Development and Evaluation of Hourly and Kilometer Resolution Retrospective and Real-Time Surface Meteorological Blended Forcing Dataset (SMBFD) in China
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...
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| Vydané v: | Journal of Meteorological Research Ročník 33; číslo 6; s. 1168 - 1181 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Beijing
The Chinese Meteorological Society
01.12.2019
National Meteorological Information Center, China Meteorological Administration, Beijing 100081 |
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| ISSN: | 2095-6037, 2198-0934 |
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| Abstract | 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. |
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| AbstractList | 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. A real-time, long-term surface meteorological blended forcing dataset (SMBFD) has been developed based on sta-tion 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 radi-ation, 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 analy-sis, 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 ob-servations. The SMBFD dataset is also compared with similar datasets produced in other major meteorological opera-tional centers in the world. The results are as follows. (1) All variables show reasonable geographic distribution fea-tures 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 in-dependent 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, ag-riculture, 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 Administra-tion. The dataset with the network Common Data Form (NETCDF) can be decoded by various programming lan-guages, and it is freely available to non-commercial users. |
| Author | Xu, Bin Liang, Xiao Han, Shuai Shi, Chunxiang Jiang, Lipeng Sun, Shuai Zhang, Tao |
| AuthorAffiliation | National Meteorological Information Center, China Meteorological Administration, Beijing 100081 |
| AuthorAffiliation_xml | – name: National Meteorological Information Center, China Meteorological Administration, Beijing 100081 |
| Author_xml | – sequence: 1 givenname: Shuai surname: Han fullname: Han, Shuai organization: National Meteorological Information Center, China Meteorological Administration – sequence: 2 givenname: Chunxiang surname: Shi fullname: Shi, Chunxiang email: shicx@cma.gov.cn organization: National Meteorological Information Center, China Meteorological Administration – sequence: 3 givenname: Bin surname: Xu fullname: Xu, Bin organization: National Meteorological Information Center, China Meteorological Administration – sequence: 4 givenname: Shuai surname: Sun fullname: Sun, Shuai organization: National Meteorological Information Center, China Meteorological Administration – sequence: 5 givenname: Tao surname: Zhang fullname: Zhang, Tao organization: National Meteorological Information Center, China Meteorological Administration – sequence: 6 givenname: Lipeng surname: Jiang fullname: Jiang, Lipeng organization: National Meteorological Information Center, China Meteorological Administration – sequence: 7 givenname: Xiao surname: Liang fullname: Liang, Xiao organization: National Meteorological Information Center, China Meteorological Administration |
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| Cites_doi | 10.1007/s13351-014-4034-2 10.1175/BAMS-83-11-1631 10.5194/hess-21-5805-2017 10.1002/qj.49712252905 10.1175/1520-0442(1998)011<0018:STAOTU>2.0.CO;2 10.1007/s13351-017-6073-y 10.1016/j.rse.2007.08.025 10.1007/s11430-010-4160-3 10.1007/s13351-018-7151-5 10.1016/S0022-1694(96)03128-9 10.1175/2010MWR3338.1 10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2 10.1002/met.1770 10.1002/qj.828 10.1002/jgrd.50353 10.1007/s13351-017-7050-1 10.1364/AO.27.002502 10.1007/s13351-018-8041-6 10.1175/1520-0442(2003)016<1032:SASVOA>2.0.CO;2 10.1175/1520-0442(1989)002<0656:SPFEOH>2.0.CO;2 10.1029/2008JG000723 10.1016/S0038-092X(00)00121-3 10.3969/j.issn.1671-6345.2015.06.001 10.3878/j.issn.1006-9585.2004.02.04 10.19513/j.cnki.issn2096-3599.2018.01.011 10.3969/j.issn.1001-7313.2009.04.001 10.3972/westdc.002.2014.db |
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| Copyright | The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019 Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Title | Development and Evaluation of Hourly and Kilometer Resolution Retrospective and Real-Time Surface Meteorological Blended Forcing Dataset (SMBFD) in China |
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