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Near-surface wind data are particularly important for Hainan Island and the South China Sea, and there is a wide range of wind data sources. A detailed understanding of the reliability of these datasets can help us to carry out related research. In this study, the hourly near-surface wind data from the High-Resolution China Meteorological Administration (CMA) Land Data Assimilation System (HRCLDAS) and the fifth-generation ECMWF atmospheric reanalysis data (ERA5) were evaluated by comparison with the ground automatic meteorological observation data for Hainan Island and the South China Sea. The results are as follows: (1) the HRCLDAS and ERA5 near-surface wind data trend was basically the same as the observation data trend, but there was a smaller bias, smaller root-mean-square errors, and higher correlation coefficients between the near-surface wind data from HRCLDAS and the observations; (2) the quality of HRCLDAS and ERA5 near-surface wind data was better over the islands of the South China Sea than over Hainan Island land. However, over the coastal areas of Hainan Island and island stations near Sansha, the quality of the HRCLDAS near-surface wind data was better than that of ERA5; (3) the quality of HRCLDAS near-surface wind data was better than that of ERA5 over different types of landforms. The deviation of ERA5 and HRCLDAS wind speed was the largest along the coast, and the quality of the ERA5 wind direction data was poorest over the mountains, whereas that of HRCLDAS was poorest over hilly areas; (4) the accuracy of HRCLDAS at all wind levels was higher than that of ERA5. ERA5 significantly overestimated low-grade winds and underestimated high-grade winds. The accuracy of HRCLDAS wind ratings over the islands of the South China Sea was significantly higher than that over Hainan Island land, especially for the higher wind ratings; and (5) in the typhoon process, the simulation of wind by HRCLDAS was closer to the observations, and its simulation of higher wind speeds was more accurate than the ERA5 simulations.
Yi Jiang; Shuai Han; Chunxiang Shi; Tao Gao; Honghui Zhen; Xiaoyan Liu. Evaluation of HRCLDAS and ERA5 Datasets for Near-Surface Wind over Hainan Island and South China Sea. Atmosphere 2021, 12, 766 .
AMA StyleYi Jiang, Shuai Han, Chunxiang Shi, Tao Gao, Honghui Zhen, Xiaoyan Liu. Evaluation of HRCLDAS and ERA5 Datasets for Near-Surface Wind over Hainan Island and South China Sea. Atmosphere. 2021; 12 (6):766.
Chicago/Turabian StyleYi Jiang; Shuai Han; Chunxiang Shi; Tao Gao; Honghui Zhen; Xiaoyan Liu. 2021. "Evaluation of HRCLDAS and ERA5 Datasets for Near-Surface Wind over Hainan Island and South China Sea." Atmosphere 12, no. 6: 766.
A common approach used for multi-source observation data blending is the fusion method. This study assesses the applicability of the first-generation fusion sea surface temperature (SST) product of the China Meteorological Administration (CMA) in the Yellow–Bohai Sea region for numerical weather predictions. First, daily and 6 h fusion SST measurements are compared with data derived from 21 buoy sites for 2019 to 2020. The error analysis results show that the root-mean-square error (RMSE) of the daily SST ranges from 0.64 to 1.36 °C (overall RMSE of 0.996 °C). The RMSE of the 6 h SST varies from 0.64 to 1.73 °C (overall RMSE of 1.06 °C). According to the simulation result, the SST difference could affect the value and location distribution of liquid water content in the fog area. A lower SST is favorable for increasing the liquid water content, which fits the mechanisms of advection fog formation by warm air flowing over colder water.
Ping Qu; Wei Wang; Zhijie Liu; Xiaoqing Gong; Chunxiang Shi; Bin Xu. Assessment of a Fusion Sea Surface Temperature Product for Numerical Weather Predictions in China: A Case Study. Atmosphere 2021, 12, 604 .
AMA StylePing Qu, Wei Wang, Zhijie Liu, Xiaoqing Gong, Chunxiang Shi, Bin Xu. Assessment of a Fusion Sea Surface Temperature Product for Numerical Weather Predictions in China: A Case Study. Atmosphere. 2021; 12 (5):604.
