    • Export Citation
  • Peng, M. S., J. Bao-Fong, and R. T. Williams, 1999: A numerical study on tropical cyclone intensification. Part I: Beta effect and mean flow effect. J. Atmos. Sci., 56, 14041423, https://doi.org/10.1175/1520-0469(1999)056<1404:ANSOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Prat, O. P., and B. R. Nelson, 2013: Mapping the world’s tropical cyclone rainfall contribution over land using the TRMM multi-satellite precipitation analysis. Water Resour. Res., 49, 72367254, https://doi.org/10.1002/wrcr.20527.

    • Search Google Scholar
    • Export Citation
  • Ren, F., and C. Xiang, 2017: Review and prospect of researches on the prediction of precipitation associated with landfalling tropical cyclones (in Chinese). J. Mar. Meteor., 37, 818.

    • Search Google Scholar
    • Export Citation
  • Ren, F., G. Wu, X. Wang, Y. Wang, W. Dong, J. Liang, and L. Bai, 2011: Tropical Cyclone Affecting China in Recent 60 Years (in Chinese). Meteorology Press, 203 pp.

  • Rodgers, E. B., S. W. Chang, and H. F. Pierce, 1994: A satellite observational and numerical study of precipitation characteristics in western North Atlantic tropical cyclones. J. Appl. Meteor., 33, 129139, https://doi.org/10.1175/1520-0450(1994)033<0129:ASOANS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Roe, G. H., 2005: Orographic precipitation. Annu. Rev. Earth Planet. Sci., 33, 645671, https://doi.org/10.1146/annurev.earth.33.092203.122541.

    • Search Google Scholar
    • Export Citation
  • Sean, H. V., H. W. Hiser, and R. C. Bourret, 1957: Studies of hurricane spiral bands as observed on radar. National Hurricane Research Project Rep. 12, 13 pp.

  • Shanghai Typhoon Institute of Chinese Meteorological Administration, 2008: Yearbook of Tropical Cyclone in China for 2006 (in Chinese). Meteorological Press, 228 pp.

  • Shanghai Typhoon Institute of Chinese Meteorological Administration, 2016: Yearbook of Tropical Cyclone in China for 2014 (in Chinese). Meteorological Press, 195 pp.

  • Shanghai Typhoon Institute of Chinese Meteorological Administration, 2018: Yearbook of Tropical Cyclone in China for 2016 (in Chinese). Meteorological Press, 212 pp.

  • Smith, A. B., and R. W. Katz, 2013: US billion-dollar weather and climate disasters: Data sources, trends, accuracy and biases. Nat. Hazards, 67, 387410, https://doi.org/10.1007/s11069-013-0566-5.

    • Search Google Scholar
    • Export Citation
  • Smith, R. B., 1979: The influence of mountains on the atmosphere. Adv. Geophys., 21, 87230, https://doi.org/10.1016/S0065-2687(08)60262-9.

    • Search Google Scholar
    • Export Citation
  • Smith, R. B., P. Schäfer, D. J. Kirshbaum, and E. Regina, 2009a: Orographic enhancement of precipitation inside Hurricane Dean. J. Hydrometeor., 10, 820831, https://doi.org/10.1175/2008JHM1057.1.

    • Search Google Scholar
    • Export Citation
  • Smith, R. B., P. Schäfer, D. J. Kirshbaum, and E. Regina, 2009b: Orographic precipitation in the tropics: Experiments in Dominica. J. Atmos. Sci., 66, 16981716, https://doi.org/10.1175/2008JAS2920.1.

    • Search Google Scholar
    • Export Citation
  • Tao, S., and Coauthors, 1980: Rainstorms in China (in Chinese). Science Press, 225 pp.

  • Tuleya, R. E., M. Demaria, and R. J. Kuligowski, 2007: Evaluation of GFDL and simple statistical model rainfall forecasts for U.S. landfalling tropical storms. Wea. Forecasting, 22, 5670, https://doi.org/10.1175/WAF972.1.

