Experimental Design Overview

Southern China Monsoon Rainfall Experiment (SCMREX)

A WMO/WWRP Research and Development Project (RDP)

March 25, 2013

Table of Content Executive

Summary

1. Introduction 1.1 History

1.2 Motivation 1.3 Goals

2. Scientific Objectives and Hypotheses 3. Field Campaigns 3.1 Experiment time

3.2 Experiment area 3.3 Experiment design 3.4 Experiment measurements 3.4.1 Upper air observation

3.4.2 Surface observation 3.4.3 Precipitation system observation 3.4.4 Satellite observation

3.5 Experiment organization 4. Numerical Modeling Study

4.1 Improvement of the initial field 4.2 Improvement of the model physical processes 4.3 Meso-scale ensemble prediction (MEP) experiments 5. Data Collection, Processing and Sharing

6. Programme Management 7. International Participation and Collaboration 8. Implementation Schedule

Acknowledgments

References

Executive Summary

During the early summer rainy season (April to June) of China, frequency and intensity of

torrential-rain in South China increase after the onset of South China Sea (SCS) Monsoon in

late May, being a serious threat to people’s lives and properties and causing great economic

losses. To expedite our understanding of processes key to the torrential rain formation and our

efforts to improve simulation and prediction of the torrential rain, the China Meteorological

Administration initiated and are organizing Southern China Monsoon Rainfall Experiment

(SCMREX). SCMREX consists of four integrated components: field campaign, data

collection/processing/sharing, numerical modeling study, and physical mechanism study.

The field campaign of SCMREX aims to obtain unique and composite observations of the

fine-scale structures of the convective systems and of their atmospheric environment during

the early summer rainy season. The field experiments will be launched in a region mainly

covering Guangdong, Guangxi, Hainan, Hong Kong and the offshore of SCS during

mid-April to mid-June of 2013-2014. The experiments will capture the thermodynamic and

dynamic properties of the atmosphere, the boundary-layer wind field in particular which is

of significant importance to convection initiation, by utilizing the wind profilers,

rawinsondes, GPS/MET stations, and dropsondes. Dynamic and microphysical structures of

precipitating systems will be detected not only by the Doppler weather radars and lightning

location system, but also multiple advanced remote sensing equipments in two enhanced

observing areas: 2 C-band dual-polarization radars, 1 X-band dual-polarization radar, 1

micro-rain radar, 4 raindrop distrometers, and 2 millimeter-wave cloud radars.

The numerical modeling study of SCMREX aims to reduce initial errors of numerical

weather prediction, improve physical schemes in numerical model, and conduct/evaluate

meso-scale ensemble prediction experiments. The physical mechanism studies are mainly

about (a) the roles of low-level jet, planetary boundary layer, and underlying surface on

initiation and development of the rainy storms; (b) the microphysical features and

processes of the convective systems. A scientific database and a website will be setup to

share the data sets from the field campaign and modeling study

.

SCMREX has been endorsed as a Research and Development Project (RDP) of

WMO/WWRP, and has attracted widespread participation and cooperation from many

countries such as India, Japan, Korea, Australia, and the Philippines.

1. Introduction

1.1 History

In September 2011, the Chinese Academy of Meteorological Sciences (CAMS) of China

Meteorological Administration (CMA) submitted a proposal conducting field experiments

on “Deep Convection and Intense Rainfall in Southern China during the Monsoon Outbreak

over South China Sea” to the Tropical Meteorology Research Working Group (WGTMR)

Monsoon Panel of the World Meteorological Organization/World Weather Research

Programme (WMO/WWRP). This proposal was later accepted as the WMO/WWRP

Research and Development Project (RDP), and it was treated as the key subject of the

WMO/WWRP’s “International Workshop on Heavy Monsoon Rainfall”, which was held at

CMA on 12-14 October 2011. During the morning session of October 13, Prof. Renhe

Zhang from CAMS introduced plans to conduct the above-mentioned field experiments.

Scientists from CMA/CAMS and CMA/Numerical Prediction Center reported their related

work on the numerical model development, and the modeling and analysis of monsoon

rainfall. After one-day discussion, the workshop participants affirmed the scientific

objectives of the experimental plans and CMA implement plan. Meanwhile, they made

some recommendations to improve the plans. Based on these recommendations and those

by Expert Group of the WWRP WGTMR Monsoon Panel, the above-mentioned plans were

renamed as the "Southern China Monsoon Rainfall Experiment (SCMREX), with an

improved version of the proposal. This revised proposal was submitted in January 2012 to

and later accepted by the WMO Monsoon Panel, which was recommended to the WWRP in

February 2012.

The SCMREX RDP proposal was approved during the 5th

meeting of the WWRP Joint

Scientific Committee which was held on 11-13 April 2012 at the WMO’s Headquarters in

Geneva. The WMO Executive Council’s Annual Meeting, held in Geneva on 25 June – 3

July 2012 officially approved the SCMREX RDP as the WMO/WWRP RDP.

During the WMO/WWRP’s 2nd International Workshop on Heavy Monsoon Rainfall,

which was held on 10-12 December 2012 in Kuala Lumpur, Malaysia, as an important part

of the workshop, Prof. Yali Luo from CAMS introduced the origin, scientific objectives

and implementation plans of SCMREX RDP, in the afternoon of December 11. Extensive

discussions on the scientific goals, observing systems, data sharing were carried out by

experts from China, the United States, Japan, South Korea and Australia, and other

countries. Many useful recommendations and required actions were put forward.

On 26 December 2012, CMA/Division of Science and Technology and Climate Change

organized a meeting to develop the SCMREX implementation plan, with divided tasks

for several authors. This plan was completed a couple of month later.

In recent years, the WMO/WWRP has established several research development projects

and Forecast Demonstration Project (FDP). They include the mesoscale Alpine Programme

(MAP, Bougeault et al 2001; Ranzi et al. 2007) and its predecessor; the MAP

Demonstration of Probabilistic Hydrological and Atmospheric Simulation of Flood Events

(i.e., the MAP D-Phase, Zappa, et al 2008); the IMPROVE experiment over the Cascade

Mountains of western North America aiming at improving cloud microphysical schemes

(Stoelinga et al 2003); Sydney 2000 FDP (Anderson-Berry et al. 2004) aiming at

promoting and showcasing summer convection nowcasting systems; Beijing 2008 FDP

(Wilson et al 2010) demonstrating the progress in nowcasts and the technological advances

in transforming research to applications since 2000; Beijing 2008 RDP (Duan et al. 2012)

focusing on the mesoscale ensemble forecasts; Beijing 2008 FDP and RDP both

emphasizing quantitative precipitation forecasts (QPFs), convective initiation, and summer

severe weather; the Science of Nowcasting Olympic Weather for Vancouver 2010 Olympic

(SNOW V10; Isaac et al. 2009) aiming at the winter weather nowcasts under complex

terrain.

The SCMREX RDP aims at expediting our efforts to improve the prediction of heavy

rainfall events in South China during the first rainy season through field campaigns and the

subsequent data processing and sharing, numerical modeling and

analysis. The project will improve our understanding of the structures and evolution of the

South China heavy-rain-producing storms during the monsoon outbreak period, and

improve our ability to predict these high-impact events through minimizing the errors in

initial conditions and uncertainties in physics schemes in NWP models, and

conduct/evaluate mesoscale ensemble prediction experiments. The unique background of

the East Asian monsoon (and its associated abundant moisture) plus the complex surface

conditions over South China (e.g., mesoscale mountains, plains, coastal, urban complexes;

see Figs. 1 and 3d) gives rise to the uniqueness and complexity of heavy rainfall

development during the first rainy season in South China. This also provides a great

opportunity for SCMREX RDP to extend the WMO/WWRP RDP/FDP previous

programs.

Fig. 1 Schematic surface conditions over the central field observing areas: vegetation (green), cities

(red dots) including those around the Zhu-River Delta (red circles with filled green shadings).

Triangles in red denote the location of Doppler radar in two intensive observing areas.

1.2 Motivation

Southern China is one of the rainiest regions in China, with an annual amount of over 2000

mm. The rainfall amount, accumulated from the onset in April to the end in middle or late

June, the so-called first rainy season, as the monsoon rainbelt moves northward to the

Yangtze-Huai River basin (Ding, 1994), accounts for about 50% of the annual amount. This

period often experiences the most frequent occurrences of heavy rainfall, leading to severe

flooding and inundations. So these storms endanger the safety of lives, and cause marked

property damages, often producing devastating economic losses. The first rainy season in

southern China reaches its peak in terms of occurrence frequency and rainfall intensity

during the period (Zhou et al., 2003; Fig.2). Most precipitation during the first rainy season

in South China is of convective nature with mesoscale organizational characteristics, and

more than 50% contributions to the total rainfall are from those heavy rainfall events with

the rates of more than 50 mm day-1

(Fig. 3b). Major heavy rainfall centers are typically

distributed over the coastal areas of Guangdong and Guangxi provinces, northern Guangxi

as well as the western and northern Fujian, with the maximum rainfall located in Guandong

Province (Fig. 3a). These rainfall centers correspond well to the most frequent occurrences

of deep convection.

