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- Hindawi Publishing Corporation
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- Copyright © 2020 Zhimin Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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- 10.1155/2020/8827071
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Abstract
Hindawi Advances in Meteorology Volume 2020, Article ID 8827071, 17 pages https://doi.org/10.1155/2020/8827071 Research Article Simulating Heavy Meiyu Rainfall: A Note on the Choice of the Model Microphysics Scheme 1 2 1 1 Zhimin Zhou , Yi Deng , Yang Hu, and Zhaoping Kang Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332-0340, USA Correspondence should be addressed to Yi Deng; yi.deng@eas.gatech.edu Received 27 July 2020; Revised 30 October 2020; Accepted 11 November 2020; Published 29 November 2020 Academic Editor: Francisco Molero Copyright © 2020 Zhimin Zhou et al. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Better simulations and predictions of heavy rainfall associated with Meiyu fronts are critical for flood management in the Yangtze River Valley, China. *is work systematically evaluates and compares the performances of three microphysics schemes in Weather Research and Forecasting (WRF) Model with regard to simulating properties of a classic Meiyu rainstorm in central China which occurred during a 30-hour period in July 2016, including spatial distribution, rain rate PDF, and lifecycle behavior of local rainfall. Model simulations are validated using both in situ and remote sensing observations. It is found that all three schemes capture the overall spatial distribution of precipitation and the average rainfall intensity changes more rapidly with time in the simulation than in the observation. Further insights are gained through an examination of the budget terms of raindrop and ice-phase hy- drometeors in the model. Accretion of cloud droplets by raindrops and melting of ice-phase hydrometeors are the major source of rainwater. Bergeron and riming processes are found to play a prevailing role in the growth of ice-phase hydrometeors in Meiyu rainfall. Large differences in the parameterization of riming process in different schemes lead to significant differences in the simulated growth of ice-phase hydrometeors. microphysical processes contribute to the uncertainty of 1. Introduction numerical models [13]. Heavy rainfall in East Asia during summer is identified as It is difficult to track the evolution of cloud micro- Meiyu front rainfall in China. It is the main cause of me- physical processes in observations, and numerical models teorological disasters and often produces large-scale could be a powerful tool to investigate the microphysical flooding, which brings serious about threat and loss to the characteristics of the Meiyu front. *e proper representation economy and people’s life and property in the basin [1, 2]. of cloud microphysical processes is critical for numerical *e Meiyu front is a complex of stratiform and em- models. Microphysics schemes are classified into two types bedded convective precipitation, which includes large- according to the parameterization of ice-phase hydrome- scale [3, 4], synoptic, and meso-scale processes [5–7]. teors. In typical schemes, hydrometeor categories include Over the last few decades, research on the Meiyu front has cloud droplet, raindrop, ice crystals, snow, and graupel (hail) mainly focused on the macroscopic characteristics, such [14–18]. *e hydrometeor characteristics, such as bulk as the structure and maintenance of the Meiyu front [8], density, shape, and terminal fall speeds, are predefined. front and frontogenesis dynamics [9], and thermody- However, ice-phase particles have complex shapes, and the namic characteristics [10, 11]. In fact, the strong feedback range of their densities is large, which can have a great from in-cloud microphysical processes on dynamic and impact on growth processes [19]. *e other type of mi- thermodynamic processes of rainstorms is crucial to the crophysics scheme includes a single ice category [15, 20], and evolution of the Meiyu front [12], and this feedback and uncertainties from conversions between ice-phase particles 2 Advances in Meteorology are eliminated. However, uncertainties from aggregation are conducive to rainfall formation over central China and and riming efficiencies affect the performance of numerical consistent with the typical synoptic-scale characteristics of simulations. Meiyu front systems [30, 31]. Although there are differences in the two types of mi- crophysics parameterizations, both can still provide rea- 2.2. Data. *e precipitation observation data used in this sonable simulation results in different cases [21, 22]. Of study are from CMORPH covering mainland China from course, the performance of precipitation simulations varies 1200 UTC on 1 July to 1200 UTC on 2 July and are provided with not only the microphysics scheme but also the studied by the China Meteorological Administration with systematic domain [23–29]. *erefore, it is crucial to investigate the quality control (Shen et al., 2014). *ere is bias varying from microphysical characteristics of the Meiyu front’s heavy −0.3mm/h to 0.4mm/h between the hourly CMORPH data rainfall due to its complexity and importance. and gauge data. *e National Centers for Environmental *e sensitivity of microphysics schemes in rainfall Prediction (NCEP) global analysis dataset (FNL) with a simulations [23–26] has indicated that one scheme may ° ° coarse resolution of 1 ×1 and a six-hour interval (00, 06, 12, provide better simulation results than others in one rainfall and 18 UTC) provides initialization and lateral boundary case, while it may provide worse results in another case. conditions for the model experiments. *e FNL data are also *ere are many causes contributing to this uncertainty, and