Estimating observation and model error variances using multiple data sets

In this paper we show how multiple data sets, including observations and models, can be combined using the N-cornered hat method to estimate vertical profiles of the errors of each system. Using data from 2007, we estimate the error variances of radio occultation, radiosondes, ERA-Interim and GFS model data sets at four radiosonde locations in the tropics and subtropics. A key assumption is the neglect of error covariances among the different data sets, and we examine the consequences of this assumption on the resulting error estimates.