Sensitivity of Precipitation and Structure of Typhoon Hato to Bulk and Explicit Spectral Bin Microphysics Schemes

Shen, Xiaodian; Jiang, Baolin; Cao, Qimin; Lin, Wenshi; Zhang, Lan

doi:https://doi.org/10.1155/2019/2536413

Advances in Meteorology

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Abstract Introduction Results and Discussion Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2019 | Article ID 2536413 | https://doi.org/10.1155/2019/2536413

Sensitivity of Precipitation and Structure of Typhoon Hato to Bulk and Explicit Spectral Bin Microphysics Schemes

Xiaodian Shen,¹Baolin Jiang,^1,2,3Qimin Cao,¹Wenshi Lin,^1,2,3and Lan Zhang⁴

Academic Editor: Yoshihiro Tomikawa

Received18 Jan 2019

Revised17 Oct 2019

Accepted26 Oct 2019

Published12 Dec 2019

Abstract

This study simulated the evolution of Typhoon Hato (2017) with the Weather Research and Forecasting model using three bulk schemes and one bin scheme. It was found that the track of the typhoon was insensitive to the microphysics scheme, whereas the degree of correspondence between the simulated precipitation and the cloud structure of the typhoon was closest to the observations when using the bin scheme. The different microphysical structure of the bin and three bulk schemes was reflected mainly in the cloud water and snow content. The three bulk schemes were found to produce more cloud water because the application of saturation adjustment condensed all the water vapor at the end of each time step. The production of more snow by the bin scheme could be attributed to several causes: (1) the calculations of cloud condensation nucleus size distributions and supersaturation at every grid point that cause small droplets to form at high levels, (2) different fall velocities of different sizes of particles that mean small particles remain at a significant height, (3) sufficient water vapor at high levels, and (4) smaller amounts of cloud water that reduce the rates of riming and conversion of snow to graupel. The distribution of hydrometeors affects the thermal and dynamical structure of the typhoon. The saturation adjustment hypothesis in the bulk schemes overestimates the condensate mass. Thus, the additional latent heat makes the typhoon structure warmer, which increases vertical velocity and enhances convective precipitation in the eyewall region.

1. Introduction

Recent rapid developments in computing technology and numerical modeling capability have enabled progress in simulation-based research using horizontal grid spacing with <5 km resolution. Because models with such fine resolution can resolve most convective clouds, explicit cloud microphysical schemes have replaced the cumulus convective parameterization schemes used previously in cloud-resolving models [1]. Thus, the explicit representation of microphysical processes is becoming increasingly important in numerical cloud-resolving models. Many microphysical parameterization schemes from bulk microphysics parameterization schemes (hereafter, bulk schemes) to spectral bin microphysics parameterization schemes (hereafter, SBM schemes) have been developed, improved, and applied to study the precipitation processes of typhoons [2].

Bulk schemes were developed earlier than SBM schemes, and they have been applied extensively in cloud-resolving models because of their advantageous computational efficiency and capability to reproduce observed features of clouds and precipitation in typhoons [3–13]. For example, after satisfactory simulation of Typhoon Hagupit (2008) in terms of its track, intensity change, wind, and precipitation distribution by the Weather Research and Forecasting (WRF) model with the WSM6 bulk scheme, further research quantified every conversion rate and then analyzed the cloud microphysical processes, which revealed that the precipitation and structure of typhoons are influenced substantially by changes in microphysical processes [14, 15].

In recent years, simulations have increasingly used SBM schemes instead of bulk schemes, especially following a demonstration of the SBM Hebrew University Cloud Model [16–21]. By applying different microphysical schemes with various aerosol concentrations, the simulations of Hurricane Irene showed that storm intensity varied greatly and that only the SBM experiment with different aerosol concentrations could reproduce the observed temporal shift between the maximum wind and the minimum pressure [22].

In essence, the same microphysical processes are represented differently in bulk and SBM schemes. Bulk schemes calculate microphysical equations for several particle size distribution (PSD) moments, that is, n(m) with m being the particle mass, rather than for the PSDs of all types of hydrometeors. The k-th moment of the PSD can be defined as follows:in which k is usually taken as an integer. In contrast, SBM schemes do not require an a priori functional form of the PSDs. The primary differences between SBM and bulk schemes are that SBM schemes include parameterizations of various microphysical processes and interactions between particles that are more specific. Through dividing the particle distribution into a number of finite size or mass categories and then calculating the explicit microphysical equations, the representation of microphysical processes in SBM schemes is more reasonable than that in bulk schemes, which could improve simulation of precipitation. Furthermore, SBM schemes use identical equations and parameters to represent microphysical processes; therefore, any particular scheme could be applied without modification to different meteorological phenomena [2].

Although bulk schemes are used widely, they incorporate many unreasonable representations of microphysical processes that could cause errors in numerical simulations [2]. For example, many simulations using bulk schemes overestimate the amount of rainfall. Furthermore, the various bulk schemes tend to differ considerably in terms of the number of interactions between microphysical particles, which means different simulations using different bulk schemes produce results with large dispersion [22].