Chicago/Turabian StylePing Qu; Wei Wang; Zhijie Liu; Xiaoqing Gong; Chunxiang Shi; Bin Xu. 2021. "Assessment of a Fusion Sea Surface Temperature Product for Numerical Weather Predictions in China: A Case Study." Atmosphere 12, no. 5: 604.
Land data assimilation (DA) is an effective method to provide high-quality spatially and temporally continuous soil moisture datasets that are crucial in weather, climate, hydrological, and agricultural research. However, most existing land DA applications have used remote sensing observations, and are based on one-dimensional (1D) analysis, which cannot be directly employed to reasonably assimilate the recently expanded in-situ soil moisture observations in China. In this paper, a two-dimensional (2D) localized ensemble-based optimum interpolation (EnOI) scheme for assimilating in-situ soil moisture observations from over 2200 stations into land surface models (LSMs) is introduced. This scheme uses historical LSM simulations as ensemble samples to provide soil moisture background error covariance, allowing the in-situ observation information to be propagated to surrounding pixels. It is also computationally efficient because no additional ensemble simulations are needed. A set of ensemble sampling and localization length scale sensitivity experiments are performed. The EnOI performs best for in-situ soil moisture fusion over China with an ensemble sampling of hourly soil moisture from the previous 7 days and a localization length scale of 100 km. Following the evaluation, simulations for in-situ soil moisture fusion are also performed from May 2016 to September 2016. The EnOI analysis is notably better than that without in-situ observation fusion, as the wet bias of 0.02 m3 m−3 is removed, the root-mean-square error (RMSE) is reduced by about 37%, and the correlation coefficient is increased by about 25%. Independent evaluation shows that the EnOI analysis performs considerably better than that without fusion in terms of bias, and marginally better in terms of RMSE and correlation.
Lipeng Jiang; Chunxiang Shi; Shuai Sun; Xiao Liang. Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China. Journal of Meteorological Research 2020, 34, 1335 -1346.
AMA StyleLipeng Jiang, Chunxiang Shi, Shuai Sun, Xiao Liang. Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China. Journal of Meteorological Research. 2020; 34 (6):1335-1346.
Chicago/Turabian StyleLipeng Jiang; Chunxiang Shi; Shuai Sun; Xiao Liang. 2020. "Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China." Journal of Meteorological Research 34, no. 6: 1335-1346.
The MicroWave Humidity Sounder 2 (MWHS-2) onboard the FY-3C satellite provides an extra important data source for atmospheric water vapor monitoring besides the Microwave Humidity Sounder (MHS) and the Advanced Technology Microwave Sounder (ATMS). This paper introduces MWHS-2 radiance data into the community Gridpoint Statistical Interpolation (GSI) global analysis system. More than one-year cycling assimilation experiments with and without MWHS-2 data are performed. Results show that MWHS-2 has similar data quality to MHS and ATMS. The biases of MWHS-2 are stable except some sudden jumps that can be removed nicely by the variational bias correction scheme within GSI. Assimilating MWHS-2 makes the 6-h forecasts fit more closely to radiosonde observations, with a reduction of 0.55–1% for the observation-minus-simulation standard deviation of specific humidity. The 500 hPa geopotential height anomaly correlation scores are increased by around 0.006 for the 144-h forecast, indicating that assimilating MWHS-2 may also help to improve 3–8-day forecasts.
Lipeng Jiang; Chunxiang Shi; Tao Zhang; Yang Guo; Shuang Yao. Evaluation of Assimilating FY-3C MWHS-2 Radiances Using the GSI Global Analysis System. Remote Sensing 2020, 12, 2511 .
AMA StyleLipeng Jiang, Chunxiang Shi, Tao Zhang, Yang Guo, Shuang Yao. Evaluation of Assimilating FY-3C MWHS-2 Radiances Using the GSI Global Analysis System. Remote Sensing. 2020; 12 (16):2511.
Chicago/Turabian StyleLipeng Jiang; Chunxiang Shi; Tao Zhang; Yang Guo; Shuang Yao. 2020. "Evaluation of Assimilating FY-3C MWHS-2 Radiances Using the GSI Global Analysis System." Remote Sensing 12, no. 16: 2511.
Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS), we blended the Climate Prediction Center (CPC) morphing technique (CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China (CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2 (GLDAS-V2.1) precipitation, and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement (GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.
