A11R-06
Evaluation of Atmospheric Precipitable Water from Reanalysis Products Using Homogenized Radiosonde Observations over China

Monday, 14 December 2015: 09:15
3012 (Moscone West)
Tianbao Zhao1, Juanhuai Wang1 and Aiguo Dai2, (1)Institute of Atmospheric Physics, Beijing, China, (2)University at Albany State University of New York, Albany, NY, United States
Abstract:
Many multi-decadal atmospheric reanalysis products are avialable now, but their consistencies and reliability are far from perfect. In this study, atmospheric precipitable water (PW) from the NCEP/NCAR, NCEP/DOE, MERRA, JRA-55, JRA-25, ERA-Interim, ERA-40, CFSR and 20CR reanalyses is evaluated against homogenized radiosonde observations over China during 1979-2012 (1979-2001 for ERA-40). Results suggest that the PW biases in the reanalyses are within ∼20% for most of northern and eastern China, but the reanalyses underestimate the observed PW by 20%–40% over western China, and by ∼60% over the southwestern Tibetan Plateau. The newer-generation reanalyses (e.g., JRA25, JRA55, CFSR and ERA-Interim) have smaller root-mean-square error (RMSE) than the older-generation ones (NCEP/NCAR, NCEP/DOE and ERA-40). Most of the reanalyses reproduce well the observed PW climatology and interannual variations over China. However, few reanalyses capture the observed long-term PW changes, primarily because they show spurious wet biases before about 2002. This deficiency results mainly from the discontinuities contained in reanalysis RH fields in the mid-lower troposphere due to the wet bias in older radiosonde records that are assimilated into the reanalyses. An empirical orthogonal function (EOF) analysis revealed two leading modes that represent the long-term PW changes and ENSO-related interannual variations with robust spatial patterns. The reanalysis products, especially the MERRA and JRA-25, roughly capture these EOF modes, which account for over 50% of the total variance. The results show that even during the post-1979 satellite era, discontinuities in radiosonde data can still induce large spurious long-term changes in reanalysis PW and other related fields. Thus, more efforts are needed to remove spurious changes in input data for future long-term reanlayses.