Typhoons are important weather systems characterized by extreme winds and torrential rainfall. Cloud microphysical processes play a major role in the development and evolution of the cloud structure and precipitation of typhoons [10]. Analysis of microphysical processes to investigate the sensitivities of typhoons or precipitation to different factors has become increasingly popular in recent years. Considerable research has been undertaken to verify and improve the efficacy of microphysical schemes through comparison and analysis of the effects of different schemes on the typhoon structure or precipitation [23–28]. For example, some sensitivity analyses have been undertaken to identify the optimal combination of microphysical and convective schemes [29]. However, many previous studies have focused on analysis of different bulk schemes despite the rapid development of the new approach using SBM schemes. In comparison with bulk schemes, the representation of microphysical processes in SBM schemes is more reasonable, which could lead to improved simulation of precipitation. Thus, it is valuable to study and compare the effects of bulk and SBM schemes on typhoon precipitation and structure.

In this study, we used the WRF model to conduct simulation experiments of Typhoon Hato (2017) at cloud-resolving 3 km resolution. To examine the sensitivity of both the precipitation and the structure of typhoons to microphysical schemes, three bulk schemes (i.e., Lin, WDM6, and Morrison) and one bin scheme (SBM) were chosen.

2. Typhoon Development and Experimental Design

2.1. Typhoon Development

Typhoon Hato (1713) was originally developed from an easterly wave. After strengthening to a severe tropical storm at 0000 UTC on August 22, Hato entered the northeastern South China Sea and was upgraded to a typhoon at 0700 UTC and then to a super typhoon at 2300 UTC with the minimum pressure of 950 hPa and the maximum wind speed of 42 m/s. Although it was close to the Chinese mainland, it continued to strengthen. It reached the peak strength (48 m/s, 940 hPa) at 0300 UTC on August 23 and made landfall at the coastal city of Zhuhai, Guangdong Province, at 0450 UTC with a maximum wind speed of 45 m/s. Then, Hato weakened and gradually dissipated in Guangxi Province. The landing of Hato coincided with the astronomical high tide, causing great damage to Zhuhai, Hong Kong, and Macao, resulting in 24 deaths and $6.82 billion in economic losses. The 50th annual conference of the Typhoon Committee in the Vietnamese capital, Hanoi, from February 28 to March 3, 2018, decided to retire the name of Hato in view of the extensive damage and death toll it caused.

2.2. Scheme Description and Experimental Design

The Advanced Research WRF modeling system, whose solver is based on fully compressible nonhydrostatic equations, is a state-of-the-art atmospheric simulation system with terrain-following hydrostatic-pressure vertical coordinates. Arakawa C-grid staggering, the Runge–Kutta second- and third-order time integration options, and second- and sixth-order advection options (horizontal and vertical) are used in the Advanced Research WRF solver. It is portable and efficient on a single processor as well as being available for parallel computing platforms. The cloud model has been applied widely in numerical simulations on various scales ranging from meters to thousands of kilometers, which demonstrates its capability and suitability for use in atmospheric research [30].

This study used WRF version 3.6.1 to simulate Typhoon Hato by performing 60 h integrations that were initialized at 1200 UTC on August 21, 2017, and ended at 0000 UTC on August 24, 2017. The initial 12 h period was treated as the model spin-up time. As shown in Figure 1, the simulation incorporated two nested domains: d01 and d02, which had 9 and 3 km horizontal resolution [31] and corresponding time steps of 30 and 10 s, respectively. In the vertical, all simulations had 43 sigma layers with the model top at 50 hPa. For the initial and boundary conditions, we used the NCEP/NCAR global grid reanalysis dataset (NCEP-FNL; http://rda.ucar.edu/datasets/ds083.2), which is available with 1° × 1° spatial resolution and 6 h temporal resolution. None of the four experiments included assimilated observational data.

Domain d01 used the Kain–Fritsch cumulus parameterization scheme [32–34]; however, no cumulus parameterization scheme was used for domain d02 because updrafts can be resolved sufficiently at 3 km resolution, resulting in explicit convective vertical transport [1]. Long and shortwave radiation was computed using the Rapid Radiative Transfer Model for global climate models [35–37]. Boundary layer processes were parameterized using the Yonsei University planetary boundary layer scheme [33, 36, 38]. For the surface layer option, the revised MM5 Monin–Obukhov scheme was selected. The land surface option was set as the unified Noah land surface model. The scheme combination we used has been proved to be suitable for simulating typhoons [23, 39].

In this study, all four experiments shared the same configuration shown above, but they incorporated different microphysical schemes. We chose three widely used bulk schemes (i.e., Lin, WDM6, and Morrison) for comparison with the SBM scheme, and each experiment was named in accordance with the microphysical parameterization scheme adopted.

The four microphysical schemes used have all been implemented in WRF model version 3.6.1, and they all contain six hydrometeors: water vapor, cloud water, rainwater, cloud ice, snow, and graupel. The Lin microphysics scheme [40–42] is a single-moment bulk scheme that outputs the particle mixing ratio. It has been developed by incorporating equations for snow in the bulk water microphysics scheme of Orville and Kopp [43]. In the Lin scheme, snow can be generated both by the Bergeron process and contact freezing and by aggregation of cloud ice, following which it must then grow further to transform into hail. Cloud ice is allowed to coexist with cloud water in the temperature region of −40 to 0°C. Both the autoconversion of cloud water to form rain and the accretion of cloud water by rain are two major processes included for the formation of warm rain. The WDM6 scheme is a double moment developed by adding only double-moment warm-rain microphysics to the WSM6 scheme. The revised approach to the microphysical processes of ice has two distinguishing assumptions: (1) the number concentration of ice nuclei is a function of temperature and (2) the number concentration of ice crystals is a function of the amount of ice. Because of the indirect influence of the predicted number concentrations of cloud and rainwater on ice processes, it is an improvement on the single-moment scheme [42, 44, 45]. The Morrison scheme is a complete double-moment scheme [46]. The mass mixing ratios of five hydrometeor categories (cloud drops, ice, snow, rain drops, and graupel) are predicted, along with their total number concentrations.