Shuai Sun; Chunxiang Shi; Yang Pan; Lei Bai; Bin Xu; Tao Zhang; Shuai Han; Lipeng Jiang. Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China. Journal of Meteorological Research 2020, 34, 879 -892.
AMA StyleShuai Sun, Chunxiang Shi, Yang Pan, Lei Bai, Bin Xu, Tao Zhang, Shuai Han, Lipeng Jiang. Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China. Journal of Meteorological Research. 2020; 34 (4):879-892.
Chicago/Turabian StyleShuai Sun; Chunxiang Shi; Yang Pan; Lei Bai; Bin Xu; Tao Zhang; Shuai Han; Lipeng Jiang. 2020. "Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China." Journal of Meteorological Research 34, no. 4: 879-892.
Hyperspectral data have important research and application value in the fields of meteorology and remote sensing. With the goal of improving retrievals of atmospheric temperature profiles, this paper outlines a novel temperature channel selection method based on singular spectrum analysis (SSA) for the Geostationary Interferometric Infrared Sounder (GIIRS), which is the first infrared sounder operating in geostationary orbit. The method possesses not only the simplicity and rapidity of the principal component analysis method, but also the interpretability of the conventional channel selection method. The novel SSA method is used to decompose the GIIRS observed infrared brightness temperature spectrum (700–1130 cm−1), and the reconstructed grouped components can be obtained to reflect the energy variations in the temperature-sensitive waveband of the respective sequence. At 700–780 cm−1, the channels selected using our method perform better than IASI (Infrared Atmospheric Sounding Interferometer) and CrIS (Cross-track Infrared Sounder) temperature channels when used as inputs to the neural network retrieval model.
Peipei Yu; Chunxiang Shi; Ling Yang; Shuai Shan. A New Temperature Channel Selection Method Based on Singular Spectrum Analysis for Retrieving Atmospheric Temperature Profiles from FY-4A/GIIRS. Advances in Atmospheric Sciences 2020, 37, 735 -750.
AMA StylePeipei Yu, Chunxiang Shi, Ling Yang, Shuai Shan. A New Temperature Channel Selection Method Based on Singular Spectrum Analysis for Retrieving Atmospheric Temperature Profiles from FY-4A/GIIRS. Advances in Atmospheric Sciences. 2020; 37 (7):735-750.
Chicago/Turabian StylePeipei Yu; Chunxiang Shi; Ling Yang; Shuai Shan. 2020. "A New Temperature Channel Selection Method Based on Singular Spectrum Analysis for Retrieving Atmospheric Temperature Profiles from FY-4A/GIIRS." Advances in Atmospheric Sciences 37, no. 7: 735-750.
As one of the most principal meteorological factors to affect global climate change and human sustainable development, temperature plays an important role in biogeochemical and hydrosphere cycle. To date, there are a wide range of temperature data sources and only a detailed understanding of the reliability of these datasets can help us carry out related research. In this study, the hourly and daily near-surface air temperature observations collected at national automatic weather stations (NAWS) in China were used to compare with the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the Global Land Data Assimilation System (GLDAS), both of which were developed by using the advanced multi-source data fusion technology. Results are as follows. (1) The spatial and temporal variations of the near-surface air temperature agree well between CLDAS and GLDAS over major land of China, except that spatial details in high mountainous areas were not sufficiently displayed in GLDAS; (2) The near-surface air temperature of CLDAS were more significantly correlated with observations than that of GLDAS, but more caution is necessary when using the data in mountain areas as the accuracy of the datasets gradually decreases with increasing altitude; (3) CLDAS can better illustrate the distribution of areas of daily maximum above 35 °C and help to monitor high temperature weather. The main conclusion of this study is that CLDAS near-surface air temperature has a higher reliability in China, which is very important for the study of climate change and sustainable development in East Asia.
Shuai Han; Buchun Liu; Chunxiang Shi; Yuan Liu; Meijuan Qiu; Shuai Sun. Evaluation of CLDAS and GLDAS Datasets for Near-Surface Air Temperature over Major Land Areas of China. Sustainability 2020, 12, 4311 .
AMA StyleShuai Han, Buchun Liu, Chunxiang Shi, Yuan Liu, Meijuan Qiu, Shuai Sun. Evaluation of CLDAS and GLDAS Datasets for Near-Surface Air Temperature over Major Land Areas of China. Sustainability. 2020; 12 (10):4311.