    • Search Google Scholar
    • Export Citation
  • Vickery, P. J., P. F. Skerjl, and L. A. Twisdale, 2000: Simulation of hurricane risk in the U.S. using empirical track model. J. Struct. Eng., 126, 12221237, https://doi.org/10.1061/(ASCE)0733-9445(2000)126:10(1222).

    • Search Google Scholar
    • Export Citation
  • Villarini, G., R. Goska, J. A. Smith, and G. A. Vecchi, 2014: North Atlantic tropical cyclones and U.S. flooding. Bull. Amer. Meteor. Soc., 95, 13811388, https://doi.org/10.1175/BAMS-D-13-00060.1.

    • Search Google Scholar
    • Export Citation
  • Wang, J., C. Li, and P. Gong, 2015: Adaptively weighted decision fusion in 30 m land-cover mapping with Landsat and MODIS data. Int. J. Remote Sens., 36, 36593674, https://doi.org/10.1080/01431161.2015.1047049.

    • Search Google Scholar
    • Export Citation
  • Wang, R., L. Wu, and C. Wang, 2011: Typhoon track changes associated with global warming. J. Climate, 24, 37483752, https://doi.org/10.1175/JCLI-D-11-00074.1.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., 2012: Recent research progress on tropical cyclone structure and intensity. Trop. Cyclone Res. Rev., 1, 254275, https://doi.org/10.6057/2012TCRR02.05.

    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., 1978: A possible mechanism for the formation of hurricane rainbands. J. Atmos. Sci., 35, 838848, https://doi.org/10.1175/1520-0469(1978)035<0838:APMFTF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., F. D. Marks Jr., and R. J. Feinberg, 1984: Stationary and moving convective bands in hurricanes. J. Atmos. Sci., 41, 31893211, https://doi.org/10.1175/1520-0469(1984)041<3189:SAMCBI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., R. W. R. Darling, and M. E. Rahn, 2006: Parametric representation of the primary hurricane vortex. Part II: A new family of sectionally continuous profiles. Mon. Wea. Rev., 134, 11021120, https://doi.org/10.1175/MWR3106.1.

    • Search Google Scholar
    • Export Citation
  • Xu, Y., H. Qian, L. Luo, and H. Yu, 2019: A study of terrain correction method on typhoon precipitation based on ECMWF forecasts (in Chinese). Acta Meteor. Sin., 77, 674685, https://doi.org/10.11676/qxxb2019.037.

    • Search Google Scholar
    • Export Citation
  • Ying, M., W. Zhang, H. Yu, X. Lu, J. Feng, Y. Fan, Y. Zhu, and D. Chen, 2014: An overview of the China Meteorological Administration tropical cyclone database. J. Atmos. Oceanic Technol., 31, 287301, https://doi.org/10.1175/JTECH-D-12-00119.1.

    • Search Google Scholar
    • Export Citation
  • Yu, C. K., and L. W. Cheng, 2013: Distribution and mechanisms of orographic precipitation associated with Typhoon Morakot (2009). J. Atmos. Sci., 70, 28942915, https://doi.org/10.1175/JAS-D-12-0340.1.

    • Search Google Scholar
    • Export Citation
  • Yu, H., and L. Chen, 2019: Impact assessment of landfalling tropical cyclones: Introduction to the special issue. Front Earth Sci., 13, 669671, https://doi.org/10.1007/s11707-019-0809-1.

    • Search Google Scholar
    • Export Citation
  • Yu, Z., H. Yu, P. Chen, C. Qian, and C. Yue, 2009: Verification of tropical cyclone-related satellite precipitation estimates in mainland China. J. Appl. Meteor. Climatol., 48, 22272241, https://doi.org/10.1175/2009JAMC2143.1.