The SBM schemes developed at the Hebrew University of Jerusalem (Israel) have been implemented in the WRF model. All the particles they contain are divided into 33 mass bins, and the equation system is solved for the size distribution functions of such particles. The full-SBM scheme has demonstrated strong universality in simulating various weather phenomena without requiring modification; however, its use remains restricted because of the enormous computational time required for the calculation of the equations. Therefore, the fast-SBM scheme, which is based on the full-SBM scheme, was developed to improve the overall computational efficiency. By calculating all ice crystal and snow aggregates on one mass grid, the fast-SBM scheme reduces the computation time to less than 20% that of the full-SBM scheme, which means it can be run on standard PC clusters. The fast-SBM scheme describes identical microphysical processes as the full-SBM scheme by solving kinetic equations with no a priori function, which means it retains all the advantages of an SBM scheme. Simulations of squall lines conducted using both fast-SBM and full-SBM schemes have shown they produce similar microphysical and dynamical structures and equivalent amounts of precipitation. Thus, the fast-SBM scheme was considered suitable for use in this study [2, 17, 20–22, 47].

3. Results and Discussion

3.1. Comparison with Observations

Figure 2 shows the observed track of Typhoon Hato, obtained from the China Meteorological Administration Shanghai Typhoon Institute (CMA; http://www.typhoon.org.cn/), and the results of the four simulations at 3 h intervals from 0000 UTC on August 22 to 0000 UTC on August 24, 2017. It can be seen that the results of the four experiments are similar and that the simulated tracks agreed well with the observations before the typhoon made landfall, reflecting the capability of the model in simulating the typhoon track. The simulated times of landfall (at about 0900 UTC on August 23) lag behind the observed time by about 4 h. After the typhoon made landfall, the tracks varied a little, which may be caused by the landfall time lag and the influence of topography.

Figure 3 shows the minimum sea level pressure and the maximum surface wind speed of the four simulations of Typhoon Hato together with the observed values. The simulated minimum sea level pressure and maximum surface wind speeds were in agreement with the observed values while three bulk experiments simulated a stronger typhoon than the SBM scheme. The minimum sea level pressure values of Lin and Morrison at 0700 UTC on August 23 were 937.0 and 942.8 hPa, respectively, which were very close to the observed pressure (i.e., 940 hPa at 1000 UTC). After landfall, the observed and simulated intensities all weakened rapidly, but the simulated intensities weakened more slowly than the observed ones, which might be attributable to the delayed time of landfall.

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Figure 4 shows the observed and simulated 24 h accumulated precipitation (0000 UTC on August 23 to 0000 UTC on August 24, 2017) of Typhoon Hato. Generally, the precipitation distribution in each of the experiments was similar to that observed. For example, the main precipitation event observed in western Guangdong Province was reproduced by all the experiments; however, the maximum rainfall amount of the Lin, WDM6, Morrison, and SBM experiments was 506.53, 408.05, 454.26, and 261.24 mm, respectively, while the observed value was 322.4 mm. The bulk schemes produced extremely torrential rainfall. Moreover, the area in which precipitation was >200 mm was obviously too large in the three bulk experiments, whereas it was closer to the observations in the SBM experiment. Figure 5 reveals that the three bulk schemes overestimated the rainfall in the eyewall area, which led to overestimation of the rainfall amount along the typhoon track.

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From the above analysis, it was evident that the different microphysical schemes produced similar typhoon tracks as in previous studies but with different precipitation [6, 48, 49]. The occurrence of precipitation is closely related to cloud microphysics; therefore, it is important to consider how the different cloud microphysical schemes might produce differences in precipitation.

3.2. Cloud and Snow

Figure 6 compares the fields of vertically integrated mixing ratios of cloud water in the four experiments at the time of landfall. The maximum vertically integrated mixing ratio of cloud water reached only 3.2 kg/m² in the SBM experiment; however, it exceeded 8.3 kg/m² in all three bulk experiments (it even reached over 16.0 kg/m² in WDM6). Furthermore, in the bulk experiments, cloud water was located across the entire range of the typhoon, and its areal coverage was wider than that in the SBM experiment. In bulk schemes, new nucleated droplets obey a gamma distribution similar to other cloud droplets [2]. The formation of large amounts of cloud water might increase the mixing ratio of cloud water, although its existence could be unreasonable. Furthermore, the application of saturation adjustment in bulk schemes leads to condensation of all water at the end of each time step [2], which might form additional cloud water. Moreover, large cloud droplets could absorb more water vapor, which would decrease the supersaturation and hinder growth of the effective radius, preventing rainwater formation. Our simulated results showed there was less rainwater in the bulk schemes than in the SBM experiment (Table 1).

