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Open AccessArticle

Validation of GPM Precipitation Products by Comparison with Ground-Based Parsivel Disdrometers over Jianghuai Region

1
Department of Atmosphere Science and Engineering, College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China
2
Beijing Tracking and Communication Technology Research Institute, Beijing 100000, China
3
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100000, China
*
Authors to whom correspondence should be addressed.
Water 2019, 11(6), 1260; https://doi.org/10.3390/w11061260
Received: 5 May 2019 / Revised: 6 June 2019 / Accepted: 13 June 2019 / Published: 16 June 2019
(This article belongs to the Special Issue Satellite Application on Support to Water Monitoring and Management)

Abstract

In this study, we evaluated the performance of rain-retrieval algorithms for the Version 6 Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) products, against disdrometer observations and improved their retrieval algorithms by using a revised shape parameter µ derived from long-term Particle Size Velocity (Parsivel) disdrometer observations in Jianghuai region from 2014 to 2018. To obtain the optimized shape parameter, raindrop size distribution (DSD) characteristics of summer and winter seasons over Jianghuai region are analyzed, in terms of six rain rate classes and two rain categories (convective and stratiform). The results suggest that the GPM DPR may have better performance for winter rain than summer rain over Jianghuai region with biases of 40% (80%) in winter (summer). The retrieval errors of rain category-based µ (3–5%) were proved to be the smallest in comparison with rain rate-based µ (11–13%) or a constant µ (20–22%) in rain-retrieval algorithms, with a possible application to rainfall estimations over Jianghuai region. Empirical DmZe and NwDm relationships were also derived preliminarily to improve the GPM rainfall estimates over Jianghuai region.
Keywords: satellite precipitation products; ground-based validation; precipitation; cloud physics satellite precipitation products; ground-based validation; precipitation; cloud physics

1. Introduction

Precipitation plays an important role in global climate systems and has significant spatial and temporal variability [1]. The accurate estimation of the global distribution of precipitation is crucial for the better performance of hydrological models [2]. The East Asian Summer Monsoon (EASM) is one of the most important rainfall systems that brings the major rainy season to East Asian countries [3,4,5]. Extreme rainfall events frequently occur over Jianghuai region (Figure 1) due to the impact from the East Asian Monsoon, and sometimes result in severe natural disasters like flash floods and mudslides [6,7,8,9,10]. Hence, the accurate prediction of precipitation in Jianghuai region is of great importance.
From ground-based gauges, disdrometers and radars to satellite-based sensors, the spatial and temporal variability of precipitation systems can be easily captured, particularly over rural areas where in situ measurements are scarce [11]. The Global Precipitation Measurement (GPM) mission, as a successor of the Tropical Rainfall Measuring Mission (TRMM), shows a significant advantage over gauge-based or radar-based estimates [12,13,14], which is expected to improve our knowledge of precipitation processes by providing greater dynamic range, more detailed information on microphysics, and better accuracy values in rainfall retrievals [15].
Raindrop size distribution (DSD) is the most fundamental microphysical property of precipitation. Extensive literature has revealed that the DSD characteristics vary with rain categories, geographical locations, storm to storm, and season to season [16,17,18,19,20,21,22,23]. Modeled DSD parameters are crucial for satellite-based rainfall estimation algorithms. For instance, three-parameter gamma distribution has been utilized in the GPM Dual-frequency Precipitation Radar (DPR) [24,25,26]. It was also reported by Tokay et al. [27] that the robust features of DSD parameters can be obtained from long-term observations of ground-based disdrometers and are useful in eliminating the assumptions of constant shape parameters in radar rainfall-retrieval and GPM DPR algorithms [25,26,28]. This motivated us to continue our research with this principle objective: the validation of GPM precipitation products using ground-based Parsivel disdrometers over Jianghuai region. It was found that DSD characteristics are highly affected by seasonal variations of precipitation in the Asian monsoon region [29]. Accordingly, seasonal DSD variability between summer and winter over Jianghuai region are studied, in terms of six rain rate classes and two rain categories (convective and stratiform). Optimized shape parameters under different seasons, rain rates and rain categories are obtained from the Parsivel observations over Jianghuai region. Furthermore, we revised the currently adopted shape parameter used in the GPM DPR with the results from our Parsivel observations and evaluated the performance of the new algorithm by comparison with the latest GPM precipitation products.
Following the introduction part, the data and methods are described in Section 2, the validation of GPM precipitation products are implemented in Section 3, and different GPM rainfall-retrieval methods are discussed in Section 4. Finally, Section 5 presents a summary and conclusions.

2. Data and Methods

2.1. Observational Sites and Datasets

Observations from in situ Parsivel disdrometers and GPM satellites are collected at Nanjing (NJ, 118.5° E, 32.0° N; 15 m ASL), Chuzhou (CZ, 118.3° E, 32.3° N; 18 m ASL). NJ and CZ stations are located in central Jianghuai region (Figure 1). Under the influence of the East Asian Monsoon, there are uneven distributions of rainfall in different seasons, causing significant microphysical variability between winter rain and summer rain [30]. In this paper, we separate the total rainfall samples into winter samples (December and January) and summer samples (June and July) for further research. The data used in this work are presented as follows:

2.1.1. In Situ Parsivel2 Disdrometers

The 1-min DSD data selected for the analysis were measured by Parsivel2 disdrometers from 2014 to 2018. The Parsivel2 disdrometer used herein was a second-generation optical disdrometer manufactured by OTT Hydromet, Germany [31,32]. To minimize the measurement error, a data quality control procedure was implemented. Fallers with diameters over 8 mm or falling speeds outside ±60% of the empirical speed–diameter relationship for rain [33] were eliminated. In addition, 1-min samples with raindrop numbers less than 10 or rain rates less than 0.1 mm h−1 were excluded [27]. Following the definition of a rain event proposed by Tokay and Bashor [34], 48 rain events incorporating 52,056 1-min effective DSD samples were identified for the summer season, 53 rain events incorporating 16,831 samples were identified for the winter season. For simplicity, herein, we only listed the DSD data collected in 2014 (Table 1). Notably, the present study only focused on rainfall samples, to endorse that there were no snow samples or mix-phased particles in the winter season, a phase identification method (velocity and diameter relations) proposed by Friedrich et al. [35] was adopted herein (Figure 2).