used as the background field to produce the hourly regional one of the main causes is the parameterization of ice-phase (i.e., central-eastern China) analysis data via the Local microphysics. Unlike liquid hydrometeors, which are ap- Analysis and Prediction System [32, 33]. *e average RMSE proximated by spheres, ice-phase hydrometeors have more of the geopotential height, temperature, relative humidity, complex shapes and a wide range of densities [15, 20]. wind velocity, and wind direction from LAPS are 44–45 *erefore, microphysical processes involving ice-phase −1 ° ° gpm, 1.0–1.1 C, 35%, 2.5ms , and 24–25 , respectively [34]. hydrometeors are very difficult to be parameterized. In fact, ERA5 reanalysis data with a horizontal resolution of they have been parameterized with a highly simplified ° ° 0.25 ×0.25 from the European Center for Medium-Range representation of nature in microphysics schemes. Although Weather Forecasts are used to represent the observed large- liquid-phase hydrometeors are easier to be parameterized, scale circulation. the interaction between liquid and ice hydrometeors will have large effects on model results. *ese effects depend on the parameterizations in cloud microphysics schemes. 2.3. Model Configuration. *e Advanced Research WRF *erefore, a special goal of this study is to understand the model (version 3.4.1) is used to conduct a series of simu- differences in cloud microphysics schemes when simulating lations in this paper. *e WRF model is run at a time step of heavy Meiyu rainfall. *e paper is organized as follows. A 20s and at high spatial resolution with 1200 ×600 grid brief introduction to the observation data and experimental points using a horizontal grid spacing of 3km over the entire design are given in Section 2. Section 3 presents the model domain. *e domain has 45 vertical levels and a model top of results and analysis. A summary and conclusions are given 50hPa. *ree cloud microphysics schemes are evaluated in Section 4. (i.e., EXP1: Morrison scheme [15]; EXP2: *ompson scheme [18]; and EXP3: Milbrandt and Yao (MY) scheme [16, 17]). Cloud microphysics processes producing hail are repre- 2. Materials and Methods sented in the MY scheme. For the Morrison scheme, the 2.1. Study Case. Several Meiyu front heavy rainfall cases focus is mainly on the evolution of the trailing stratiform region in a squall line when being implemented in the WRF during June and July 2016 have been extensively studied, and these cases have shown a certain degree of similarity. From model. *e coefficients used in the scheme are set to graupel. 1200 UTC on 1 July to 1200 UTC on 2 July, a rainstorm One of the objectives of the *ompson scheme is to improve disaster occurred (the accumulative rainfall exceeded aviation applications to forecast aircraft icing. Hail pro- 200mm), and the distribution of the Meiyu front was in its duction is not included in this scheme. *e model inte- typical nearly zonal direction. *erefore, the following gration starts from 0600 UTC on 1 July to 1200 UTC on 2 discussion will focus on this time period. July, and the output from the model is at a 1-hour interval. *e observed large-scale circulation is shown in Figure 1. Table 1 summarizes the WRF physics schemes used to Central-eastern China is beneath the right entrance of the simulate the heavy rainfall event associated with the Meiyu front. Sensitivity tests [35] have shown that a combination of upper-level jet, which favors upper-level divergence and upward motion (Figure 1(a)). *e southeastern part of shortwave and longwave radiation schemes, PBL scheme, ° ° China (25–30 surface layers, and land surface schemes listed in Table 1 N, 110–120 E) is dominated by strong southwesterly winds at low levels that transport large provide reasonable results. *e number density distribution, amounts of moisture to eastern China (see Figures 1(b) and mass-dimensional relation, and terminal velocity of hy- 1(c)). Wind shear is observed over central China at 700hPa drometeors are, respectively, expressed as follows.where x (Figure 1(b)), and the composite location of the Meiyu front denotes the hydrometeor category (i.e., c, r, i, s, or g for is found along 30–32 N with a nearly zonal direction. *e droplets, rainwater, cloud ice, snow, or graupel, respec- area with the maximum moisture convergence in central tively). *e characteristics of the three microphysics schemes China is located slightly south of the Meiyu front (30 N, mentioned above are compared in Table 2. Monodisperse 113–115 E). In general, these large-scale circulation features (MONO), exponential (EXP), and gamma (GAMA) Advances in Meteorology 3 200 hPa 700hPa 38°N 38°N 20 m/s 10m/s 36°N 36°N 34°N 34°N 32°N 32°N 30°N 30°N 28°N 28°N 26°N 26°N 24°N 24°N 90°E 95°E 100°E 105°E 110°E 115°E 120°E 90°E 95°E 100°E 105°E 110°E 115°E 120°E 0 5 10 15 20 25 30 35 40 45 2 34 567 8 9 10 11 12 13 14 15 16 (a) (b) 850 hPa 38°N 10.0 g/(s hPa cm) 36°N 34°N 32°N 30°N 28°N 26°N 24°N 90°E 95°E 100°E 105°E 110°E 115°E 120°E –25 –20 –15 –10 –5 0 5 10 15 20 25 (c) Figure 1: Time-mean (a) wind vectors (units: m/s) and zonal wind velocity (shaded; units: m/s) at 200hPa, (b) wind vectors (units: m/s) and −1 −1 −1 air temperature (units: C) at 700hPa, and (c) moisture flux (units: gs hPa cm ), moisture flux divergence (shaded; units: −7 −1 −1 −2 10 gs hPa cm ), and the time-mean location of the Meiyu front at 850hPa (solid purple line). distributions are used in these schemes. In the Morrison departure of the center latitudes from the averaged center scheme, the coefficients can be set to graupel or hail, and they latitude is less than 1.0 (i.e., |lat(i) − lat|<1). are set to graupel in this study. N � N D exp −λ D , x 0x x x x 2.4.2. Hydrometeor Budget Analysis. Precipitation is a result x of interactions among dynamics, thermodynamics, and m D � a D , (1) x x x x cloud microphysics, with contributions from each factor V D � c D exp −c D . varying across individual events [37]. x x x x For each hydrometeor species in the model, the kinetic equation for the mixing ratio q is expressed as follows: zq z 2.4. Analysis Methods (2) � −∇.