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As all hydrometeors are distributed in three dimensions, we plotted the vertical profiles of the hydrometeors by calculating the domain average within a 210 km radius of the typhoon center at the time of landfall (Figure 7). It can be seen that the SBM and bulk experiments differed most in terms of cloud water and snow. The differences in the horizontal distribution and magnitude of cloud water have been discussed above. Figure 7 shows that little cloud water occurred above the height of 9 km in the SBM experiment, whereas significant cloud water content reached the height of 11 km in the bulk experiments (even reaching 13 km when using the Lin scheme). The overestimation of cloud water at such levels in the bulk experiments could increase the rates of riming and of the conversion of snow to graupel [22], leading to the lower snow content and larger graupel content. The snow content represented the greatest difference among all the hydrometeors between the bulk and SBM experiments, which can be seen in both vertical and horizontal distributions. In the SBM experiment, the snow content was considerable from 5 to 16 km; it was >0.7 g/kg between 7 and 12 km with a maximum of about 0.95 g/kg at the height of 9 km. Conversely, in all the bulk experiments, the maximum snow content was less than 0.5 g/kg. The horizontal distribution and magnitude of snow content are shown in Figure 8. The maximum vertically integrated mixing ratio of snow in the SBM experiment was >24 kg/m², whereas in the bulk experiments, it was about 8 kg/m². Furthermore, the horizontal distribution of snow in the SBM experiment was obviously larger than that in the bulk experiments. The process of in-cloud nucleation in the SBM experiment, which determines the cloud condensation nucleus size distribution with supersaturation at every grid point, leads to penetration of greater numbers of small droplets to higher levels. The presence of these small droplets has an effect on ice microphysics [2], which could explain the large snow content.

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The collision and aggregation of particles in all the bulk schemes used averaged values, and those particles obeyed a gamma distribution after each microphysical process; however, this could result in extremely unreasonable vertical distribution of the particles [2]. Conversely, in the SBM scheme, these microphysical processes depended on the type and mass of particles, which lead to size sorting in the vertical direction that was considered a benchmark. From the vertical profiles, it can be seen that the content of solid particles in the SBM experiment was concentrated mainly above the height of 6 km and that the water vapor extended to a higher level than that in the bulk experiments. Table 1 shows that the ice water path (IWP) in the SBM experiment was larger than that in the bulk schemes. These results indicate that the generation of solid particles might be caused by nucleation with subsequent condensational growth from water vapor. In the SBM experiment, the higher fall velocities of large particles in comparison with small particles meant large particles descended, whereas small particles remained floating at higher levels. In the presence of abundant water vapor, these small particles could be nucleated as ice nuclei. In contrast, the gamma distribution of particles in the bulk experiments produced minimal content of ice crystals above the cloud base. Furthermore, the averaged fall velocities of the particles introduced errors in the self-collision and aggregation processes [2] that could lead to miscalculation of the snow content. All these mechanisms, including the interaction of warm and ice microphysics, resulted in different distributions of hydrometeors, which could influence the thermal and dynamical structure of a typhoon, as well as its precipitation rate.

3.3. Precipitation Types

To explore the reasons for the different distributions and magnitudes of precipitation associated with the different microphysical schemes, we calculated certain microphysical quantities and we separated the precipitation types. We followed the method of Sui et al. [50] to separate precipitation into convective, stratiform, and mixed types. First, we calculated the vertically integrated mixing ratios of cloud hydrometeors. Then, the sum of the vertically integrated mixing ratio of ice/water hydrometeors was calculated as the IWP/liquid water path (LWP). Then, the cloud ratio, defined as the ratio of IWP to LWP, was used to evaluate the relative importance of ice and warm clouds. The grid was designed to represent convective rainfall when the corresponding cloud ratio was <0.2 or when the IWP value was >2.55 kg/m², and to represent stratiform rainfall when the corresponding IWP value was <2.55 kg/m² and when the cloud ratio was >1.0; the remaining grids were regarded as mixed precipitation.

Table 1 shows that the grid number percentages of both stratiform-type and convective-type precipitation in the SBM experiment were larger than those in the three bulk experiments both before and after typhoon landfall. The spiral distribution at 0900 UTC on August 23, 2017, is plotted in Figure 9. Corresponding to the distribution of 24 h accumulated rainfall, the distribution of both convective-type and stratiform-type precipitation in the SBM experiment was wider than that in the bulk experiments. It can be seen that the area of convective-type precipitation in the three bulk experiments was concentrated in the typhoon eyewall region, especially in the single-moment experiments (i.e., Lin), which caused extremely torrential rainfall in that area with a maximum value far greater than that observed. In the SBM experiment, the wider distribution of stratiform-type precipitation, which has a smaller precipitation rate relative to convective-type precipitation, produced a wider area of precipitation closer to the observations.

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According to the method adopted, when the IWP value of a grid was >2.55 kg/m², the grid was designated as convective-type precipitation. This was because grids with large IWP values might have strong ascent. Stratiform-type precipitation was assigned to grids with a cloud ratio <1.0, meaning ice cloud processes were more important than warm cloud processes in a typhoon [50]. The larger distributions of stratiform-type and convective-type precipitation in the SBM experiment than those found in the bulk experiments corresponded to the larger value and wider distribution of snow, indicating the importance of ice cloud processes in a typhoon. Table 1 shows that the IWP values in the SBM experiment before and after typhoon landfall were 3.73 and 3.65 kg/m², respectively, whereas the IWP value in the bulk experiments was just below 2.9 kg/m².