Chicago/Turabian StyleShuai Han; Buchun Liu; Chunxiang Shi; Yuan Liu; Meijuan Qiu; Shuai Sun. 2020. "Evaluation of CLDAS and GLDAS Datasets for Near-Surface Air Temperature over Major Land Areas of China." Sustainability 12, no. 10: 4311.
As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG “Early,” “Late,” and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs.
Jiayong Shi; Fei Yuan; Chunxiang Shi; Chongxu Zhao; Limin Zhang; Liliang Ren; Yonghua Zhu; Shanhu Jiang; Yi Liu. Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region. Water 2020, 12, 1006 .
AMA StyleJiayong Shi, Fei Yuan, Chunxiang Shi, Chongxu Zhao, Limin Zhang, Liliang Ren, Yonghua Zhu, Shanhu Jiang, Yi Liu. Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region. Water. 2020; 12 (4):1006.
Chicago/Turabian StyleJiayong Shi; Fei Yuan; Chunxiang Shi; Chongxu Zhao; Limin Zhang; Liliang Ren; Yonghua Zhu; Shanhu Jiang; Yi Liu. 2020. "Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region." Water 12, no. 4: 1006.
Precipitation serves as a crucial factor in the study of hydrometeorology, ecology, and the atmosphere. Gridded precipitation data are available from a multitude of sources including precipitation retrieved by satellites, radar, the output of numerical weather prediction models, and extrapolation by ground rain gauge data. Evaluating different types of products in ungauged regions with complex terrain will not only help researchers in applying scientific data, but also provide useful information that can be used to improve gridded precipitation products. The present study aims to evaluate comprehensively 12 precipitation datasets made by raw retrieved products, blended with rain gauge data, and blended multiple source datasets in multi-temporal scales in order to develop a suitable method for creating gridded precipitation data in regions with snow-dominated regions with complex terrain. The results show that the Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Satellite Mapping of Precipitation with Gauge Adjusted (GSMaP_GAUGE), Tropical Rainfall Measuring Mission (TRMM_3B42), Climate Prediction Center Morphing Technique blended with Chinese observations (CMORPH_SUN), and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) can represent the spatial pattern of precipitation in arid/semi-arid and humid/semi-humid areas of the Qinghai-Tibet Plateau on a climatological spatial pattern. On interannual, seasonal, and monthly scales, the TRMM_3B42, GSMaP_GAUGE, CMORPH_SUN, and MSWEP outperformed the other products. In general, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN_CCS) has poor performance in basins of the Qinghai-Tibet Plateau. Most products overestimated the extreme indices of the 99th percentile of precipitation (R99), the maximal of daily precipitation in a year (Rmax), and the maximal of pentad accumulation of precipitation in a year (R5dmax). They were underestimated by the extreme index of the total number of days with daily precipitation less than 1 mm (dry day, DD). Compared to products blended with rain gauge data only, MSWEP blended with more data sources, and outperformed the other products. Therefore, multi-sources of blended precipitation should be the hotspot of regional and global precipitation research in the future.
Lei Bai; Yuanqiao Wen; Chunxiang Shi; Yanfen Yang; Fan Zhang; Jing Wu; Junxia Gu; Yang Pan; Shuai Sun; Junyao Meng. Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data? Remote Sensing 2020, 12, 683 .
AMA StyleLei Bai, Yuanqiao Wen, Chunxiang Shi, Yanfen Yang, Fan Zhang, Jing Wu, Junxia Gu, Yang Pan, Shuai Sun, Junyao Meng. Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data? Remote Sensing. 2020; 12 (4):683.
Chicago/Turabian StyleLei Bai; Yuanqiao Wen; Chunxiang Shi; Yanfen Yang; Fan Zhang; Jing Wu; Junxia Gu; Yang Pan; Shuai Sun; Junyao Meng. 2020. "Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data?" Remote Sensing 12, no. 4: 683.
