Filling Gaps in Hourly Air Temperature Data Using Debiased ERA5 Data

被引:24
作者
Lompar, Milos [1 ]
Lalic, Branislava [2 ]
Dekic, Ljiljana [1 ]
Petric, Mina [3 ,4 ,5 ]
机构
[1] Republ Hydrometeorol Serv Serbia, Dept Meteorol, Belgrade 11000, Serbia
[2] Univ Novi Sad, Fac Agr, Novi Sad 21000, Serbia
[3] Univ Ghent, Dept Phys & Astron, Fac Sci, B-9000 Ghent, Belgium
[4] Univ Novi Sad, Fac Sci, Dept Phys, Novi Sad 21000, Serbia
[5] Avia GIS NV, B-2980 Zoersel, Belgium
基金
欧盟地平线“2020”;
关键词
air temperature data gap; gap filling; ERA5; debiasing techniques; SURFACE-TEMPERATURE; PRECIPITATION; INTERPOLATION; RESOLUTION;
D O I
10.3390/atmos10010013
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Missing data in hourly and daily temperature data series is a common problem in long-term data series and many observational networks. Agricultural and environmental models and climate-related tools can be used only if weather data series are complete. To support user communities, a technique for gap filling is developed based on the debiasing of ERA5 reanalysis data, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate. The debiasing procedure includes in situ measured temperature. The methodology is tested for different landscapes, latitudes, and altitudes, including tropical and midlatitudes. An evaluation of results in terms of root mean square error (RMSE) obtained using hourly and daily data is provided. The study shows very low average RMSE for all gap lengths ranging from 1.1 degrees C (Montecristo, Italy) to 1.9 degrees C (Gumpenstein, Austria).
引用
收藏
页数:24
相关论文
共 40 条
[11]  
Daly SF, 2000, HYDROL PROCESS, V14, P3257, DOI 10.1002/1099-1085(20001230)14:18<3257::AID-HYP199>3.0.CO
[12]  
2-Z
[13]   Evaluation of the Reanalysis Products from GSFC, NCEP, and ECMWF Using Flux Tower Observations [J].
Decker, Mark ;
Brunke, Michael A. ;
Wang, Zhuo ;
Sakaguchi, Koichi ;
Zeng, Xubin ;
Bosilovich, Michael G. .
JOURNAL OF CLIMATE, 2012, 25 (06) :1916-1944
[14]   Daily air temperature interpolated at high spatial resolution over a large mountainous region [J].
Dodson, R ;
Marks, D .
CLIMATE RESEARCH, 1997, 8 (01) :1-20
[15]   Changes in temperature and temperature gradients in the French Northern Alps during the last century [J].
Dumas, M. Dominique .
THEORETICAL AND APPLIED CLIMATOLOGY, 2013, 111 (1-2) :223-233
[16]  
Eischeid JK, 2000, J APPL METEOROL, V39, P1580, DOI 10.1175/1520-0450(2000)039<1580:CASCND>2.0.CO
[17]  
2
[18]  
Evans J.D., 1996, Straightforward Statistics for the Behavioral Sciences
[19]  
FDA U, 2004, NUREG1576 FDA U NIST
[20]  
GAREN DC, 1994, WATER RESOUR BULL, V30, P481