Fig. 2 (a) The mean frequency of rainfall rates (> 50 mm day-1); and (b) the frequency

distribution of rainfall rates occurring in South China for the phases of before and after the

monsoon outbreak in South China Sea during the years of 1998-2010. The maps are obtained using

the daily rainfall observations at 0.25° resolution. The South China region is defined as the

continental area of 21o

–28o

N, 109o

–120o

E. The pre-monsoon and monsoon phases are defined as

the one month prior to the monsoon outbreak in South China Sea, and the period from the monsoon

outbreak to the beginning of the Meiyu season in the Yangtze-Huai River Basin (Luo et al. 2013).

Fig. 3 (a) The annual mean rainfall (mm); (b) the percentage of heavy rainfall (> 50 mm day-1)

contribution; and (c) the frequency of convective precipitating features (PFs) observed by TRMM

during the mature stage of the first rainy season in the years of 1998-2010; and (d) the geography

and terrain distribution in South China. A PF is defined as a contiguous area consisting of the

TRMM 2A25 (Iguchi et al. 2000) near surface raining pixels. A convective PF contains at least one

convective pixel. The criterion of a convective pixel is based on the TRMM 2A23 product ( Awaka

et al. 1998 ).

In order to better understand the heavy rainfall development during the first rainy season in

South China, we have conducted for the first time in 1977-1980 “the Southern China

Monsoon Rainfall Experiment”. Results indicate that these heavy rainfall events took place

in the warm sector, and they are accompanied by the low-level jets, mesoscale convective

systems (MCSs), and moist planetary boundary layer (PBL). The second such field

experiment was conducted in 1998 (Zhou et al. 2003). Results from this experiment reveal

some meso-β-scale structures of MCSs, and certain mechanisms by which they form. In

particular, it is shown that

high-resolution NWP models, initialized with large-scale observations, can simulate some

observed meso-β-scale features associated with the heavy rainfall events. During the

months of May-July 2008 and 2009, CAMS/the State Key Laboratory of Severe Weather

conducted the South China Heavy Rainfall Experiment (SCHeREX) with four mesoscale

observing networks distributed in South China, the low valley, and the middle valley of

Yantze-River, and the Yangtze-Huai River basin, respectively (Zhang et al., 2011). One

millimeter-wave radar and one C-band dual polarmetric Doppler radar were utilized; some

dropsondes were used over the northern South China Sea. Mesos-analysis of the SCHeREX

data and three-dimensional wind retrieval have revealed some mesoscale characteristics of

heavy rainfall events.

The previous observational analyses show that the heavy rainfall events during the first

rainy season in South China are mostly associated with mesoscale convective complexes

(MCCs). However, the linkages from convective cells to convective clusters, to MCCs and

further to regional heavy rainfall, as well as their multi-scale interactions are still unknown.

Thus, it is extremely difficult to predict the timing and location of the heavy rainfall

occurrence and its associated intensity changes. In general, we are lacking the predictability

of heavy rainfall during the first rainy season (Fig. 4), and our ability to predict the

warm-sector rainfall is much lower than that associated with the front rainfall.

Fig. 4 The TS scores of the monthly accumulated rainfall forecasts in South China for the month of

May (top), and June (bottom) 2010 (provided by CMA/NWP Center). Different color bars denote

the TS scores of forecasters, the global model of T639 and that used in Japan and German,

mesoscale models of MM5 and GRAPES. (Data source: Numerical Prediction Center, CMA)

To improve the forecast skill of heavy rainfall, it is desirable to explore the mechanisms whereby

convective initiation and subsequent mesoscale organization occur, as well as the processes leading

to rainfall intensity changes. On the other hand, it is important to improve the short-and

medium-range quantitative precipitation forecasts (QPFs) with NWP models. To achieve this

requires the accurate representation of clouds and precipitation as well as mesoscale information in

the model initial conditions, the reasonable description of the model physical processes, and the

development of ensemble prediction systems. For the model physics schemes,

the PBL and cloud microphysics deserve special attention, because (a) these heavy rainfall events

are highly associated with the PBL processes (b) the triggering of deep convection is closely related

to the flows, moisture and thermal conditions in the PBL;

(c) convective development and rainfall intensity are controlled by cloud-precipitation

physical processes, especially as the grid size decreases to 1-3 km; and (d) few studies

have been done to verify the model-simulated fine-scale structures of the PBL and

convective systems, mainly due to the lack of suitable observations. Thus, we are uncertain

about the ability of the current PBL and cloud microphysics schemes to simulate the

development of heavy rainfall during the first rainy season in South China.

Through the previous field experiments, Chinese scientists have gained valuable experience

in collecting and processing various non-conventional observational datasets (Ni et al.,

2011), and in using the data in their research and operational forecasts (Zhang et al., 2011).

However, the previous field experiments were unable to capture the inner-core

characteristics of MCSs and the underlying PBL processes leading to heavy rainfall events.

In addition, the high-resolution observations so obtained have not been applied to the

verification and improvement of physical parameterization schemes in NWP models.

Today, we have better observing facilities measuring the inner-core structures and PBL

processes during the first rainy season in South China. For example, since 2007, we have

installed 16 windprofiler radars, including 2 in Hong Kong, over Guangdong Province,

which consists of 14 PBL windprofilers and 2 tropospheric windprofilers. These

instruments will greatly improve the observational capability of low-level flows triggering

deep convection, and help us capture the low-level shear or convergence line, and

fluctuations in the LLJs. Moreover, as compared to SCHeREX, we now have more

advanced portable remote sensing equipment, to be described in section 3.4.3, which can

provide more comprehensive detection of the inner-core structures of MCSs. We have also

made significant progress in data quality control and retrieval algorithm (Hu et al. 2010,

2012; Liu et al. 2010), which would allow us to obtain more accurate information of

the microphysical and dynamic structures in the inner regions of MCSs. In addition, since

2009, CMA/National Meteorological Information Center, has began to perform the

real-time quality control of all automated surface observatories, and established a real-time

quality control system for basic meteorological variables (i.e., air temperature, air pressure,

humidity, wind speed and direction, and precipitation), with error information feedback

mechanisms (Ren et al. 2012; Ju et al. 2010a,b; Zhao et al. 2011). These high-resolution

(i.e., hourly and a grid spacing of about 10 km) continuous surface observations have

provided us with new opportunities to carry out surface meso-analysis, and investigate the

mechanisms whereby heavy precipitation develops.

1.3 Goals

With the above-mentioned favorable background conditions, CMA conducts the

international research and development project of “Southern China Monsoon Rainfall

Experiment” with the following goals: (i) To obtain comprehensive observational datasets

that could be shared among researchers of interest and then used to describe MCSs and

their environmental conditions during the first rainy season in South China;

(ii) To improve simulation and QPF of the torrential rain through advancing data

assimilation technique and improving physics parameterizations in model and by

conducting/evaluating mesoscale ensemble prediction (MEP) experiments. (iii) To increase

our knowledge on the development of heavy rainfall in the warm sector and the associated

dynamical and microphysical processes; and (iv) To establish a high-quality meteorological

data bank to facilitate future scientific exchange.

2. Scientific Objectives and Hypotheses

This project is targeted to the following four broad scientific questions focusing on the

prefrontal and/or warm-sector linear convective lines and nonlinear cloud clusters in

central Guangdong province during mid-April to mid-June.

(1) The role of low level jet (LLJ) in the convection initiation and the

development/maintenance of the warm-sector heavy-rain-producing MCS

LLJ is one of the main weather components that may trigger the heavy rainfall in south

China in early summer rainy season (Bao 1986; Ding et al. 2011). About 75-80% of the

heavy rainfall events (>50 mm/day) are associated with LLJ (Zhao and Wang 2009).

Statistic shows that 16 out of 19 strong LLJ processes in Guangdong during May and June

of 1970-1973 were followed by heavy rainfall of > 100 mm/day (Bao 1986). About 94% of

heavy rainfall events in North Taiwan are also accompanied by LLJ at 850hPa (Chen et al.

2005). Studies show that the appearance of LLJ above 2 km is usually a couple of hours

ahead of the occurrence of severe heavy rain (Liu et al. 2003; Cao et al. 2006). The

appearance of severe heavy rain is better associated with the intensification of LLJ and

downward expansion to below 1.5-2 km. It is hypothesized that the intensification and

downward expansion of LLJ play important roles in triggering the severe heavy rain likely

through providing moisture, heat, energy and disturbance and/or trigger inertial gravity

wave.

This project will use the obtained comprehensive data set and numerical simulation and

dynamic diagnoses to examine the evolution features of the LLJ, the interaction between

LLJ and upper tropospheric systems (such as south Asia high) and its impact on the

initiation and development of warm-sector heavy-rain-producing MCS.