As mentioned above in relation to the SBM experiment, the calculation of the cloud condensation nucleus size distribution and supersaturation at every grid means the in-cloud nucleation processes transported small droplets to higher levels, and then the different fall velocities of the particles of different size produced appropriate sorting. Small ice crystals nucleated as ice nuclei that grew at such high levels make more solid particles, leading to an increased IWP value. In the bulk experiments, the spectrum of new nucleated droplets above the cloud base remained described by a gamma function, leading to unreasonable expression of large cloud droplets and preventing raindrop formation. Thus, there might be more cloud water but less rainwater in the bulk experiments in comparison with the SBM experiment. Furthermore, the cloud water at high levels in the bulk experiments had an effect on ice microphysical processes, leading to unreasonable representation of solid particles. Overall, these differences in hydrometeor content between the experiments not only affected the warm/ice microphysics but also changed the dynamical structure of the typhoon (as discussed below) and, ultimately, influenced the rate and type of precipitation.

3.4. Thermodynamic Structure

The different distributions of hydrometeors attributable to cloud microphysics could be expected to change the thermodynamics of a typhoon and produce a dynamical response. Figure 10 shows the axially symmetric vertical structure of temperature deviation and vertical velocity before the typhoon made landfall. It is evident that all experiments produced a warm core structure in the center of the typhoon, consistent with the facts, although the range and intensity of the warm core vary. The warm core structure in Lin and WDM6 experiments reached a height of about 8 km over the typhoon center, while in Morrison and SBM experiments, it was higher. The maximum temperature in each of the three bulk experiments was larger than 8.0°C, but in the SBM experiment, it was only 6.8°C. We considered the stronger warm core in the bulk experiments was attributable to the application of saturation adjustment in simulating a typhoon with large updrafts. In convective updrafts, especially in a typhoon, supersaturation could reach several percent because not all the water vapor condenses when supersaturation is greater than zero [2]. Saturation adjustment assumes that supersaturation over water is forced to zero. As mentioned earlier, water vapor condensed to produce excess cloud water in bulk schemes, meaning it overestimated the condensate mass and released more latent heat, which is in accord with the simulation of structures with stronger warm cores. Furthermore, the warm core in the SBM experiment was higher, which had a substantial effect on ice microphysical processes. It was also more peripheral, indicating greater condensation that was reflected in the larger distributions of both stratiform-type and convective-type precipitation.

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The greater the release of latent heat, the deeper the warm core structure and the larger the vertical velocity. From Figure 10, it can be seen that the vertical velocity in the typhoon eyewall is accompanied by the warm core. In all bulk experiments, the density of the contour lines illustrated the strong velocity gradient and reflected the rate of velocity increase. As vertical velocity increased, convection was strengthened and the rate of precipitation was enhanced. It can be seen from Figure 10 that the maximum vertical velocity in the SBM experiment was the smallest of all the simulations. It can also be seen that the largest value of vertical velocity in the bulk experiments was located within 50 km of the typhoon center, whereas in the SBM experiment, it had a relatively lower value and was more peripheral. Such a dynamic structure in the bulk experiments produced short-term heavy rainfall in the eyewall area. Overall, in comparison with the bulk experiments, we attributed the better simulation of precipitation in the SBM experiment to more reasonable representation of droplet growth by diffusion.

4. Conclusions

This study used the WRF model with 3 km resolution to simulate Typhoon Hato (2017) to test the sensitivity of the precipitation and the structure of the typhoon to bulk (Lin, WDM6, and Morrison) and explicit SBM schemes.

The results produced little evidence to suggest the different schemes could alter the track of the typhoon. However, it was evident that the rainfall simulated using the SBM scheme was closest to that observed. In comparison with observations, the magnitude of precipitation produced by all three bulk schemes was larger in the eyewall region and the area with accumulated precipitation of >200 mm was obviously too wide.

The horizontal and vertical distributions of hydrometeors showed that the cloud water content in the SBM experiment was smaller than that in the bulk experiments, whereas the converse was true for rainwater content. This was because of different nucleation and diffusion growth of the particles in the different schemes. The bulk experiments overestimated cloud water content because particles were forced to adopt a gamma distribution. The large cloud water content could absorb more water vapor, decreasing the supersaturation, which hindered the growth of the effective radius and prevented rainwater formation. In addition, in bulk schemes, droplet growth by diffusion was achieved by applying a saturation adjustment that condensed all the water vapor, which also contributed to overestimation of cloud water content.

Among all the hydrometeors, the vertically integrated mixing ratio of snow was found to represent the greatest difference between the bulk and SBM experiments. Snow content in the SBM experiment was obviously larger than that in the bulk experiments, as was the IWP. This was because the SBM scheme calculated the cloud condensation nucleus size distribution together with supersaturation at every grid point. This caused small droplets to form at high levels, which could affect ice microphysical processes. Differential fall velocities can lead to size sorting that leaves small particles to act as ice nuclei at higher levels. Together with abundant water vapor, the nucleation and growth of small particles on ice nuclei can increase the snow content. In addition, smaller amounts of cloud water can decrease the rates of riming and of the conversion of snow to graupel.

