Open AccessArticle

Microphysical Characteristics of Precipitation for Four Types of Typical Weather Systems on Hainan Island

Wupeng Xiao

^1,†,

Yun Zhang

^1,*,

Hepeng Zheng

^1,†,

Zuhang Wu

Yanqiong Xie

² and

Yanbin Huang

College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China

College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China

Hainan Institute of Meteorological Science, Haikou 570100, China

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Remote Sens. 2024, 16(22), 4144; https://doi.org/10.3390/rs16224144

Submission received: 5 September 2024 / Revised: 26 October 2024 / Accepted: 30 October 2024 / Published: 6 November 2024

(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)

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Abstract

The microphysical characteristics of precipitation and their differences among four typical weather systems over Hainan Island were investigated via multi-source observations from 2019 to 2023. We find that the cold fronts (CFs) have the greatest concentration of small raindrops, with a more substantial raindrop condensation process. The subtropical highs (SHs), with primarily deep convection and more prominent evaporation at low levels, lead to greater medium-to-large raindrops (diameters > 1 mm). Tropical cyclones (TCs) are characterized mainly by raindrop condensation and breakup, resulting in high concentrations of small raindrops and low concentrations of large raindrops. The trough of low pressures (TLPs) produces the lowest concentration of small raindrops because of evaporation processes. The convective clusters of the SHs are between maritime-like and continental-like convective clusters, and those of the other three types of weather systems are closer to maritime-like convective clusters. The relationships between the shape parameter (μ) and the slope parameter (Λ), as well as between the reflectivity factors (Z) and the rain rates (R), were established for the four weather systems. These results could improve the accuracy of radar quantitative precipitation estimation and the microphysical parameterizations of numerical models for Hainan Island.

Keywords:

raindrop size distribution (DSD); weather systems; precipitation microphysics; Hainan Island

1. Introduction

Precipitation is the result of dynamic, thermodynamic, and microphysical processes within clouds [1,2,3]. The raindrop size distribution (DSD) is an essential feature of precipitation microphysics and is closely related to microphysical processes, such as raindrop collision–coalescence, breakup, and evaporation [4,5,6]. The DSD is crucial for improving not only the accuracy of radar quantitative precipitation estimation (QPE) [7,8,9] but also microphysical parameterization in numerical models [10,11].

Numerous studies have revealed that the DSD varies significantly and is affected by factors such as geographic region, season, and weather system [7,12,13,14]. For example, Bringi et al. [15] reported that continental convective precipitation is dominated by large raindrops at low concentrations, whereas maritime convective precipitation comprises mainly small raindrops at high concentrations. Kim et al. [16] investigated the effect of topography on DSDs and reported that, compared with leeward slopes, windward slopes presented low concentrations of small raindrops (diameters < 1 mm) and high concentrations of medium raindrops (1 < diameters < 2 mm). Liu et al. [17] reported that the mean diameter of raindrops and their daily variations were greater in summer than in winter in Chongqing and indicated that this difference was associated with more convective precipitation in summer. Chen et al. [18] used four disdrometers at different heights on a 356-meter-high meteorological tower in Shenzhen and reported more medium raindrops, fewer large raindrops, and narrower spectral widths at lower levels than at higher levels. The above studies emphasize mainly the spatial and temporal variations in DSDs. Some studies have shown that the DSD varies with the weather system [19,20,21,22]; for example, Janapati et al. [23] investigated the microphysical features of typhoon and non-typhoon rainfall. They reported that typhoon rainfall has a greater concentration of small raindrops and a lower concentration of large raindrops than non-typhoon rainfall. This finding can be attributed to stronger convective activity and higher humidity during the typhoon. In addition, the higher rainfall amounts and larger raindrops in the active spells are related to the abundant moisture and convective activity in their recent research [24]. The microphysical characteristics of tropical cyclones vary significantly with respect to their location, intensity, and type. Researchers have investigated tropical cyclones in the Atlantic Ocean [25], Pacific Ocean [26], and South China Sea [27,28] and reported that tropical cyclones are dominated by small and medium-sized raindrops at high concentrations, and that the warm rain process is more pronounced. Moreover, the closer to the eyewall of a tropical cyclone is, the larger the diameter and the lower the concentration of raindrops [29,30]. However, few researchers have investigated differences in DSDs in different weather systems with the same rainfall rate (R), especially for extreme rainfall. The reasons for these differences have not been effectively revealed and require further attention.

Hainan Island, located in the northern South China Sea, is affected by diverse weather systems. For example, extreme rainfall often occurs on Hainan Island in autumn, which is closely related to the activities of cold fronts [31]. In summer afternoon on Hainan Island, local heat convection often occurs, triggering short-duration heavy rainfall, which is related to the activities of the western Pacific subtropical high [31,32]. Tropical cyclones are also major weather systems affecting precipitation in Hainan and account for more than one-third of the total precipitation [33]. In addition, Hainan Island is affected by low-pressure trough systems, such as the southwest low-pressure trough, the South China Sea monsoon trough, and the southern branch trough. The monsoon trough is an obvious signal for the initiation of the South China Sea summer monsoon [34]. However, studies on the microphysics of precipitation for different weather systems on Hainan Island are lacking, which constrains the development of radar QPE and model forecasting in the region.

For the first time, in this study, the microphysical characteristics and differences among four types of typical weather systems on Hainan Island are investigated, and the potential formation mechanisms are analyzed. OTT Parsivel² disdrometers (OTTs) for 5 years from 2019 to 2023, combined with multi-source observations from automatic weather stations (AWSs), soundings, and FY-4A satellites, are used. The results enhance the understanding of the microphysical characteristics of precipitation in different weather systems and can also support improvements in radar QPE and numerical model microphysical parameterization schemes in Hainan.

The paper is structured as follows: Section 2 outlines the methods for data quality control and precipitation classification. In Section 3, the DSD characteristics and the mechanisms for four distinct weather systems are discussed, and the variability in DSD parameters with the rainfall rate is examined. Section 4 presents the conclusions and discussion.

2. Materials and Methods

2.1. Instruments and Datasets

Hainan Island has 22 OTTs and more than 500 AWSs, which are densely and evenly distributed (Figure 1). The OTTs and AWSs are stable and continuously operating. Note that the OTT in Yangpu was deployed late, with data only available after 2020. AWSs are deployed in almost all townships. Observations from the OTT and AWS could represent the precipitation characteristics of Hainan Island.

OTTs are user-friendly, stable, reliable, and cost-effective, enabling the direct acquisition of the DSD characteristics of precipitation [35]. As an optical laser disdrometer, it measures raindrop diameter (D) and falling velocity (V) by recording the shading degree and duration of the laser beam during raindrop fall [36], and the recorded results are divided into 32 × 32 bins. The first two size bins of the OTT observations were excluded because of a low signal-to-noise ratio [37]. Raindrops exceeding 8 mm in diameter are extremely rare under natural atmospheric conditions. Therefore, the range of D is from 0.25 to 8 mm, corresponding to 3–23 bins. The recording interval is 1 min. To ensure accurate data sampling, only data that were within 60% of the curve of the raindrop fall velocity [38] were retained in this study [39]. Additionally, precipitation samples with R values less than 0.1 mm h⁻¹ were excluded to avoid outliers.