(Vq) + q.V + ∇ q + SS, qx D zt zz 2.4.1. Meiyu Front Detection. *e Meiyu front is a narrow where V is the 3D velocity vector and V is the mass- convergence zone between moist and dry air masses and thus qx weighted fall speed. *e terms on the right-hand side represent provides a favorable background for rainstorm formation. advection/divergence, turbulent mixing, sedimentation, and *e composite location of the Meiyu front is the average ° ° microphysical sources, respectively. *e kinetic equation for location of the Meiyu front within 25–35 N and 106–125 E the number concentration N is similar. *e direct effect of during this period, while the procedure of defining the lo- different microphysics schemes on the evolution of heavy cation of the Meiyu front at each time step is based on locating rainfall is from the SS term. Despite the importance of sink and the area with a strong gradient in the equivalent potential source terms, their indirect effects on the feedback of the temperature (θ ) [36] (see Figure 2): (1) we first check whether dynamical and thermodynamic processes, which contribute to there is a band at each longitude with |(zθ /zy)|> 0.04K/km at the difference in simulating accumulated rainfall, also played 850hPa; (2) if the band exists, the center latitude of the band important roles. at a specific longitude is then calculated; (3) a location can be Many studies have been performed which use hydro- defined as the Meiyu front when (a) the total number of grids meteor budget analysis to understand cloud microphysics with (|(zθ /zy)|>0.04K/km) at all longitudes exceeds 600, (b) processes and mechanisms related to surface precipitation the average difference in the center latitudes between adjacent [37–42]. Previous work on budget analysis of precipitation longitudes is less than 1 (i.e., N−2 has mainly focused on tropical rainfall systems, and a similar (1/N − 1) × |lat(i + 1) − lat(i)|<1), or (c) the standard analysis has not been carried out for Meiyu front rainfall. deviation of the center latitudes is less than 2.0, and the 4 Advances in Meteorology Table 1: Configuration of the WRF model considered for the among the three schemes. In fact, the rainfall intensity from simulation of rainfall. the model output, especially in the heavy rainfall area (i.e., where Rain24 exceeds 50mm), is overestimated compared Model options Dataset/value to observations. It can be seen that a spatial bias exists for the Domains 1 modeled heavy rainfall area, and the coverage of the region Grid resolution (spacing) 3 km receiving heavy rainfall is overestimated in the models. Projection system Mercator Compared with the observations, the simulated rain belts are Initial conditions NCEP FNL located slightly south over central-eastern China Shortwave radiation scheme Dudhia Longwave radiation scheme RRTM (113–115 E). *e root mean square errors (RMSE) of model PBL scheme ACM2 results are calculated. For Morrison, *ompson, and MY 1. Morrison schemes, the value of RMSE is 0.263mm, 0.258mm, and Microphysics schemes 2. *ompson 0.261mm, respectively. 3. Milbrandt-Yao (MY) Regarding the model results, Rain24 is overestimated Surface layer option Monin-Obukhov over the lower reaches of the Yangtze River (117–119 E), Land surface model Noah especially in the MY scheme. *e *ompson scheme pro- ° ° duces the weakest rainfall, especially between 25 N and 27 N. *e empirical probability density function (PDF) dis- *erefore, it is necessary to analyze the budget of rain and tributions of precipitation obtained from the WRF simu- ice-phase hydrometeors in Meiyu front rainfall. lations are compared to the observed precipitation (see Five microphysical processes (in which mass transfer Figure 5). Figure 5 follows the same interpolation method as occurs) related to the growth of raindrops are analyzed in Figure 4. *e data in this figure are normalized by the total this paper: evaporation of rain, accretion of cloud droplets number of model grid points to ensure a fair comparison by raindrops, autoconversion of cloud droplets to raindrops, between the PDFs of the WRF simulations and observations. riming, and melting. *e comparison results show that the model overestimates Excluding the melting process, the riming and deposi- nearly all rain categories with a cumulative precipitation in tion (or sublimation) processes are discussed in detail. In 24 hours (Rain24) greater than 0.1mm except for Rain24 fact, other processes related to ice-phase hydrometeors (e.g., cases of 10–50mm. In a comparison of model outputs, the ice nucleation, etc.) are considered. However, they play less Morrison scheme simulates the highest percentage of rain significant roles compared to the processes mentioned except for when Rain24<10mm. *e difference in Rain24 above; therefore, we focus the discussion on the key pro- between the MY and Morrison schemes is small for light cesses. First, ice-phase hydrometeors are discussed as a rainfall (i.e., Rain24 ranging from 0.1 to 10mm). *e whole to eliminate the need for conversion among ice *ompson scheme produces the lightest Rain24 except for categories. *en, deposition and riming processes related to when Rain24 is between 10 and 100mm. In fact, the dif- snow and graupel are separately discussed for different cloud ference in Rain24 between the MY and *ompson schemes microphysical schemes. is not obvious. *e model overestimates the percentage of drizzle (i.e., Rain24<0.1mm), which may be partly caused 3. Results and Discussion by the limitation of the sensors of rain-gauge instruments [43, 44]. 