Through determination of precipitation type, it was found that the grid number percentages of both stratiform-type and convective-type precipitation in the SBM experiment were larger than those in the three bulk experiments, indicating that ice cloud microphysical processes were important in typhoons. In addition, cross sections of temperature and vertical velocity showed that the SBM experiment produced the weakest, outermost, and highest warm core typhoon structure with the smallest vertical velocity. The application of saturation adjustment in the bulk schemes during simulation of the typhoon overestimated the condensation mass and latent heat. Consequently, in comparison with the SBM experiment, the warmer thermodynamic structure in the bulk experiments produced stronger dynamics, and the larger vertical velocity caused stronger convective precipitation in the eyewall area.

In summary, the representation of microphysical processes in the different schemes changed the distribution of hydrometeors, and the interaction of all hydrometeors then affected the cloud structure and the rates of both warm and cold microphysical processes. Finally, the microphysical processes lead to the release of latent heat, which affected the vertical velocity and the precipitation rate.

The observation of cloud microphysical processes is extremely difficult, especially ice cloud processes. Because of the lack of adequate observational data, the representation of microphysical processes in SBM schemes still had certain shortcomings. Nevertheless, we have proven that the inherent insensitivity of bulk schemes to particle size means they always overestimate precipitation, whereas SBM schemes that describe microphysical processes more reasonably are better able to simulate the precipitation and cloud structure of a typhoon. Considering the rapid increase in computational capacity over recent years, SBM schemes may be adopted to realize optimal prediction of precipitation in future numerical simulations.

Data Availability

The NCAR Mesoscale and Microscale Meteorology Division provided the WRF model, which is available at http://www.mmm.ucar.edu/wrf/users. The National Centers for Environmental Prediction Final Global Tropospheric Analysis (NCEP-FNL) data were taken from https://rda.ucar.edu/datasets/ds083.2. The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the National Key R&D Program of China (2018YFC1507402), National Natural Science Foundation of China (41875168 and 41705117), and Guangzhou Science and Technology Plan (201707010088).