(2) The roles of boundary layer processes and underlying surface on the initiation

and evolution of the heavy-rain-producing MCS

The topography of Guangdong province is very complicated which involves land-sea,

mountain-valley, and city-rural contrasts. The nonlinear interaction between underlying

surface and large-scale environmental flow may lead to the development of mesoscale

disturbance such as the convergent lines and/or vortex to trigger convection initiation.

Land-sea contrast may produce apparent convergence in coastal area during land breeze

phase from mid-night to early morning and apparent convergence in inner land area during

sea breeze phase from afternoon to early evening (Bao 1986). The trumpet shape

mountain-valley distribution around Zhujiang

River delta area is favorable for the convergence between westerly or southwesterly flow

and southeasterly flow. Heating differences between city and rural areas in the central part

of Guangdong province may trigger local mesoscale circulation and thus mesoscale

disturbance. Mesoscale boundaries such as convergent lines may form due to the complex

interaction between above mentioned various local mesoscale circulations forced by

topography and PBL processes or between the local mesoscale circulations and background

flow.

This project will use the obtained comprehensive data set and numerical simulation and

dynamic diagnoses to examine the roles of boundary layer processes and underlying

surface (such as sea breeze, mountain-valley wind, urban heat island, PBL rolls, convergent

lines, and mesoscale convective vortex) on the initiation and evolution of the

heavy-rain-producing MCS.

(3) The microphysical processes in the heavy-rain-producing MCS

Microphysical processes in the heavy-rain-producing MCS are closely related to the

precipitation properties such as the intensity, efficiency, lightening, and more importantly

the accuracy of microphysics parameterization scheme of NWP. The rain rate in the warm

sector is generally much larger than that at front which may be closely related to

microphysical properties. Observations on the microphysical properties of the MCSs in

early summer rainy season in South China were very limited in previous field experiments

due to the unavailability of polarimetric radars. Numerical studies indicated that both ice

cloud and warm cloud could be the main microphysical process of the MCSs in early

summer rainy season in South China (e.g., Wang et al. 2002).

This project is aimed to examine the key microphysics and the distribution of hydrometers

in warm-sector heavy-rainfall-producing MCS in South China based on the

dual-polarimetric radar, micro rain radar, millimeter wave cloud radar, raindrop

disdrometer in combination with satellite products such as TRMM and CloudSat.

(4) Key differences of the warm sector and frontal heavy rainfall before and after the

onset of SCS monsoon

Heavy rains in warm sector and at front are the two main rainfall categories in early summer

in South China. Previous case studies show that these two kinds of heavy rain have apparent

differences in the rainfall features and their formation environment (Zhao et al. 2008; Zhang

and Ni 2009). Heavy rains in warm sector are more convective and intensive than at front.

The environment of the heavy rain in warm sector has stronger upper level divergence than

lower level convergence, greater convective instability, while the environment of the heavy

rain at front has stronger lower level convergence than upper level divergence and greater

symmetric instability. Warm-sector heavy rain has more direct influence from monsoon,

less direct influence from cold air, stronger low-level vertical wind shear, smaller

convective inhibition, higher column precipitable water relative to that at front. The

behavior of inertial gravity wave triggered by the enhancement of vertical wind shear due to

cold air intrusion and their roles in the convection initiation of frontal and warm-sector

heavy rainfall could be different. The two kinds of rainfall may also have different

microphysical processes and different initiation and maintenance mechanisms partly due to

possible different impacts from mountain-valley and land-sea contrasts.

This project will use the obtained comprehensive data set and numerical simulation and

dynamic diagnoses to examine the different convection-initiation mechanism and

microphysical properties of the warm sector rainfalls before and after the onset of monsoon

under the apparently different common flow and moisture supply, as well as the differences

in the evolution and microphysical processes of warm-sector and frontal rainfall, the

isolated and linearly organized convections.

3. Field Campaigns

3.1 Experiment time

The field experiment is planned to take place in southern China during

mid-April to mid-June 2013/2014. The first rainy season in Southern

China is from April to June

with a peak occurring between the onset of the South China Sea monsoon and the

northward shift of its rain belt to the Yangtz-Huai River Valleys. In the past 15

years (1998-2012), the earliest onset of the SCS monsoon took place on 5 May

(2008) and the latest on 26 May (2005/2009)

(http://cmdp.ncc.cma.gov.cn/Monitoring/monsoon.htm). The average duration

from the SCS monsoon onset to the ending of the first rainy season in Southern

China is about 31 days (Luo et al. 2013). Therefore, observation between April and

June would most probably capture the heavy rainfall processes before and after the

monsoon’s onset during the first rainy season in Southern China.

3.2 Experiment area

The observation areas of the field campaign are designed at three levels of scale, i.e., the

large-, meso-, and cloud-convective-scales. The large-scale observing network covers

Guangdong, Guangxi, Hainan, most part of Fujian, the southern part of Jiangxi, Hunan and

Guizhou, as well as Hong Kong and the adjacent oceanic areas (Fig. 5). The major

observation area (also called “the mesoscale observing area”) includes Guangdong and the

adjacent oceanic regions as well as eastern Guangxi (Figs. 5 and Fig. 6). The underlying

conditions over the mesoscale observation area are very complicated (Fig. 1 and Fig. 3d):

Guangdong is adjacent to northern South China Sea with complicated land topography

lower in the southern side and higher in the northern side; mountains such as Yunwu,

Lianhua, Qingyun, Huashi are scattered in this area; the Pearl River Delta city group is

located in the central plain area of Guangdong. Thus, this observation area is a natural

experimental base to carry out research on the southwest monsoon, land and sea breeze,

mountain-valley winds and urban influences. Within the meso-scale observation area, there

are two cloud-convective-scale observation areas.

Figure 5 Distribution of major instruments in the observing network. Blue and red rectangles

represent the boundaries of the large-scale and meso-scale observing networks, respectively.

3.3 Experiment design

Large-scale observing network

This network builds upon the meteorological operational observation network at

Guangdong, Guangxi, Hainan, and Hong Kong, that consists mainly of the radiosonde

sounding stations, weather radars, ground-based GPS water vapor stations, AWSs, and

satellites. During intensive observation period, observations in the sounding stations in

Guangdong, Hainan and Hong Kong will increase from the routine of twice per day to 4

times per day. These data should provide information on thermodynamics in the

atmosphere.

Meso-scale observing network

Information to be obtained is important for the understanding of the trigger mechanism in

the meso-scale. The equipments employed in this campaign include 16 fixed wind-profiling

radars (14 boundary layer wind-profiling radars plus 2 troposphere profiling radars) and 2

mobile boundary layer wind-profiling radars. The two mobile boundary layer wind profiler

radars, which are used to record information on the atmospheric boundary layer wind field,

(blue five-pointed star in Fig. 5 and Fig. 6) will be placed at Yangjiang and Qingyuan to

complement the fixed wind profiler radar (red five-pointed star in Fig. 5 and Fig. 6)

network and provide data comparable with upper air radiosounding observations. We also

plan to carry out dropsonde experiments by using airforce aircrafts over the northern South

China Sea when possible. Moreover, 11 operational Doppler weather radars are put in place

to cover the entire range of the studied region, making it possible to record the evolution of

the precipitating convective system.

Intensive Observation area of Convective Systems

Two intensive observation stations will been built. The dynamic and microphysical

structures inside convective systems will be acquired by remote sensing instruments

including 2 C-band dual polarization radars, 1 X-band dual

polarization radar, 2 millimeter wave radars, 1 micro precipitation radar and 4 rain drop

disdrometers. Two sites for intensive observation have been selected based on the

distribution of the averaged precipitation over the South China during the first rainy season

in South China (Fig. 3a and Fig. 3b). One is near Yangjiang-Enping located along the

southwestern coast of Guangdong, the other is near Fugang-Longmen located in the central

part of Guangdong and about 100 km from the southwestern coast. This design help capture

the different aspects of the nature of precipitation systems. In addition, observations

obtained by the Doppler weather radar stations in Yangjiang and Heyuan can be compared

with that obtained by the mobile dual polarization radar.

The 2 mobile C-band dual polarization radars will be placed at Enping and Xinfeng

meteorological stations, respectively. The radar in Enping station is about 49 km from the

S-band Doppler radar in Yangjiang station, and that in Xinfeng station is about 58 km from

S-band Doppler radar in Heyuan station. The two set of radar not only allow data to be

analyzed in parallel, but also make it is possible to examine wind retrievals. A X-band dual

polarization radar will be placed near Enping and Yangjiang to be compared with C-band

and S-band Doppler radar observations.

The placement of instruments including 1 micro rain radar, 4 raindrop disdrometers, 2

millimeter wave cloud radars in 2013 will differ from that in 2014. During the pre-phase

experiment period, all of the instruments will be concentrated near Yangjiang and Enping.