The accuracy of land surface hydrological simulations using an offline land surface model (LSM) depends largely on the quality of the atmospheric forcing data. In this study, Global Land Data Assimilation System (GLDAS) forcing data and the newly developed China Meteorological Administration Land Data Assimilation System (CLDAS) forcing data are used to drive the Noah LSM with multiple parameterizations (Noah-MP) and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over mainland China. The monthly soil moisture (SM) and evapotranspiration (ET) simulations are then compared and evaluated against observations. The results show that the Noah-MP driven by the CLDAS forcing data (referred to as CLDAS_Noah-MP) significantly improves the simulations in most cases over mainland China and its eight river basins. CLDAS_Noah-MP increases the correlation coefficient (R) values from 0.451 to 0.534 for the SM simulations at a depth range of 0–ss10 cm in mainland China, especially in the eastern monsoon area such as the Huang-Huai-Hai Plain, the southern Yangtze River basin, and the Zhujiang River basin. Moreover, the root-mean-square error is reduced from 0.078 to 0.068 m3 m−3 for the SM simulations, and from 12.9 to 11.4 mm month−1 for the ET simulations over mainland China, especially in the southern Yangtze River basin and Zhujiang River basin. This study demonstrates that, by merging more in situ and remote sensing observations in regional atmospheric forcing data, offline LSM simulations can better simulate regional-scale land surface hydrological processes.
Jianguo Liu; Chunxiang Shi; Shuai Sun; Jingjing Liang; Zong-Liang Yang. Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data. Journal of Meteorological Research 2019, 33, 1194 -1206.
AMA StyleJianguo Liu, Chunxiang Shi, Shuai Sun, Jingjing Liang, Zong-Liang Yang. Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data. Journal of Meteorological Research. 2019; 33 (6):1194-1206.
Chicago/Turabian StyleJianguo Liu; Chunxiang Shi; Shuai Sun; Jingjing Liang; Zong-Liang Yang. 2019. "Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data." Journal of Meteorological Research 33, no. 6: 1194-1206.
A real-time, long-term surface meteorological blended forcing dataset (SMBFD) has been developed based on station observations, satellite retrievals, and reanalysis products in China. The observations are collected at national and regional automatic weather stations, satellite data are obtained from the Fengyun (FY) series satellites retrievals, and the reanalysis products are obtained from the ECMWF. The 90-m resolution digital terrain elevation data in China are obtained from the Shuttle Radar Topographic Mission (SRTM) for temperature and humidity elevation adjustment. The dataset includes 2-m air temperature and humidity, 10-m zonal and meridional winds, downward shortwave radiation, surface pressure, and precipitation. The spatial resolution is 1 km, and the temporal resolution is 1 h. During the data processing procedure, various data fusion techniques including the space-time multiscale variational analysis, the discrete ordinates radiative transfer (DISORT) model, the hybrid radiation estimation model, and a terrain correction algorithm are employed. Dependent and independent evaluations of the dataset are performed against observations. The SMBFD dataset is also compared with similar datasets produced in other major meteorological operational centers in the world. The results are as follows. (1) All variables show reasonable geographic distribution features and realistic spatial and temporal variations. (2) Dependent and independent evaluations both indicate that the gridded SMBFD dataset is close to the observations, while the dependent evaluation yields better results than the independent evaluation. (3) Compared with similar datasets produced in other meteorological operational centers, the real-time and retrospective surface meteorological fusion data obviously have higher quality. The dataset introduced in the present study is in general stable and accurate, and can be applied in various practice such as meteorology, agriculture, ecology, environmental protection, etc. Meanwhile, this dataset has been used as the atmospheric forcing data to drive the operational High-resolution Land Data Assimilation System of China Meteorological Administration. The dataset with the network Common Data Form (NETCDF) can be decoded by various programming languages, and it is freely available to non-commercial users.
Shuai Han; Chunxiang Shi; Bin Xu; Shuai Sun; Tao Zhang; Lipeng Jiang; Xiao Liang. Development and Evaluation of Hourly and Kilometer Resolution Retrospective and Real-Time Surface Meteorological Blended Forcing Dataset (SMBFD) in China. Journal of Meteorological Research 2019, 33, 1168 -1181.
AMA StyleShuai Han, Chunxiang Shi, Bin Xu, Shuai Sun, Tao Zhang, Lipeng Jiang, Xiao Liang. Development and Evaluation of Hourly and Kilometer Resolution Retrospective and Real-Time Surface Meteorological Blended Forcing Dataset (SMBFD) in China. Journal of Meteorological Research. 2019; 33 (6):1168-1181.