2.1.2. GPM DPR Level-2 Products

In this paper, three types of GPM DPR level-2 products, including the Ka-band high-sensitivity product (KaHS), Ku-band product (KuPR), and dual-frequency matched product (DPR_MS) are used to obtain the radar reflectivity and rain rate near the surface. The GPM DPR is comprised of a Ku-band precipitation radar (20 mm) and a Ka-band precipitation radar (8 mm). DPR scans are taken along and cross track of the spacecraft orbit (about 7 km s−1) with a 5 × 5 km2-footprint. It typically takes 1–2 min to cross the analysis domain (Figure 1). The matching between DPR and surface disdrometer observations depends both on the availability of the Parsivel composite and the presence of rainy events over the area. As a result, we have chosen 20 (25) effective observations from 40 (43) instantaneous cases in summer (winter) for the DPR–Parsivel comparisons. Table 2 simply shows the rain events observed by the GPM DPR during the summer season in June–July 2014 and the winter season in December–January 2015.

2.2. Raindrop Size Distribution

The raindrop size distribution is calculated from the Parsivel2 disdrometer counts and the integral rainfall parameters, including the radar reflectivity factor Z (mm6 m−3), rain rate R (mm h−1), rain water content W (g m−3) and total concentration of raindrops Nt (mm−3), are derived from measured DSDs as described in Wen et al. [36].
The three-parameter [N0, µ and Λ] gamma function model is widely used to represent the measured raindrop spectra [37] and is expressed as
N ( D ) = N 0 D μ exp ( Λ D )
where D (mm) is the raindrop diameter, N0 is the intercept parameter, µ is the shape parameter, and Λ is the slope parameter. The truncated moment method has been well described in Wu et al. [22] to obtain the gamma model as well as other DSD parameters from Parsivel observations.
The standard deviation of the mass spectrum σM (mm), which can be used to measure the spectral width and shape of the DSD [38], is defined as
σ M = [ D min D max ( D D m ) 2 N ( D ) D 3 d D D min D max N ( D ) D 3 d D ] 1 / 2
where Dm (mm) is the mass-weighted mean diameter [39], given by
D m = 4 + μ Λ
The normalized gamma model has been proposed to solve the nonindependence problem of the parameters of gamma DSD model [39,40,41,42], which makes it possible to compare DSDs regardless of the time scale and rain rate and accurately examine the substantial variations associated with the physical rainfall regimes. We follow the normalized gamma DSD model as adopted in Wu et al. [22].

2.3. Calculated GPM DPR Variables

Based on the normalized gamma model, the effective radar reflectivity factor Ze for each wavelength can be calculated as below:
Z e = λ 4 π 5 | k w 2 | i = 3 32 N ( D i ) σ b ( D i , λ ) Δ D i
where λ is the radar wavelength and σb (Di,λ) is the backscattering cross section of a raindrop with diameter Di, which can be directly calculated based on Mie theory. Kw2 is the dielectric factor, which is related to the complex refractive index of the region and is conventionally taken to be 0.93.
The difference between the dual-band reflectivity measurements is described by the dual-frequency ratio (DFR). The DFR (dB) is independent of the intercept parameter Nw and is defined as follows:
D F R = 10 log 10 ( Z K u Z K a )
where ZKu and ZKa are the effective radar reflectivity factors at Ku- and Ka-band frequencies, calculated via Equation (4).

2.4. GPM–Parsivel Comparison

For validation, GPM–Parsivel statistics [the normalized bias (NB) and the normalized standard error (NSE)] are computed with the following definitions:
N B = ( G P M D S D ) D S D
N S E = | G P M D S D | D S D
where GPM and DSD represent GPM DPR observation data and Parsivel measurement values, respectively (e.g., rain rate, reflectivity).