Furthermore, the environmental factors influencing precipitation are analyzed via other data from FY-4A, sounding, and ERA5. FY-4A has been in operation since 2017 and has an Advanced Geostationary Radiation Imager (AGRI). The spatial resolution is 4 km, and the temporal resolution is 5 min for observations in the Chinese region. Black body temperature (TBB) and cloud top height (CTH) are available as secondary products from AGRI “http://www.nsmc.org.cn/nsmc/cn/home/index.html (accessed on 27 March 2024)”. TBB serves as an effective indicator of convection intensity [40]. In general, a lower TBB indicates a higher cloud top and stronger convection. Combining these data with CTH data can help us better understand the microphysical properties of clouds and precipitation.

Sounding is an effective way to measure the three-dimensional structure of the atmosphere directly. Sounding provides vertical profiles of temperature, pressure, humidity, and wind, revealing changes in the structure of the atmosphere and plays a crucial role in research on atmospheric dynamics, thermodynamics, and microphysical processes. Soundings from the University of Wyoming “http://weather.uwyo.edu/upperair/seasia.html (accessed on 15 March 2024)” were used. ERA5 reanalysis data were also used, with a horizontal resolution of 0.25° × 0.25°, the vertical resolution of 37 pressure levels, and the temporal resolution of 1 h.

2.2. Weather System Classification

On the basis of weather maps and circulation characteristics, weather systems are classified into four categories: cold fronts (CFs), subtropical highs (SHs), tropical cyclones (TCs), and troughs of low pressure (TLPs) [41]. The specific classification steps are as described below:

The identification of CFs is based mainly on the location of the cold, high-pressure ridge and the density of isobars. The high-pressure ridge reaches coastal areas of South China, with three dense isobars observed in the Guangdong region. Weather maps and AWS data show that CFs precipitation results in an increase in pressure, a decrease in temperature, a shift in the wind direction, and a jump in the wind speed.

The intensity and location of SHs have a significant effect on Hainan precipitation When the subtropical high range influences South China, the 5880 gpm contour covers Hainan Island, with distinct east—west movement of SHs, and the wind field on Hainan Island showing a clockwise trend.

TCs are highly disruptive and extreme. We focus on five TCs that made landfall on Hainan Island, as well as two non-landfalling TCs that significantly impacted the region (Table 1). The intensity levels of TCs are classified as follows: tropical depression (TD), tropical storm (TS), severe tropical storm (STS), and typhoon (TY). TCs origins are the South China Sea (SCS) and the Western North Pacific (WNP).

TLPs selected in this paper include one Southern Branch trough, three Southwest low-pressure troughs, and four South China Sea monsoon troughs, which are chosen for their different trough line locations. Figure 2 shows the typical circulation patterns associated with each weather system type.

Figure 2a shows the CFs, with the 500 hPa contours approximately parallel and the subtropical highs distributed in bands. The shear line along the coast of South China of the 850 hPa wind vector indicates the position of the cold front at the surface. In Figure 2b, the SH controls central and southern China, with relatively little water vapor and overall dryness in the control area, and Hainan Island is at the edge of the SH. Figure 2c shows the weather pattern of tropical cyclone Lionrock landfalling in Qionghai, Hainan, in 2021. Under the impact of Lionrock, Hainan Island experienced an extraordinarily heavy rainfall event that resulted in economic losses of nearly $30 million. Figure 2d shows the TLPs, in which Hainan Island is located in front of the trough and the 850 hPa wind vector changes from northwesterly to southwesterly. The remaining examples of the four types of weather systems are similar to those in Figure 2, and the number of samples and the precipitation statistics of the four types of weather systems are provided in Table 2.

In addition, stratiform and convective precipitation have significant microphysical differences. In this study, we distinguish stratiform precipitation from convective precipitation on the basis of R [6,26,42] and classify samples with rainfall rates of R ≤ 10 mm h⁻¹ as stratiform precipitation and those with R > 10 mm h⁻¹ as convective precipitation. Moreover, raindrops with diameters < 1 mm are classified as small raindrops, raindrops with 1 < diameter < 3 mm are classified as medium raindrops, and raindrops with diameters > 3 mm are classified as large raindrops [12,37].

2.3. Calculation of DSD

The DSD was calculated from the OTT disdrometers in Hainan Province as follows:

N (D_{i}) = \frac{1}{S_{e f f} (D_{i}) T Δ D_{i}} \sum_{i = 1}^{32} \frac{n_{i j}}{V_{j}}

(1)

The size and velocity of the precipitation particles are divided into 32 bins.

N (D_{i})

(m⁻³ mm⁻¹) represents the number concentration of raindrops per unit volume per unit size interval in the ith size bin.

T

(1 min) is the sampling time;

Δ D_{i}

(mm) and

D_{i}

are the width and mean value, respectively, of the

i

th size bin;

V_{j}

(m s⁻¹) is the mean falling speed categorized by the

j

th velocity bin; and

n_{i j}

is the number of raindrops in the

i

th size bin and the

j

th velocity bin of the original output.

The sampling area of the OTT is 54 cm² (beam length of 180 mm and beam width of 30 mm), and the effective sampling area

S_{e f f} (D_{i})

(m²) is calculated after the raindrop shading and boundary effects are considered as follows:

S_{e f f} (D_{i}) = 180 \times (30 - \frac{D_{i}}{2}) \times 10^{- 6}

(2)

After

N (D)

is calculated, the radar reflectivity factor

Z

(mm⁶ m⁻³), liquid water content LWC (g m⁻³), the total raindrop concentration

N_{t}

(m⁻³), and rainfall rate

R

(mm h⁻¹) are further calculated as follows:

Z = \int_{D_{\min}}^{D_{\max}} D^{6} N (D) d D

(3)

L W C = \frac{π}{6} ρ_{w} \times 10^{- 3} \int_{D_{\min}}^{D_{\max}} D^{3} N (D) d D

(4)

N_{t} = \int_{D_{\min}}^{D_{\max}} N (D) d D

(5)

R = \frac{6 π}{10^{4}} \int_{D_{\min}}^{D_{\max}} D^{3} V (D) N (D) d D

(6)

where

D_{\min}

and

D_{\max}

are the minimum diameter and maximum diameter, respectively, observed by the OTT and

ρ_{w}

(1 g cm⁻³) is the density of liquid water.

The mass-weighted mean diameter

D_{m}

(mm) and the normalized intercept parameter

N_{w}

are calculated for the normalized gamma distribution via the following formula:

D_{m} = \frac{\int_{D \min}^{D \max} D^{4} N (D) d D}{\int_{D \min}^{D \max} D^{3} N (D) d D}

(7)

N_{w} = \frac{4^{4}}{π ρ_{w}} \frac{10^{3} L W C}{D_{m}^{4}}

(8)

3. Results

3.1. Overall DSD

Figure 3 shows the distributions of the average raindrop size for the four types of weather systems on Hainan Island. The CFs have the highest number concentration of small raindrops and the lowest number concentration of medium raindrops. The SHs have the highest number concentration of medium-to-large raindrops (diameters > 1 mm) and the widest DSD spectrum. The TCs have the lowest number concentration of large raindrops and the narrowest DSD spectrum, and the TLPs have the lowest number concentration of small raindrops.