    • Search Google Scholar
    • Export Citation
  • Yu, Z., Y. Wang, H. Xu, N. Davidson, Y. Chen, and H. Yu, 2017: On the relationship between intensity and rainfall distribution in tropical cyclones making landfall over China. J. Appl. Meteor. Climatol., 56, 28832901, https://doi.org/10.1175/JAMC-D-16-0334.1.

    • Search Google Scholar
    • Export Citation
  • Yue, C., P. Chen, X. Lei, and Y. Yang, 2006a: A preliminary study on method of quantitative precipitation estimation (QPE) for landfall typhoon (in Chinese). Sci. Meteor. Sin., 26, 1723.

    • Search Google Scholar
    • Export Citation
  • Yue, C., P. Chen, X. Lei, and Y. Yang, 2006b: Preliminary study of short-term quantitative precipitation forecast method for landfalling typhoon (in Chinese). Sci. Meteor. Sin., 34, 711.

    • Search Google Scholar
    • Export Citation
  • Zhang, W., G. Villarini, G. A. Vecchi, and J. A. Smith, 2018: Urbanization exacerbated the rainfall and flooding caused by Hurricane Harvey in Houston. Nature, 563, 384388, https://doi.org/10.1038/s41586-018-0676-z.

    • Search Google Scholar
    • Export Citation
  • Zhou, H., 2017: Rainfall intensities simulation based on boundary model and rainfall risk analysis of typhoon (in Chinese). M.S. thesis, Dept. of Shenzhen Graduate, Harbin Institute of Technology, 70 pp.

Save
  • Chen, J., C. Chang, and T. Li, 2003: Annual cycle of the South China sea surface temperature using the NCEP/NCAR reanalysis. J. Meteor. Soc. Japan, 81, 879884, https://doi.org/10.2151/jmsj.81.879.

    • Search Google Scholar
    • Export Citation
  • Chen, L., and Y. Ding, 1979: An Introduction to West Pacific Typhoons (in Chinese). Chinese Science Press, 491 pp.

  • Chen, P., X. Lei, and M. Ying, 2013: Introduction and application of a new comprehensive assessment index for damage caused by tropical cyclones. Trop. Cyclone Res. Rev., 2, 176183, https://doi.org/10.6057/2013TCRR03.05.

    • Search Google Scholar
    • Export Citation
  • Chen, P., H. Yu, M. Xu, X. T. Lei, and F. Zeng, 2019: A simplified index to assess the combined impact of tropical cyclone precipitation and wind on China. Front Earth Sci., 13, 672681, https://doi.org/10.1007/s11707-019-0793-5.

    • Search Google Scholar
    • Export Citation
  • Chen, P., H. Yu, K. Cheung, J. Xin, and Y. Lu, 2021: A potential risk index dataset for landfalling tropical cyclones in the Chinese mainland (PRITC dataset V1.0). Adv. Atmos. Sci., 38, 17911802, https://doi.org/10.1007/s00376-021-0365-y.

    • Search Google Scholar
    • Export Citation
  • Czajkowski, J., G. Villarini, M. Montgomery, E. Michel-Kerjan, and R. Goska, 2017: Assessing current and future freshwater flood risk from North Atlantic tropical cyclones via insurance claims. Sci. Rep., 7, 41609, https://doi.org/10.1038/srep41609.

    • Search Google Scholar
    • Export Citation
  • DeHart, J. C., 2017: Orographic modification of precipitation processes in a tropical cyclone moving over a continental mountain range. Ph.D. thesis, University of Washington, 119 pp.

  • Dong, M., L. Chen, Y. Li, and C. Lu, 2010: Rainfall reinforcement associated with landfalling tropical cyclones. J. Atmos. Sci., 67, 35413558, https://doi.org/10.1175/2010JAS3268.1.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2013: Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc. Natl. Acad. Sci. USA, 110, 12 21912 224, https://doi.org/10.1073/pnas.1301293110.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2017: Assessing the present and future probability of hurricane Harvey’s rainfall. Proc. Natl. Acad. Sci. USA, 114, 12 68112 684, https://doi.org/10.1073/pnas.1716222114.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., R. Sundararajan, and J. Williams, 2008: Hurricanes and global warming: Results from downscaling IPCC AR4 simulations. Bull. Amer. Meteor. Soc., 89, 347368, https://doi.org/10.1175/BAMS-89-3-347.