3.1. Simulated Large-Scale Dynamic and 3ermodynamic Characteristics. As shown in Figure 3, the large-scale cir- culation characteristics are reasonably reproduced in all 3.3. Comparison of Average Rainfall Amount and Mean Mass three experiments with different microphysics schemes, Concentration of Rain in the Heavy Rainfall Area. which increases the confidence in further analysis of the Figure 6 compares the 24-hour time series of the average microphysical processes. Despite the good performances of rainfall intensity (ave-Rain, hereafter) and the averaged all experiments in simulating the large-scale circulation rainwater content (ave-Qr, vertically and horizontally av- pattern, weak biases can be found in the composite location eraged) in the heavy rainfall area between the model outputs of the Meiyu front. *ese biases may be ascribed to local and observations. Figure 6(a) shows that rainfall starts slowly thermal and dynamical differences, which are associated but maintains a relatively steady intensity throughout the with the selected cloud microphysical scheme. period in the observations, while, in the model simulations, rainfall starts rapidly (from 1200 to 1400 UTC on 1 July), gaining strength at the very beginning but dissipating 3.2. Observed and Simulated Precipitation Characteristics. rapidly from 0200 UTC on 2 July. Figure 4 shows the spatial distribution of the cumulative rainfall within 24 hours (Rain24) over central China It can be seen from Figure 6(b) that the time evolutions of ave-Rain and ave-Qr are consistent in most time periods, according to the observations and the WRF simulations with different microphysics schemes. especially when they change rapidly. Moreover, ave-Qr from the Morrison scheme experiences the most abrupt evolution, Here, the model outputs are interpolated to a resolution of 0.1 degrees for comparison with the observations. An and ave-Qr from the *ompson scheme shows a relatively smooth evolution compared to the other two schemes. *e initial comparison of these three figures shows that the MY scheme simulates more ave-Rain than the Morrison structure and location of precipitation are fairly similar Advances in Meteorology 5 Table 2: Summary of hydrometeor size distributions in the Morrison, *ompson, and MY schemes. −4 x −1 − d Scheme Category Distribution N (m ) b c (m s m ) d ρ c M 0x − b x x x x x x (m ) Varies to maintain a constant total Dependent on droplet Cloud GAMA number πρ /6 3 3 ×10 2 997 0 number density density of Morrison 250 ×10 −3 (m ) Rain EXP Prognostic πρ /6 3 841.99667 0.8 997 0 0 Ice EXP Prognostic πρ /6 3 700 1 500 0 0 Snow EXP Prognostic πρ /6 3 11.72 0.41 100 0 0 πρ /6 Graupel EXP Prognostic 3 19.3 0.37 400 0 0 Cloud GAMA 100 ×10 πρ /6 3 0 0 1000 195 Min(15(10 /N ) + 2) c 0c Rain EXP Prognostic πρ /6 3 4854.0 1 1000 195 0 Ice EXP Prognostic πρ /6 3 1847.5 1 890 0 0 Varies Temperature- Snow EXP+GAMA 0.069 3 40 0.55 with 100 0.636 dependent *ompson Varies according to a function of Graupel EXP πρ /6 3 442 0.89 500 0 0 total mass and size distribution Varies to maintain a constant total Cloud GAMA number πρ /6 3 0 0 1000 0 0 density of 200 ×10 MY −3 (m ) Rain EXP Prognostic πρ /6 3 149.1 0.5 1000 0 0 Ice EXP Prognostic 440 3 71.34 0.6635 500 0 0 Varies Snow EXP Prognostic 0.1597 2.08 11.72 0.41 0 0 with D Graupel EXP Prognostic πρ /6 3 19.3 0.37 400 0 0 scheme during most of the simulation time, while the former central China) at 700hPa except for the maximum values found near the coastline of the Yellow Sea, indicating an simulates less ave-Qr before 0200 UTC on 2 July. *e *ompson scheme simulates the smallest ave-Rain, which overall slow evolution of the synoptic-scale circulation over coincides with the ave-Qr. *is demonstrates that a larger central China during this period. amount of ave-Qr is not always consistent with more ave- In addition, a maximum at 500hPa is found over Hubei ° ° Rain. After all, ave-Qr includes the distribution of raindrops province (30–32 N, 110–114 E), consistent with the devel- at different levels, while ave-Rain only includes the distri- opment/decay of the rainfall system over this area. *e spatial bution of raindrops at the surface. Regardless, the time patterns of the standard deviation in the three experiments are evolution of precipitation and raindrop content shows good generally in agreement with the observations with the fol- consistency during most of the time period. lowing biases: the center at 500hPa in all experiments is Figure 7 shows the spatial distributions of the standard located west of that in the analysis data (see Figures 8 and 9). deviation of the geopotential height over central-eastern *e modeled geopotential height over central-eastern China China at 700hPa and 500hPa based on hourly regional has greater temporal variability than that observed during this analysis data (i.e., LAPS product). Generally, a relatively low period (see Figure 9), which is consistent with the greater- standard deviation is found over the entire domain (i.e., than-observed rainfall temporal variability in the models. 6 Advances in Meteorology Each longitude from 106‐125°E Does a band exist with gradient of equivalent potential temperature exceeding 0.04 K/km at 850 hPa spanning 25‐35°N? Yes Yes Calculate the center latitudes If the total number of grid of the Meiyu front at each points in the band exceeds 600 latitude Yes Yes If the average difference in If the standard deviation of the center latitudes the center latitudes is less between adjacent than 2.0 and the departure longitudes is less than 1 of the center latitudes from the average center latitude is less than 1.0 Yes Determine the location (latitudes) of the Meiyu front at each time step and at each longitude Composite the location of the Meiyu front Figure 2: Flow chart determining the composite location of the Meiyu front. hydrometeors to form rain is called the “cold-rain” mi- 3.4. Budget Analysis of Raindrops. A comparison shows that the microphysics processes involved in