Figure 4 shows the distributions of the average raindrop sizes of the four types of weather systems in different ranges of

R

. Weak precipitation (

R

< 10 mm h⁻¹) indicates mainly stratiform precipitation; the concentration of small raindrops is greater, and the concentration of large raindrops is lower for the CFs and TCs than for the SHs and TLPs. Moderate precipitation (10 <

R

< 50 mm h⁻¹) indicates predominantly convective precipitation, with the highest number concentration of large raindrops (diameters > 3 mm) in the SHs and the lowest number concentration of large raindrops in the TCs, whereas the differences between the CFs and TLPs are small. For heavy precipitation (

R

> 50 mm h⁻¹), the differences in the DSDs are small for the four types of weather systems. Overall, the DSD spectral widths and number concentrations of the four types of weather systems gradually increase with increasing rainfall rates. Moreover, the increase in the number concentration of large raindrops in the CFs is more obvious, and the number concentration of large raindrops is the lowest under weak precipitation and the highest under heavy precipitation.

The relative contributions of raindrops of different diameter ranges to the rainfall rate R, total number concentration

N_{t}

, and reflectivity factor Z for the four types of weather systems are shown in Figure 5. According to the results, small raindrops (diameters < 1 mm) contribute the most to

N_{t}

, with the CFs contributing 89.0% (Figure 5b); medium raindrops (1 < diameters < 3 mm) contribute the most to R, with the TCs contributing 75.3% (Figure 5a); and large raindrops (diameters > 3 mm) contribute greatly to Z, with the CFs contributing 73.3% (Figure 5c). Therefore, precipitation on Hainan Island is dominated by small raindrops with high concentrations (70.6–89.0%), but the contributions to R (7.5–21.1%) and Z (0.4–1.7%) are small.

3.2. Statistical Characteristics of DSD Parameters

The DSD characteristics and differences among the four weather systems on Hainan Island can be better understood by comparing stratiform and convective precipitation. The mean values of the DSD parameters are shown in Table 3. In stratiform precipitation, CFs have a smaller

D_{m}

and R and higher

N_{t}

, indicating weaker precipitation and the greatest concentration of small raindrops. TCs have a larger R and

L W C

values because of the presence of abundant water vapor. SHs (TLPs) have a larger

D_{m}

and smaller LWC, possibly because of stronger evaporation. In convection precipitation, SHs (TCs) have a larger (smaller)

D_{m}

N_{t}

, R, and

L W C

values than others do.

Figure 6 presents the

D_{m}

N_{w}

distributions of stratiform and convective precipitation for the four types of weather systems. Figure 6a shows that the raindrop concentration of precipitation in the Hainan CFs is greater than that of Meiyu frontal precipitation in central China [43] and eastern China [44,45], which may be attributed to the sufficient water vapor supply on Hainan Island. Compared with the western Pacific subtropical high precipitation at sea [21] in Figure 6b, raindrops in Hainan SH convective precipitation are larger, and raindrops in stratiform precipitation have higher concentrations, probably due to the involvement of more aerosols on land on Hainan Island. For example, Fan et al. [46] found that ultrafine aerosol particles can increase the convective strength. TCs that make landfall in Taiwan have greater D_m values and lower N_w values, which may be due to terrain-influenced deep convective systems [26]. In contrast, TCs that make landfall in eastern and southern China are affected by a combination of topography, anthropogenic aerosols, and cold air from the north, resulting in higher N_w and smaller D_m [42]. In Hainan, TCs are affected by unique terrain, continent-like aerosols, and an ample supply of water vapor. The raindrop diameter and concentration of the Hainan TCs in Figure 6c are between those of the tropical cyclones that made landfall in East China, South China, and Taiwan and are closer to those of the tropical cyclone Kajiki [27], which made landfall in Hainan. This finding suggests that Hainan Island is a coastal area of China that is characterized by both maritime convection and inland convection. The distributions of raindrop size and concentration in the TLPs in Figure 6d are more consistent with monsoon precipitation [22], especially stratiform precipitation. The convective precipitation is closer to the post-monsoon precipitation.

3.3. Variations in DSD Parameters with R

Figure 7 shows the fitting curves of

D_{m} - R

relationship and

N_{t} - R

relationship. Under weak precipitation (R < 10 mm h⁻¹), the distributions of

D_{m}

and

N_{t}

are more dispersed, with a wide range of raindrop diameters and concentrations, possibly because of the different contributions of large raindrops of low concentration and small and medium raindrops of high concentration to the rainfall rate (small raindrops make a large contribution to

N_{t}

but a small contribution to R, whereas medium-to-large raindrops make a small contribution to

N_{t}

but a large contribution to R in Figure 5a,b). As the rainfall rate increases,

D_{m}

and

N_{t}

gradually concentrate and then slowly increase, indicating that the diameter and concentration of raindrops during strong precipitation (R > 10 mm h⁻¹) do not change much and that the process of raindrop collision–coalescence and breakup is close to the equilibrium state.

According to Figure 8, the distributions of raindrop size and concentration in the four types of weather systems differ with increasing rainfall rates. Overall, the raindrops in the SHs have larger diameters and lower concentrations, whereas those in the TCs have smaller diameters and higher concentrations. Those of the CFs and TLPs are between those of the SHs and TCs. In weak precipitation (R < 10 mm h⁻¹), the CFs and TCs have smaller raindrops and higher concentrations, whereas the SHs and TLPs have larger raindrops and lower concentrations. For strong precipitation (10 < R < 50 mm h⁻¹), especially for heavy precipitation (R > 50 mm h⁻¹),

D_{m}

is significantly greater for the SHs and CFs than for the TCs and TLPs, whereas

N_{t}

has the opposite trend. This finding is consistent with the high concentrations of large raindrops in the SHSHs and CFs in Figure 4d. In particular, the CFs have smaller raindrop diameters and higher concentrations under weak precipitation, whereas the CFs raindrops become larger in diameter and lower in concentration as precipitation tends toward the extremes.

3.4. Mechanisms of DSD Formation

To reveal the different mechanisms of DSD formation under the four weather systems on Hainan Island, in this study, the environmental conditions of the four weather systems are further analyzed by sounding and FY-4A geostationary satellite observations. These observations include the convective available potential energy (CAPE), black body temperature (TBB), lifting condensation level (LCL), 0 °C level height, cloud top height (CTH), temperature, wind speed, specific humidity, and relative humidity. The larger the CAPE value is, the more likely convection is to occur; the lower the TBB value is, the stronger the convective activity [47]; the LCL and the CTH can approximate the cloud base height and the cloud top height; and, on the basis of the distance of the LCL and the CTH from the height of the 0 °C level, the warm cloud depth and the cold cloud depth were available by Zeng et al. [22]. Relative humidity reflects how close the air is to saturation but is influenced by temperature. In contrast, specific humidity directly measures the mass of water vapor in the air and is unaffected by temperature. The results are shown in Figure 9 and Figure 10.

Precipitation results from a combination of macro- and micro- effects. The microphysical processes of raindrop formation and growth can be reflected in macroscopic environmental conditions. For example, lower temperatures and higher humidity promote the condensation process of cloud droplets. Higher temperatures, lower humidities, and greater wind speeds favor the evaporation of raindrops. The breakup of raindrops is associated with greater wind speeds.

