China's daily snow depth simulation and prediction data set is the estimated daily snow depth data of China in the future based on the nex-gdpp model data set. The artificial neural network model of snow depth simulation takes the maximum temperature, minimum temperature, precipitation data and snow depth data of the day as the input layer of the model, The snow depth data of the next day is used as the target layer of the model to build the model, and then the snow depth simulation model is trained and verified by using the data of the national meteorological station. The model verification results show that the iterative space-time simulation ability of the model is good; The spatial correlations of the simulated and verified values of cumulative snow cover duration and cumulative snow depth are 0.97 and 0.87, and the temporal and spatial correlations of cumulative snow depth are 0.92 and 0.91, respectively. Based on the optimal model, this model is used to iteratively simulate the daily snow depth data in China in the future. The data set can provide data support for future snow disaster risk assessment, snow cover change research and climate change research in China. The basic information of the data is as follows: historical reference period (1986-2005) and future (2016-2065), as well as rcp4.5 and rcp8.5 scenarios and 20 climate models. Its spatial resolution is 0.25 ° * 0.25 °. The projection mode of the data is ease GR, and the data storage format is NC format. The following is the data file information in NC Time: duration (unit: day) Lon = 320 matrix, 320 columns in total Lat = 160 matrix, 160 rows in total X Dimension: Xmin = 60.125; // Coordinates of the corner points of the lower left corner grid in the X direction of the matrix Y Dimension: Ymin = 15.125; // Coordinates of the corner points of the grid at the lower left corner of the Y-axis of the matrix
CHEN Hongju, YANG Jianping, DING Yongjian
The SZIsnow dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System version 2 (GLDAS-2) with the Noah land surface model. This SZIsnow dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZIsnow dataset is able to well capture the large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information of drought, and also can serve as a valuable resource for drought studies.
WU Pute, TIAN Lei, ZHANG Baoqing
The data set of ice core-snow black carbon content on the Tibetan plateau (1950-2006) contains five (5) tables: 1 Xu et al. 2006 AG, 2 Xu et al. 2009 PNAS_Conc., 3 Xu et al. 2009 PNAS_flux, 4 Xu et al. 2012 ERL, 5 Wang et al. 2015 ACP. The data collection sites include the Meikuang glacier, Dongkemadi, Qiangyong, Kangwure, Naimona’nyi, Muztagata, Rongbuk, Tanggula Mountain, Ningjin Gangsang, Zuoqipu, and Glacier No. 1 at the headwaters of the Ürüqi River. The latitudes and longitudes of the collection locations, elevations and other information are marked in the data. The main indicators of the data are location, time, organic carbon (OC), elemental carbon (EC), black carbon (BC) content and flux. Location: latitude and longitude Time: year or date OC: organic carbon EC: elemental carbon BC: Black carbon Conc.: content, unit: ng g-1 Flux: flux, unit: mg m-2a-1 The data come from the following subjects. 1. National Program on Key Basic Research Project (973 Program)：Temporal and Spatial Characteristics and Remote Sensing Modeling of Global Change Sensitive Factors; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the Ministry of Science and Technology. 2. National Key Basic Research Program: The Response of Formation and Evolution on the Tibetan Plateau to Global Changes and Adaptation Strategy; Person in charge: Tandong Yao; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the Ministry of Science and Technology. 3. The General Program of National Natural Science Foundation of China: High-resolution Carbon Black Recording in Snow Ice of the Tibetan Plateau; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). 4. The General Program of the National Natural Science Foundation of China: Extraction of Climate and Environment Information from Ice Core Encapsulated Gas on the Tibetan Plateau; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). 5. National Natural Science Foundation of China for Distinguished Young Scholars: Snow and Ice-Atmospheric Chemistry and Environmental Changes on the Tibetan Plateau; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). 6. National Natural Science Foundation of China for Distinguished Young Scholars: Study on the Changes of Aerosol Emissions and Combustion in Human Activities in South Asia in the Past 100 Years; Person in charge: Mo Wang; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). Observation methods: two-step heating method, thermal/optical carbon analysis method, and single-particle black carbon aerosol photometer.