3. Validation of GPM Precipitation Products

The Ka–Ku DPR and the Microwave Imager (MI) onboard the GPM Core Observatory Satellite have been collecting data for several years, providing precipitation products over the globe, including oceans and remote areas where ground-based precipitation measurements are not available [43]. The validation work for the GPM constellation devotes significant effort and resources to improve the basic understanding required for physically based algorithms. Thus, we have compared concurrent DPR observations and Parsivel derivations in terms of mean rain rate and radar reflectivity near the surface.
In this work, we first selected the DPR_MS product to obtain the rain rate detected by DPR, due to the fact that the precipitation retrieval algorithm of DPR_MS is highly self-governed and performs better in retrieving both weak and intense rains than other algorithms [44,45,46]. Further, GPM DPR level-2 products, including KaHS and KuPR, were selected herein to obtain the observational single-band radar reflectivity factor for Ku and Ka band. The KaHS shows great advantages in observing weak precipitations, while KuPR performs well for intense precipitation observations [46].
Based on Parsivel2 observations, we calculated the Ku-band (Ka-band) effective radar reflectivity factor ZKu (ZKa) for two seasons using Equation (4). Thereby, we can calculate the dual-frequency ratio (DFR) for the GPM–Parsivel comparison via Equation (5). The DFR provides valuable information that can be used to attain a better understanding of the microphysics associated with rain-retrieval algorithms. Thus, we analyzed and compared the relationship between the DFR and rain rate R from GPM and Parsivel observations. The comparison results are shown in Figure 3.
Figure 3 shows an example of the scatterplots near the surface from both DPR observations (Figure 3, left) and DSD derivations (Figure 3, right) for two seasons. At higher rain rates (deep convective rain), the DFR may reach an equilibrium state, where ZKu and ZKa are in linear correlation. The values of the DFR from observation and derivation remain nearly constant at approximately 0.3 and 0.5 (dB) for winter, 0.1 and 0.5 (dB) for summer, respectively. We computed the GPM–Parsivel normalized bias (NB) via Equation (6). The comparison results (NB = 0.4 for winter, NB = 0.8 for summer) suggest that the GPM underestimate the DFR more in the summer season than in the winter season, which indicates that the GPM might have better performance for winter rain than summer rain over Jianghuai region.
Particularly, the GPM DPR observations show a distinct lower frequency ratio of strong convective echoes compared with those from Parsivel (Figure 3). Except for the attenuation caused by multiple scattering, the possible reason for such a discrepancy could be the impact of nonuniform beam filling on DPR estimates of convective echoes, which affects not only errors in the estimate of path attenuation but also the retrieval of microphysical parameters regarding the properties (phase state, shape, nonuniform distributions, etc.) of precipitation particles [47]. In addition, due to the broader spectral width of the precipitation during summer than that of winter, the discrepancy would be worse in the summer season.
To explain the unique characteristics above, the σMDm variations of the observed DSD samples in two seasons are analyzed via Equations (2) and (3) and are shown in Figure 4. These variations are represented in terms of the frequency of occurrence, as joint PDF where the colors represent the number of cases with the corresponding σMDm pairs. In two seasons, a similar variation is found that σM generally increases with Dm. The variation for each part precipitation samples was fitted to a power law, and the fitted curve is superimposed on a corresponding plot in Figure 4. The fitted power law equation for winter is given by:
σM = 0.295Dm1.50
for summer, it is given by:
σM = 0.275Dm1.36
Comparing the two seasons, though winter precipitation has a larger exponent for σMDm relations, the mean values of σM and Dm are smaller in winter rain than summer rain (σM = 0.38, Dm = 1.31 for winter and σM = 0.40, Dm = 1.44 for summer), resulting in a broader spectral width with more large droplets in summer. In addition, this further implies the microphysics variability between different seasons.
To compare our data with those of other climatic regimes reported in Bringi et al. [48], Figure 5 shows the values of log10(Nw) versus Dm for the convective and stratiform rain types over Jianghuai region. The two outlined boxes on the scatterplot correspond to the maritime-like and continental-like convective clusters as defined by Bringi et al. [48]. Note that the stratiform rain samples of both seasons are very close to the stratiform line reported by Bringi et al. [48]. The convective precipitation in summer can be identified as both maritime-like and continental-like, which could be related to the abundant moisture transported from tropical ocean during summer, while that of winter is close to continental-like convective precipitation. In addition, this could be due to the typical dry and cold weather during winter monsoon season over Jianghuai region.

4. Improvement of GPM Retrieval Algorithms

Though spaceborne radars show great advantages, the rainfall retrieval algorithm of the GPM DPR is not yet mature. A physically based algorithm requires greater insight into the properties and behavior of both ice microphysics and land surface processes. Surface disdrometers could be employed to improve the retrieval algorithms locally by providing detailed particle microphysical information (sizes, shapes, types, numbers, etc.). In this study, we employ Parsivel measurements to develop constraints that are optimized for DPR retrieval algorithms with possible application to rainfall estimation over Jianghuai region.
The DFR is usually used for the retrieval of Dm. Nevertheless, when the DFR is negative, Dm cannot be uniquely retrieved because of the well-known “dual value” problem as indicated in Meagher and Haddad [49], which is an obstacle for dual-frequency radar DSD retrievals. For our data, the dual-valued phenomenon also existed (Figure 3). To avoid such a dual-value problem, only the effective radar reflectivity was used in this work to obtain the empirical relationship of Dm.
Based on disdrometer observations during two seasons, scatter plots and fitting results of DmZKu, DmZKa and log10(Nw)–Dm were obtained as shown in Figure 6. Dm increase tends to be highly correlated with increasing ZKu or ZKa. There is an inverse relationship between log10(Nw) and Dm. Following a least squares method, we derived the second-degree polynomial relationships of DmZKu, DmZKa and log10(Nw)–Dm as presented in Table 3. Using the three relations, the parameters Nw and Dm can be derived first. Then combined with the normalized gamma model described in Section 2.2, the DSD can be preliminarily reconstructed from the derived Nw and Dm given a local µ value. Thereby, the rain rate can be estimated eventually with the derived DSD. It was reported by Liao et al. [25] that a fixed-µ value (µ = 3) generally yields the smallest error. However, a single constant µ value may not exist. To acquire an optimized shape parameter, statistical results of gamma model parameters under different seasons, rain rates and rain categories are obtained from the Parsivel observations over Jianghuai region. The results are evaluated by comparison with Parsivel observations.

4.1. Under Different Rain Rates

To discern the precipitation differences between two seasons, the DSD samples are stratified into six rain rate classes: R ≤ 2, 2 < R ≤ 5 1, 5 < R ≤ 10, 10 <R ≤ 20, 20 < R ≤ 40, and R > 40 mm h−1 and large (small) drops are assumed to be D > 4 (D < 1) mm. The average raindrop spectra of two seasons in six rain rates are shown in Figure 7, which suggests that the particle number concentration and spectral width both increase with an increase in rain rate. Comparing the two seasons, the concentration of large drops is larger in winter season than summer season at the same rain rate class, which could be due to a stronger collision–coalescence process within small drops as well as the easier evaporation of small drops in drier winter conditions. The concentration of small drops is larger in the summer season than in the winter season, with an increase in rain rate that could be due to the stronger collision–breakup process within large drops.
Table 4 provides the mean values of gamma model parameters under different rain rates. It is notable that N0 and λ both increase with an increase in rain rate, while μ values increase to a maximum value first, then decrease to a minimum value. Comparing the two seasons, the three parameters of summer rain are slightly larger than winter rain at the same rain rate class. Such a difference could be attributed to the different rain categories in two seasons. The convective rain of the summer season was fed with moisture transported by the southwesterly monsoon. The winter rain is impacted by the northeasterly monsoon, causing large-scale frontal rainfall.
Based on the µ value obtained at different rain rates (Table 4), we derived the DSD using DmZKu, DmZKa and log10(Nw)–Dm relationships and calculated the rain rate. The NB and NSE statistics are also computed via Equations (6) and (7) for evaluation (Table 5). Compared with a constant µ in Liao et al. [25], the rain rate-based µ performs better in Jianghuai region with a NB of −11.3% and a NSE of 33.2% for winter, and a NB of −13.1% and a NSE of 36.3% for summer.