The SHs have a greater probability that −60 °C < TBB < −75 °C. The mean values of the LCL (466.9 m) and CAPE (1843 kJ kg⁻¹) are significantly greater in the SHs than in the other three types of systems (Table 4). This finding reflects deeper convection, which favors the microphysical processes of ice and produces large raindrops, resulting in the largest

D_{m}

in convective precipitation (Table 3). Moreover, lower wind speeds with little change in the vertical direction and higher temperatures imply that SHs are dominated by localized heat convection. The weak wind speed at the lower level is unfavorable for the breakup of large raindrops (Figure 10a). The high specific humidities of the SHs indicates that the water vapor content is substantial. However, at higher temperatures, water vapor does not reach saturation easily, resulting in low relative humidity. Consequently, the evaporation of small raindrops is more pronounced, which leads to the highest concentration of medium and large raindrops in the SH among the four weather systems (Figure 3).

The CFs have a higher relative humidity at low levels and lower temperatures, than the SHs do, which is conducive to the condensation of cloud droplets and small raindrops. In addition, at low levels, the wind speed increases with decreasing altitude, having the potential to facilitate the accretion and coalescence, which aids in the transition of cloud droplets into raindrops, leading to relatively high concentrations of small raindrops under weak precipitation (Figure 4a). However, the relatively high probability of extreme values of TBB in the CFs (Figure 9b) is likely to produce larger concentrations of large raindrops in heavy precipitation events with R > 50 mm h⁻¹ (Figure 4d).

TCs have the lowest LCL (411.8 m) and the highest CTH (10,524.6 m), but TBB values have a higher probability in the shallow convective zone and the smallest CAPE (707 kJ kg⁻¹) value, indicating weaker convective activity in TCs, which is consistent with Janapati et al. [23], who reported that convection is weaker in typhoon rainfall than in non-typhoon rainfall. The highest relative humidity and specific humidity favor the condensation of water vapor into small raindrops, as well as the formation of higher CTH, warm cloud depth, and cold cloud depth. The wind speed in TCs is significantly greater, which favors the breakup of large raindrops. These factors lead to a lower concentration at the large raindrop end than for those of the other three types of systems (Figure 3 and Figure 4).

The wind speed at a low level in the TLPs are comparable to that in the CFs, but the relative humidity and specific humidity are significantly lower, which may be accompanied by a stronger evaporation process, leading to a significantly lower concentration of small raindrops (Figure 3 and Figure 4a). The probability of the TLPs is the lowest at TBB < −60 °C (Figure 9b), and deeper convection is less common, resulting in the lowest concentration of large raindrops in heavy precipitation at R > 50 mm h⁻¹ (Figure 4d).

3.5. μ-Λ and Z-R Relationships

Figure 11 shows the fitted relationships between the shape parameter μ and the slope parameter Λ, as well as between the reflectivity factor (Z) and the rain rates (R). for different weather systems. Figure 11e shows that the Λ value of the CFs is high at Λ < 3. For Λ > 3, the fitted curves of the μ-Λ relation greatly differ. The SHs and TLPs are high, whereas the TCs and CFs are low. There is also a more pronounced difference in the Z-R relationship in Figure 11f. The CFs and TCs have more small raindrops (Figure 5a), which yields a lower Z value for the same R, especially when R is small. The SH has fewer small raindrops, and R is contributed by larger raindrops, which yields a higher Z value when R is small. The SH relationship is closer to the WSR-88D empirical relationship, whereas the direct use of the fixed empirical relationship underestimates the precipitation of the other three weather systems for R < 50 mm h⁻¹ and overestimates the precipitation of the CFs for R > 50 mm h⁻¹. Thus, the establishment of relative fitting relationships based on different weather systems can improve the accuracy of radar quantitative precipitation estimation and forecasting.

4. Discussion

In this study, the microphysical characteristics of precipitation under the influence of four typical weather systems on Hainan Island were statistically analyzed and the potential mechanisms were discussed.

CFs precipitation on Hainan Island has a higher concentration of raindrops than Meiyu frontal precipitation in central China [43] and eastern China [44,45]. Compared with the western Pacific subtropical high precipitation at sea [21], raindrops in convective precipitation in the Hainan SH are larger, and raindrops in stratiform precipitation have higher concentrations. Hainan TCs are characterized by both maritime and inland convection. The raindrop diameter and concentration of the Hainan TCs are between those of the tropical cyclones that made landfall in East China and South China [42] and Taiwan [26] and are closer to those of the tropical cyclone Kajiki [27], which made landfall on Hainan Island. The distributions of raindrop diameter and concentration in the TLPs are consistent with those in monsoon precipitation [22], especially stratiform precipitation. The convective precipitation is closer to the post-monsoon precipitation. The effects of the unique terrain, continent-like aerosols, and ample water vapor supply in Hainan may contribute to these differences. Overall, the convective clusters of the SHs are between maritime-like and continental-like convective clusters, and those of the other three types of weather systems are closer to maritime-like convective clusters.

The observations in this study can be used to verify the accuracy and applicability of numerical model microphysical parameterization schemes. By analyzing the particle size distributions (DSDs) and their environmental conditions in this paper, the microphysical parameterization scheme can be evaluated. This will help develop a localized parameterization scheme suitable for the Hainan region, thereby improving the accuracy of precipitation forecasts in the region. The use of corresponding μ-Λ and Z-R relationships for different weather systems can improve the accuracy of radar quantitative precipitation estimation and forecasting. However, the microphysical mechanism of the occurrence of precipitation on Hainan Island is still not fully understood. In particular, the extreme convection associated with cold frontal precipitation may be closely related to the unique extreme heavy rain in autumn on Hainan Island. Although observations from 22 disdrometers on Hainan Island over the past 5 years have been used and the number of samples is sufficient, the station distribution is still not sufficiently dense, and only the characteristics of ground precipitation can be recorded. The microphysical characteristics can be obtained via dual-polarization radar or satellite measurements. Using dual-polarization parameters, it is possible to understand the three-dimensional raindrop size distribution within the system of precipitation and to reveal the formation of its microphysical structure.

In the future, we will analyze the differences in the vertical structure of precipitation in different weather systems through data from dual-polarization radar and other information to further reveal the three-dimensional spatial distribution of DSDs and the microphysical mechanism of precipitation.

5. Conclusions

The microphysical characteristics of four different types of weather systems (cold fronts (CFs), subtropical highs (SHs), tropical cyclones (TCs), and troughs of low pressure (TLPs)) are statistically analyzed via the OTT raindrop disdrometers in Hainan Province, and the precipitation formation mechanisms are discussed. The main conclusions are summarized as follows:

