Detecting long-term trends in precipitable water over the Tibetan Plateau by synthesis of station and MODIS observations

PW products from Moderate Resolution Imaging Spectroradiometer (MODIS) are able to provide good spatial coverage of PW over the TP but limited in time coverage, while the meteorological stations in the TP can estimate long-term PW but unevenly distributed. To detect the decadal trend in PW over the TP, Bayesian inference theory is used to construct long-term and spatially continuous PW data for the TP based on the station and MODIS observations.

The prior information on the monthly-mean PW from MODIS and the 63 stations over the TP for 2000-06 is used to get the posterior probability knowledge that is utilized to build a Bayesian estimation model. This model is then operated to estimate continuous monthly-mean PW for 1970-2011 and its performance is evaluated using the monthly MODIS PW anomalies (2007-11) and annual GPS PW anomalies (1995-2011), with RMSEs below 0.65 mm, to demonstrate that the model estimation can reproduce the PW variability over the TP in both space and time.