Chicago/Turabian StyleShuai Han; Chunxiang Shi; Bin Xu; Shuai Sun; Tao Zhang; Lipeng Jiang; Xiao Liang. 2019. "Development and Evaluation of Hourly and Kilometer Resolution Retrospective and Real-Time Surface Meteorological Blended Forcing Dataset (SMBFD) in China." Journal of Meteorological Research 33, no. 6: 1168-1181.
Assimilation of snow cover is an important method to improve the accuracy of snow simulation. However, the effects of snow assimilation are poor because satellite observed snow cover data contain erroneous information, such as cloud contamination. In this paper, an improved approach is proposed to reduce the effects of observational errors during assimilation of snow cover fraction acquired by the Fengyun-3 (FY-3) satellite in northeastern China. A snow depth constraint was imposed on quality control of a snow depth product from a microwave radiation imager. The assimilation experiments were carried out before and after quality control (denoted as SCFDA and SCFDA_WSD, respectively). The snow cover fraction results were evaluated against the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products. When assimilating the snow cover fraction with the snow depth constraint (i.e., SCFDA_WSD), substantially larger improvement was obtained than that without such a constraint/quality control (SCFDA), and the deviation and root mean square error of the snow cover fraction were significantly reduced. The assimilation performance was also evaluated against in-situ snow depth observations. The SCFDA_WSD also showed greater improvements during the snow accumulation and snowmelt periods than the SCFDA. The SCFDA_WSD improvements in woodland and shrubland were the most obvious. At different altitudes, the effects of the SCFDA_WSD were basically equivalent, and the deeper the snow depth was, the better the effect. In addition, the SCFDA_WSD method was found in close agreement with the observations during a sudden snowfall event.
Shuai Zhang; Chunxiang Shi; Runping Shen; Jie Wu. Improved Assimilation of Fengyun-3 Satellite-Based Snow Cover Fraction in Northeastern China. Journal of Meteorological Research 2019, 33, 960 -975.
AMA StyleShuai Zhang, Chunxiang Shi, Runping Shen, Jie Wu. Improved Assimilation of Fengyun-3 Satellite-Based Snow Cover Fraction in Northeastern China. Journal of Meteorological Research. 2019; 33 (5):960-975.
Chicago/Turabian StyleShuai Zhang; Chunxiang Shi; Runping Shen; Jie Wu. 2019. "Improved Assimilation of Fengyun-3 Satellite-Based Snow Cover Fraction in Northeastern China." Journal of Meteorological Research 33, no. 5: 960-975.
The spatiotemporal pattern of precipitation is significantly changing with global climate change. Snowfall is a solid phase of precipitation and an important water resource. With two gridded datasets of APHRO (Asia Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation of Water Resources) and CN05.1, this study analyzes the changes in the spatiotemporal pattern of snowfall in a snow‐dominant region of China from 1961 to 2015. The results indicate the significant increasing trend of winter snowfall in horizontal and altitude dimension in snow‐dominant regions, but the winter snowing season length shortened. For the frequency of snowfall intensity level, light, and heavy snowfall and snowstorms increased, but moderate snowfall showed no change. However, the intensity of extreme snowfall in once‐in‐a‐century was decreasing in all of the snow‐dominant regions. In the altitude dimension, the increasing trend in snow‐dominant conditions was not uniform, which may be related to change in air temperature and water vapor through the vertical atmospheric levels. The upward trend in snowfall may be caused by the increase of atmospheric water content rather than the change of snowy weather conditions. In addition, the change values of climate indices can also contribute to snowfall increasing in snow‐dominant regions. This article is protected by copyright. All rights reserved.
Lei Bai; Chunxiang Shi; Qingdong Shi; Lanhai Li; Jing Wu; Yanfen Yang; Shuai Sun; Feiyun Zhang; Junyao Meng. Change in the spatiotemporal pattern of snowfall during the cold season under climate change in a snow‐dominated region of China. International Journal of Climatology 2019, 39, 5702 -5719.
AMA StyleLei Bai, Chunxiang Shi, Qingdong Shi, Lanhai Li, Jing Wu, Yanfen Yang, Shuai Sun, Feiyun Zhang, Junyao Meng. Change in the spatiotemporal pattern of snowfall during the cold season under climate change in a snow‐dominated region of China. International Journal of Climatology. 2019; 39 (15):5702-5719.