(1): The average raindrop size distributions (DSDs) of the four types of weather systems over Hainan Island vary significantly. Overall, the SH has the widest DSD spectrum and the highest concentration of medium-to-large raindrops (diameters > 1 mm). The convective clusters of the SH are between maritime-like clusters and continental-like clusters, and those of the other three types of weather systems are closer to maritime-like clusters. The TCs have the lowest concentration of large raindrops, which corresponds to a smaller $D_{m}$ ; the spectral patterns of the TLPs and CFs are similar, and the distributions of large raindrops are between those of the TCs and those of the SHs. The contribution of small raindrops to the total number concentration is relatively high in all four types of weather systems, especially up to 89% in the CFs. The contribution of medium raindrops to the rainfall rate is much greater, including up to 75% in TCs.
(2): The differences in the DSDs among the four types of weather systems are mainly in large raindrops under heavy precipitation (R > 50 mm h⁻¹). The D_m values of the SH and CFs are significantly greater than those of the TLPs and TCs as the rainfall rate increases. The DSD for moderate precipitation events (10 < R < 50 mm h⁻¹) is similar to the overall average DSD. Under weak precipitation (R < 10 mm h⁻¹), the SHs have a relatively high concentration of large raindrops, whereas the CFs and TCs have relatively high concentrations of small raindrops.
(3): DSD formation is related to the environmental conditions of the weather system. The SHs are dominated by localized heat convection, with higher temperatures and CAPE values that favor the formation of large raindrops. Furthermore, a weak wind speed at the lower level is unfavorable for the breakup of large raindrops. The combination of high specific humidity and low relative humidity makes it easy for small raindrops to evaporate. This leads to the highest concentration of large raindrops in the SH among the four weather systems. Compared with the SHs, CFs have higher relative humidity at low levels and slower temperatures, favoring the condensation of raindrops and leading to higher concentrations of small raindrops under weak precipitation. The relatively high probability of extreme TBB values in the CFs is likely to produce relatively high concentrations of large raindrops during heavy precipitation. The wind speed of the TC is significantly greater at low levels, which favors the breakup of large raindrops, leading to the lowest concentration of large raindrops. In addition, the TCs have a lower CAPE value, i.e., weaker convective activity, and the highest relative humidity and specific humidity, which is favorable for condensation, leading to higher small and medium raindrop concentrations. The TLPs have lower relative humidity and specific humidity, which may be accompanied by a stronger evaporation process, leading to a lower concentration of small raindrops. The probability of a TLP is the lowest at TBB < −60 °C, and deeper convection is less common, resulting in the lowest concentration of large raindrops in heavy precipitation.

Author Contributions

Conceptualization, W.X., Y.X. and Y.Z.; methodology, Z.W.; software, H.Z.; validation, H.Z., Y.Z. and Z.W.; formal analysis, W.X., H.Z. and Y.Z.; investigation, W.X.; resources, Y.Z. and Y.H.; data curation, W.X.; writing—original draft preparation, W.X. and H.Z.; writing—review and editing, W.X. and H.Z.; visualization, W.X. and H.Z.; supervision, Y.X.; project administration, Y.Z.; and funding acquisition, Y.Z., Z.W. and Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (grant numbers 42075080, 42305081, and 42365011), the Science and Technology Innovation Program of Hunan Province (grant number 2024RC3141), and the Research Project of the National University of Defense Technology (grant number ZK23-55).

Data Availability Statement

Global sounding data can be obtained from the University of Wyoming via “http://weather.uwyo.edu/upperair/seasia.html (accessed on 15 March 2024)”. ERA5 data can be obtained from the Climate Data Store (CDS) via “https://cds.climate.copernicus.eu/datasets?q=ERA5+single+levels (accessed on 30 June 2023)”. FY-4A satellite data can be obtained from the National Satellite Meteorological Center via “http://www.nsmc.org.cn/nsmc/cn/home/index.html (accessed on 27 March 2024)”. Disdrometer data can be acquired by sending requests to the first author.

Acknowledgments

The authors acknowledge the Hainan Province Weather Modification Center for observation maintenance and data processing. We also thank the reviewers for their constructive comments.

Conflicts of Interest

The authors declare no conflicts of interest.