raindrop growth crophysics mechanism. *is mechanism is important in Meiyu front precipitation [21]. mainly include the evaporation of rain (Evap), the riming of raindrops by ice-phase hydrometeors (Col1), the auto- Although simulations using all three microphysics conversion of cloud droplets to rain (Auto), the accretion of schemes demonstrate the importance of Mlt and CLcr for cloud droplets by raindrops (CLcr), and the melting of ice- Meiyu front precipitation, the detailed roles Mlt and CLcr phase hydrometeors (Mlt). *e accretion of raindrops by play in the temporal evolution of rainfall are different in each frozen particles is neglected because of the relatively small scheme. In the Morrison scheme (see Figure 10(a)), ave- magnitude of the transferred mass. CLcr is larger than ave-Mlt before 0200 UTC on 2 July, and Figure 10 shows the time series of the mean content of the latter is larger than the former from 0200 UTC to 0500 the main sources and sinks of rain (computed with the UTC on 2 July. During the remaining period, ave-CLcr is slightly larger than ave-Mlt. In the *ompson scheme (see same method as ave-Rain in Figure 6) in the heavy rainfall area. For brevity, the prefix “ave-” is used to represent the Figure 10(b)), ave-CLcr is larger than ave-Mlt before 2300 mean content of the mass transferred through different UTC on 1 July and then less than ave-Mlt after that time. *e microphysical processes. *e simulation results from all MY scheme simulates more ave-Mlt than ave-CLcr three microphysics schemes indicate the importance of throughout most of the simulation period. Mlt and CLcr, which appear to play dominant roles in the In contrast, ave-Col1 is approximately several times growth of raindrops. *e melting of ice-phase smaller than the other two processes in the Morrison and Advances in Meteorology 7 200 hPa 200 hPa 200 hPa 38°N 38°N 38°N 20 m/s 20 m/s 20 m/s 34°N 34°N 34°N 30°N 30°N 30°N 26°N 26°N 26°N 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E 0 5 10 15 20 25 30 35 40 45 0 5 10 15 20 25 30 35 40 45 0 5 10 15 20 25 30 35 40 45 700 hPa 700 hPa 700 hPa 38°N 38°N 38°N 10 m/s 10 m/s 10 m/s 34°N 34°N 34°N 30°N 30°N 30°N 26°N 26°N 26°N 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 850 hPa 850 hPa 850 hPa 38°N 38°N 38°N 10.0 g/(s hPa cm) 10.0 g/(s hPa cm) 10.0 g/(s hPa cm) 34°N 34°N 34°N 30°N 30°N 30°N 26°N 26°N 26°N 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E –25–20 –15 –10 –5 0 5 10 15 20 25 –25–20 –15 –10 –5 0 5 10 15 20 25 –25–20 –15 –10 –5 0 5 10 15 20 25 (a) (b) (c) Figure 3: *e same as Figure 1 except for the model output with the composite location of the Meiyu front in each experiment (observations) being indicated by a solid brown (purple) line. OBS Morrison 35N 35N 30N 30N 25N 25N 90E 100E 110E 120E 90E 100E 110E 120E 0.1 10 25 50 100 250 0.1 10 25 50 100 250 Units: mm Units: mm (a) (b) ompson MY 35N 35N 30N 30N 25N 25N 90E 100E 110E 120E 90E 100E 110E 120E 0.1 10 25 50 100 250 0.1 10 25 50 100 250 Units: mm Units: mm (c) (d) Figure 4: Spatial distribution of cumulative rainfall in 24 hours for (a) the observations and ((b)-(d)) the WRF simulations using three microphysics schemes: (b) Morrison, (c) *ompson, and (d) MY. 8 Advances in Meteorology <0.1 0.1–10 10–50 50–100 >100 Range of daily precipitation (mm) OBS MY Morrison ompson Figure 5: PDFs of the cumulative precipitation in 24 hours between the observations and WRF simulations using the three microphysics schemes (Morrison, *ompson, and MY). 6.0 15 5.0 4.0 10 3.0 2.0 5 1.0 0.0 0 1200 1600 2000 0000 0400 0800 1200 1200 1600 2000 0000 0400 0800 1200 July 1 July 2 July 1 July 2 Time (UTC) Time (UTC) Morrison MY Morrison ompson OBS ompson MY (a) (b) Figure 6: Time series of (a) area-averaged rainfall and (b) rainwater content in a heavy rainfall area of observation and simulation results from 1200 UTC on 1 July to 1200 UTC on 2 July 2016. *ompson schemes. *e MY scheme produces a larger ave- the evaporation of raindrops. *e differences in evaporation Auto than the other two schemes, although ave-Auto is still processes for these 3 schemes are relatively small. an order of magnitude smaller than ave-Mlt and ave-CLcr. Figure 11 shows the vertically averaged content of *e simulation results suggest that the dominant source transferred mass from the melting of ice-phase hydrome- terms of rain are the melting of ice-phase hydrometeors and teors (h_ave_Mlt) and accretion of cloud droplets by accretion of cloud droplets by raindrops. *e key sink term is raindrops (h_ave_CLcr). Averaged precipitation (mm) PDF (%) Averaged content (g/m ) Advances in Meteorology 9 700hPa 500hPa 36°N 36°N 34°N 34°N 32°N 32°N 30°N 30°N 28°N 28°N 26°N 26°N 24°N 24°N 106°E 110°E 114°E 118°E 122°E 106°E 110°E 114°E 118°E 122°E 0 0.5 1 1.5 2 0 0.5 1 1.5 2 (a) (b) Figure 7: *e spatial distribution of the standard deviation of the geopotential height (units:10 ×gpm) at (a) 700hPa and (b) 500hPa from 1 July to 2 July 2016. 700hPa Morrison 700hPa Thompson 700hPa MY 36°N 36°N 36°N 34°N 34°N 34°N 32°N 32°N 32°N 30°N 30°N 30°N 28°N 28°N 28°N 26°N 26°N 26°N 24°N 24°N 24°N 106°E 110°E 114°E 118°E 122°E 106°E 110°E 114°E 118°E 122°E 106°E 110°E 114°E 118°E 122°E 0 0.5 1 1.5 2 0 0.5 1 1.5 2 0 0.5 1 1.5 2 500hPa Morrison 500hPa o Th mpson 500hPa MY 36°N 36°N 36°N 34°N 34°N 34°N 32°N 32°N 32°N 30°N 30°N 30°N 28°N 28°N 28°N 26°N 26°N 26°N 24°N 24°N 24°N 106°E 110°E 114°E 118°E 122°E 106°E 110°E 114°E 118°E 122°E 106°E 110°E 114°E 118°E 122°E 0 0.5 1 1.5 2 0 0.5 1 1.5 2 0 0.5 1 1.5 2 (a) (b) (c) Figure 8: *e same as Figure 7 except for the three experiments: (a) Morrison, (b) *ompson, and (c) MY. 1 and 2 indicate 700hPa and 500hPa, respectively. It can be seen that differences in h_ave_Mlt and From 1200 to 1400 UTC on 1 July, precipitation ex- h_ave_CLcr exist in local areas for all schemes. Even in one periences a sharp increase. *e evolution of two key pro- scheme, there