The comparisons will be made among the 4 raindrop distrometers, between the raindrop

disdrometer and rain gauge observations, between the lowest-level (10-200 m height)

precipitation retrieved by micro rain radar and rain gauge observations, and between the 2

millimeter wave cloud radars. During the formal experiment in 2014, the 4 raindrop

spectrometers and 2 millimeter wave cloud radar will be divided into 2 groups to observe

in the two intensive observing areas, respectively, and the micro rain radar will still be

placed around Yangjiang and Enping.

Figure 6 Distribution of major instruments in the meso-scale observing network.

Figure 7 Schematic diagram of the coverage of dual-Doppler radar 3D wind field retrieval on 2, 4,

6, 8 km altitudes for the S-band radar at Yangjiang and the C-band polarimetric radar at Enping.

The distance between the two radars is about 49 km.

Figure 8 Similar to Figure 7, except for the radars at Heyuan and Xinfeng.

3.4 Experiment measurements

3.4.1 Upper air observation

1) Wind-profiling radars

In the meso-scale observing network, there will be 16 fixed (red stars in Fig. 6; 14

boundary layer radars, 2 troposphere radars) and 2 mobile wind-profiling radars (blue stars

in Fig. 6). Wind profiler is a five-wave beam radio remote sensing instrument. Based on

Bragg scattering theory, each antenna can measure radial velocity through several signal

and data processing such as coherent accumulate, FFT transform, spectrum average, etc.

Final products such as profile of horizontal wind, vertical velocity and atmospheric

structure constant of refractive index can then be obtained .

According to the different working frequency and detecting range, wind profilers are

classified into three types: stratosphere wind profiler (46–68MHz), troposphere wind

profiler (440–450MHz) and boundary layer wind profiler (1270–1295MHz,

1300–1375MHz). The two latter wind profilers are widely used in China at present.

Based on wind profiler standards of functional specification and data format established by

China Meteorology Administration, wind profiler observation data include: real time wind

profile data, half-hour averaged wind profile data and one-hour averaged wind profile data.

All of these data contain radial velocity, spectrum width, SNR, horizontal wind, vertical

velocity and power spectrum density.

Table 1 wind profiler fundamental characteristics

2) Radiosonding stations

Twenty-two upper air sounding stations (blue dots in Fig. 5) , built by China

Meteorological Administration (CMA) are put in place in the experiment area to conduct

operational observation twice per day. Eight of them (4 in Guangdong, 3 in Hainan and 1

in Hong Kong) will release radiosondes four times a day during intensive observation.

3) GPS/MET stations

There are 85 ground-based GPS water vapor observation stations in the large-scale

observing network, among which 35 are located in the meso-scale region (black triangles in

Figs. 5 and 6). They are designed to measure precipitable water in the atmospheric column

at one hour interval.

4) Air-borne dropsonde observation

Hong Kong Observatory, one of the international participants of the SCMREX

Boundary layer wind

profiler

Troposphere wind

profiler

Frequency of operation 1320MHz 445 MHz

Beam width ≤4.5º ≤4.5º

Transmitter power ≥2kW ≥6kW

Maximum detecting range 3-5km 6-8km

Minimum detecting range 100m 150m

Range resolution 60m 120m

Time resolution ≤6分钟 ≤6分钟

Maximum detecting range of

vertical velocity ±20m/s ±24m/s

Measuring precision of wind

speed ≤1.5m/s (RMS) ≤1.5m/s (RMS)

Measuring precision of

vertical velocity ≤0.2m/s ≤0.2m/s

Measuring precision of wind

direction ≤10°(RMS) ≤10°(RMS)

RDP, plans to implement the air-borne dropsonde observation in the northern region of the

South China Sea to collect the upper-air meteorological data of upstream atmosphere

within the trial period in 2014.

3.4.2 Surface observation

Land-based (in situ) observation network consists of national-level automatic weather

stations (AWSs) with observers on duty and regional AWSs. There are 366 national-level

stations (black dots in Fig. 5) within the large-scale area; 113 national-level stations and

1,503 regional AWSs are located in the meso-scale region with an average distance of

about 10 km. They will provide temperature, sea level pressure, humidity, wind

direction/speed, and precipitation observations every hour.

3.4.3 Precipitation system observation

1) Doppler weather radar

There are 22 Doppler weather radars in the large-scale observing network. The 11 radars in

the mesoscale region are S band, of which nine are SA radars, named CINRAD-SA in

China and Internationally known as WSR-98D, produced by Beijing METSTAR Radar

Co,. Ltd. The specifications of the SINRAD-SA (Table 2) are similar to those of the

WSR-88D of US (http://www.roc.noaa.gov/). The volume scan pattern of the radars is

VCP21, with 9 elevation angles from 0.5o

to 19.5o

. The scan period is 6 minutes.

Table 2 CINRAD-SA Radar Parameters

2) Dual polarimetric radar

Wavelength

(cm)

Beam

width

(°)

Antenna

Gain (dB)

First

Sidelobe

(dB)

Terminal

Sidelobe

(dB)

Transmitting

Power (kW)

Sensitivity (dBm)

Dynamic

Range (dB)

10 ≤0.99 ≥44dB ≤－29 ≤－42 ≥750 ≤-107 ≥85

Two C-and one X-band mobile dual polarimetric radars (the main technical parameters are

listed in Table 3, and the performance parameters in Table 4, respectively) will participate

in the field experiment. Dual polarimetric radar has the significant advantage over ordinary

Doppler weather radar on quantitative precipitation estimation, drop size distribution

inversion, hydrometeor phase identification, etc. (Hu et al., 2010, 2012; Liu et al., 2010；

Chu et al. 1997). The reason is that the amplitude, phase, frequency, and polarization state

of electromagnetic wave will be changed during scattering and propagation process. In

contrast with ordinary Doppler radar, polarization radar has the capability of detecting the

echoes in different polarimetric directions, which can alternately or simultaneously transmit

and receive the echo signals in horizontal and vertical directions. It can also measure

polarimetric parameters, such as the difference of

reflectivity between the two directions (differential reflectivity, ZDR

), cross-

polarization reflectivity (de-polar reflectivity, LDR

), the difference of phase

(differential propagation phase shift, Φdp

), specific differential propagation phase

shift ( K ), and correlation coefficient ( ρ ) between the two directions, as well as DP HV

the echo intensity in horizontal direction (ZH), radial velocity (Vr), and spectrum width (SW)

(Seliga and Bringi 1976; Matrosov et al. 2002; Hu et al. 2008, 2010, 2012).

As examples, Fig. 9 shows the plane position indicator (PPI) pictures of ZH (dBZ), ZDR

(dB), KDP (0

km-1

), and ρHV detected with the C-band dual polarimetric radar, which was

developed by State Key Laboratory of Severe Weather (LaSW) in 2008, in the 4.500

elevation at 0943 BST June 6, 2008 in Boluo, Guangdong province. Fig. 10 shows the

inversion products, which include the phase identification, raindrop mean diameter (D0,

unit: mm), numerical density (N0, unit: 105

mm-1

m-3

), and liquid water content (Lw, unit: g

m-3

). (Fig. 9 and 10 are attached at the end).

Table 3 Main technical parameters of the dual polarimetric radar

Table 4. Specification of dual polarimetric radar

3) Micro Rain Radar (MRR)

One MRR made by METEK (http://www.metek.de/) will be used in the

Wave length C-band X-band

Antenna

Diameter Gain

Beam width First

side lobe Isolation

3.2 m 40 dB 1.4° -32 dB 31

dB

2.4 m 45 dB 1.0° -31 dB 41

dB

Transmitter

Wavelength Pulse

width Peak power

PRF

5.5 cm 1.0/2.0 μs; 150/300m

268 kW 300-1300 Hz

3.2 cm 1.0/0.5 μs; 125/62.5

m 52 kW 300-2000 Hz

Receiver

Polarization

Minimum

detectable power

Noise Dynamic

range

Simultaneous transmitting

and receiving (STSR) -110

dBm 2.9 dB 87 dB (linear)

Alternate transmitting but

simultaneous receiving

(ATSR) -110 dBm 2.3 dB 90

dB (linear)

Band Performance Parameters

Volume

scan

(min)

Detection

Range (km)

Elevaztion

Number

C-band Reflectivity (ZH), Radial velocity (Vr),

Spectral Width (SW), Differential

reflectivity (ZDR), phase shift (ΦDP),

cross-correcting coefficient (ρHV)

6 150 9, 14

X-band

Reflectivity (ZH), Radial velocity (Vr),

Spectral Width (SW), Differential

reflectivity (ZDR), phase shift (ΦDP),

cross-correcting coefficient (ρHV),

Linear depolarization reflectivity (LDR)

6 125 9, 14

experiment, and its main technique variables are described in Table 5. The MRR is a

compact 24.1 GHz FM-CW radar for the measurement of profiles of drop size distributions

and, derived from this, rain rates, liquid water content and characteristic falling velocity

resolved into 30 range gates. MRR can detect very small amounts of precipitation, which

isbelow the detecting threshold of conventional rain gauges. Due to the large scattering

volume (compared to in situ sensors), statistically stable drop size distributions can be

derived within a few seconds. The droplet number concentration in each drop-diameter bin

is derived from the backscatter intensity in each corresponding frequency bin, with

assumption of the relation between terminal falling velocity and drop size.