    • Search Google Scholar
    • Export Citation
  • Fang, P., B. Zhao, Z. Zeng, H. Yu, X. Lei, and J. Tan, 2018: Effects of wind direction on variations in friction velocity with wind speed under conditions of strong onshore wind. J. Geophys. Res. Atmos., 123, 73407353, https://doi.org/10.1029/2017JD028010.

    • Search Google Scholar
    • Export Citation
  • Fang, P., G. Ye, and H. Yu, 2020: A parametric wind field model and its application in simulating historical typhoons in the western North Pacific Ocean. J. Wind Eng. Ind. Aerodyn., 199, 104131, https://doi.org/10.1016/j.jweia.2020.104131.

    • Search Google Scholar
    • Export Citation
  • Fang, W., and X. Shi, 2012: A review of stochastic modeling of tropical cyclone track and intensity for disaster risk assessment. Adv. Earth Sci., 27, 866875.

    • Search Google Scholar
    • Export Citation
  • Georgiou, P. N., 1985: Design wind speeds in tropical cyclone-prone regions. Ph.D. thesis, Faculty of Engineering Science, University of Western Ontario, 292 pp.

  • Gong, P., and Coauthors, 2019: Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Sci. Bull., 64, 370373, https://doi.org/10.1016/j.scib.2019.03.002.

    • Search Google Scholar
    • Export Citation
  • Grieser, J., and S. Jewson, 2012: The RMS TC-rain model. Meteor. Z., 21, 7988, https://doi.org/10.1127/0941-2948/2012/0265.

  • Guinn, T. A., and W. H. Schubert, 1993: Hurricane spiral bands. J. Atmos. Sci., 50, 33803380, https://doi.org/10.1175/1520-0469(1993)050<3380:HSB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Holland, G. J., 1980: An analytic model of the wind and pressure profiles in hurricanes. Mon. Wea. Rev., 108, 12121218, https://doi.org/10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157163, https://doi.org/10.1038/ngeo779.

  • Knutson, T. R., and Coauthors, 2013: Dynamical downscaling projections of twenty-first-century Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. J. Climate, 26, 65916617, https://doi.org/10.1175/JCLI-D-12-00539.1.

    • Search Google Scholar
    • Export Citation
  • Langousis, A., and D. Veneziano, 2009: Theoretical model of rainfall in tropical cyclones for the assessment of long-term risk. J. Geophys. Res., 114, D02106, https://doi.org/10.1029/2008JD010080.

    • Search Google Scholar
    • Export Citation
  • Langousis, A., D. Veneziano, and S. Chen, 2008: Boundary layer model for moving tropical cyclones. Hurricanes and Climate Change, Springer, 265286.

    • Search Google Scholar
    • Export Citation
  • Li, Q., Y. Wang, and Y. Duan, 2017: A numerical study of outer rainband formation in a sheared tropical cyclone. J. Atmos. Sci., 74, 203227, https://doi.org/10.1175/JAS-D-16-0123.1.

    • Search Google Scholar
    • Export Citation
  • Li, Y., 2014: Parametric modeling on tropical cyclone rainfall based on structure analysis: A case study in northwest Pacific basin with China as focus (in Chinese). Ph.D. thesis, Dept. of Disaster Reduction and Emergency Management, Beijing Normal University, 187 pp.

  • Lu, P., N. Lin, K. Emanuel, D. Chavas, and J. Smith, 2018: Assessing hurricane rainfall mechanisms using a physics-based model: Hurricanes Isabel (2003) and Irene (2011). J. Atmos. Sci., 75, 23372358, https://doi.org/10.1175/JAS-D-17-0264.1.