XU Baiqing
Snow water equivalent (SWE) is an important parameter of the surface hydrological model and climate model. The data is based on the ridge regression algorithm of machine learning, which integrates a variety of existing snow water equivalent data products to form a set of snow water equivalent data products with continuous time series and high accuracy. The spatial range of the data is Pan-Arctic (45 N° to 90 N °), The data time series is 1979-2019. The dataset is expected to provide more accurate snow water equivalent data for the hydrological and climate model, and provide data support for cryosphere change and global change.
LI Hongyi, SHAO Donghang, LI Haojie, WANG Weiguo, MA Yuan, LEI Huajin
The dataset include ground-based passive microwave brightness temperature, multi-angle brightness temperature, ten-minute 4-component radiation and snow temperature, daily snow pit data and hourly meteorological data observed at Altay base station（lon：88.07、lat: 44.73）from November 27, 2015 to March 26, 2016. Daily snow pit parameters include: snow stratification, stratification thickness, density, particle size, temperature. These data are stored in five NetCDF files: TBdata. nc, TBdata-multiangle. nc, ten-minute 4 component radiation and snow temperature. nc, hourly meteorological and soil data. nc and daily snow pit data.nc. TBdata. nc is brightness temperature at 3 channels for both polarizations automatically collected by a six-channel dual polarized microwave radiometer RPG-6CH-DP. The contents include Year, month, day, hour, minute, second, Tb1h, Tb1v, Tb18h, Tb18v, Tb36h, Tb36v, incidence angle, azimuth angle. TBdata-multiangle.nc is 7 groups of multi-angle brightness temperatures at 3 channels for both polarizations. The contents include Year, month, day, hour, minute, second, Tb1h, Tb1v, Tb18h, Tb18v, Tb36h, Tb36v, incidence angle, azimuth angle. The ten-minute 4 component radiation and snow temperature.nc contains 4 component radiation and layered snow temperatures. The contents include Year, month, day, hour, minute, SR_DOWN, SR_UP, LR_DOWN, LR_UP, T_Sensor, ST_0cm, ST_5cm, ST_15cm, ST_25cm, ST_35cm, ST_45cm, ST_55cm. The hourly meteorological and soil data.nc contains hourly weather data and layered soil data. The contents include Year, month, day, hour, Tair, Wair, Pair, Win, SM_10cm, SM_20cm, Tsoil_5cm, Tsoil_10cm, Tsoil_15 cm, Tsoil_20cm. The daily snow pit data.nc. is manual snow pit data. The observation time was 8:00-10:100 am local time. The contents include Year, month, day, snow depth, thickness_layer1, thickness_layer2, thickness_layer3, thickness_layer4, thickness_layer5, thickness_layer6, Long_layer1, Short_layer1, Long_layer2, Short_layer2, Long_layer3, Short_layer3, Long_layer 4, Short_layer4, Long_layer5, Short_layer5, Long_layer6, Short_layer 6, Stube, Snow shovel_0-10, Snow shovel _10-20, Snow shovel _20-30, Snow shovel _30-40, Snow shovel _40-50, Snow fork_5, Snow fork _10, Snow fork _15, Snow fork_20, Snow fork_25, Snow fork_30, Snow fork_35, Snow fork_40, Snow fork_45, Snow fork_50, shape1, shape2, shape3, shape4, shape5,
DAI Liyun
Supported by the Strategic Priority Research Program of the Chinese Academy of Science (XDA19070100). Tao Che, the director of this program, who comes from Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, CAS. They used machine learning methods combined with multi-source gridded snow depth product data to derive a long-time series over the Northern Hemisphere. Firstly, the applicability of artificial neural network (ANN), support vector machine (SVM) and random forest (RF) method in snow depth fusion are compared. It is found that random forest method shows strong advantages in snow depth data fusion. Secondly, using the random forest method, combined with remote sensing snow depth products such as AMSR-E, AMSR-2, NHSD and GlobSnow and reanalysis data such as ERA-Interim and MERRA-2. These gridded snow depth products and environmental factor variables are used as the input independent variables of the model. In situ observations of China Meteorological Station (945), Russia Meteorological Station (620), Russian snow survey data (514), and global historical meteorological network (41261) are used as reference truth to train and verify the model. The daily gridded snow depth dataset of the snow hydrological year from 1980 to 2019 (September 1 of the previous year to May 31 of the current year) is prepared on the cloud platform provided by the CASEarth. Since the passive microwave brightness temperature data from 1980 to 1987 is the data of every other day, there will be a small number of missing trips in the data during this period. Using the ESM-SnowMIP and independent ground observation data for verification, the quality of the fusion data set has been improved. According to the comparison between the ground observation data and the snow depth products before fusion, the determination coefficient (R2) of the fusion data is increased from 0.23 (GlobSnow snow depth product) to 0.81, and the corresponding root mean square error (RMSE) and mean absolute error (MAE) are also reduced to 7.7 cm and 2.7 cm.