Table 5 Specification of MRR

4) Raindrop disdrometer

Four raindrop disdrometers made by OTT

( http://www.ott.com/web/ott_uk.nsf/id/pa_parsivel2_e.html ) will be used in the experiment.

Based on operational principle of photoelectric effects, raindrop special density, fall speed and

raindrop size can be measured when raindrops fall through the rectangle sample connection

(180×30 mm). Raindrop diameter and fall speed are

Specification

Frequency (wavelength) 24.1 GHz (1.25 cm)

Transmit power 50 mW

Modulation FM-CW

Beam width（two way, 6 dB） 2°

Antenna Offset parabolic dish of 60cm diameter

Pointing direction vertical

Range resolution 10 m – 200 m

Lowest analyzed range 10 m – 200m

Number of range gates 30

Temporal resolution 1 min

Physical dimension 60 cm X 60 cm X 60 cm

Total weight 12 kg

classified into 32 grades. The measuring precision of raindrop size and fall speed and their

corresponding grades are: 0.125 mm and 0.1m/s in 1-10 grades, 0.25 mm and 0.2m/s in

11-15 grades, 0.5 mm and 0.4m/s in 16-20 grades, 1mm and 0.8m/s in 21-25 grades, 2mm

and 1.6m/s in 26-30 grades, 3mm and3.2m/s in 31-32 grades, respectively. From the

raindrop diameter and fall speed, a variety of properties can be deduced: raindrop size

distribution, rain intensity, visibility, energy of raindrop motion and precipitation type

(such as drizzle, rain, sleet, hail, snow, fog).

Table 6 Specification of raindrop disdrometer

5) Millimeter-wave radar

The Ka band cloud radar from State Key laboratory of Severe weather (LaSW), Chinese

Academy of Meteorological Sciences, and Institute of Heavy Rain, Wuhan (WHIHR)

(Table 7) will be used to observe the internal structures of nonprecipitating and weakly

precipitating clouds with excellent sensitivity, spatial and temporal resolutions, and

accuracy. The cloud and precipitation parameters, such as cloud and precipitation drop size

distribution (DSD), air vertical speed and turbulent fluctuation standard deviation (σ) could

be retrieved from reflectivity, velocity , spectral width and Doppler spectral density data

that are directly measured by the cloud radars (Gossard, 1994; Gossard et al., 1997). The

cloud radar of LaSW normally operates in vertically pointing mode while the cloud radar

of WHIHR can operate in scan mode.

Specification

Measurement range of raindrop diameter 0.2—25 mm, 32 grades

Measurement range of raindrop fall speed 0.2—20 m/s, 32 grades

Precipitation type None, drizzle, light rain, rain, sleet, snow, hail, fog

Judgmental accuracy of precipitation type Coincidence rate exceed 97% under the condition of

drizzle, hail and snow

Measurement range of precipitation 0.001—1200 mm/h

Measurement accuracy of precipitation ± 5% (liquid state), ± 10% (snow), ± 20% (solid state)

Visibility 10—5000 m, ±10%

Measurement interval 15sec—60min

Measuring and sampling area 54 cm2

The minimum observation ranges are 500m and 150m for the LaSW and WHIHR radars,

respectively.

Table7 Specification of Ka band (35GHz) radar

6) Lightning location system (LLS)

Meteorological bureaus in Guangdong province, Hongkong, and Macao started to jointly

construct the Guangdong-Hong Kong-Macao LLS in 2005. Five sub-stations were built in

2005, with the 6th sub-station added in Sep 2007, and another 11 sub-stations in 2012. As a

whole, 7 IMPACT sensors and 10 LS-7000 sensors are employed in the Guangdong-Hong

Kong-Macao LLS (Fig. 11). The combined MDF/TOA technology is also used to detect CG

lightning stroke information such as longitude and latitude, GPS time, peak current, polarity

and

Institute Institute of Heavy Rainfall,

Wuhan, CMA CAMS

Antenna

Diameter Gain

Beam width First

side lobe Cross

polarization

isolation

1.5 m 53 dB 0.4° -27 dB 32

dB

1.3 m 50 dB 0.44 -30 dB 33

dB

Transmitter

Wavelength Pulse

length Peak power

PRF

0.86 mm 1.32/0.66/0.33μs

0.67kW 2000-4000Hz

0.86 mm 0.3/1.5/20/40μs

0.6kW 2500 or 5000Hz

Receiver

Mode Sensitivity

Noise figure

Dynamic range

Transmit H, receive H and

-110 dBm 4.9 dB 84dB

Transmit H, receive H and

≤-98.4 dBm ≤5.6 dB 70dB

Platform mobile mobile

reporting sensors, etc. The comparison of triggered lightning and natural lightning

observation shows that the detection efficiency and location precision had been obviously

improved since the increase of detection sub-stations in 2012. The flash detection

efficiency and stroke detection efficiency were about 97% and 91%, respectively; the

arithmetic mean location error was about 600 m. However, the peak currents of return

strokes were systematically overestimated.

Figure 11 Distribution of sub-stations in the Guangdong-Hong Kong-Macao LLS

3.4.4 Satellite observation

In normal mode, the FY-2D and FY-2E geostationary satellites each take

28 images in a daily basis. With 20 additional images taken by each

satellite when operated in the dual-satellite intensive observation mode, it

will provide geostationary satellite data over target area once every 15

minutes. Such data can be obtained once every 6 minutes by FY-2F

geostationary satellite during the intensive observation period. The FY-3A

and FY-3B polar orbiting satellites will work in the normal operational

mode, and data that cover the target area will be available once in the

morning and once in the afternoon. More details of satellite products are

listed in

Table

8. Table 8 Satellite Products

Product name Spatial coverage and resolutions Frequency

FY-2E Precipitation Estimation 55°E-155°E,50°N-0°N, 0.1°×0.1° 24 times per day

FY-2E Atmospheric Motion Vectors 55°E-155°E,50°N-50°S, 1°×1° 4 times per day

FY-2E Cloud total Amount 55°E-155°E,50°N-50°S, 0.1°×0.1° 24 times per day

FY-2E Upper Troposphere Humidity 55°E-155°E,50°N-50°S, 0.1°×0.1° 8 times per day

FY-2E Humidity Profile derived from

Cloud Analysis

55°E-155°E,50°N-50°S, 0.1°×0.1° 8 times per day

FY-2E Total Precipitation Water for

Clear Sky

55°E-155°E,50°N-50°S, 0.1°×0.1° 8 times per day

FY-2E Cloud Classification 55°E-155°E,50°N-50°S, 0.1°×0.1° 24 times per day

FY-2D precipitation estimation

products

37°E-137°E,50°N-0°N, 0.1°×0.1° 24 times per day

FY-2D Atmospheric Motion Vectors 37°E-137°E,50°N-50°S, 1°×1° 4 times per day

FY-2D Cloud total Amount 27°E-147°E,60°N-60°S, 0.1°×0.1° 24 times per day

FY-2D Upper Troposphere Humidity 27°E-147°E,60°N-60°S, 0.1°×0.1° 8 times per day

FY-2D Humidity Profile derived

from Cloud Analysis

27°E-147°E,60°N-60°S, 0.1°×0.1° 8 times per day

FY-2D Total Precipitation Water for

Clear Sky

27°E-147°E,60°N-60°S, 0.1°×0.1° 8 times per day

FY-2D Cloud Classification 27°E-147°E,60°N-60°S, 0.1°×0.1° 24 times per day

FY-3A/VIRR Cloud Physical

Parameters

Global, 5km 1 times per day

FY-3A/VIRR Cloud Amount and

Cloud Type

Global, 5km 1 times per day

FY-3B/VIRR Cloud Physical

Parameters

Global, 5km 1 times per day

FY-3B/VIRR Cloud Amount and

Cloud Type

Global, 5km 1 times per day

3.5 Experiment organization/frame

31 / 52

In order to direct the science experiment in a more unified and integrated manner, the field

experiment operation center will be established at China Meteorological Administration

(CMA) Southern China regional meteorological center. The operation center is responsible

for the command and coordination of the science experiment over the testing regions. The

observational period of the field experiment (mid-April to mid-June) is divided into two

categories: routine observational period and intensive observation period. Routine

observational period is defined when the forecast predicts that no precipitation systems

would be affecting the testing region, and that the operational weather radar network does

not detect any precipitation systems moving into the testing region in the next 24 hours.