Chicago/Turabian StyleLei Bai; Chunxiang Shi; Qingdong Shi; Lanhai Li; Jing Wu; Yanfen Yang; Shuai Sun; Feiyun Zhang; Junyao Meng. 2019. "Change in the spatiotemporal pattern of snowfall during the cold season under climate change in a snow‐dominated region of China." International Journal of Climatology 39, no. 15: 5702-5719.
We describe the construction of a very important forcing dataset of average daily surface climate over East Asia—the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool model (CMADS). This dataset can either drive the SWAT model or other hydrologic models, such as the Variable Infiltration Capacity model (VIC), the Soil and Water Integrated Model (SWIM), etc. It contains several climatological elements—daily maximum temperature (°C), daily average temperature (°C), daily minimum temperature (°C), daily average relative humidity (%), daily average specific humidity (g/kg), daily average wind speed (m/s), daily 24 h cumulative precipitation (mm), daily mean surface pressure (HPa), daily average solar radiation (MJ/m2), soil temperature (K), and soil moisture (mm3/mm3). In order to suit the various resolutions required for research, four versions of the CMADS datasets were created—from CMADS V1.0 to CMADS V1.3. We have validated the source data of the CMADS datasets using 2421 automatic meteorological stations in China to confirm the accuracy of this dataset. We have also formatted the dataset so as to drive the SWAT model conveniently. This dataset may have applications in hydrological modelling, agriculture, coupled hydrological and meteorological modelling, and meteorological analysis.
Xianyong Meng; Hao Wang; Chunxiang Shi; Yiping Wu; Xiaonan Ji. Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS). Water 2018, 10, 1555 .
AMA StyleXianyong Meng, Hao Wang, Chunxiang Shi, Yiping Wu, Xiaonan Ji. Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS). Water. 2018; 10 (11):1555.
Chicago/Turabian StyleXianyong Meng; Hao Wang; Chunxiang Shi; Yiping Wu; Xiaonan Ji. 2018. "Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS)." Water 10, no. 11: 1555.
Precipitation is the main component of global water cycle. At present, satellite quantitative precipitation estimates (QPEs) are widely applied in the scientific community. However, the evaluations of satellite QPEs have some limitations in terms of the deficiency in observation, evaluation methodology, the selection of time windows for evaluation and short periods for evaluation. The objective of this work is to make some improvements by evaluating the spatio-temporal pattern of the long-terms Climate Hazard Group InfraRed Precipitation Satellite’s (CHIRPS’s) QPEs over mainland China. In this study, we compared the daily precipitation estimates from CHIRPS with 2480 rain gauges across China and gridded observation using several statistical metrics in the long-term period of 1981–2014. The results show that there is significant difference between point evaluation and grid evaluation for CHIRPS. CHIRPS has better performance for a large amount of precipitation than it does for arid and semi-arid land. The change in good performance zones has strong relationship with monsoon’s movement. Therefore, CHIRPS performs better in river basins of southern China and exhibits poor performance in river basins in northwestern and northern China. Moreover, CHIRPS exhibits better in warm season than in Winter, owing to its limited ability to detect snowfall. Nevertheless, CHIRPS is moderately sensitive to the precipitation from typhoon weather systems. The limitations for CHIRPS result from the Tropical Rainfall Measuring Mission (TRMM) 3B42 estimates’ accuracy and valid spatial coverage.
Lei Bai; Chunxiang Shi; Lanhai Li; Yanfen Yang; Jing Wu. Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China. Remote Sensing 2018, 10, 362 .
AMA StyleLei Bai, Chunxiang Shi, Lanhai Li, Yanfen Yang, Jing Wu. Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China. Remote Sensing. 2018; 10 (3):362.
Chicago/Turabian StyleLei Bai; Chunxiang Shi; Lanhai Li; Yanfen Yang; Jing Wu. 2018. "Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China." Remote Sensing 10, no. 3: 362.