CHE Tao, HU Yanxing, DAI Liyun, XIAO Lin
Based on AVHRR-CDR SR products, a daily cloud-free snow cover extent dataset with a spatial resolution of 5 km from 1981 to 2019 was prepared by using decision tree classification method. Each HDF4 file contains 18 data elements, including data value, data start date, longitude and latitude, etc. At the same time, to quickly preview the snow distribution, the daily file contains the snow area thumbnail, which is stored in JPG format. This data set will be continuously supplemented and improved according to the real-time satellite remote sensing data and algorithm update (up to may 2019), and will be fully open and shared.
HAO Xiaohua
Soluble organic carbon (DOC) in snow and ice can effectively absorb the solar radiation in the ultraviolet and near ultraviolet band, which is also one of the important factors leading to the enhancement of snow and ice ablation. Through the continuous snow samples from November 2016 to April 2017 in Altay area, the data of DOC, TN and BC of snow in kuwei station in Altay area were obtained through the experimental analysis and test with the instrument. The time resolution was weeks and the ablation period was daily. 1. Unit: Doc and TN unit μ g-1 (PPM), BC unit ng g-1 (ppb), MAC unit M2 g-1
SHANGGUAN Donghui
The fraction snow cover (FSC) is the ratio of the snow cover area SCA to the pixel space. The data set covers the Arctic region (35 ° to 90 ° north latitude). Using Google Earth engine platform, the initial data is the global surface reflectance product with a resolution of 1000m with mod09ga, and the data preparation time is from February 24, 2000 to November 18, 2019. The methods are as follows: in the training sample area, the reference data set of FSC is prepared by using Landsat 8 surface reflectance data and snomap algorithm, and the data set is taken as the true value of FSC in the training sample area, so as to establish the linear regression model between FSC in the training sample area and NDSI based on MODIS surface reflectance products. Using this model, MODIS global surface reflectance product is used as input to prepare snow area ratio time series data in the Arctic region. The data set can provide quantitative information of snow distribution for regional climate simulation and hydrological model.
MA Yuan, LI Hongyi
The Tibetan Plateau has an average altitude of over 4000 m and is the region with the highest altitude and the largest snow cover in the middle and low latitudes of the Northern Hemisphere regions. Snow cover is the most important underlying surface of the seasonal changes on the Tibetan Plateau and an important composing element of ecological environment. Ice and snow melt water is an important water resource of the plateau and its downstream areas. At the same time, plateau snow, as an important land-surface forcing factor, is closely related to disastrous weather (such as droughts and floods) in East Asia, the South Asian monsoon and in the middle and lower reaches of the Yangtze River. It is an important indicator of short-term climate prediction and one of the most sensitive responses to global climate change. The snow depth refers to the vertical depth from the surface of the snow to the ground. It is an important parameter for snow characteristics and one of the conventional meteorological observation elements. It is the key parameter of snow water equivalent estimation, climate effect studies of snow cover, the basin water balance, the simulation and monitoring of snow-melt, and snow disaster evaluation and grading. In this data set, the Tibetan Plateau boundary was determined by adopting the natural topography as the leading factor and by comprehensive consideration of the principles of altitude, plateau and mountain integrity. The main part of the plateau is in the Tibetan Autonomous Region and Qinghai Province, with an area of 2.572 million square kilometers, accounting for 26.8% of the total land area of China. The snow depth observation data are the monthly maximum snow depth data after quality detection and quality control. There are 102 meteorological stations in the study area, most of which were built during the 1950s to 1970s. The data for some months or years for sites existing during this period were missing, and the complete observational records from 1961 to 2013 were adopted. The temporal resolution is daily, the spatial coverage is the Tibetan Plateau, and all the data were quality controlled. Accurate and detailed plateau snow depth data are of great significance for the diagnosis of climate change, the evolution of the Asian monsoon and the management of regional snow-melt water resources.