Intensive observation period is defined, when any precipitation system is forecasted to

influence the testing region, or when the operational weather radar network detects any

precipitation systems that will be moving into the testing region in the next 24 hours, in

which case the operation center will give the instruction immediately and enter the

intensive observation period. The intensive observation will not be stopped until the

systems fade away or move out the testing region. During the intensive observation period,

Mobile C, X-band dual linear polarization radar, millimeter wave cloud detection radar

will be launched, and the temporal intense observations (from 2-time per day to 4-time per

day) will be made in the observation stations over Guangdong, Guangxi and Hainan

provinces. Furthermore, air-borne dropsonde observation (Hong Kong Observatory) plans

to be launched over northern SCS when necessary. The other observational systems and

equipments will keep running as regular during both the routine observation period and the

intense observation period.

During the middle of the observation period, the review meeting will be convened in

middle or late May. Main directors of each team should report the data situation collected in

the test, discuss whether the experimental design should be partially adjusted, and

meanwhile accordingly modify the deficiency of the experimental design.

4. Numerical Modeling Study

Because errors in the initial conditions of model simulations are present and the model

parameterization schemes are insufficiently representative of meso-and micro-scale

physical processes in reality, the forecast of heavy rainfall in the warm sector is less

accurate than that of the front-associated rainfall. It has been demonstrated that numerical

simulations of a meso-scale convective system in the warm season is quite sensitive to the

initial atmospheric conditions and the model physical parameterization schemes adopted

(Luo et al. 2010; Wu et al. 2013). Compared with the single forecast, ensemble forecasts

can provide further information on the uncertainty (Toth and Kalnay 1993, Tracton and

Kalnay 1993), accuracy and predictability of the numerical models. This project will use

data collected during the field experiment (2013-2014) and, along with other historical

observations/data, to improve the model initial conditions and model physical processes, to

carry out ensemble experiments, and to examine the model capability.

4.1 Improvement of the initial field

The quality of the initial field is important for the short-term numerical weather prediction

(NWP). It is a also the key to improving heavy rainfall forecasts during the early summer

rainy season in South China and understanding corresponding physical mechanisms. A

good initial field can also serve as the foundation stone for the construction of

representative ensemble forecasts and their perturbation. Data assimilation has been used to

reduce initial condition error. Applications of different methodologies in data assimilations

of weather systems have been proposed and well developed, ranging from single

conventional measurement to sorts of nonconventional measurements and from the single

time observation to multi-times, such as the evaluation from the simple interpolation,

successive correlation (Barnes，1964), optimal interpolation (OI) (Bratseth，1986) and three

dimensional variation (3DVAR) (Lorenc，1981；Parrish and Derber，1992；Anderson et al.，

1998) to ensemble Kalman filter (EnKF) (EnKF，Houtekamer et al.，1996； Hamill and

Snyder，2000) and four dimensional variation (4DVAR) (Lewis and

Derber 1985；Courtier and Talagrand 1990). More and more measurements are better

assimilated due to the continuous advance in data assimilation methodology. Nowadays,

various studies based on different measurements take account of the impact on analysis and

forecast, and propose a lot of assessment methods (Purser and Huang 1993；Cardinali et al.

2004；Chapnik et al. 2004). The advanced methodologies help reveal how the assimilated

measurements affect the model forecast via the data assimilation system, and what roles the

measurements and background field in the analysis and forecast play. Based on these

findings, an improved method of assimilation system can be proposed.

This project will use various measurements collected by different observation platforms to

investigate the correspondent data assimilation methods and to quantitatively verify the

analysis obtained through data assimilation. The dataset created by our data assimilation

system will be open to researchers of interests. A number of assimilation methods, such as

3DVAR, 4DVAR, EnKF and the hybrid method (Hamill and Snyder，2000), will be

adopted to generate the accurate analytic data during the early summer rainy season in

South China and study the impacts aroused by various situ measurements on forecast.

Application of observation data obtained in the field experiment and the construction of

analytic data via data assimilation that provide a good initial field for NWP are our primary

propositions. This project aims to create a dataset, which contains the best analytic grid

data of the early summer rainy season in South China and the most valued situ

observations. Meanwhile, this project provides a unique opportunity to identify the most

efficient and effective data assimilation algorithm.

4.2 Improvement of the model physical processes

The model error is another major contributor to an inadequate numerical weather

prediction. The triggering mechanisms for warm sector rainstorm generally involve

complicated boundary layer processes, the surface topography and different properties

between land and sea, the interaction among multi-scale systems, and

sophisticated cloud-precipitation microphysical processes. A warm sector rainstorm is

rarely well forecasted by numerical models, implying caveats in the physical processes

among models. Since most key parameters of the applied physical processes in mesoscale

numerical models are primarily based on some empirical values of similar weather

backgrounds obtained in foreign areas. The representativeness of this empirical value is of

major concern and may lead to model forecast errors associated with the peculiar monsoon

rainstorm in China, especially in the warm sector scenario.

The project will take full advantage of the data collected during the field experiment. The

integration/combination of the historical observation data, and the TRMM and

CloudSat/CALIPSO satellite active measurements that reflect the cloud-precipitation

characteristics, will be utilized to improve the parameterization schemes in mesoscale

model by a variety of strategies (for instance: updating and improving the values of the key

parameters in the model physical schemes via data assimilation based on the EnKF

parameter estimation). Here, we focus on improving the boundary layer processes and the

cloud-precipitation microphysical processes to reduce the model error and then improve

the forecasting capability of warm sector rainstorm before and after the outbreak of

summer monsoon rainfall.

4.3 Mesoscale Ensemble Prediction (MEP) Experiments

Errors from model dynamics, physics schemes and initial conditions create

uncertainties in model simulations. While a single simulation or prediction

cannot provide information on uncertainty, ensemble prediction serves to

provide the statistical state of a collection of possible results, which can

help improve the forecast quality and extend valid prediction periods. The

ensemble members created for one single model are generally approached

with two methods, either by adding perturbations to initial condition,

including random Monte Carlo (Leith 1974), Lagged Average Forecast,

Singular Vector (Molteni et al. 1996, Ono et al. 2010), Breeding of

Growing Modes (Toth and Kalnay 1993) and etc., or by selecting different

model physical schemes and adopting different values of key parameters.

Moreover,

ensemble members across a couple of models can be used to provide the

so-called “super ensemble forecasts”.

Making good use of initial conditions and physics perturbation methods (by changing

physical schemes or parameters), we will carry out MEP experiments with model

horizontal grid spacing less than 10 km for heavy rainfall in the first rainy season and cold

front cases in South China. Super ensemble prediction will also be constructed based on

individual model MEP experiments at all institutions in the presented project, so as to

evaluate the performance of MEP of heavy rainfall in South China.

Based on current operational system, CMA will set up a specified verification system for a

careful assessment of all participating MEPSs. The verification of meso-scale ensemble

forecast systems will be carried out against the shareable observational datasets in Southern

China. Variables to be examined and verified are those of highest concerns in meso-scale

forecasts, such as the surface and low-level troposphere parameters, including surface

precipitation, 2-m temperature, 2-m dew point temperature, 2-m relative humidity, sea

level pressure, 850-hPa temperature, 850 relative humidity, etc. Two main types of

methods will be used to verify the ensemble forecasts conducted: one is the probability

density function (PDF), and the other is the probability forecast exceeding some specific

thresholds (e.g., equitable threat score, ETS).

5. Data Management

5.1 Data transmission, collection and processing

The project will produce three parts of data: the operational observations, the

non-operational observations from the mobile equipments, and data from the modeling

study.

The operational observations: The observational data from the meteorological

operational network including the AWSs, radiosonde observation stations, GPS/MET

water vapor stations, weather radars, and satellites will be conveyed to the National

Meteorological Information Centre (NMIC) in real time. NMIC will conduct quality

control for and gridding analysis based on the AWS observations. 3D gridded radar

reflectivity (at coordinates of longitude, latitude, and altitude) will be produced using the

Radar Mosaic System developed by the State Key Laboratory of Severe Weather, Chinese

Academy of Meteorological Sciences.

The non-operational observations: This part of data will be collected by the instrument

people that participate in the field campaign, and sorted out after the field experiment. The

raw data and the data after quality control should be submitted within about one month

after the end of the field experiment.

The numerical simulation data: The people who participate in the modeling study will

submit two types of gridded data. One is produced by the corresponding data assimilation

systems. The other is from the MEP experiments.

Information of the key contact persons in charge of the observational data and/or

equipments are listed in Table 9.

5.2 Data sharing

Data sharing will be in accordance with the WMO rules and the CMA relevant rules. A

data base will be set up at the National Meteorological Information Center/CMA. For

user’s convenience, the data products will be stored in standard, internationally common,

“self explanatory” formats. Furthermore, a website will be built as the main platform for

data and information sharing.