References

J. Molinari and M. Dudek, “Parameterization of convective precipitation in Mesoscale numerical models: a critical review,” Monthly Weather Review, vol. 120, no. 2, pp. 326–344, 1992.
View at: Publisher Site | Google Scholar
A. P. Khain, K. D. Beheng, A. Heymsfield et al., “Representation of microphysical processes in cloud-resolving models: spectral (bin) microphysics versus bulk parameterization,” Reviews of Geophysics, vol. 53, no. 2, pp. 247–322, 2015.
View at: Publisher Site | Google Scholar
D. Wang, X. Li, W.-K. Tao, and Y. Wang, “Effects of vertical wind shear on convective development during a landfall of severe tropical storm Bilis (2006),” Atmospheric Research, vol. 94, no. 2, pp. 270–275, 2009.
View at: Publisher Site | Google Scholar
J. C. H. Fung and G. Gao, “Evaluations on profiles of the eddy diffusion coefficients through simulations of super typhoons in the northwestern pacific,” Advances in Meteorology, vol. 2016, Article ID 2397873, 14 pages, 2016.
View at: Publisher Site | Google Scholar
I. Gultepe, A. J. Heymsfield, P. R. Field, and D. Axisa, “Ice-phase precipitation,” Meteorological Monographs, vol. 58, pp. 1–36, 2017.
View at: Publisher Site | Google Scholar
K. W. Maw and J. Min, “Impacts of microphysics schemes and topography on the prediction of the heavy rainfall in Western Myanmar associated with tropical cyclone ROANU (2016),” Advances in Meteorology, vol. 2017, Article ID 3252503, 22 pages, 2017.
View at: Publisher Site | Google Scholar
B. Jiang, B. Huang, W. Lin, and S. Xu, “Investigation of the effects of anthropogenic pollution on typhoon precipitation and microphysical processes using WRF-Chem,” Journal of the Atmospheric Sciences, vol. 73, no. 4, pp. 1593–1610, 2016.
View at: Publisher Site | Google Scholar
Y. Liu, W. Lin, J. Li, G. Wang, S. Yang, and Y. Feng, “A numerical simulation of latent heating within Typhoon Molave,” Acta Oceanologica Sinica, vol. 36, no. 7, pp. 39–47, 2017.
View at: Publisher Site | Google Scholar
W. S. Lin, S. Xu, and C. H. Sui, “A numerical simulation of the effect of the number concentration of cloud droplets on Typhoon Chanchu,” Meteorology & Atmospheric Physics, vol. 113, no. 3-4, pp. 99–108, 2011.
View at: Publisher Site | Google Scholar
D. J. Stensrud, Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models, vol. 459, Cambridge University Press, Cambridge, UK, 2009.
S. Xu, W. Lin, and C.-H. Sui, “The separation of convective and stratiform precipitation regions of simulated Typhoon Chanchu and its sensitivity to the number concentration of cloud droplets,” Atmospheric Research, vol. 122, no. 3, pp. 229–236, 2013.
View at: Publisher Site | Google Scholar
D. Wang, X. Li, W.-K. Tao, Y. Liu, and H. Zhou, “Torrential rainfall processes associated with a landfall of severe tropical storm Bilis (2006): a two-dimensional cloud-resolving modeling study,” Atmospheric Research, vol. 91, no. 1, pp. 94–104, 2009.
View at: Publisher Site | Google Scholar
D. Wang, X. Li, and W. K. Tao, “Torrential rainfall responses to radiative and microphysical processes of ice clouds during a landfall of severe tropical storm Bilis (2006),” Meteorology & Atmospheric Physics, vol. 109, no. 3-4, pp. 107–114, 2010.
View at: Publisher Site | Google Scholar
J. Li, G. Wang, W. Lin, Q. He, Y. Feng, and J. Mao, “Cloud-scale simulation study of Typhoon Hagupit (2008) part I: microphysical processes of the inner core and three-dimensional structure of the latent heat budget,” Atmospheric Research, vol. 120-121, pp. 170–180, 2013.
View at: Publisher Site | Google Scholar
J. Li, G. Wang, W. Lin, Q. He, Y. Feng, and J. Mao, “Cloud-scale simulation study of Typhoon Hagupit (2008) part II: impact of cloud microphysical latent heat processes on typhoon intensity,” Atmospheric Research, vol. 120-121, pp. 202–215, 2013.
View at: Publisher Site | Google Scholar
J. Fan, L. R. Leung, D. Rosenfeld et al., “Microphysical effects determine macrophysical response for aerosol impacts on deep convective clouds,” Proceedings of the National Academy of Sciences, vol. 110, no. 48, pp. E4581–E4590, 2013.
View at: Publisher Site | Google Scholar
A. P. Khain, V. Phillips, N. Benmoshe, and A. Pokrovsky, “The role of small soluble aerosols in the microphysics of deep maritime clouds,” Journal of the Atmospheric Sciences, vol. 69, no. 9, pp. 2787–2807, 2012.
View at: Publisher Site | Google Scholar
X. Li, W.-K. Tao, A. P. Khain, J. Simpson, and D. E. Johnson, “Sensitivity of a cloud-resolving model to bulk and explicit bin microphysical schemes—part I: comparisons,” Journal of the Atmospheric Sciences, vol. 66, no. 1, pp. 3–21, 2009.
View at: Publisher Site | Google Scholar
X. Li, W.-K. Tao, A. P. Khain, J. Simpson, and D. E. Johnson, “Sensitivity of a cloud-resolving model to bulk and explicit bin microphysical schemes—part II: cloud microphysics and storm dynamics interactions,” Journal of the Atmospheric Sciences, vol. 66, no. 1, pp. 22–40, 2009.
View at: Publisher Site | Google Scholar
B. H. Lynn, A. P. Khain, J. W. Bao et al., “The sensitivity of Hurricane Irene to aerosols and ocean coupling: simulations with WRF spectral bin microphysics,” Journal of the Atmospheric Sciences, vol. 73, no. 2, pp. 467–486, 2016.
View at: Publisher Site | Google Scholar
A. Khain, A. Pokrovsky, M. Pinsky, A. Seifert, V. Phillips, and Vaughan, “Simulation of effects of atmospheric aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model—part I: model description and possible applications,” Journal of the Atmospheric Sciences, vol. 61, no. 24, pp. 2963–2982, 2004.
View at: Publisher Site | Google Scholar
A. Khain, B. Lynn, and J. Shpund, “High resolution WRF simulations of Hurricane Irene: sensitivity to aerosols and choice of microphysical schemes,” Atmospheric Research, vol. 167, pp. 129–145, 2016.
View at: Publisher Site | Google Scholar
J. Liu, W. Zhong, S. Liu, and H. Lu, “Allocation difference analyses of water substances during typhoon landing processes,” Journal of Tropical Meteorology, vol. 24, no. 3, 2018.
View at: Google Scholar
S. Liu, D. Tao, K. Zhao, M. Minamide, and F. Zhang, “Dynamics and predictability of the rapid intensification of super typhoon Usagi (2013),” Journal of Geophysical Research: Atmospheres, vol. 123, no. 14, pp. 7462–7481, 2018.