4.2. Under Different Rain Categories

To study the DSD characteristics of different rain types, the 1-min samples were further categorized into two types (convective and stratiform). Several rainfall classification methods have been well developed based on disdrometer observations [16,39,48]. However, the result of Tokay and Short [16] was obtained from tropical rainfall clusters, which is inappropriate for our study due to different regional characters. The categorization method of Testud et al. [39] was based on the variability of rain rate (R) with time. In addition, the categorization method of Bringi et al. [48] was based on the standard deviation value (σR) of the rain rate. In this work, two categorization schemes are combined together to separate total samples into convective and stratiform clusters. Specifically, for ten consecutive 1-min samples, if the R values of ten adjacent values are all less than 10 mm h−1 and the standard deviation σR is less than 1.5 mm h−1, then the rain is defined as stratiform, otherwise it is classified as convective clusters. Consequently, winter and summer consist of 94.7% (5.3%), 80% (20%) stratiform (convective) rainfall samples, respectively. The results indicate a dominant stratiform rain type in winter.
The average DSDs of the two rain types are described in Figure 8. Comparing the convective rain in the two seasons, summer rain contains more sufficient droplets than winter rain, which could be due to the stronger convective activity during summer. Further, the gamma model statistics are listed in Table 6. Comparing the two seasons, the three parameters [N0, µ and Λ] of the gamma model all exhibit larger values in summer than winter—both for convective rain and stratiform rain. Comparing the two rain types, the stratiform rain shows larger values of [N0, µ and Λ] than convective rain.
Based on the µ value obtained at different rain categories (Table 6), we derived the DSD using DmZKu, DmZKa and log10(Nw)–Dm relationships and calculated the rain rate. The NB and NSE statistics are also computed via Equations (6) and (7) for evaluation (Table 5). Compared with a constant µ or a rain rate-based µ, the rain category-based µ performs best in Jianghuai region with a NB of −3.5% and a NSE of 17.9% for winter, and a NB of −5.3% and a NSE of 18.8% for summer.

4.3. Possible Application of the µ–Λ Relationship

The µΛ empirical relationship has been widely demonstrated to better describe the DSD variability in natural rain ([50,51]. Studies have also shown that radar rainfall estimations improve after adjusting the µΛ relationship to ground observations [52]. In recent years, it was found that the µΛ relationship varies within different precipitation types, different climate regimes and different terrains [50,51,52,53,54]. Zhang et al. [50] obtained the µΛ relationship in Florida using DSD data collected in summer. Chen et al. [51] obtained the µΛ relationship in Nanjing during Meiyu. It needs to be customized in order to obtain the unique µΛ relationships for precipitation during different seasons.
Following the data processing method of Zhang et al. [50], the samples with rain rate R > 5 mm h−1 and number concentration N > 1000 were fitted by the truncated moment method to obtain µ and Λ values in two seasons. A second-degree polynomial µΛ relationship was further derived. The relationship for winter is:
Λ = 0.053μ2 + 0.437μ + 1.540
The relationship for summer is:
Λ = 0.017μ2 + 0.724μ + 1.958
and the results are shown in Figure 9. Given the same Λ, we notice the parameter µ of winter is less than that of summer. Such differences could be due to the relatively higher concentration of small drops in summer. For a gamma model, the µΛ relationship can be also expressed as ΛDm = 4 + μ [39]. Thus, given the Dm and μ values, the corresponding Λ value can be estimated. As shown in Figure 8, compared to the fit of convective rain from Chen et al. [51], our fits appear in the lower Dm region, which suggests that the DSDs in Meiyu precipitation have higher Dm values than those observed in Jianghuai region.
To diminish the retrieval errors from assuming a constant shape parameter, a µΛ relationship can be imposed [55]. Thus, a native µΛ relationship could possibly be applied to improve GPM DPR rainfall estimates in a specific area (herein Jianghuai region). By using the µΛ relationship obtained in Jianghuai, as well as Equation (3), µ and Λ can be solved with the Dm value calculated from DmZe relationships presented in Table 3, given a reflectivity factor. Therefore, the rain rate can be eventually estimated with the derived normalized gamma model as described in Section 2.2. The performance of DSD retrieval using the above three equations should be assessed. However, the retrieval is mostly theoretical and needs more research in future work.

5. Summary and Conclusions

In this work, we studied seasonal DSD variability between summer and winter over Jianghuai region using measurements taken from Parsivel disdrometers, as well as GPM observations. The validation and improvement of GPM precipitation products are implemented based on the DSD properties. The major conclusions can be drawn as below:
  • GPM underestimates the DFR more in summer than winter, which indicates that GPM might have better performance in the winter than summer season over Jianghuai region with biases of 40% (80%) in winter (summer). Such a discrepancy could be due to the broader spectral width of the precipitation during summer than that of winter in this specific area.
  • The shape parameters µ under different rain rates as well as rain categories are obtained from 5-year Parsivel observations over Jianghuai region. The retrieval errors of rain category-based µ (3–5%) are proved to be smaller than that of rain rate-based µ (11–13%) or a constant µ (20–22%) in rain-retrieval algorithms, with a possible application to rainfall estimations over Jianghuai region.
  • The effective radar reflectivity factor Ze is calculated using Parsivel disdrometer data. Empirical DmZe and NwDm relationships are further derived to improve the GPM rainfall estimates over Jianghuai region.

Author Contributions

Conceptualization, Y.Z. and Z.W.; methodology, Z.W.; software, L.Z.; validation, Z.W., Y.Z. and L.Z.; formal analysis, Z.W.; investigation, X.H.; resources, Y.Z.; data curation, H.L.; writing—original draft preparation, Z.W.; writing—review and editing, H.Z.; visualization, X.H.; supervision, Y.Z.; project administration, L.Z.; funding acquisition, L.Z.