Table 9 Information of the key contact persons in charge of the observational data and/or

equipments

6. Programme Management

1) CMA Steering Committee

The CMA leader will act as the head of the Steering Committee including members from

the Department of Science and Technology and Climate Change, Department of

Forecasting and Information System, Department of Integrated Observations, Department

of International Cooperation of CMA, Chinese Academy of Meteorological Sciences as

well as the leaders of the meteorological bureaus of Guangdong, Guangxi, Hainan, and

Hong Kong. The Steering Committee is responsible for leading the field experiment and

organizing and coordinating the participating units.

2) International Science Steering Committee

The International Science Steering Committee (ISSC) consists of leading international

experts. The purpose of the committee is to instruct and deliberate the design of this field

experiment and other research-related work, and to oversee the operation of the field

experiment.

3) Project Chief Scientist

The Chief Scientist is responsible for the field experiments and scientific aspects of the

project.

4) Working Groups

The project has established four working groups, including Observation Working Group,

Data Working Group, Modeling Working Group, and Physics Working Group. Each

working group consists of some interested scientists/researchers with a few leaders, who

will be responsible for the research and various work related to the field campaign under

the leadership of the Chief Scientist.

5) Leading and participating units and scientists

This field experiment is leaded and organized by the State Key Laboratory of

Severe Weather at CAMS. The participating units in mainland China include Guangzhou

Institute of Tropical and Marine Meteorology, Wuhan Institute of Heavy Rains of CMA,

National Meteorological Centre, National Satellite Meteorological Centre, National

Meteorological Information Centre, National Meteorological Observation Center,

Guangdong Meteorological Bureau, Guangxi Meteorological Bureau, Hainan

Meteorological Bureau, the Meteorological Department of Hong Kong Special

Administrative Region, Institute of Atmospheric Physics of Chinese Academy of Sciences,

Peking University, Nanjing University, Nanjing University of Information Science and

Technology.

7. International Participation and Collaboration

We will collaborate closely with the Mesoscale Working Group of WWRP on modeling

and model verification and the Monsoon Panel of the WMO/CAS Working Group on

Tropical Meteorology Research (WGTMR) on monsoon heavy rainfall study. Both the

Pukyong University in Korea and Nagoya University in Japan may join the project through

coordination of the observation periods of their related projects in and around Korea, Japan

and East China Sea with the proposed field experiment. Some southern and southeastern

Asian countries are willing to participate by enhancing the frequency of radiosonde

observations.

8. Implementation Schedule

Year/Month Data WG

• Format and QC data of historical cases

• Design & develop website

2013/1-4

Update website Collect operational data Process the operational data Improve data base and website Submit annual report

Year/Month Modeling WG

• Setup MEPS • Conduct relevant studies 2013/4-6

Conduct MEP experiments for 2013 cases 2013/7

Submit data products ~

Evaluate MEP experiments 2014/6

Conduct relevant studies

Submit annual report

During 2013-2014 the physics WG will conduct research that is relevant to the scientific

objectives of SCMREX, by analyzing the data sets available from the project.

During the year of 2015, each WG will continue to complete their work.

Acknowledgments

A meeting was organized by the Department of Science and Technology and Climate

Change (DSTCC) of China Meteorological Administration (CMA) on 21 March 2013 to

review this Implementation Plan of SCMREX. The invited experts from Chinese Academy

of Science, Zhongshan University, National Natural Science Fundation of China, and CMA

in the meeting are greatly appreciated for providing helpful suggestions for improving the

plan. Professors C.P. Chang (Naval Postgraduate School of US) and Ben Jou (ACTS)

reviewed an earlier version of the plan. Their constructive comments are greatly

appreciated. Professors Ben Jou (NCTS) and Da-Lin Zhang (Maryland University of US)

are also deeply appreciated for refining the English expression in parts of the plan.

References

Anderson, E., and Coauthors, 1998: The ECMWF implementation of three-dimensional

variational assimilation (3D-var). III: experimental results. Quart. J. Roy. Meteor. Soc., 124,

1831-1860.

Anderson-Berry, L., and Coauthors, 2004: The societal, social, and economic impacts of the

World Weather Research Programme Sydney 2000 Forecast Demonstration Project (WWRP

S2000 FDP). Weather and Forecasting, 19, 168-178.

Awaka, J., T. Iguchi, and K. Okamoto, 1998: Early results on rain type classification by

the Tropical Rainfall Measuring Mission (TRMM) precipitation radar, Proc. 8th URSI

Commision F Open Symp., Aveiro, Portugal, pp. 143-146.

Barnes, S., 1964: A techniques for maximizing details in numerical map analysis. J. Appl.

Meteor. 3, 395-409.

Bougeault, P., and Coauthors, 2001: The MAP Special Observing Period. Bull. Amer.

Meteor. Soc., 82, 433–462.

Bratseth, A., 1986: Statistical interpolation by means of successive corrections. Tellus,

38A, 439-447.

Cardinali C, Pezzulli S, Andersson E., 2004: Influence-matrix diagnostic of a data

assimilation system. Q. J. R. Meteorol. Soc., 130, 2767–2786.

Chapnik B, Desroziers G, Rabier F, Talagrand O., 2004: Properties and first application of

an error-statistics tuning method in variational assimilation. Q. J.

R. Meteorol. Soc., 130, 2253–2275.

Chen, G., C. Wang, and D. Lin, 2005: Characteristics of low-level jets over Northern

Taiwan in meiyu season and their relationship to heavy rain. Mon. Wea. Rev., 133(1),

20-43.

Courtier, P., and O. Talagrand, 1990: Variational assimilation of meteorological

observations with the direct and adjoint shallow water equations. Tellus, 42A, 531-549.

Ding, Y.，1994：Monsoons 0ver China．Dordrecht／Boston／London：Kluwer Academic

Publishers，419pp.

Duan, Y.-H., and Coauthors, 2012: An overview of the Beijing 2008 Olympics Research

and Development Project (B08RDP). Bull. Amer. Meteor. Soc., 93, 381–403.

Frisch, A. S., C. W. Fairall, and J. B. Snider. 1995. Measurement of stratus cloud and

drizzle parameters in ASTEX with a Ka-band Doppler radar and a microwave radiometer

[J]. J. Atmos. Sci., 52: 2788–2799.

Gossard, E. E. 1994. Measurement of cloud droplet spectra by Doppler radar [J]. J. Atmos.

Oceanic Technol., 11: 712–726.

Gossard E. E.,J. B. Snider,E. E. Clothiaux,B. Martner, J. S. Gibson,R. A. Kropfli, and

A. S. Frisch. 1997. The Potential of 8-mm Radars for Remotely Sensing Cloud Drop

Size Distributions, J. Atmos. Oceanic Technology [J]. 14(1): 76–87.

Hamill, T. M. and C. Synder, 2000: A hybrid ensemble Kalman filter-3D variational

analysis scheme. Mon. Wea. Rev., 128, 2905-2919.

Houtekamer, P. L., and Coauthors, 1996: A system simulation approach to ensemble

prediction. Mon. Wea. Rev., 124, 1225-1242.

Hu, Z.Q., L.P. Liu, R.Z. Chu, and R.H. Jin, 2010: Study of Different Attenuation

Correction Methods in Association with Rainfall Estimation for X-Band Polarimetric

Radars. Acta Meteor. Sinica, 24 (5), 602-613.

Hu, Z.Q., L.P. Liu, and L.R. Wang, 2012: A Quality Assurance Procedure and Evaluation

of Rainfall Estimates for C-Band Polarimetric Radar. Adv. Atmos. Sci., 29(1), 144 -156.

Iguchi, T., T. Kozu, R. Meneghin, J. Awaka, and K. Okamoto, 2000: Rain-profiling

algorithm for the TRMM precipitaton radar. J. Appl. Meteor., 39, 2038-2052.

Isaac, G. A., and Coauthors, 2009: Science and Nowcasting Olympic Weather for

Vancouver 2010 (SNOW-V10) --A new World Weather Research Program Project.

Abstract at The 25th

Conference on International Interactive Information and Processing

Systems (IIPS) for Meteorology, Oceanography, and Hydrology. The 89th

American

Meteorological Society Annual Meeting, Phoenix, AZ, 10-16 January 2009.

Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpron, 1998: The Tropical Rainfall

Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809–817.

Leith, C. E., 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102,

409-418.

Lewis, J., and J. Derber, 1985: The use of adjoint equations to solve a variational adjust

problem with advective constraints. Tellus, 37A, 309-322.

Liu, L.P., Z.Q. Hu, W.G. Fang, and Coauthors, 2010: Calibration and Data Quallity

Analysis with Mobile C-Band Polarimetric Radar. Acta Meteor. Sinica, 24 (4): 501–509.

Liu, S., Y. Zheng, and Z. Tao, 2003: The analysis of the relationship between pulse of LLJ

and heavy rain using wind profiler data. J. Tropic. Meteor., 19(3), 285-290.

Lorenc, A., 1981: A global three-dimensional multivariate statistical interpolation scheme.

