Empirical estimation of daytime net radiation from shortwave radiation and ancillary information

tAll-wave net surface radiation is greatly needed in various scientific research and applications. Satellitedata have been used to estimate incident shortwave radiation, but hardly to estimate all-wave net radia-tion due to the inference of clouds on longwave radiation. A practical solution is to estimate all-wave netradiation empirically from shortwave radiation and other ancillary information. Since existing modelswere developed using a limited number of ground observations, a comprehensive evaluation of thesemodels using a global network of representative measurements is urgently required. In this study, wedeveloped a new day-time net radiation estimation model and evaluated it against seven commonly usedexisting models using radiation measurements obtained from 326 sites around the world from 1991 to2010. MERRA re-analysis products from which the meteorological data were derived and remotely sensedproducts during the same period were also used. Model evaluations were performed in both global mode(all data were used to fit the models) and conditional mode (the data were divided into four subsetsbased on the surface albedo and vegetation index, and the models were fitted separately). Besides, thefactors (i.e., albedo, air temperature, and NDVI) that may impact the estimation of all-wave net radiationwere also extensively explored. Based on these evaluations, the fitting RMSE of the new developed modelwas approximately 40.0 Wm−2in the global mode and varied between 18.2 and 54.0 Wm−2in the condi-tional mode. We found that it is better to use net shortwave radiation (including surface albedo) than theincident shortwave radiation nearly in all models. Overall, the new model performed better than otherexisting linear models.

JIANG Bo;  ZHANG Y;  LIANG Shunlin;  WOHLFAHRT Georg;  ARAIN Altaf M.;  CESCATTI Alessandro;  GEORGIADIS T.;  JIA Kun;  KIELY Gerald;  LUND Magnus;  MONTAGNANI Leonardo;  MAGLIULO Vincenzo;  SERRANO-ORTIZ Penelope;  OECHEL Walter;  VACCARI Francesco Primo;  YAO Yunjun;  ZHANG Xiaotong; 

2018-01-16

ELSEVIER SCIENCE BV

JRC99982

0168-1923,   

http://www.sciencedirect.com/science/article/pii/S0168192315001458?via%3Dihub,    https://publications.jrc.ec.europa.eu/repository/handle/JRC99982,   

10.1016/j.agrformet.2015.05.003,