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

Autor: Morales-Salinas L; Laboratory for Research in Environmental Sciences (LARES), Faculty of Agricultural Sciences, University of Chile, Santiago, Chile. lmorales@uchile.cl., Ortega-Farias S; Research and Extension Center for Irrigation and Agroclimatology (CITRA), Faculty of Agricultural Sciences, Universidad de Talca, Campus Talca, Talca, Chile., Riveros-Burgos C; Departamento de Producción Agrícola, Facultad de Ciencias Agronómicas, Universidad de Tarapacá, Casilla 6-D, Arica, Chile.; Institute of Agri-Food, Animal and Environmental Sciences (ICA3), Universidad de O'Higgins, San Fernando, Chile., Chávez JL; Civil & Environmental Engineering Department, Colorado State University, Fort Collins, CO, USA., Wang S; Center for Agricultural Water Research in China, China Agricultural University, Beijing, 100083, China., Tian F; Center for Agricultural Water Research in China, China Agricultural University, Beijing, 100083, China., Carrasco-Benavides M; Department of Agricultural Sciences, Universidad Católica del Maule, Curicó, Chile., Neira-Román J; Department of Agricultural Sciences, Universidad Católica del Maule, Curicó, Chile., López-Olivari R; Instituto de Investigaciones Agropecuarias, INIA Carillanca, km 10 Camino Cajón-Vilcún s/n, Casilla 929, Temuco, Chile., Fuentes-Jaque G; Laboratory for Research in Environmental Sciences (LARES), Faculty of Agricultural Sciences, University of Chile, Santiago, Chile.; Master in Territorial Management of Natural Resources, Postgraduate School, Faculty of Agricultural Sciences, University of Chile, Santiago, Chile.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Sep 02; Vol. 13 (1), pp. 14465. Date of Electronic Publication: 2023 Sep 02.
DOI: 10.1038/s41598-023-40499-6
Abstrakt: Atmospheric longwave downward radiation (L d ) is one of the significant components of net radiation (R n ), and it drives several essential ecosystem processes. L d can be estimated with simple empirical methods using atmospheric emissivity (ε a ) submodels. In this study, eight global models for ε a 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 R n 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 ε a estimation, nor in time or location.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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