Mon. Wea. Rev., 109, 701-721.

Luo, Y.L., Y.J. Wang, H.Y. Wang, Y.J. Zheng, and H. Morrison, 2010: Modeling

convective-stratiform precipitation processes on a Mei-Yu front with the WRF model:

Comparison with observations and sensitivity to cloud microphysics parameterizations. J.

Geophys. Res., 115, D18117, doi:10.1029/2010JD013873.

Luo, Y.L., H. Wang, R.H. Zhang, W.M. Qian, Z.Z. Luo, 2013: Comparison of rainfall

characteristics and convective properties of monsoon precipitation systems over South

China and the Yangtze and Huai River Basin. J. Climate, 26, 110-132.

Matrosov S Y, Clark K A, Martner B E, and Coauthors, 2002: X-band polarimetric radar

measurements of rainfall. J Appl Meteor, 41: 941–952.

Molteni, F., T. N. Palmer, R. Buizza and T. Pertroliagis, 1996: The ESMWF ensemble

prediction system methodology and verification. Quart. J. Roy. Met. Soc., 122, 73-121.

Ono, K., Y. Honda and M. Kunii, 2010: Development of a mesoscale ensemble prediction

system using a singular vector method. CAS/JSC WGNE Res. Act. Atmos. Ocea. Modell.,

40, 5.17-5.18.

Parrish, D.F., and J. D. Derber, 1992: The National Meteorological Center spectral

statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763. 45 / 52

Purser, R.J., and H.L. Huang, 1993: Estimating effective data density in a satellite

retrieval or an objective analysis. J. Appl. Meteorol., 32, 1092–1107.

Ranzi, R., M. Zappa, and B. Bacchi, 2007: Hydrological aspects of the Mesoscale Alpine

Programmes: Findings from field experiments and simulations. Q. J. R. Meteorol. Soc. 133,

867–880.

Ray, P.S., C.L. Ziegler, W. Bumgarner, and R.J. Serafin, 1980: Single-and multiple-Doller

radar observations of tornadic storms. Mon. Wea. Rev., 108, 1607-1625.

Seliga, T. A., and V. N. Bringi, 1976: Potential use of radar differential reflectivity

measurements at orthogonal polarizations for measuring precipitation. J. Appl. Meteor.,

1976 15: 69-76.

Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train. Bull. Amer.

Meteor. Soc., 83, 1771–1790.

Stephens, G. L., and Coauthors, 2008a: CloudSat mission: Performance and early science

after the first year of operation. J. Geophys. Res., 113, D00A18,

doi:10.1029/2008JD009982.

Stoelinga, Mark T., and Coauthors, 2003: Improvement of Microphysical Parameterization

through Observational Verification Experiment. Bull. Amer. Meteor. Soc., 84, 1807–1826.

Toth, Zoltan, Eugenia Kalnay, 1993: Ensemble Forecasting at NMC: The Generation of

Perturbations. Bull. Amer. Meteor. Soc., 74, 2317–2330.

Tracton, M. Steven, Eugenia Kalnay, 1993: Operational Ensemble Prediction at the

National Meteorological Center: Practical Aspects. Wea. Forecasting, 8, 379–398.

Wilson, J. W., Y.-R. Feng, M. Chen, and R. D. Roberts, 2010: Nowcasting challenged

during the Beijing Olympics: Successes, failures, and implications for future nowcasting

systems. Wea. Forecasting, 25, 1691-1714.

Wu, D., Z. Meng, and D. Yan, 2013: The predictability of a squall line in South China on

23 April 2007. Adv. Atmos. Sci., 30(2), 485-502.

Zappa, M., and Coauthors, 2008: MAP D-PHASE: real-time demonstration of hydrological

ensemble prediction systems. Atmospheric Science Letters, 9, 80-87.

Zhang, X., and Y. Ni, 2009: A comparative study of a frontal and a non-frontal convective

systems. Acta Meteorologica Sinica, 67(1), 108-121.

Zhang, R., Y. Ni, L. Liu, Y. Luo, and Y. Wang, 2011: South China heavy rainfall

experiments (SCHeREX). J. Meteor. Soc. Japan, 89A, 153-166.

Bao, C., 1986: Research progress of heavy rainfall of south China in pre-summer

flood season. Acta Ocean. Sinca (in Chinese), 8(1), 31-40.

Cao, Y., Y. Jiang, and X. Sun, 2006: Relationship between low altitude jet stream and

heavy precipitation in a heavy rainfall event. Meteoro. Mon. (in Chinese), 32(6),

102-106.

Chu, R., Z. Wang, L. Liu, W. Lei, and X. Li, 1997: Preliminary analysis of rainfall

estimate utilizing dual linear polarization radar. Acta Meteor. Sinica (in Chinese),

55(1), 103-109.

Ding, Z., C. Liu, and X. Shen, 2011: Statistical analysis of the relationship among

warm sector heavy rainfall, upper and lower tropospheric jet stream and south Asia

high in May and June from 2005 to 2008. J. Trop. Meteoro. (in Chinese), 27(3),

307-316.

Hu, Z., L. Liu, and Y. Xiao, 2008: A simulation study on raindrop orientation

variation to dual linear polarimetric radar. J. Appl. Meteor. Sci. (in Chinese), 19(3),

362-366.

<<Heavy Rainfall of South China in Early-Summer Flood Season>> drafting group,

1986: Heavy Rainfall of South China in Early-Summer Flood Season. Guangdong

Science and Technology Press, 244pp.

Liang, Q., S. Xiang, L. Lin, and W. Meng, 2012: MCS characteristics over south

China during the annually first rainy season and their organization types. J. Trop.

Meteoro. (in Chinese), 28(4), 541-551.

Liu, L., L. Wu, and Y. Yang, 2007: Development of fuzzy-logical two-step ground

clutter detection algorithm. Acta Meteor. Sinica (in Chinese), 65(2), 252-260.

Liu, L., R. Zong, Y. Qi, and J. Liu, 2012: Microphysical parameters retrieval by cloud

radar and comparing with aircraft observation in stratiform cloud. Engineering

Sciences (in Chinese), 14(9), 64-71.

Ju, X., Z. Ren, and Q. Zhang, 2010: Research on quality control technology of

automatic hourly pressure. J. Anhui Agri. Sci. (in Chinese), 38(27), 15130-15133.

Ju, X., L. Cao, and J. Zhu, 2010: Check method on surface pressure extreme value

from ground automatic weather station. J. Meteor. Environment (in Chinese), 26(3),

48-52.

Ni, Y., R. Zhang, L., Liu and Coauthors, 2013: South China heavy rainfall

experiments (SCHeREX). China Meteorological Press, in press.

Ren, Z., P. Zhao, Q. Zhang, and Coauthors, 2010: Quality control procedures for

hourly precipitation data from automatic weather stations in China. Meteor. Mon. (in

Chinese), 36(7), 123-132.

Wang, P., Z. Ruan, and H. Kang, 2002: Numerical study of cloud physical

processes of heavy rainfall in south China. J. Appl. Meteor. Sci. (in Chinese),

13(1), 78-87.

Zhang, H, H. Zuo, X. Qie, and S. Guo, 2002: Analysis of echo characteristics of

Pingliang hailstorm. Acta Meteor. Sinica (in Chinese), 60(1), 110-115.

Zhang, X., and Y. Ni, 2009: A comparative study of a frontal and a non-frontal

convective systems. Acta Meteor. Sinica (in Chinese), 67(1), 108-121.

Zhao, Y., and Y. Wang, 2009: A review of studies on torrential rain during

pre-summer flood season in south China since the 1980’s. Torrential Rain and

Disasters (in Chinese), 28(3), 1993-202.

Zhao, Y., Z. Li, and Z. Xiao, 2008: Comparison analysis of south China front and

warm-area heavy rain systems in June 2006. Meteoro. Sci. Tech. (in Chinese), 36(1),

47-54.

Zhao, Y., Z. Ren, and Q. Zhang, 2011: Quality control procedures for hourly relative

humidity data from national automatic weather stations. J. Meteor. Sci., 31(6),

687-693.

Zhou, X., J. Xue, Z. Tao, S. Zhao, Q. Yi and B. Su, 2003: ’98 Huanan Area Meso

scale Experiment (HUAMEX). China Meteorological Press, 220pp.

Figure 9 The 4.50 PPIs detected by the C-band dual polarization radar of LaSW at 0943 LST on 6

June, 2008 in Boluo, Guangdong province: (a) ZH (dBZ), (b) ZDR (dB), (c) KDP,(o km-1), and (d)

ρHV.

Figure 10 Similar to Fig. 9, but for the retrieved variables: (a) Phase identify, (b) mean diameter of

particles (D0; mm), (c) particle number density ( N0; 10-5mm-1m-3), and (d) liquid water content

(Lw; g m-3).

50 / 52

䘀椀最甀爀攀............................................................

....... ........................................................................................ ............................... .. ...........................

..................