Assessment of atmospheric emissivity models for clear-sky conditions with reanalysis data

被引:0
作者
Luis Morales-Salinas
Samuel Ortega-Farias
Camilo Riveros-Burgos
José L. Chávez
Sufen Wang
Fei Tian
Marcos Carrasco-Benavides
José Neira-Román
Rafael López-Olivari
Guillermo Fuentes-Jaque
机构
[1] University of Chile,Laboratory for Research in Environmental Sciences (LARES), Faculty of Agricultural Sciences
[2] Universidad de Talca,Research and Extension Center for Irrigation and Agroclimatology (CITRA), Faculty of Agricultural Sciences
[3] Universidad de Tarapacá,Departamento de Producción Agrícola, Facultad de Ciencias Agronómicas
[4] Universidad de O’Higgins,Institute of Agri
[5] Colorado State University,Food, Animal and Environmental Sciences (ICA3)
[6] China Agricultural University,Civil & Environmental Engineering Department
[7] Universidad Católica del Maule,Center for Agricultural Water Research in China
[8] Instituto de Investigaciones Agropecuarias,Department of Agricultural Sciences
[9] INIA Carillanca,Master in Territorial Management of Natural Resources, Postgraduate School, Faculty of Agricultural Sciences
[10] University of Chile,undefined
来源
Scientific Reports | / 13卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Atmospheric longwave downward radiation (Ld) is one of the significant components of net radiation (Rn), and it drives several essential ecosystem processes. Ld can be estimated with simple empirical methods using atmospheric emissivity (εa) submodels. In this study, eight global models for εa were evaluated, and the best-performing model was calibrated on a global scale using a parametric instability analysis approach. The climatic data were obtained from a dynamically consistent scale resolution of basic atmospheric quantities and computed parameters known as NCEP/NCAR reanalysis (NNR) data. The performance model was evaluated with monthly average values from the NNR data. The Brutsaert equation demonstrated the best performance, and then it was calibrated. The seasonal global trend of the Brutsaert equation calibrated coefficient ranged between 1.2 and 1.4, and the K-means analysis identified five homogeneous zones (clusters) with similar behavior. Finally, the calibrated Brutsaert equation improved the Rn estimation, with an error reduction, at the worldwide scale, of 64%. Meanwhile, the error reduction for each cluster ranged from 18 to 77%. Hence, Brutsaert’s equation coefficient should not be considered a constant value for use in εa estimation, nor in time or location.
引用
收藏
相关论文
共 172 条
[31]  
Zolina O(2008)Evaluation of a model to simulate net radiation over a Vineyar cv. Cabernet Sauvignon Chil. J. Agric. Res. 113 2380-14
[32]  
Grigoriev S(2009)Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery Remote Sens. Environ. 15 2089-557
[33]  
Von Randow RCS(2002)A comparative study of maximum and minimum temperatures over Argentina: NCEP-NCAR reanalysis versus station data J. Clim. 113 1-13
[34]  
Alvalá RCS(2008)Surface heat fluxes from the NCEP/NCAR and NCEP/DOE reanalyses at the Kuroshio Extension Observatory buoy site J. Geophys. Res. Ocean 31 545-1168
[35]  
Silva JB(2011)Comparison of ERA-40, ERA-Interim and NCEP/NCAR reanalysis data with observed surface air temperatures over Ireland Int. J. Climatol. 28 1-903
[36]  
Herrero J(2021)How well do atmospheric reanalyses reproduce observed winds in coastal regions of Mexico? Meteorol. Appl. 27 1151-2041
[37]  
Polo MJ(2010)Validation of ECMWF and NCEP-NCAR reanalysis data in Antarctica Adv. Atmos. Sci. 144 885-493
[38]  
Crawford TM(2021)Performance evaluation of NCEP/NCAR reanalysis blended with observation-based datasets for estimating reference evapotranspiration across Iran Theor. Appl. Climatol. 138 2021-1576
[39]  
Duchon CE(2019)Comparison between ERA Interim/ECMWF, CFSR, NCEP/NCAR reanalysis, and observational datasets over the eastern part of the Brazilian Northeast Region Theor. Appl. Climatol. 12 477-1358
[40]  
Kjaersgaard JH(1999)Evaluation of the earth radiation budget in NCEP–NCAR reanalysis with ERBE J. Clim. 21 1561-39