Funding

This research was financially supported by the Beijige Open Research Fund for Nanjing Joint Center of Atmospheric Research (NJCAR2018ZD03). The article processing charge (APC) was funded by NJCAR2018ZD03.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ning, S.; Wang, J.; Jin, J.; Ishidaira, H. Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland. Water 2016, 8, 481. [Google Scholar] [CrossRef]
  2. Ma, Z.; Tan, X.; Yang, Y.; Chen, X.; Kan, G.; Ji, X.; Lu, H.; Long, J.; Cui, Y.; Hong, Y. The First Comparisons of IMERG and the Downscaled Results Based on IMERG in Hydrological Utility over the Ganjiang River Basin. Water 2018, 10, 1392. [Google Scholar] [CrossRef]
  3. Tao, S.; Chen, L. A review of recent research on the East Asian summer monsoon in China. In Monsoon Meteorology; Chang, C.-P., Krishnamurti, T.N., Eds.; Oxford University Press: London, UK, 1987; pp. 60–92. [Google Scholar]
  4. Ding, Y.H.; Chan, J.C.L. The East Asian summer monsoon: an overview. Meteor. Atmos. Phys. 2005, 89, 117–142. [Google Scholar]
  5. Ha, K.J.; Heo, K.Y.; Lee, S.S.; Yun, K.S.; Jhun, J.G. Variability in the East Asian Monsoon: A review. Meteorol. App. 2012, 19, 200–215. [Google Scholar] [CrossRef]
  6. Ninomiya, K.; Shibagaki, Y. Cloud system families in the Meiyu-Baiu front observed during 1–10 July 1991. J. Meteor. Soc. Japan 2003, 81, 193–209. [Google Scholar] [CrossRef]
  7. Kato, T.; Aranami, K. Formation factors of 2004 Niigata-Fukushima and Fukui heavy rainfalls and problems in the predictions using a cloud-resolving model. SOLA 2005, 1, 1–4. [Google Scholar] [CrossRef]
  8. Kato, T. Structure of the band-shaped precipitation system inducing the heavy rainfall observed over northern Kyushu, Japan on 29 June 1999. J. Meteor. Soc. Japan 2006, 84, 129–153. [Google Scholar] [CrossRef]
  9. Li, Q.; Wei, F.; Li, D. Interdecadal variation of East Asian summer monsoon and drought/flood distribution over eastern China in the last 159 years. J. Geogr. Sci. 2011, 21, 579–593. [Google Scholar] [CrossRef]
  10. Shan, X.; Jiang, N.; Qian, W. Regional heavy rain locations associated with anomalous convergence lines in eastern China. Nat. Hazards 2015, 77, 1731–1750. [Google Scholar] [CrossRef]
  11. Tong, K.; Zhao, Y.; Wei, Y.; Hu, B.; Lu, Y. Evaluation and Hydrological Validation of GPM Precipitation Products over the Nanliu River Basin, Beibu Gulf. Water 2018, 10, 1777. [Google Scholar] [CrossRef]
  12. Hou, A.Y.; Kakar, R.K.; Neeck, S.; Azarbarzin, A.A.; Kummerow, C.D.; Kojima, M.; Oki, R.; Nakamura, K.; Iguchi, T. The Global Precipitation Measurement Mission. Bull. Am. Meteorol. Soc. 2014, 95, 701–722. [Google Scholar] [CrossRef]
  13. Huffman, G.J.; Bolvin, D.T.; Nelkin, E.J. Integrated Multi-Satellite Retrievals for GPM (IMERG) Technical Documentation. 2015. Available online: https://pmm.nasa.gov/sites/default/fifiles/ document_fifiles/IMERG_doc.pdf (accessed on 22 March 2019).
  14. Gilewski, P.; Nawalany, M. Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland. Water 2018, 10, 1665. [Google Scholar] [CrossRef]
  15. Le, M.; Chandrasekar, V. Hydrometeor Profile Characterization Method for Dual-Frequency Precipitation Radar Onboard the GPM. IEEE Trans. Geosci. Remote Sens. 2013, 51, 3648–3658. [Google Scholar] [CrossRef]
  16. Tokay, A.; Short, D.A. Evidence from tropical raindrop spectra of the origin of rain from stratiform versus convective clouds. J. Appl. Meteorol. 1996, 35, 355–371. [Google Scholar] [CrossRef]
  17. Kumar, S.B.; Reddy, K.K. Raindrop size distribution characteristics of cyclonic and north east monsoon thunderstorm precipitating clouds observed over Kadapa (14.47°N, 78.82°E), tropical semi-arid region of India. Mausam 2013, 64, 35–48. [Google Scholar]
  18. Kumari, N.P.A.; Kumar, S.B.; Jayalakshmi, J.; Reddy, K.K. Raindrop size distribution variations in JAL and NILAM cyclones induced precipitation observed over Kadapa (14.47°N, 78.82°E), a tropical semi-arid region of India. Indian J. Radio Space Phys. 2014, 43, 57–66. [Google Scholar]
  19. Seela, B.K.; Janapati, J.; Lin, P.-L.; Reddy, K.K.; Shirooka, R.; Wang, P.K. A comparison study of summer season raindrop size distribution between Palau and Taiwan, two islands in western Pacifific. J. Geophys. Res. Atmos. 2017, 122, 11787–11805. [Google Scholar] [CrossRef]
  20. Dolan, B.; Fuchs, B.; Rutledge, S.A.; Barnes, E.A.; Thompson, E.J. Primary modes of global drop-size distributions. J. Atmos. Sci. 2018, 75, 1453–1476. [Google Scholar] [CrossRef]
  21. Wang, D.; Giangrande, S.E.; Bartholomew, M.J.; Hardin, J.; Feng, Z.; Thalman, R.; Machado, L.A.T. The Green Ocean: Precipitation insights from the GoAmazon2014/5 experiment. Atmos. Chem. Phys. 2018, 18, 9121–9145. [Google Scholar] [CrossRef]
  22. 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]
  23. Giangrande, S.E.; Wang, D.; Bartholomew, M.J.; Jensen, M.P.; Mechem, D.B.; Hardin, J.C.; Wood, R. Midlatitude oceanic cloud and precipitation properties as sampled by the ARM Eastern North Atlantic Observatory. J. Geophys. Res. Atmos. 2019, 124, 4741–4760. [Google Scholar] [CrossRef]
  24. Hou, A.Y.; Skofronick-Jackson, G.; Kummerow, C.D.; Shepherd, J.M. Global precipitation measurement. In Precipitation: Advances in Measurement, Estimation and Prediction; Michaelides, S., Ed.; Springer: New York, NY, USA, 2008; pp. 131–169. [Google Scholar]