    • Search Google Scholar
    • Export Citation
  • Lu, X. Q., H. Yu, M. Ying, B. K. Zhao, S. Zhang, L. M. Lin, L. N. Bai, and R. J. Wan, 2021: Western North Pacific tropical cyclone database created by the China Meteorological Administration. Adv. Atmos. Sci., 38, 690699, https://doi.org/10.1007/s00376-020-0211-7.

    • Search Google Scholar
    • Export Citation
  • Lu, Y., F. M. Ren, and W. J. Zhu, 2018: Risk zoning of typhoon disasters in Zhejiang Province, China. Nat. Hazards Earth Syst. Sci., 18, 29212932, https://doi.org/10.5194/nhess-18-2921-2018.

    • Search Google Scholar
    • Export Citation
  • Peng, M. S., J. Bao-Fong, and R. T. Williams, 1999: A numerical study on tropical cyclone intensification. Part I: Beta effect and mean flow effect. J. Atmos. Sci., 56, 14041423, https://doi.org/10.1175/1520-0469(1999)056<1404:ANSOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Prat, O. P., and B. R. Nelson, 2013: Mapping the world’s tropical cyclone rainfall contribution over land using the TRMM multi-satellite precipitation analysis. Water Resour. Res., 49, 72367254, https://doi.org/10.1002/wrcr.20527.

    • Search Google Scholar
    • Export Citation
  • Ren, F., and C. Xiang, 2017: Review and prospect of researches on the prediction of precipitation associated with landfalling tropical cyclones (in Chinese). J. Mar. Meteor., 37, 818.

    • Search Google Scholar
    • Export Citation
  • Ren, F., G. Wu, X. Wang, Y. Wang, W. Dong, J. Liang, and L. Bai, 2011: Tropical Cyclone Affecting China in Recent 60 Years (in Chinese). Meteorology Press, 203 pp.

  • Rodgers, E. B., S. W. Chang, and H. F. Pierce, 1994: A satellite observational and numerical study of precipitation characteristics in western North Atlantic tropical cyclones. J. Appl. Meteor., 33, 129139, https://doi.org/10.1175/1520-0450(1994)033<0129:ASOANS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Roe, G. H., 2005: Orographic precipitation. Annu. Rev. Earth Planet. Sci., 33, 645671, https://doi.org/10.1146/annurev.earth.33.092203.122541.

    • Search Google Scholar
    • Export Citation
  • Sean, H. V., H. W. Hiser, and R. C. Bourret, 1957: Studies of hurricane spiral bands as observed on radar. National Hurricane Research Project Rep. 12, 13 pp.

  • Shanghai Typhoon Institute of Chinese Meteorological Administration, 2008: Yearbook of Tropical Cyclone in China for 2006 (in Chinese). Meteorological Press, 228 pp.

  • Shanghai Typhoon Institute of Chinese Meteorological Administration, 2016: Yearbook of Tropical Cyclone in China for 2014 (in Chinese). Meteorological Press, 195 pp.

  • Shanghai Typhoon Institute of Chinese Meteorological Administration, 2018: Yearbook of Tropical Cyclone in China for 2016 (in Chinese). Meteorological Press, 212 pp.

  • Smith, A. B., and R. W. Katz, 2013: US billion-dollar weather and climate disasters: Data sources, trends, accuracy and biases. Nat. Hazards, 67, 387410, https://doi.org/10.1007/s11069-013-0566-5.

    • Search Google Scholar
    • Export Citation
  • Smith, R. B., 1979: The influence of mountains on the atmosphere. Adv. Geophys., 21, 87230, https://doi.org/10.1016/S0065-2687(08)60262-9.

    • Search Google Scholar
    • Export Citation
  • Smith, R. B., P. Schäfer, D. J. Kirshbaum, and E. Regina, 2009a: Orographic enhancement of precipitation inside Hurricane Dean. J. Hydrometeor., 10, 820831, https://doi.org/10.1175/2008JHM1057.1.