are obvious differences in h_ave_Mlt and cesses (melting of ice-phase hydrometeors and accretion of h_ave_CLcr. Compared with Figure 4, the maximum cloud droplets by raindrops) in all three schemes shows good h_ave_Mlt and h_ave_CLcr values are consistent with the consistency with the evolution of precipitation. *e ave-Rain maximum cumulative precipitation. *is indicates that the trend differs in three schemes from 1400 to 1500 UTC on 1 difference in the two key microphysical processes in different July. *e three key processes show similar trends with ave- Rain in the *ompson scheme, although they differ in the microphysics schemes leads to the differences in the spatial distribution of heavy rainfall, especially in the maximum other two schemes. *e precipitation experiences a sharp cumulative rainfall area. decrease from 0300 to 0600 UTC on 2 July. During this time Table 3 shows the simulated ave-Rain, ave-Mlt, ave-CLcr, period, the ave-Mlt and ave-CLcr trends are nearly con- and ave-Evap trends in two continuous time periods, which sistent with that of ave-Rain in the MY scheme. In the other representtherapidincreaseanddecreaseperiodsofprecipitation. two schemes, ave-Mlt and ave-CLcr show opposite trends in 10 Advances in Meteorology 700hPa Morrison 700hPa Thompson 700hPa MY 36°N 36°N 36°N 34°N 34°N 34°N 32°N 32°N 32°N 30°N 30°N 30°N 28°N 28°N 28°N 26°N 26°N 26°N 24°N 24°N 24°N 106°E 110°E 114°E 118°E 122°E 106°E 110°E 114°E 118°E 122°E 106°E 110°E 114°E 118°E 122°E –0.5 –0.25 0 0.25 0.5 –0.5 –0.25 0 0.25 0.5 –0.5 –0.25 0 0.25 0.5 500hPa Morrison 500hPa Thompson 500hPa MY 36°N 36°N 36°N 34°N 34°N 34°N 32°N 32°N 32°N 30°N 30°N 30°N 28°N 28°N 28°N 26°N 26°N 26°N 24°N 24°N 24°N 106°E 110°E 114°E 118°E 122°E 106°E 110°E 114°E 118°E 122°E 106°E 110°E 114°E 118°E 122°E –0.5 –0.25 0 0.25 0.5 –0.5 –0.25 0 0.25 0.5 –0.5 –0.25 0 0.25 0.5 (a) (b) (c) Figure 9: *e same as Figure 8 except for the differences between the model output and LAPS product (Model-LAPS). Morrison ompson 0.20 0.20 0.15 0.15 0.10 0.10 0.05 0.05 0.00 0.00 1200 1600 2000 0000 0400 0800 1200 1200 1600 2000 0000 0400 0800 1200 July 1 July 2 July 1 July 2 Time (UTC) Time (UTC) Mlt Clcr Mlt Clcr Coll Evap Coll Evap Auto Auto (a) (b) Figure 10: Continued. Averaged content (g/m ) Averaged content (g/m ) 50 Advances in Meteorology 11 MY 0.20 0.15 0.10 0.05 0.00 1200 1600 2000 0000 0400 0800 1200 July 1 July 2 Time (UTC) Mlt Clcr Coll Evap Auto (c) Figure 10: Time series of the mean content of the main sources and sinks of rain in heavy rainfall areas from 1200 UTC on 1 July to 1200 UTC on 2 July in 2016: (a) Morrison, (b) *ompson, and (c) MY. 3 3 Morrison 24h Melt (g/m ) + Pr (mm) Morrison 24h Clcr (g/m ) + Pr (mm) 7 7 32°N 32°N 6 6 30°N 5 30°N 5 4 4 28°N 28°N 3 3 2 2 26°N 26°N 1 1 106°E 108°E 110°E 112°E 114°E 116°E 118°E 120°E 106°E 108°E 110°E 112°E 114°E 116°E 118°E 120°E (a) 3 3 ompson 24h Melt (g/m ) + Pr (mm) ompson 24h Clcr (g/m ) + Pr (mm) 7 7 32°N 32°N 6 6 30°N 30°N 5 5 4 4 28°N 28°N 3 3 2 2 26°N 26°N 1 1 106°E 108°E 110°E 112°E 114°E 116°E 118°E 120°E 106°E 108°E 110°E 112°E 114°E 116°E 118°E 120°E (b) 3 3 MY 24h Melt (g/m ) + Pr (mm) MY 24h Clcr (g/m ) + Pr (mm) 7 7 32°N 32°N 6 6 30°N 30°N 5 5 4 4 28°N 28°N 3 3 2 2 26°N 26°N 1 1 106°E 108°E 110°E 112°E 114°E 116°E 118°E 120°E 106°E 108°E 110°E 112°E 114°E 116°E 118°E 120°E (c) Figure 11: Vertically averaged h_ave_Mlt and h_ave_CLcr (shaded area) and heavy rainfall area (contour of 50 mm): (a), Morrison; (b) *ompson; (c) MY. 1 and 2 indicate melting of ice-phase hydrometeors and accretion of cloud droplet by rain, respectively. Averaged content (g/m ) 12 Advances in Meteorology Table 3: Simulated ave-Rain, ave-Mlt, ave-CLcr, and ave-Evap trends from 1200 to 1500 UTC on 1 July and from 0300 to 0600 UTC on 2 July. Red upward arrows and blue downward arrows represent increases and decreases, respectively. “-” indicates a negligible change. Morrison *ompson MY Time Prec Mlt CLcr Evap Prec Mlt CLcr Evap Prec Mlt CLcr Evap 12z01–13z01 ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ 13z01–14z01 ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ 14z01–15z01 ↓ ↑ — ↑ ↑ ↑ ↑ ↑ ↓ ↓ — ↑ 03Z02–04Z02 ↓ ↓ ↓ — ↓ ↑ ↓ — ↓ ↓ ↓ — 04Z02–05Z02 ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↓ ↓ ↓ — 05Z02–06Z02 ↓ ↓ ↑ ↓ ↓ ↓ ↓ ↑ ↓ ↓ ↓ — some time periods. Moreover, ave-Evap does not change as the simulated ave-Mlt, ave-Dep, ave-Col1, and ave-Col2 during these two time periods, similar to Table 2. rapidly as the other two processes. *ese two different evolution periods of precipitation From 1200 to 1400 UTC on 1 July, ave-Rain and ave-Qr increase rapidly. All three schemes simulate the same ave- indicate that the trend in key processes is still not consistent with that of ave-Rain when ave-Rain experiences rapid Mlt, ave-Dep, ave-Col1, and ave-Col2 trends. From 1400 to 1500 UTC on 1 July, ave-Rain and ave-Qr from the changes, and the microphysical parameterization scheme affects the consistency between processes and precipitation. *ompson scheme do not change as rapidly as those in the previous time period, and the changes in the four sink and source terms of rain do not show good consistency. *e 3.5. Budget Analysis of Ice-Phase Hydrometeors. Previous consistency in the other two schemes is better than that of studies have shown that the coexistence and interaction of the *ompson scheme. During the time period when ave- ice-phase and liquid-phase hydrometeors in mixed-phase Rain and ave-Qr decrease rapidly (from 0300 to 0600 UTC cloud processes contribute most to the formation and de- on 2 July), the trends in the four budget terms show good velopment of heavy rainfall in Meiyu frontal systems [21]. To consistency. *is demonstrates that when the precipitation simplify the discussion, all ice categories are considered as a and water content experience rapid changes, the trends in whole. Except for melting (Mlt) and