  25. Liao, L.; Meneghini, R.; Tokay, A. Uncertainties of GPM DPR rain estimates caused by DSD parameterizations. J. Appl. Meteor. Climatol. 2014, 53, 2524–2537. [Google Scholar] [CrossRef]
  26. Nakamura, K.; Iguchi, T. Dual-wavelength radar algorithm. In Measuring Precipitation from Space; Levizanni, V., Bauer, P., Turk, F.J., Eds.; Springer: New York, NY, USA, 2007; pp. 225–234. [Google Scholar]
  27. Tokay, A.; Petersen, W.A.; Gatlin, P.; Wingo, M. Comparison of raindrop size distribution measurements by collocated disdrometers. J. Atmos. Oceanic. Technol. 2013, 30, 1672–1690. [Google Scholar] [CrossRef]
  28. Brandes, E.A.; Zhang, G.; Vivekanandan, J. Experiments in rainfall estimation with a polarimetric radar in a subtropical environment. J. App. Meteorol. 2002, 41, 674–685. [Google Scholar] [CrossRef]
  29. Kozu, T.; Akramin, Z.; Shimomai, T. Seasonal and diurnal variations of raindrop size distribution in Asian monsoon region. J. Meteor. Soc. Japan 2006, 84, 195–209. [Google Scholar] [CrossRef]
  30. Sun, Q.; Miao, C.; Duan, Q. Changes in the Spatial Heterogeneity and Annual Distribution of Observed Precipitation across China. J. Clim. 2017, 30, 9399–9416. [Google Scholar] [CrossRef]
  31. Tokay, A.; Wolff, D.B.; Petersen, W.A. Evaluation of the New Version of the Laser-Optical Disdrometer, OTT Parsivel2. J. Atmos. Oceanic Technol. 2014, 31, 1276–1288. [Google Scholar] [CrossRef]
  32. Löffler-Mang, M.; Joss, J. An optical distrometer for measuring size and velocity of hydrometeors. J. Atmos. Ocean. Technol. 2000, 17, 130–139. [Google Scholar] [CrossRef]
  33. 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]
  34. Tokay, A.; Bashor, P.G. An experimental study of small-scale variability of raindrop size distribution. J. Appl. Meteorol. Climatol. 2010, 49, 2348–2365. [Google Scholar] [CrossRef]
  35. Friedrich, K.; Kalina, E.A.; Masters, F.J. Drop-size distributions in thunderstorms measured by optical disdrometers during VORTEX2. Mon. Weather Rev. 2013, 141, 1182–1203. [Google Scholar] [CrossRef]
  36. Wen, L.; Zhao, K.; Zhang, G.; Xue, M.; Zhou, B.; Liu, S.; Chen, X. Statistical characteristics of raindrop size distributions observed in East China during the Asian summer monsoon season using 2-D video disdrometer and Micro Rain Radar data. J. Geophys. Res. Atmos. 2016, 121, 2265–2282. [Google Scholar] [CrossRef]
  37. Ulbrich, C.W. Natural variations in the analytical form of the drop size distribution. J. Clim. Appl. Meteor. 1983, 22, 1764–1775. [Google Scholar] [CrossRef]
  38. Ulbrich, C.W.; Atlas, D. Rainfall microphysics and radar properties: Analysis methods for drop size spectra. J. Appl. Meteorol. 1998, 37, 912–923. [Google Scholar] [CrossRef]
  39. 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. Meteor. 2001, 40, 1118–1140. [Google Scholar] [CrossRef]
  40. Willis, P.T. Functional fits to some observed drop size distributions and parameterization of rain. J. Atmos. Sci. 1984, 41, 1648–1661. [Google Scholar] [CrossRef]
  41. Sempere Torres, D.; Porrà, J.M.; Creutin, J.D. A general formulation for raindrop size distribution. J. Appl. Meteor. 1994, 33, 1494–1502. [Google Scholar] [CrossRef]
  42. Sempere Torres, D.; Porrà, J.M.; Creutin, J.D. Experimental evidence of a general description for raindrop size distribution properties. J. Geophys. Res. 1998, 103, 1785–1797. [Google Scholar] [CrossRef]
  43. Skofronick-Jackson, G.; Petersen, W.A.; Berg, W.; Kidd, C.; Stocker, E.F.; Kirschbaum, D.B.; Kakar, R.; Braun, S.A.; Huffman, G.J.; Iguchi, T.; et al. The Global Precipitation Measurement (GPM) mission for science and society. Bull. Amer. Meteor. Soc. 2017, 98, 1679–1695. [Google Scholar] [CrossRef]
  44. Kotsuki, S.; Terasaki, K.; Miyoshi, T. GPM/DPR precipitation compared with a 3.5-km-resolution NICAM simulation. Scientific online letters on the atmosphere. SOLA 2014, 10, 204–209. [Google Scholar] [CrossRef]
  45. Chandrasekar, V.; Le, M. Evaluation of profile classification module of GPM-DPR algorithm after launch. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26–31 July 2015; pp. 5174–5177. [Google Scholar] [CrossRef]
  46. Zhang, A.; Fu, Y. The structural characteristics of precipitation cases detected by dual-frequency radar of GPM satellite. Chin. J. Atmos. Sci. 2018, 42, 33–51. (In Chinese) [Google Scholar]
  47. Iguchi, T.; Kozu, T.; Kwiatkowski, J.; Meneghini, R.; Awaka, J.; Okamoto, K. Uncertainties in the Rain Profiling Algorithm for the TRMM Precipitation Radar. J. Meteor. Soc. Japan. 2009, 87, 1–30. [Google Scholar] [CrossRef]
  48. 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]
  49. Meagher, J.P.; Haddad, Z.S. To what extent can raindrop size be determined by a multiple-frequency radar? J. Appl. Meteor. Climatol. 2006, 45, 529–536. [Google Scholar] [CrossRef]
  50. Zhang, G.; Vivekanandan, J.; Brandes, E.A.; Meneghini, R.; Kozu, T. The shape-slope relation in observed gamma raindrop size distributions: Statistical error or useful information? J. Atmos. Ocean. Technol. 2003, 20, 1106–1119. [Google Scholar] [CrossRef]
  51. Chen, B.; Yang, J.; Pu, J. Statistical Characteristics of Raindrop Size Distribution in the Meiyu Season Observed in Eastern China. J. Meteor. Soc. Japan 2013, 91, 215–227. [Google Scholar] [CrossRef]
  52. Cao, Q.; Zhang, G.; Brandes, E.; Schuur, T.; Ryzhkov, A.; Ikeda, K. Analysis of video disdrometer and polarimetric radar data to characterize rain microphysics in Oklahoma. J. Appl. Meteorol. Climatol. 2008, 47, 2238–2255. [Google Scholar] [CrossRef]