View at: Publisher Site | Google Scholar
J. Li, K. Wu, F. Li, Y. Chen, Y. Huang, and Y. Feng, “Effects of latent heat in various cloud microphysics processes on autumn rainstorms with different intensities on Hainan Island, China,” Atmospheric Research, vol. 189, pp. 47–60, 2017.
View at: Publisher Site | Google Scholar
K.-B. Kim, E.-H. Lee, and K.-H. Seol, “Sensitivity of typhoon simulation to physics parameterizations in the global model,” Atmosphere, vol. 27, no. 1, pp. 17–28, 2017.
View at: Publisher Site | Google Scholar
P. R. Mohan, C. V. Srinivas, V. Yesubabu, R. Baskaran, and B. Venkatraman, “Simulation of a heavy rainfall event over Chennai in Southeast India using WRF: sensitivity to microphysics parameterization,” Atmospheric Research, vol. 210, pp. 83–99, 2018.
View at: Google Scholar
R. Karki, S. U. Hasson, L. Gerlitz et al., “WRF-based simulation of an extreme precipitation event over the central Himalayas: atmospheric mechanisms and their representation by microphysics parameterization schemes,” Atmospheric Research, vol. 214, pp. 21–35, 2018.
View at: Publisher Site | Google Scholar
P. V. S. Raju, J. Potty, and U. C. Mohanty, “Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model,” Meteorology & Atmospheric Physics, vol. 113, no. 3-4, pp. 125–137, 2011.
View at: Publisher Site | Google Scholar
W. C. Skamarock, J. B. Klemp, J. Dudhia et al., A Description of the Advanced Research WRF Version 3, vol. 125, NCAR Technical note-475+ STR, Boulder, CO, USA, 2008.
Y. Wang, K.-H. Lee, Y. Lin, M. Levy, and R. Zhang, “Distinct effects of anthropogenic aerosols on tropical cyclones,” Nature Climate Change, vol. 4, no. 5, pp. 368–373, 2014.
View at: Publisher Site | Google Scholar
J. S. Kain, “The Kain-Fritsch convective parameterization: an update,” Journal of Applied Meteorology, vol. 43, no. 1, pp. 170–181, 2004.
View at: Publisher Site | Google Scholar
Z. Xian and K. Chen, “Numerical analysis on the effects of binary interaction between typhoons tembin and bolaven in 2012,” Advances in Meteorology, vol. 2019, Article ID 7529263, 16 pages, 2019.
View at: Publisher Site | Google Scholar
L. Zhou, D. F. Wang, and X. U. Ya-Qin, “Numerical simulation of tiny super typhoon Saomai using WRF,” Journal of Zhejiang University, vol. 39, no. 6, pp. 703–710, 2012.
View at: Google Scholar
M. J. Iacono, J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, “Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models,” Journal of Geophysical Research, vol. 113, 2008.
View at: Publisher Site | Google Scholar
J. C. H. Fung and G. Gao, “A new vortex initialization scheme coupled with WRF-ARW,” Advances in Meteorology, vol. 2017, Article ID 6272158, 15 pages, 2017.
View at: Publisher Site | Google Scholar
T. Du Duc, L. R. Hole, D. Tran Anh, C. Hoang Duc, and T. Nguyen Ba, “Verification of forecast weather surface variables over vietnam using the national numerical weather prediction system,” Advances in Meteorology, vol. 2016, Article ID 8152413, 11 pages, 2016.
View at: Publisher Site | Google Scholar
S.-Y. Hong, Y. Noh, and J. Dudhia, “A new vertical diffusion package with an explicit treatment of entrainment processes,” Monthly Weather Review, vol. 134, no. 9, pp. 2318–2341, 2006.
View at: Publisher Site | Google Scholar
R. Nakamura, T. Shibayama, M. Esteban, and T. Iwamoto, “Future typhoon and storm surges under different global warming scenarios: case study of typhoon Haiyan (2013),” Natural Hazards, vol. 82, no. 3, pp. 1645–1681, 2016.
View at: Publisher Site | Google Scholar
Y.-L. Lin, R. D. Farley, and H. D. Orville, “Bulk parameterization of the snow field in a cloud model,” Journal of Climate and Applied Meteorology, vol. 22, no. 6, pp. 1065–1092, 1983.
View at: Publisher Site | Google Scholar
S. J. Ghan, L. R. Leung, R. C. Easter, and H. Abdul-Razzak, “Prediction of cloud droplet number in a general circulation model,” Journal of Geophysical Research, vol. 102, no. 1022, pp. 21777–21794, 1997.
View at: Publisher Site | Google Scholar
S.-Y. Hong, J. Dudhia, and S.-H. Chen, “A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation,” Monthly Weather Review, vol. 132, no. 1, pp. 103–120, 2004.
View at: Publisher Site | Google Scholar
H. D. Orville and F. J. Kopp, “Numerical simulation of the life history of a hailstorm,” Journal of the Atmospheric Sciences, vol. 34, no. 34, pp. 1596–1618, 1977.
View at: Publisher Site | Google Scholar
K.-S. S. Lim and S.-Y. Hong, “Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models,” Monthly Weather Review, vol. 138, no. 5, pp. 1587–1612, 2010.
View at: Publisher Site | Google Scholar
S.-Y. Hong and J.-O. J. Lim, “The WRF single-moment 6-class microphysics scheme (WSM6),” Asia-Pacific Journal of Atmospheric Sciences, vol. 42, no. 2, pp. 129–151, 2006.
View at: Google Scholar
H. Morrison, G. Thompson, and V. Tatarskii, “Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: comparison of one- and two-moment schemes,” Monthly Weather Review, vol. 137, no. 3, pp. 991–1007, 2009.
View at: Publisher Site | Google Scholar
A. Khain, B. Lynn, and J. Dudhia, “Aerosol effects on intensity of landfalling hurricanes as seen from simulations with the WRF model with spectral bin microphysics,” Journal of the Atmospheric Sciences, vol. 67, no. 2, pp. 365–384, 2010.
View at: Publisher Site | Google Scholar
D. L. Douluri and K. Annapurnaiah, “Impact of microphysics schemes in the simulation of cyclone hudhud using WRF-ARW model,” International Journal of Oceans and Oceanography, vol. 10, no. 1, pp. 49–59, 2016.
View at: Google Scholar
J. Choi, J. Lee, and S. Kim, “Impact of sea surface temperature and surface air temperature on maximizing typhoon rainfall: focusing on typhoon maemi in Korea,” Advances in Meteorology, vol. 2019, Article ID 1930453, 12 pages, 2019.
View at: Publisher Site | Google Scholar
C.-H. Sui, C.-T. Tsay, and X. Li, “Convective-stratiform rainfall separation by cloud content,” Journal of Geophysical Research, vol. 112, no. D14, Article ID D14213, 2007.
View at: Publisher Site | Google Scholar

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Copyright © 2019 Xiaodian Shen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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