evaporation deposition the sink and source terms of ice-phase hydrometeors are (VD), Col1 and the collision of cloud droplets with ice-phase largely consistent. hydrometeors (Col2) are discussed. Similar to Figure 6, the Figure 13 compares the contributions of snow and prefix “ave-” is used to represent the mean content of graupel to riming. Collisions between cloud droplets transferred mass by different sink and source terms. (raindrops) and snow are represented by CLcs (CLrs), and Figure 12 indicates that there are similarities in the CLcg (CLrg) indicates the collision between cloud droplets evolution of the key budget terms of raindrops in all mi- (raindrops) and graupel. *e meaning of the prefix “ave-“is crophysics schemes, especially when rapid changes occur. the same as that mentioned above. *e MY and *ompson schemes simulate the largest and It can be seen that graupel and snow contribute very least amounts of melted ice-phase hydrometeors, respec- differently to the riming process in different schemes. *e tively (see Figure 12(a)). *e ave-Des values simulated by the accretion of cloud droplets by snow and graupel is the Morrison and *ompson schemes, which are larger than major cause of growth in the Morrison scheme. *e those simulated by the MY scheme (see Figure 12(b)), show accretion of cloud droplets by snow (graupel) is the major similar temporal evolutions. Cloud droplets contribute more cause of growth in the *ompson (MY) scheme. More- than raindrops to the growth of ice-phase hydrometeors in over, ave-CLrs is much smaller than the other terms and the riming process (see Figures 12(c) and 12(d)). Because is nearly negligible, especially in the MY scheme. *e cloud droplets are smaller and lighter than raindrops, it is Morrison scheme simulates more ave-CLcg than ave- easier for the former to be lifted to high levels and collide CLcs in general and more ave-CLcs than ave-CLcg from with ice-phase hydrometeors. *erefore, ave-Col2 is larger 0300 to 1000 UTC on 2 July (see Figure 13(a)). *is than ave-Col1, especially in the MY scheme, in which ave- scheme simulates more ave-CLrg than ave-CLrs at the Col2 is several times larger than ave-Col1. same time, and they are less than the other two source For the Morrison and *ompson schemes, deposition terms of ice-phase hydrometeors. Compared to the and riming processes play nearly equally important roles, *ompson scheme, which produces more ave-CLcs than while the riming of cloud droplets by ice-phase hydrome- ave-CLcg over the entire time period (see Figure 13(b)), teors contributes more than deposition. *is demonstrates the MY scheme simulates very different results (see that the contribution of the same cloud microphysical Figure 13(c)); for example, ave-CLcg is nearly an order of processes may be very different across different parame- magnitude larger than ave-CLcs. Consequently, more terization schemes. snow is simulated in the *ompson scheme, and more As mentioned above, precipitation increases (decreases) graupel is simulated in the MY scheme (see Figure 14). from 1200 to 1500 UTC on 1 July (0300 to 0500 UTC on 2 *ese results demonstrate that great differences in the July), while the ice-phase hydrometeors experience similar riming process of different cloud microphysics schemes may evolutions during the two time periods. To further understand be one of the major causes leading to distribution differences the results shown in Figure 6, Table 4 compares the changes in Advances in Meteorology 13 MLT VD 0.20 0.20 0.15 0.15 0.10 0.10 0.05 0.05 0.00 0.00 1200 1600 2000 0000 0400 0800 1200 1200 1600 2000 0000 0400 0800 1200 July 1 July 2 July 1 July 2 Time (UTC) Time (UTC) Morrison Morrison ompson ompson MY MY (a) (b) COL1 COL2 0.20 0.20 0.15 0.15 0.10 0.10 0.05 0.05 0.00 0.00 1200 1600 2000 0000 0400 0800 1200 1200 1600 2000 0000 0400 0800 1200 July 1 July 2 July 1 July 2 Time (UTC) Time (UTC) Morrison Morrison ompson ompson MY MY (c) (d) Figure 12: Time series of the mean contents of sink and source terms of ice-phase hydrometeors in heavy rainfall areas (R greater than 50mm/24 (h) from 1200 UTC on 1 July to 1200 UTC on 2 July 2016: (a) melting of ice-phase hydrometeors; (b) deposition of vapor; (c) riming (for raindrops); (d) riming (for cloud droplets). *e black, red, and blue lines indicate the Morrison, *ompson, and MY schemes, respectively. Table 4: *e same as Table 2 except for the melting of ice-phase hydrometeors (Mlt), deposition (Dep), and riming of raindrops (Col1) and cloud droplets (Col2) by ice-phase hydrometeors. “—” indicates a negligible change. Morrison *ompson MY Time Mlt VD Col1 Col2 Mlt VD Col1 Col2 Mlt VD Col1 Col2 12z01–13z01 ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ 13z01–14z01 ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ 14z01–15z01 — ↓ — ↓ ↑ ↓ — ↓ ↓ — ↓ ↓ 03Z02–04Z02 ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ 04Z02–05Z02 ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ 05Z02–06Z02 ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ 3 3 Averaged content (g/m ) Averaged content (g/m ) 3 3 Averaged content (g/m ) Averaged content (g/m ) 14 Advances in Meteorology Morrison ompson 0.20 0.20 0.15 0.15 0.10 0.10 0.05 0.05 0.00 0.00 1200 1600 2000 0000 0400 0800 1200 1200 1600 2000 0000 0400 0800 1200 July 1 July 2 July 1 July 2 Time (UTC) Time (UTC) CLcs CLrg CLcs CLrg CLrs CLcg CLrs CLcg (a) (b) 0.20 0.15 0.10 0.05 0.00 1200 1600 2000 0000 0400 0800 1200 July 1 July 2 Time (UTC) CLcs CLrg CLrs CLcg (c) Figure 13: Time series of the mean contents of the riming process in heavy rainfall areas from 1200 UTC on 1 July to 1200 on 2 July 2016: (a) Morrison; (b) *ompson; (c) MY. *e black, red, green, and blue lines indicate cloud droplet-snow, raindrop-snow, cloud droplet- graupel, and raindrop-graupel interactions, respectively. QS QG MY ompson Morrison Figure 14: Vertically and domain-averaged contents of snow (QS) and graupel (QG) from three schemes in heavy rainfall areas from 1200 UTC on 1 July to 1200 UTC on 2 July 2016. Averaged content (g/m ) 24 h total content (g/m3) Averaged content (g/m ) Averaged content (g/m ) Advances in Meteorology 15 assumed distribution of hydrometeors, should not be in graupel and snow, which can cause differences in melting and thus affect the distribution of rainfall directly or neglected. Because of the complexity of dealing with all cloud indirectly. microphysical processes, to avoid discussing the conversion between ice-phase hydrometeors, all frozen hydrometeors 4. Summary and Conclusions were classified into only one category for an initial com- Microphysical processes affecting Meiyu front precipitation parison. After differences in the ice-phase processes were in central-eastern China are complex and challenging to found, they were individually compared. *eir direct effects model. *is study examined the sensitivity of a simulated impacted the distribution of ice-phase hydrometeors. It Meiyu heavy rainfall event over central China to three would be easy to judge which scheme is the best one when all different cloud microphysical parameterizations in the WRF hydrometeors can be distinguished correctly. However, it is difficult to deduce the hydrometeor types and amounts model. *e general large-scale circulation and thermodynamic because the predefined characteristics of the hydrometeors lead to some uncertainties [46]. characteristics of the Meiyu rainfall case were reasonably reproduced by all the three experiments with different Which scheme could most accurately describe the cloud microphysics schemes. *e overall distribution of the structure of heavy Meiyu rainfall? Regarding this question, simulated precipitation matched well with that of the ob- from the comparison of the RMSE of accumulated rainfall servations, while the average rainfall amount was over- over 24 hours, it appears that the *ompson scheme per- estimated, especially in heavy rainfall areas. *e simulated formed the best in this study. However, determining rainfall in all three schemes began rapidly and gained whether or not this scheme simulated the detailed micro- physical structure of the clouds requires the simulated strength at the very beginning but dissipated rapidly, while the observed precipitation began slowly but maintained a distribution of hydrometeors, especially ice-phase particles, to be compared with observations in the future. Field work relatively steady intensity throughout the period. *e more rapid evolution of the rainfall rate in the model was con- and in situ, remote sensing observations of cloud micro- and macroproperties for Meiyu rainfall events in central-eastern sistent with the greater-than-observation temporal vari- ability in the geopotential height over central-eastern China. China are needed. *e simulated precipitation in the model experiments was also overestimated compared to observations from the South Data Availability China Monsoon Rainfall Experiment [25], while the rainfall rate from all microphysics schemes was more than that of *e precipitation data used in this study were provided by the observations for nearly the entire simulation period. the China Meteorological Administration. *e global A microphysical budget analysis indicated that melting analysis dataset (FNL) was acquired from the National of ice-phase hydrometeors and accretion of cloud droplets Centers for Environmental Prediction (https://www.ncep. by raindrops were the key processes contributing to the noaa.gov/). *e ERA5 reanalysis data used are from Euro- growth of raindrop and formation of heavy rainfall in the pean Center for Medium-Range Weather Forecasts (https:// model. Bergeron and riming processes prevailed in the climate.copernicus.eu/climate-reanalysis). Data from LAPS growth of ice-phase hydrometeors. *e rapid changes in the are archived and distributed by the Institute of Heavy microphysics budget terms with time in the model were Rainfall, CMA, Wuhan, as part of the dataset during the consistent with the rapid evolution of rainfall with time in Integrative Monsoon Frontal Rainfall Experiment (IMFRE) the model. Large differences in riming processes existed in in 2018. the three schemes, leading to different distributions of ice- phase hydrometeors, especially for snow and graupel. Conflicts of Interest *ere are nearly 40 microphysical processes in each scheme. For some microphysical processes, there may be *e authors declare that they have no conflicts of interest. several different parameterization methods. *erefore, it is difficult to compare all processes at the same time in this Acknowledgments study. Furthermore, the differences between schemes are not only due to different parameterizations but also due to the *is research was supported by the NSFC project of Cloud predefined hydrometeor characteristics (see Table 2). For Analysis and Microphysical Investigation of Meiyu Frontal example, although the mass production rates of cloud water System Based on Ground-Airborne Observations are the same between two schemes, the number concen- (41620104009), the National Natural Science Foundation of tration will differ because the predefined distribution of China (91637211 and 41905071), and Key Scientific and cloud droplets differs [45]. *is will lead to differences in Technological Development Projects in Hubei Province in collisions between cloud droplets and ice-phase hydrome- China (2018Z05). teors which is a key microphysics process for the growth of graupel between the schemes. *erefore, it is difficult to References conclude how the differences in the parameterizations of certain microphysics process affect the model results. [1] G. T.-J. Chen, C.-C. Wang, and D. T.-W. 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Advances in Meteorology – Hindawi Publishing Corporation
Published: Nov 29, 2020