  53. Vivekanandan, J.; Zhang, G.; Brandes, E. Polarimetric radar estimators based on a constrained gamma drop size distribution model. J. Appl. Meteorol. 2004, 43, 217–230. [Google Scholar] [CrossRef]
  54. Atlas, D.; Ulbrich, C.W. Drop size spectra and integral remote sensing parameters in the transition from convective to stratiform rain. Geophys. Res. Lett. 2006, 33, L16803. [Google Scholar] [CrossRef]
  55. Brandes, E.A.; Zhang, G.; Vivekanandan, J. An evaluation of a drop distribution–based polarimetric radar rainfall estimator. J. Appl. Meteorol. 2003, 42, 652–660. [Google Scholar] [CrossRef]
Figure 1. Locations of two observational sites (Nanjing (NJ) and Chuzhou (CZ)) over Jianghuai region. The superimposed rectangle represents Jianghuai region.
Figure 1. Locations of two observational sites (Nanjing (NJ) and Chuzhou (CZ)) over Jianghuai region. The superimposed rectangle represents Jianghuai region.
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Figure 2. Identification and elimination of (a) snow sample case and (b) mix-phase sample case in total winter rainfall observations. The black curve represents the empirical fitting result of raindrops as reported by Friedrich et al. [35]. The black box represents rainfall area. The color mark represents particle number.
Figure 2. Identification and elimination of (a) snow sample case and (b) mix-phase sample case in total winter rainfall observations. The black curve represents the empirical fitting result of raindrops as reported by Friedrich et al. [35]. The black box represents rainfall area. The color mark represents particle number.
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Figure 3. Scatterplots of the dual-frequency ratio (DFR) versus rain rate from DPR observations and raindrop size distribution (DSD) derivations on the same scale during winter (blue circles) and summer (red circles) over Jianghuai region. The left two panels represent results from GPM observations, and the right two panels represent results from DSD derivations. The probability density functions (PDF) of the DFR are also given in each panel.
Figure 3. Scatterplots of the dual-frequency ratio (DFR) versus rain rate from DPR observations and raindrop size distribution (DSD) derivations on the same scale during winter (blue circles) and summer (red circles) over Jianghuai region. The left two panels represent results from GPM observations, and the right two panels represent results from DSD derivations. The probability density functions (PDF) of the DFR are also given in each panel.
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Figure 4. σMDm two-dimensional PDF distributions and fitting results based on Parsivel disdrometer observations in two seasons. The resolution of PDF is 0.1 mm × 0.1 mm and the color mark value represents the frequency of occurrence (%) of σM versus Dm.
Figure 4. σMDm two-dimensional PDF distributions and fitting results based on Parsivel disdrometer observations in two seasons. The resolution of PDF is 0.1 mm × 0.1 mm and the color mark value represents the frequency of occurrence (%) of σM versus Dm.
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Figure 5. Distribution of log10(Nw) and Dm observed from the Parsivel disdrometer for convective (blue filled circles) and stratiform precipitation (red hollow circles) during (a) winter and (b) summer over Jianghuai region (along with ± standard deviation). The green symbol represents the average value of convective rain. The two outlined rectangles correspond to the maritime and continental convective clusters reported by Bringi et al. [48]. The black dashed line indicates the fitting result of stratiform rain [48].
Figure 5. Distribution of log10(Nw) and Dm observed from the Parsivel disdrometer for convective (blue filled circles) and stratiform precipitation (red hollow circles) during (a) winter and (b) summer over Jianghuai region (along with ± standard deviation). The green symbol represents the average value of convective rain. The two outlined rectangles correspond to the maritime and continental convective clusters reported by Bringi et al. [48]. The black dashed line indicates the fitting result of stratiform rain [48].
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Figure 6. Scatterplots of Dm (mm) and ZKu (dBZ); Dm (mm) and ZKa (dBZ); log10(Nw) (mm−1m−3) and Dm (mm) derived from the Parsivel disdrometer data for two seasons. The left ones stand for winter and the right ones for summer. The overlaid red lines represent the fitted curves.
Figure 6. Scatterplots of Dm (mm) and ZKu (dBZ); Dm (mm) and ZKa (dBZ); log10(Nw) (mm−1m−3) and Dm (mm) derived from the Parsivel disdrometer data for two seasons. The left ones stand for winter and the right ones for summer. The overlaid red lines represent the fitted curves.
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Figure 7. The average DSDs from the Parsivel disdrometer data at six indicated rain rate classes in two seasons, as well as their comparison result, where dotted lines represent summer rainfall and solid lines represent winter rainfall.
Figure 7. The average DSDs from the Parsivel disdrometer data at six indicated rain rate classes in two seasons, as well as their comparison result, where dotted lines represent summer rainfall and solid lines represent winter rainfall.