    • Search Google Scholar
    • Export Citation
  • Smith, R. B., P. Schäfer, D. J. Kirshbaum, and E. Regina, 2009b: Orographic precipitation in the tropics: Experiments in Dominica. J. Atmos. Sci., 66, 16981716, https://doi.org/10.1175/2008JAS2920.1.

    • Search Google Scholar
    • Export Citation
  • Tao, S., and Coauthors, 1980: Rainstorms in China (in Chinese). Science Press, 225 pp.

  • Tuleya, R. E., M. Demaria, and R. J. Kuligowski, 2007: Evaluation of GFDL and simple statistical model rainfall forecasts for U.S. landfalling tropical storms. Wea. Forecasting, 22, 5670, https://doi.org/10.1175/WAF972.1.

    • Search Google Scholar
    • Export Citation
  • Vickery, P. J., P. F. Skerjl, and L. A. Twisdale, 2000: Simulation of hurricane risk in the U.S. using empirical track model. J. Struct. Eng., 126, 12221237, https://doi.org/10.1061/(ASCE)0733-9445(2000)126:10(1222).

    • Search Google Scholar
    • Export Citation
  • Villarini, G., R. Goska, J. A. Smith, and G. A. Vecchi, 2014: North Atlantic tropical cyclones and U.S. flooding. Bull. Amer. Meteor. Soc., 95, 13811388, https://doi.org/10.1175/BAMS-D-13-00060.1.

    • Search Google Scholar
    • Export Citation
  • Wang, J., C. Li, and P. Gong, 2015: Adaptively weighted decision fusion in 30 m land-cover mapping with Landsat and MODIS data. Int. J. Remote Sens., 36, 36593674, https://doi.org/10.1080/01431161.2015.1047049.

    • Search Google Scholar
    • Export Citation
  • Wang, R., L. Wu, and C. Wang, 2011: Typhoon track changes associated with global warming. J. Climate, 24, 37483752, https://doi.org/10.1175/JCLI-D-11-00074.1.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., 2012: Recent research progress on tropical cyclone structure and intensity. Trop. Cyclone Res. Rev., 1, 254275, https://doi.org/10.6057/2012TCRR02.05.

    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., 1978: A possible mechanism for the formation of hurricane rainbands. J. Atmos. Sci., 35, 838848, https://doi.org/10.1175/1520-0469(1978)035<0838:APMFTF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., F. D. Marks Jr., and R. J. Feinberg, 1984: Stationary and moving convective bands in hurricanes. J. Atmos. Sci., 41, 31893211, https://doi.org/10.1175/1520-0469(1984)041<3189:SAMCBI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., R. W. R. Darling, and M. E. Rahn, 2006: Parametric representation of the primary hurricane vortex. Part II: A new family of sectionally continuous profiles. Mon. Wea. Rev., 134, 11021120, https://doi.org/10.1175/MWR3106.1.

    • Search Google Scholar
    • Export Citation
  • Xu, Y., H. Qian, L. Luo, and H. Yu, 2019: A study of terrain correction method on typhoon precipitation based on ECMWF forecasts (in Chinese). Acta Meteor. Sin., 77, 674685, https://doi.org/10.11676/qxxb2019.037.

    • Search Google Scholar
    • Export Citation
  • Ying, M., W. Zhang, H. Yu, X. Lu, J. Feng, Y. Fan, Y. Zhu, and D. Chen, 2014: An overview of the China Meteorological Administration tropical cyclone database. J. Atmos. Oceanic Technol., 31, 287301, https://doi.org/10.1175/JTECH-D-12-00119.1.

    • Search Google Scholar
    • Export Citation
  • Yu, C. K., and L. W. Cheng, 2013: Distribution and mechanisms of orographic precipitation associated with Typhoon Morakot (2009). J. Atmos. Sci., 70, 28942915, https://doi.org/10.1175/JAS-D-12-0340.1.