National Meteorological Information Center, Tibet Meteorological Bureau, China
The “Long-term series of daily global snow depth” was produced using the passive microwave remote sensing data. The temporal range is 1980~2018, and the coverage is the global land. The spatial resolutions is 25,067.53 m and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file is a 1383*586 snow depth matrix, and each snow depth represents a 25,067.53m* 25,067.53m grid. The projection of this data is EASE-Grid, and following is the file header which describes the projection detail. File header: ncols 1383 nrows 586 xllcorner -17334193.54 yllcorner -7344787.75 cellsize 25,067.53 NODATA_value -1
CHE Tao, LI Xin, DAI Liyun
This dataset was derived from long-term daily snow depth in China based on the boundary of the three-river-source area. The snow depth ranges from 0 to 100 cm, and the temporal coverage is from January 1 1980 to December 31 2020. The spatial and temporal resolutions are 0.25o and daily, respectively. Snow depth was produced from satellite passive microwave remote sensing data which came from three different sensors that are SMMR, SSM/I and SSMI/S. Considering the systematic bias among these sensors, the inter-sensor calibrations were performed to obtain temporal consistent passive microwave remote sensing data. And the long-term daily snow depth in China were produced from this consistent data based on the spectral gradient method.For header file information, refer to the data set header.txt.
DAI Liyun
The dataset was produced based on MODIS data. Parameters and algorithm were revised to be suitable for the land cover type in the Three-River-Source Regions. By using the Markov de-cloud algorithm, SSM/I snow water equivalent data was fused to the result. Finally, high accuracy daily de-cloud snow cover data was produced. The data value is 0(no snow) or 1(snow). The spatial resolution is 500m, the time period is from 2000-2-24 to 2019-12-31. Data format is geotiff, Arcmap or python+GDAL were recommended to open and process the data.
HAO Xiaohua
The dataset of ground truth measurements for snow synchronizing with the airborne microwave radiometers (K&Ka bands) mission was obtained in the Binggou watershed foci experimental area on Mar. 30, 2008. Those provide reliable data for retrieval of snow parameters and properties, especially for dry and wet snow identification. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-A; (2) Snow parameters including snow depth, the snow surface temperature synchronizing with the airborne microwave radiometers (K&Ka bands), the snow layer temperature, the snow grain size and snow density in BG-A (10 points), BG-B (6 points), BG-F (12 points), BG-H (21 points) and BG-I (20 points); For each snow pit, the snowpack was divided into several layers with 10-cm intervals of snow depth. The layer depth (by the ruler), the snow grain size (by the handheld microscope), snow density (by the cutting ring) and the snow temperature (by the probe thermometer) were obtained at each snow pit. Two files including raw data and the preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, GU Juan, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, ZHU Shijie, LI Hua, CHANG Cun, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu, CHE Tao
The “long-term series of daily snow depth in Eurasia” was produced using the passive microwave remote sensing data. The temporal range is 1980~2016, and the coverage is the Eurasia continent. The spatial resolutions is 0.25° and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file includes a file header (projection mode) and a 720*332 snow depth matrix, and each snow depth represents a 0.25°*0.25° grid. For details of the data, please refer to data specification “Snow depth dataset of Eurasian (Version 1.0) (1980-2016).doc”
CHE Tao, DAI Liyun
The map is "1:4 Million Ice, Snow and Frozen Soil Map of China" compiled by Mr. Shi Yafeng and Mr. Meadson. The working map compiled by the map is "Chinese Pinyin Edition of the People's Republic of China", which retains the water system and mountain annotation of the map and adds some mountain annotation. The compilation of frozen soil map is based on the actual data of frozen soil survey and exploration, interpretation of remote sensing data, temperature conditions and topographic characteristics that affect the formation and distribution of frozen soil. The height of glacier snow line is expressed by isolines. Seasonal snow accumulation and seasonal icing are based on the data of 1600 meteorological observation stations and the results of many years of investigation in China. They are expressed by isoline notation and symbols. The selection of cold (periglacial) phenomena is a representative and schematic representation observed on the spot. The boundary line between permafrost and non-permafrost is mapped by calculation based on the field data, and its comprehensive degree is relatively high (Tö pfer, 1982) "China Ice and Snow Frozen Soil Map" reflects the scale, types and characteristics of distribution of glaciers, snow cover, frozen soil and periglacial, as well as its value in scientific research and the prospect of utilization and prevention in production practice. It shows our achievements in glacier and frozen soil research in the past 30 years.