References

Kumar, S.; Hazra, A.; Goswami, B.N. Role of Interaction between Dynamics, Thermodynamics and Cloud Microphysics on Summer Monsoon Precipitating Clouds over the Myanmar Coast and the Western Ghats. Clim. Dyn. 2014, 43, 911–924. [Google Scholar] [CrossRef]
Luo, L.; Xiao, H.; Yang, H.; Chen, H.; Guo, J.; Sun, Y.; Feng, L. Raindrop Size Distribution and Microphysical Characteristics of a Great Rainstorm in 2016 in Beijing, China. Atmos. Res. 2020, 239, 104895. [Google Scholar] [CrossRef]
Gupta, A.K.; Deshmukh, A.; Waman, D.; Patade, S.; Jadav, A.; Phillips, V.T.J.; Bansemer, A.; Martins, J.A.; Gonçalves, F.L.T. The Microphysics of the Warm-Rain and Ice Crystal Processes of Precipitation in Simulated Continental Convective Storms. Commun. Earth Environ. 2023, 4, 226. [Google Scholar] [CrossRef]
Wen, L.; Zhao, K.; Zhang, G.; Liu, S.; Chen, G. Impacts of Instrument Limitations on Estimated Raindrop Size Distribution, Radar Parameters, and Model Microphysics during Mei-Yu Season in East China. J. Atmos. Ocean. Technol. 2017, 34, 1021–1037. [Google Scholar] [CrossRef]
Beheng, K.D. The Evolution of Raindrop Spectra: A Review of Microphysical Essentials. In Rainfall: State of the Science; American Geophysical Union (AGU): Washington, DC, USA, 2010; pp. 29–48. ISBN 978-1-118-67023-1. [Google Scholar]
Testud, J.; Oury, S.; Black, R.A.; Amayenc, P.; Dou, X. The Concept of “Normalized” Distribution to Describe Raindrop Spectra: A Tool for Cloud Physics and Cloud Remote Sensing. J. Appl. Meteorol. Climatol. 2001, 40, 1118–1140. [Google Scholar] [CrossRef]
Zeng, Y.; Yang, L.; Zhou, Y.; Tong, Z.; Jiang, Y.; Chen, P. Characteristics of Orographic Raindrop Size Distribution in the Tianshan Mountains, China. Atmos. Res. 2022, 278, 106332. [Google Scholar] [CrossRef]
Ryzhkov, A.; Zhang, P.; Bukovčić, P.; Zhang, J.; Cocks, S. Polarimetric Radar Quantitative Precipitation Estimation. Remote Sens. 2022, 14, 1695. [Google Scholar] [CrossRef]
Huang, H.; Zhao, K.; Zhang, G.; Lin, Q.; Wen, L.; Chen, G.; Yang, Z.; Wang, M.; Hu, D. Quantitative Precipitation Estimation with Operational Polarimetric Radar Measurements in Southern China: A Differential Phase–Based Variational Approach. J. Atmos. Ocean. Technol. 2018, 35, 1253–1271. [Google Scholar] [CrossRef]
Chen, G.; Lu, Y.; Hua, S.; Liu, Q.; Zhao, K.; Zheng, Y.; Wang, M.; Zhang, S.; Wang, X. Evaluating the Variability of Simulated Raindrop Size Distributions in the “21·7” Henan Extremely Heavy Rainfall Event. Geophys. Res. Lett. 2023, 50, e2023GL102849. [Google Scholar] [CrossRef]
Morrison, H.; Van Lier-Walqui, M.; Fridlind, A.M.; Grabowski, W.W.; Harrington, J.Y.; Hoose, C.; Korolev, A.; Kumjian, M.R.; Milbrandt, J.A.; Pawlowska, H.; et al. Confronting the Challenge of Modeling Cloud and Precipitation Microphysics. J. Adv. Model. Earth Syst. 2020, 12, e2019MS001689. [Google Scholar] [CrossRef]
Seela, B.K.; Janapati, J.; Lin, P.; Wang, P.K.; Lee, M. Raindrop Size Distribution Characteristics of Summer and Winter Season Rainfall Over North Taiwan. J. Geophys. Res. Atmos. 2018, 123, 11602–11624. [Google Scholar] [CrossRef]
Li, X.; Chen, S.; Li, Z.; Huang, C.; Hu, J. Statistical Characteristics of Warm Season Raindrop Size Distribution in the Beibu Gulf, South China. Remote Sens. 2022, 14, 4752. [Google Scholar] [CrossRef]
Zhang, A.; Hu, J.; Chen, S.; Hu, D.; Liang, Z.; Huang, C.; Xiao, L.; Min, C.; Li, H. Statistical Characteristics of Raindrop Size Distribution in the Monsoon Season Observed in Southern China. Remote Sens. 2019, 11, 432. [Google Scholar] [CrossRef]
Bringi, V.N.; Chandrasekar, V.; Hubbert, J.; Gorgucci, E.; Randeu, W.L.; Schoenhuber, M. Raindrop Size Distribution in Different Climatic Regimes from Disdrometer and Dual-Polarized Radar Analysis. J. Atmos. Sci. 2003, 60, 354–365. [Google Scholar] [CrossRef]
Kim, H.-J.; Jung, W.; Suh, S.-H.; Lee, D.-I.; You, C.-H. The Characteristics of Raindrop Size Distribution at Windward and Leeward Side over Mountain Area. Remote Sens. 2022, 14, 2419. [Google Scholar] [CrossRef]
Liu, X.; Xue, L.; Chen, B.; Zhang, Y. Characteristics of Raindrop Size Distributions in Chongqing Observed by a Dense Network of Disdrometers. J. Geophys. Res. Atmos. 2021, 126, e2021JD035172. [Google Scholar] [CrossRef]
Chen, B.; Yang, J.; Gao, R.; Zhu, K.; Zou, C.; Gong, Y.; Zhang, R. Vertical Variability of the Raindrop Size Distribution in Typhoons Observed at the Shenzhen 356-m Meteorological Tower. J. Atmos. Sci. 2020, 77, 4171–4187. [Google Scholar] [CrossRef]
Houze, R.A.; Hobbs, P.V.; Herzegh, P.H.; Parsons, D.B. Size Distributions of Precipitation Particles in Frontal Clouds. J. Atmos. Sci. 1979, 36, 156–162. [Google Scholar] [CrossRef]
Han, B.; Du, Y.; Wu, C.; Liu, X. Microphysical Characteristics of the Coexisting Frontal and Warm-Sector Heavy Rainfall in South China. J. Geophys. Res. Atmos. 2021, 126, e2021JD035446. [Google Scholar] [CrossRef]
Wu, Z.; Zhang, Y.; Zhang, L.; Lei, H.; Xie, Y.; Wen, L.; Yang, J. Characteristics of Summer Season Raindrop Size Distribution in Three Typical Regions of Western Pacific. J. Geophys. Res. Atmos. 2019, 124, 4054–4073. [Google Scholar] [CrossRef]
Zeng, Q.; Zhang, Y.; Lei, H.; Xie, Y.; Gao, T.; Zhang, L.; Wang, C.; Huang, Y. Microphysical Characteristics of Precipitation during Pre-Monsoon, Monsoon, and Post-Monsoon Periods over the South China Sea. Adv. Atmos. Sci. 2019, 36, 1103–1120. [Google Scholar] [CrossRef]
Janapati, J.; Seela, B.K.; Lin, P.-L.; Lee, M.-T.; Joseph, E. Microphysical Features of Typhoon and Non-Typhoon Rainfall Observed in Taiwan, an Island in the Northwestern Pacific. Hydrol. Earth Syst. Sci. 2021, 25, 4025–4040. [Google Scholar] [CrossRef]
Seela, B.K.; Tharun, D.; Tyagi, B.; Lin, P.-L. Raindrop Size Distributions of Summer Monsoon Rainfall Observed over Eastern India. Atmos. Res. 2024, 309, 107581. [Google Scholar] [CrossRef]
Tokay, A.; Bashor, P.G.; Habib, E.; Kasparis, T. Raindrop Size Distribution Measurements in Tropical Cyclones. Mon. Weather Rev. 2008, 136, 1669–1685. [Google Scholar] [CrossRef]
Chang, W.-Y.; Wang, T.-C.C.; Lin, P.-L. Characteristics of the Raindrop Size Distribution and Drop Shape Relation in Typhoon Systems in the Western Pacific from the 2D Video Disdrometer and NCU C-Band Polarimetric Radar. J. Atmos. Ocean. Technol. 2009, 26, 1973–1993. [Google Scholar] [CrossRef]
Zheng, H.; Zhang, Y.; Zhang, L.; Lei, H.; Wu, Z. Precipitation Microphysical Processes in the Inner Rainband of Tropical Cyclone Kajiki (2019) over the South China Sea Revealed by Polarimetric Radar. Adv. Atmos. Sci. 2021, 38, 65–80. [Google Scholar] [CrossRef]
Huang, X.; Wu, Z.; Xie, Y.; Zhang, Y.; Zhang, L.; Zheng, H.; Xiao, W. Precipitation Microphysics of Locally-Originated Typhoons in the South China Sea Based on GPM Satellite Observations. Remote Sens. 2023, 15, 2657. [Google Scholar] [CrossRef]
Wang, L.; Bao, X.; Hu, Y.; Zhang, S.; Lin, W.; Zhuang, Y. Microphysics of Heavy Rain Associated With the Eyewall and Inner Rainbands of Typhoon Meranti (2016). J. Geophys. Res. Atmos. 2023, 128, e2022JD037288. [Google Scholar] [CrossRef]
Bao, X.; Wu, L.; Zhang, S.; Li, Q.; Lin, L.; Zhao, B.; Wu, D.; Xia, W.; Xu, B. Distinct Raindrop Size Distributions of Convective Inner- and Outer-Rainband Rain in Typhoon Maria (2018). J. Geophys. Res. Atmos. 2020, 125, e2020JD032482. [Google Scholar] [CrossRef]
Sui, Y.; Jiang, D.; Tian, Z. Latest Update of the Climatology and Changes in the Seasonal Distribution of Precipitation over China. Theor. Appl. Climatol. 2013, 113, 599–610. [Google Scholar] [CrossRef]
Li, Y.; Lu, R.; He, J. Tropical Large-Scale Atmospheric Circulation and Sea Surface Temperature Corresponding to Autumn Precipitation in Hainan Island. Chin. J. Atmos. Sci. 2006, 30, 1034–1042. [Google Scholar]
Wu, Y.; Wu, S.; Zhai, P. The Impact of Tropical Cyclones on Hainan Island’s Extreme and Total Precipitation. Int. J. Climatol. 2007, 27, 1059–1064. [Google Scholar] [CrossRef]
Pan, J.; Li, C. Comparison of Climate Characteristics Between Two Summer Monsoon Troughs over the South China Sea and India. Chin. J. Atmos. Sci. 2006, 30, 377–390. [Google Scholar]
Tokay, A.; Petersen, W.A.; Gatlin, P.; Wingo, M. Comparison of Raindrop Size Distribution Measurements by Collocated Disdrometers. J. Atmos. Ocean. Technol. 2013, 30, 1672–1690. [Google Scholar] [CrossRef]
Löffler-Mang, M.; Joss, J. An Optical Disdrometer for Measuring Size and Velocity of Hydrometeors. J. Atmos. Ocean. Technol. 2000, 17, 130–139. [Google Scholar] [CrossRef]
Tokay, A.; Wolff, D.B.; Petersen, W.A. Evaluation of the New Version of the Laser-Optical Disdrometer, OTT Parsivel2. J. Atmos. Ocean. Technol. 2014, 31, 1276–1288. [Google Scholar] [CrossRef]
Atlas, D.; Srivastava, R.C.; Sekhon, R.S. Doppler Radar Characteristics of Precipitation at Vertical Incidence. Rev. Geophys. 1973, 11, 1–35. [Google Scholar] [CrossRef]
Brandes, E.A.; Zhang, G.; Vivekanandan, J. Experiments in Rainfall Estimation with a Polarimetric Radar in a Subtropical Environment. J. Appl. Meteorol. Climatol. 2002, 41, 674–685. [Google Scholar] [CrossRef]
Maddox, R.A. Meoscale Convective Complexes. Bull. Am. Meteorol. Soc. 1980, 61, 1374–1387. [Google Scholar] [CrossRef]
Cai, Q.; Feng, W.; Li, X. Hainan Weather Forecast Technical Manual; China Meteorological Press: Beijing, China, 2013.
Wen, L.; Zhao, K.; Chen, G.; Wang, M.; Zhou, B.; Huang, H.; Hu, D.; Lee, W.-C.; Hu, H. Drop Size Distribution Characteristics of Seven Typhoons in China. J. Geophys. Res. Atmos. 2018, 123, 6529–6548. [Google Scholar] [CrossRef]
Fu, Z.; Dong, X.; Zhou, L.; Cui, W.; Wang, J.; Wan, R.; Leng, L.; Xi, B. Statistical Characteristics of Raindrop Size Distributions and Parameters in Central China During the Meiyu Seasons. J. Geophys. Res. Atmos. 2020, 125, e2019JD031954. [Google Scholar] [CrossRef]
Wen, L.; Zhao, K.; Yang, Z.; Chen, H.; Huang, H.; Chen, G.; Yang, Z. Microphysics of Stratiform and Convective Precipitation During Meiyu Season in Eastern China. J. Geophys. Res. Atmos. 2020, 125, e2020JD032677. [Google Scholar] [CrossRef]
Chen, B.; Yang, J.; Pu, J. Statistical Characteristics of Raindrop Size Distribution in the Meiyu Season Observed in Eastern China. J. Meteorol. Soc. Jpn. Ser. II 2013, 91, 215–227. [Google Scholar] [CrossRef]
Fan, J.; Rosenfeld, D.; Zhang, Y.; Giangrande, S.E.; Li, Z.; Machado, L.A.T.; Martin, S.T.; Yang, Y.; Wang, J.; Artaxo, P.; et al. Substantial Convection and Precipitation Enhancements by Ultrafineaerosol Particles. Science 2018, 359, 411–418. [Google Scholar] [CrossRef]
Yang, L.; Fei, J.; Huang, X.; Cheng, X.; Yang, X.; Ding, J.; Shi, W. Asymmetric Distribution of Convection in Tropical Cyclones over the Western North Pacific Ocean. Adv. Atmos. Sci. 2016, 33, 1306–1321. [Google Scholar] [CrossRef]
Fulton, R.A.; Breidenbach, J.P.; Seo, D.-J.; Miller, D.A.; O’Bannon, T. The WSR-88D Rainfall Algorithm. Weather Forecast. 1998, 13, 377395. [Google Scholar] [CrossRef]

