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Evaluation of precipitable water vapor from four satellite products and four reanalysis datasets against GPS measurements on the Southern Tibetan Plateau

Journal article
Authors Yan Wang
Kun Yang
Zhengyang Pan
Jun Qin
Deliang Chen
Changgui Lin
Yingying Chen
Lazhu
Wenjun Tang
Menglei Han
Ning Lu
Hui Wu
Published in Journal of Climate
Volume 30
Pages 5699-5713
ISSN 0894-8755
Publication year 2017
Published at Department of Earth Sciences
Pages 5699-5713
Language en
Links dx.doi.org/10.1175/JCLI-D-16-0630.1
Keywords Complex terrain, Model evaluation/performance, Reanalysis data, Regional effects, Satellite observations, Water vapor
Subject categories Water in nature and society, Meteorology and Atmospheric Sciences, Climate Research

Abstract

© 2017 American Meteorological Society. The southern Tibetan Plateau (STP) is the region in which water vapor passes from South Asia into the Tibetan Plateau (TP). The accuracy of precipitable water vapor (PWV) modeling for this region depends strongly on the quality of the available estimates of water vapor advection and the parameterization of land evaporation models. While climate simulation is frequently improved by assimilating relevant satellite and reanalysis products, this requires an understanding of the accuracy of these products. In this study, PWV data from MODIS infrared and near-infrared measurements, AIRS Level-2 and Level-3, MERRA, ERA-Interim, JRA-55, and NCEP final reanalysis (NCEP-Final) are evaluated against ground-based GPS measurements at nine stations over the STP, which covers the summer monsoon season from 2007 to 2013. The MODIS infrared product is shown to underestimate water vapor levels by more than 20% (1.84 mm), while the MODIS near-infrared product overestimates them by over 40% (3.52 mm). The AIRS PWV product appears to be most useful for constructing high-resolution and high-quality PWV datasets over the TP; particularly the AIRS Level-2 product has a relatively low bias (0.48 mm) and RMSE (1.83 mm) and correlates strongly with the GPS measurements (R = 0.90). The four reanalysis datasets exhibit similar performance in terms of their correlation coefficients (R = 0.87-0.90), bias (0.72-1.49 mm), and RMSE (2.19-2.35 mm). The key finding is that all the reanalyses have positive biases along the PWV seasonal cycle, which is linked to the well-known wet bias over the TP of current climate models.

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