SHI Yafeng, MI Desheng
1、 Data Description: the data includes the content of silica in snowmelt water and soil water in hulugou small watershed from May 2013 to April 2014. 2、 Sampling location: the sampling point of snowmelt water is located near 600m below No.2 meteorological station, with ground elevation of 3514.45m, longitude and latitude of 99 ° 53 ′ 20.655 ″ e, 38 ° 14 ′ 14.987 ″ n. The sampling point of soil water is located at 300m above and below the No.2 meteorological station, with the longitude and latitude of 99 ° 53 ′ 31.333 ″ E and 38 ° 13 ′ 50.637 ″ n. 3、 Measurement method: the content of silica in the sample was measured by ICP-AES. Silicon dioxide is replaced by the value of Si in the solution.
SUN Ziyong, CHANG Qixin
First, Data Description The data includes stable hydrogen and oxygen isotope data of snow melt water, river water and soil water from July 2013 to April 2014. Second, Sampling Sites The snowmelt water sampling point is located in the middle of the third area, with a latitude and longitude of 99°53′28.004′′E, 38°13′25.781′′N, and the number of acquisitions is 3 times; The river water sampling point is located at the exit of the Hulugou Basin, with a latitude and longitude of 99°52′47.7′′E, 38°16′11′′N, and the sampling frequency is once a week; The soil water sampling point is located in the middle and lower part of the Hongnigou catchment area, with a sampling depth of 90cm and 180cm underground, and a latitude and longitude of 99°52'25.98′′E, 38°15′36.11′′N. Third, Testing Method The samples were measured by L2130-i ultra-high precision liquid water and water vapor isotope analyzer.
CHANG Qixin, SUN Ziyong
This data set provides daily snow thickness distribution data of China from October 24, 1978 to December 31, 2012, with a spatial resolution of 25km.The original data used for the inversion of the snow depth data set came from SMMR (1978-1987), SSM/I (1987-2008) and amsr-e (2002-2012) daily passive microwave bright temperature data processed by the national snow and ice data center (NSIDC).As the three sensors are mounted on different platforms, there is a certain system inconsistency in the obtained data.The time consistency of bright temperature data is improved by cross calibration of bright temperature of different sensors.Then, based on Chang algorithm, Dr. Che tao is used to carry out snow depth inversion.Refer to the data description document for specific inversion methods.
CHE Tao, LI Xin, DAI Liyun
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No.1 (freeze/thaw status), No. 2 (snow parameters) and No. 3 (freeze/thaw status) quadrates of the A'rou foci experimental areas on Mar. 12, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:29 BJT. The quadrates were divided into 4×4 subsites, with each one spanning a 30×30 m2 plot. Center and corner points of each subsite were chosen for all observations except for the cutting ring measurements which only observed the center points. In No. 1 quadrate, numerous ground data were collected, the soil temperature, soil volumetric moisture, the loss tangent, soil conductivity, and the real part and the imaginary part of soil complex permittivity by the POGO soil sensor, soil volumetric moisture by ML2X, the soil volumetric moisture profile (10cm, 20cm, 30cm, 40cm, 60cm and 100cm) by PR2, the mean soil temperature from 0-5cm by the probe thermometer, soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). In No. 2 quadrate, simultaneous with ASAR, snow parameters were measured, the snow surface temperature by the thermal infrared probe, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, snow density by the aluminum case, the snow surface temperature and the snow-soil interface temperature by the thermal infrared probe, snow spectrum by ASD, and snow albedo by the total radiometer. In No. 3 quadrate soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity were measured by WET, the mean soil temperature from 0-5cm by the probe thermometer (5# and 7#), the surface radiative temperature by the hand-held infrared thermometer (5#), and soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Besides, GPR (Ground Penetration Radar) observations were also carried out in No. 1 quadrate of A'rou. Those provide reliable ground data for retrieval and verification of soil moisture and freeze/thaw status from active remote sensing approaches.
BAI Yanfen, CAO Yongpan, GE Chunmei, GU Juan, HAN Xujun, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, QU Wei, CHANG Cun, DOU Yan, MA Zhongguo, YU Meiyan, ZHAO Jin, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu, PATRICK Klenk, YUAN Xiaolong
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