Figure 1. Distribution of OTTs and AWSs on Hainan Island.

Figure 2. Circulation conditions of the four types of typical weather systems on Hainan Island. Composite of the 500 hPa geopotential height (black contours), the 850 hPa wind vector (blue arrows), and the 700 hPa specific humidity (g kg⁻¹, shading). (a) CFs—the blue curve approximates the position of the cold front. (b) SHs—the black bold contours represent the 5880 gpm lines. (c) TCs—the yellow typhoon marker indicates the location of the center of the tropical cyclone Lionrock. (d) TLPs—the brown curve represents the location of the trough of low pressure.

Figure 3. Average raindrop size distributions of the four types of weather systems, where the blue, red, purple, and green solid lines represent CFs, SHs, TCs, and TLPs, respectively.

Figure 4. Average raindrop size distributions of the four types of weather systems in different rainfall rates (a–d) represent

R

≤ 10 mm h⁻¹, 10 <

R

≤ 20 mm h⁻¹, 20 <

R

≤ 50 mm h⁻¹, and

R

> 50 mm h⁻¹, where the blue, red, purple, and green solid lines represent CFs, SHs, TCs, and TLPs, respectively.

Figure 4. Average raindrop size distributions of the four types of weather systems in different rainfall rates (a–d) represent

R

≤ 10 mm h⁻¹, 10 <

R

≤ 20 mm h⁻¹, 20 <

R

≤ 50 mm h⁻¹, and

R

> 50 mm h⁻¹, where the blue, red, purple, and green solid lines represent CFs, SHs, TCs, and TLPs, respectively.

Figure 5. Relative contributions of raindrops to (a) the rainfall rate

R

(b) the total raindrop concentration

N_{t}

, and (c) the reflectivity factor Z in different diameter bins, where the blue, red, purple, and green regions represent CFs, SHs, TCs, and TLPs, respectively.

Figure 5. Relative contributions of raindrops to (a) the rainfall rate

R

(b) the total raindrop concentration

N_{t}

, and (c) the reflectivity factor Z in different diameter bins, where the blue, red, purple, and green regions represent CFs, SHs, TCs, and TLPs, respectively.

Figure 6. Distribution of

D_{m}

and

l o g_{10} (N_{w})

for convective precipitation and stratiform precipitation for the four types of weather systems. The black box represents the region of maritime and continental convective precipitation, as defined by [15], and the thick black dashed line represents the stratiform precipitation fitting line. The thin black dashed lines represent the contours of the rainfall rate. The dark gray crosses and light gray dots represent convective and stratiform precipitation, respectively. The circle, square and rhombus symbols in (a) indicate the Meiyu front in Central China [43] and East China [44,45]. Red and blue shading represent convective precipitation and stratiform precipitation, respectively. (b) Western (WWP), southern (SWP), and northern (NWP) of the western Pacific subtropical high [21]. (c) The circle, square and rhombus denote the convective precipitation of tropical cyclones that made landfall in East China and South China [42], Taiwan [26], and Hainan [27], and (d) The circle, square and rhombus indicate the pre-, mid-, and post-monsoon periods in the South China Sea, respectively [22].

Figure 6. Distribution of

D_{m}

and

l o g_{10} (N_{w})

Figure 7. Scatterplot density distributions of

D_{m}

(a–d) and

N_{t}

(e–h) with R. The red curves are the least-squares-fitted

D_{m}

-R and

N_{t}

-R relationships, and the gray dashed line represents the 10 mm h⁻¹ contour.

Figure 7. Scatterplot density distributions of

D_{m}

(a–d) and

N_{t}

(e–h) with R. The red curves are the least-squares-fitted

D_{m}

-R and

N_{t}

-R relationships, and the gray dashed line represents the 10 mm h⁻¹ contour.

Figure 8. Fitted relationships for

D_{m}

(a) and

N_{t}

(b) of the four types of weather systems with rainfall rate R, with the gray dashed line representing the 10 mm h⁻¹ contour.

