Open AccessEvaluation of multireanalysis products with in situ observations over the Tibetan Plateau
Author(s) -
Wang Aihui,
Zeng Xubin
Publication year - 2012
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2011jd016553
Subject(s) - environmental science , climatology , precipitation , shortwave , plateau (mathematics) , outgoing longwave radiation , correlation coefficient , standard deviation , shortwave radiation , meteorology , wind speed , relative humidity , atmospheric sciences , geography , radiation , radiative transfer , geology , statistics , mathematics , mathematical analysis , physics , convection , quantum mechanics
As the highest plateau in the world, the Tibetan Plateau (TP) strongly affects regional weather and climate as well as global atmospheric circulations. Here six reanalysis products (i.e., MERRA, NCEP/NCAR‐1, CFSR, ERA‐40, ERA‐Interim, and GLDAS) are evaluated using in situ measurements at 63 weather stations over the TP from the Chinese Meteorological Administration (CMA) for 1992–2001 and at nine stations from field campaigns (CAMP/Tibet) for 2002–2004. The measurement variables include daily and monthly precipitation and air temperature at all CMA and CAMP/Tibet stations as well as radiation (downward and upward shortwave and longwave), wind speed, humidity, and surface pressure at CAMP stations. Four statistical quantities (correlation coefficient, ratio of standard deviations, standard deviation of differences, and bias) are computed, and a ranking approach is also utilized to quantify the relative performance of reanalyses with respect to each variable and each statistical quantity. Compared with measurements at the 63 CMA stations, ERA‐Interim has the best overall performance in both daily and monthly air temperatures, while MERRA has a high correlation with observations. GLDAS has the best overall performance in both daily and monthly precipitation because it is primarily based on the merged precipitation product from surface measurements and satellite remote sensing, while ERA‐40 and MERRA have the highest correlation coefficients for daily and monthly precipitation, respectively. Compared with measurements at the nine CAMP stations, CFSR shows the best overall performance, followed by GLDAS, although the best ranking scores are different for different variables. It is also found that NCEP/NCAR‐1 reanalysis shows the worst overall performance compared with both CMA and CAMP data. Since no reanalysis product is superior to others in all variables at both daily and monthly time scales, various reanalysis products should be combined for the study of weather and climate over the TP.