Although cumulonimbus (Cb) clouds are the main source of precipitation in south China, the relationship between Cb cloud characteristics and precipitation remains unclear. Accordingly, the primary objective of this study was to thoroughly analyze the relationship between Cb cloud features and precipitation both at the pixel and cloud patch scale, and then to apply it in precipitation estimation in the Huaihe River Basin using China’s first operational geostationary meteorological satellite, FengYun-2C (FY-2C), and the hourly precipitation data of 286 gauges from 2007. First, 31 Cb parameters (14 parameters of three pixel features and 17 parameters of four cloud patch features) were extracted based on a Cb tracking method using an artificial neural network (ANN) cloud classification as a pre-processing procedure to identify homogeneous Cb patches. Then, the relationship between Cb cloud properties and precipitation was analyzed and applied in a look-up table algorithm to estimate precipitation. The results were as follows: (1) Precipitation increases first and then declines with increasing values for cold cloud and time evolution parameters, and heavy precipitation may occur not only near the convective center, but also on the front of the Cb clouds on the pixel scale. (2) As for the cloud patch scale, precipitation is typically associated with cold cloud and rough cloud surfaces, whereas the coldest and roughest cloud surfaces do not correspond to the strongest rain. Moreover, rainfall has no obvious relationship with the cloud motion features and varies significantly over different life stages. The involvement of mergers and splits of minor Cb patches is crucial for precipitation processes. (3) The correlation coefficients of the estimated rain rate and gauge rain can reach 0.62 in the cross-validation period and 0.51 in the testing period, which indicates the feasibility of the further application of the relationship in precipitation estimation.
Yu Liu; Du-Gang Xi; Zhao-Liang Li; Chun-Xiang Shi. Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image. Atmosphere 2014, 5, 211 -229.
AMA StyleYu Liu, Du-Gang Xi, Zhao-Liang Li, Chun-Xiang Shi. Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image. Atmosphere. 2014; 5 (2):211-229.
Chicago/Turabian StyleYu Liu; Du-Gang Xi; Zhao-Liang Li; Chun-Xiang Shi. 2014. "Analysis and Application of the Relationship between Cumulonimbus (Cb) Cloud Features and Precipitation Based on FY-2C Image." Atmosphere 5, no. 2: 211-229.
Soil moisture plays an important role in land-atmosphere interactions. It is an important geophysical parameter in research on climate, hydrology, agriculture, and forestry. Soil moisture has important climatic effects by influencing ground evapotranspiration, runoff, surface reflectivity, surface emissivity, surface sensible heat and latent heat flux. At the global scale, the extent of its influence on the atmosphere is second only to that of sea surface temperature. At the terrestrial scale, its influence is even greater than that of sea surface temperatures. This paper presents a China Land Soil Moisture Data Assimilation System (CLSMDAS) based on EnKF and land process models, and results of the application of this system in the China Land Soil Moisture Data Assimilation tests. CLSMDAS is comprised of the following components: 1) A land process model—Community Land Model Version 3.0 (CLM3.0)—developed by the US National Center for Atmospheric Research (NCAR); 2) Precipitation of atmospheric forcing data and surface-incident solar radiation data come from hourly outputs of the FY2 geostationary meteorological satellite; 3) EnKF (Ensemble Kalman Filter) land data assimilation method; and 4) Observation data including satellite-inverted soil moisture outputs of the AMSR-E satellite and soil moisture observation data. Results of soil moisture assimilation tests from June to September 2006 were analyzed with CLSMDAS. Both simulation and assimilation results of the land model reflected reasonably the temporal-spatial distribution of soil moisture. The assimilated soil moisture distribution matches very well with severe summer droughts in Chongqing and Sichuan Province in August 2006, the worst since the foundation of the People’s Republic of China in 1949. It also matches drought regions that occurred in eastern Hubei and southern Guangxi in September.
Chunxiang Shi; Zhenghui Xie; Hui Qian; Miaoling Liang; Xiaochun Yang. China land soil moisture EnKF data assimilation based on satellite remote sensing data. Science China Earth Sciences 2011, 54, 1430 -1440.
AMA StyleChunxiang Shi, Zhenghui Xie, Hui Qian, Miaoling Liang, Xiaochun Yang. China land soil moisture EnKF data assimilation based on satellite remote sensing data. Science China Earth Sciences. 2011; 54 (9):1430-1440.
Chicago/Turabian StyleChunxiang Shi; Zhenghui Xie; Hui Qian; Miaoling Liang; Xiaochun Yang. 2011. "China land soil moisture EnKF data assimilation based on satellite remote sensing data." Science China Earth Sciences 54, no. 9: 1430-1440.