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Figure 8. The average DSDs of two indicated rain types: (a) convective rain, (b) stratiform rain.
Figure 8. The average DSDs of two indicated rain types: (a) convective rain, (b) stratiform rain.
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Figure 9. µΛ relationship scatterplots and fitting curves based on Parsivel disdrometer observations in two seasons. The gray circles represent winter precipitation samples and the gray crosses represent summer precipitation samples. The dashed line represents the empirical μ–Λ relationship of convective rain during Meiyu from Chen et al. [51]. The gray lines correspond to the relationship ΛDm = 4 + μ [39] given the value of Dm = 1.0, 1.5, 2.0, and 3.0 mm.
Figure 9. µΛ relationship scatterplots and fitting curves based on Parsivel disdrometer observations in two seasons. The gray circles represent winter precipitation samples and the gray crosses represent summer precipitation samples. The dashed line represents the empirical μ–Λ relationship of convective rain during Meiyu from Chen et al. [51]. The gray lines correspond to the relationship ΛDm = 4 + μ [39] given the value of Dm = 1.0, 1.5, 2.0, and 3.0 mm.
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Table 1. Precipitation events used for the present study in two seasons of 2014.
Table 1. Precipitation events used for the present study in two seasons of 2014.
SeasonsEvent No.DateTime (LST)1-min Samples (min)Accumulated Precipitation (mm)Max Rain Rate (mm h−1)
Summer115 Jun 201404:54–06:156213.719.6
215 Jun 201417:57–22:1215215.218.4
316 Jun 201407:11–17:0121618.116.8
425 Jun 201405:51–23:4736429.239.4
526 Jun 201400:02–18:1754538.643.1
61 Jul 201405:53–15:5126312.332.5
71–2 Jul 201420:49–09:3741719.437.2
84 Jul 201411:00–23:5963139.8101.1
95 Jul 201400:01–12:5372541.1115.3
1012 Jul 201406:59–22:5831371.9145.2
Winter117 Dec 201411:10–13:361463.53.9
219 Dec 201412:24–20:021925.92.9
321 Dec 201410:24–21:42990.20.6
422–23 Dec 201412:10–01:471300.10.4
524 Dec 201414:25–20:43670.10.2
626 Dec 201406:41–12:521202.56.9
729 Dec 201409:52–10:58661.12.8
830 Dec 201415:23–23:0232812.86.8
91 Jan 201504:02–09:281894.93.1
102–3 Jan 201513:05–06:442059.74.3
113 Jan 201508:21–16:371562.41.6
128 Jan 201515:34–16:57833.52.8
Table 2. Precipitation events observed by the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) when it overpasses Jianghuai region in two seasons of 2014.
Table 2. Precipitation events observed by the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) when it overpasses Jianghuai region in two seasons of 2014.
SeasonsPass No.DateTime (LST)Rainfall Observations (√/×)Max Rain Rate (mm h−1)
Summer115 Jun 201402:56–04:2910.2
215 Jun 201412:11–13:44×0
323 Jun 201400:41–02:13×0
425 Jun 201423:39–01:1124.3
526 Jun 201408:54–10:2732.1
62 Jul 201406:50–08:2319.5
76 Jul 201420:21–21:54×0
812 Jul 201404:24–05:5662.4
Winter19 Dec 201422:55–00:271.5
210 Dec 201408:10–09:420.7
318 Dec 201405:50–07:221.9
420 Dec 201419:33–21:05×0
521 Dec 201404:48–06:20×0
623 Dec 201418:31–20:030.3
726 Dec 201403:35–05:080.6
88 Jan 201513:55–15:280.4
910 Jan 201513:44–15:18×0
1013 Jan 201512:42–14:156.9
1127 Jan 201508:18–09:516.8
Table 3. Second-degree polynomial relationships of DmZKu, DmZKa and log10(Nw)–Dm derived in two seasons.
Table 3. Second-degree polynomial relationships of DmZKu, DmZKa and log10(Nw)–Dm derived in two seasons.
RelationDataabc
Dm = aZKu2 + bZKu + cWinter0.000930710.00270.5904
Summer0.001130700.00470.4911
Dm = aZKa2 + bZKa + cWinter0.000825810.01180.5011
Summer0.000924520.02130.4104
log10(Nw) = aDm2 + bDm + cWinter0.2876–2.15435.7352
Summer0.2794–2.13475.8102
Table 4. Mean values of gamma distribution parameters in terms of six rain rate classes.
Table 4. Mean values of gamma distribution parameters in terms of six rain rate classes.
SeasonRain Rate Classlog10N0μΛ
WinterR ≤ 24.823.095.85
2 < R ≤ 54.712.885.04
5 < R ≤ 104.453.144.28
10 < R ≤ 204.273.524.14
20 < R ≤ 404.111.872.88
R > 403.911.222.41
SummerR ≤ 24.863.206.01
2 < R ≤ 54.733.415.23
5 < R ≤ 104.653.795.01
10 < R ≤ 204.423.614.55
20 < R ≤ 404.272.853.54
R > 403.981.342.39
Table 5. Statistical results of the GPM–Parsivel comparison for rain rate given as a constant µ, µ under different rain rates, as well as µ under different rain categories. NB and NSE (%) represent normalized bias and normalized standard error, respectively.
Table 5. Statistical results of the GPM–Parsivel comparison for rain rate given as a constant µ, µ under different rain rates, as well as µ under different rain categories. NB and NSE (%) represent normalized bias and normalized standard error, respectively.
SeasonNB (%)NSE (%)
µ = 3Winter−20.758.3
Summer−22.160.5
µ (rain rate-based)Winter−11.333.2
Summer−13.136.3
µ (rain category-based)Winter−3.517.9
Summer−5.318.8
Table 6. Statistical results of gamma model parameters in terms of two rain types. SD and SK represent standard deviation and skewness, respectively.
Table 6. Statistical results of gamma model parameters in terms of two rain types. SD and SK represent standard deviation and skewness, respectively.
Rain TypeSeasonlog10N0μΛ
MeanSDSKMeanSDSKMeanSDSK
ConvectiveWinter4.261.390.793.813.310.894.372.681.07
Summer4.701.140.734.692.920.694.892.420.94
StratiformWinter5.121.500.384.662.930.566.943.220.49
Summer5.411.450.345.922.870.387.603.070.44
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