    • Search Google Scholar
    • Export Citation
  • Yu, H., and L. Chen, 2019: Impact assessment of landfalling tropical cyclones: Introduction to the special issue. Front Earth Sci., 13, 669671, https://doi.org/10.1007/s11707-019-0809-1.

    • Search Google Scholar
    • Export Citation
  • Yu, Z., H. Yu, P. Chen, C. Qian, and C. Yue, 2009: Verification of tropical cyclone-related satellite precipitation estimates in mainland China. J. Appl. Meteor. Climatol., 48, 22272241, https://doi.org/10.1175/2009JAMC2143.1.

    • Search Google Scholar
    • Export Citation
  • Yu, Z., Y. Wang, H. Xu, N. Davidson, Y. Chen, and H. Yu, 2017: On the relationship between intensity and rainfall distribution in tropical cyclones making landfall over China. J. Appl. Meteor. Climatol., 56, 28832901, https://doi.org/10.1175/JAMC-D-16-0334.1.

    • Search Google Scholar
    • Export Citation
  • Yue, C., P. Chen, X. Lei, and Y. Yang, 2006a: A preliminary study on method of quantitative precipitation estimation (QPE) for landfall typhoon (in Chinese). Sci. Meteor. Sin., 26, 1723.

    • Search Google Scholar
    • Export Citation
  • Yue, C., P. Chen, X. Lei, and Y. Yang, 2006b: Preliminary study of short-term quantitative precipitation forecast method for landfalling typhoon (in Chinese). Sci. Meteor. Sin., 34, 711.

    • Search Google Scholar
    • Export Citation
  • Zhang, W., G. Villarini, G. A. Vecchi, and J. A. Smith, 2018: Urbanization exacerbated the rainfall and flooding caused by Hurricane Harvey in Houston. Nature, 563, 384388, https://doi.org/10.1038/s41586-018-0676-z.

    • Search Google Scholar
    • Export Citation
  • Zhou, H., 2017: Rainfall intensities simulation based on boundary model and rainfall risk analysis of typhoon (in Chinese). M.S. thesis, Dept. of Shenzhen Graduate, Harbin Institute of Technology, 70 pp.

  • Fig. 1.

    The tracks of Saomai (2006), Rammasun (2014), and Meranti (2016). [national standard (2006) on TC intensity (GBT 19201-2006)—tropical depression (TD): 10.8–17.1 m s−1; tropical storm (TS): 17.2–24.4 m s−1; strong tropical storm (STS): 24.5–32.6 m s−1; typhoon (TY): 32.7–41.4 m s−1; strong typhoon (STY): 41.5–50.9 m s−1; supertyphoon (SuperTY): ≥51.0 m s−1].

  • Fig. 2.

    The simulated and observed total precipitation of three TCs, showing (left) simulations, (center) meteorological station observations, and (right) estimated precipitation by satellite for (a)–(c) Rammasun (2014), (d)–(f) Saomai (2006), and (g)–(i) Meranti (2016).

  • Fig. 3.

    The simulated total precipitation of three TCs by using different models, showing simulations modeled by (left) TCPM, (center) R-CLIPER, and (right) RMS for (a)–(c) Rammasun (2014), (d)–(f) Saomai (2006), and (g)–(i) Meranti (2016).

  • Fig. 4.

    The simulated and gauged total precipitation of matching points of the three historical TCs, showing (left) modeled and (right) meteorological station observations for (a),(b) Rammasun (2014); (c),(d) Saomai (2006); and (e),(f) Meranti (2016).

  • Fig. 5.

    The ecdf for (a) extreme hourly TC precipitation and (b) extreme total TC precipitation of observed (black lines) and modeled (red lines) TCs landing in China during 1960–2018. The red shading represents the 95% confidence band.

All Time Past Year Past 30 Days
Abstract Views 1195 253 0
Full Text Views 808 652 39
PDF Downloads 468 317 28