Figure 8. Fitted relationships for

D_{m}

(a) and

N_{t}

(b) of the four types of weather systems with rainfall rate R, with the gray dashed line representing the 10 mm h⁻¹ contour.

Figure 9. (a) Boxplot of CAPE. The red solid line represents the median, the blue dashed line represents the mean, and the red dots represent the anomalies; (b) Distribution of the mean probability density of the TBB derived from FY-4A, where the dashed lines represent the dividing lines of stratiform and shallow convection (−10 °C), moderate convection (−32 °C), deep convection (−60 °C), and extreme convection (−75 °C). The blue, red, purple, and green lines represent cold fronts, subtropical highs, tropical cyclones, and low-pressure troughs, respectively. (c) Boxplots of the LCL, 0 °C level height, and CTH.

Figure 10. Vertical profiles of the (a) temperature, (b) wind speed, (c) relative humidity, and (d) specific humidity for the four types of weather systems on Hainan Island.

Figure 11. μ-Λ and Z-R relationships for the four types of weather systems. (a–d) show the probability density distributions of the μ-Λ relationship and the quadratic polynomial fitting curves. The color bars on the right side represent the densities of the points in the scatterplot, where the data with precipitation rates of R < 5 mm h⁻¹ are excluded, and the fitting curves are shown in (e). (f) shows the Z-R relationship for the corresponding system and the WSR-88D empirical relationship [48].

Table 1. Statistical information on tropical cyclones.

TC	Time	Impact Intensity	Maximum Intensity	Origin	Landfall Hainan	Minimum Pressure (hPa)	Maximum Wind Speed (m s⁻¹)
Wipha	1 August 2019	TS	TS	SCS	Yes	982	23
Sinlaku	1 August 2020	TD	TS	SCS	Yes	992	18
Nangka	13 October 2020	STS	STS	WNP	Yes	988	25
Lionrock	8 October 2021	TS	TS	SCS	Yes	990	20
Kompasu	13 October 2021	STS	TY	WNP	Yes	970	33
Chaba	2 July 2022	TY	TY	SCS	No	960	38
Talim	17 July 2023	TY	TY	WNP	No	960	40

Table 2. Sample numbers and precipitation statistics for the four types of weather systems.

Weather System	Series	Time	Sites	Samples	Total Precipitation	Mean Rainfall Rate	Maximum Rainfall Rate
CFs	1	14 October 2019	479	4798	12,757.3	26.6	65.3
	2	31 December 2019	370	2390	2113.5	5.7	48.4
	3	18 April 2021	373	1578	4060.3	10.9	71.1
	4	1 May 2022	557	11,235	30,150.1	54.1	48.6
	5	7 October 2022	529	4681	15,148.8	28.6	61.0
	6	27 March 2023	447	3476	9878.4	22.1	111.2
	7	8 May 2023	473	3317	9396.8	19.9	81.7
SHs	1	7 July 2020	130	253	783.4	6.0	43.6
	2	3 October 2020	468	2779	6182.4	13.2	76.2
	3	15 July 2022	335	914	2127.6	6.4	45.6
	4	13 August 2022	300	1026	2824.2	9.4	81.6
	5	24 May 2023	181	491	1757.2	9.7	69.7
	6	26 May 2023	416	1601	4887.5	11.7	88.6
	7	28 June 2023	290	809	3481.9	12.0	63.2
	8	9 July 2023	256	659	1108.8	4.3	37.8
TCs	1	1 August 2019	491	8377	33,422.7	68.1	77.4
	2	1 August 2020	507	5877	16,321.5	32.2	67.3
	3	13 October 2020	499	9286	26,747.5	53.6	65.0
	4	8 October 2021	560	11,306	61,987.8	110.7	106.2
	5	13 October 2021	565	9126	32,706.5	57.9	188.1
	6	2 July 2022	547	8394	36,796.2	67.3	113.8
	7	17 July 2023	607	12,339	29,826.9	49.1	85.0
TLPs	1	18 February 2019	362	1683	7088.3	19.6	74.4
	2	22 July 2019	409	1542	5249.9	12.8	88.4
	3	15 June 2020	489	4841	24,976.4	51.1	94.7
	4	19 September 2020	488	8064	22,885.7	46.9	59.2
	5	7 September 2022	551	8610	30,082.5	54.6	57.5
	6	7 June 2023	540	1976	5779.5	10.7	101.0
	7	11 June 2023	497	3662	9022.8	18.2	64.8
	8	2 July 2023	454	1955	6578.7	14.5	103.0

Table 3. Mean values of DSD parameters for four types of weather systems.

Weather Types		D_m (mm)	log₁₀(N_w) (mm⁻¹ m⁻³)	log₁₀(N_t) (m⁻³)	R (mm h⁻¹)	LWC (g m⁻³)
CFs	Whole	1.01	4.45	2.87	3.47	0.22
	Stratiform	0.95	4.43	2.82	1.62	0.12
	Convective	1.85	4.39	3.22	26.2	1.38
SHs	Whole	1.44	3.77	2.72	8.27	0.42
	Stratiform	1.28	3.71	2.38	1.78	0.11
	Convective	2.16	3.96	3.25	37.8	1.82
TCs	Whole	1.18	4.17	2.80	4.79	0.30
	Stratiform	1.11	4.18	2.71	2.26	0.61
	Convective	1.72	4.16	3.16	24.8	1.35
TLPs	Whole	1.35	3.72	2.58	5.01	0.27
	Stratiform	1.28	3.65	2.37	1.77	0.11
	Convective	1.92	4.05	3.20	31.8	1.61

Table 4. Mean values of the environmental parameters for the four types of weather systems.

Parameter	CFs	SHs	TCs	TLPs
CAPE (kJ kg⁻¹)	977.2	1842.6	707.1	838.0
LCL (m)	424.8	466.9	411.8	435.3
0 °C level (m)	4549.1	4963.4	5215.3	4998.1
CTH (m)	7955.0	8784.3	10,524.6	8446.2
Cold cloud depth (m)	3405.9	3820.9	5309.3	3448.2
Warm cloud depth (m)	4124.3	4496.5	4803.5	4562.8

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Xiao, W.; Zhang, Y.; Zheng, H.; Wu, Z.; Xie, Y.; Huang, Y. Microphysical Characteristics of Precipitation for Four Types of Typical Weather Systems on Hainan Island. Remote Sens. 2024, 16, 4144. https://doi.org/10.3390/rs16224144

AMA Style

Xiao W, Zhang Y, Zheng H, Wu Z, Xie Y, Huang Y. Microphysical Characteristics of Precipitation for Four Types of Typical Weather Systems on Hainan Island. Remote Sensing. 2024; 16(22):4144. https://doi.org/10.3390/rs16224144

Chicago/Turabian Style

Xiao, Wupeng, Yun Zhang, Hepeng Zheng, Zuhang Wu, Yanqiong Xie, and Yanbin Huang. 2024. "Microphysical Characteristics of Precipitation for Four Types of Typical Weather Systems on Hainan Island" Remote Sensing 16, no. 22: 4144. https://doi.org/10.3390/rs16224144

APA Style

Xiao, W., Zhang, Y., Zheng, H., Wu, Z., Xie, Y., & Huang, Y. (2024). Microphysical Characteristics of Precipitation for Four Types of Typical Weather Systems on Hainan Island. Remote Sensing, 16(22), 4144. https://doi.org/10.3390/rs16224144

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