The three-cornered hat method for estimating error variances of three or more atmospheric data sets – Part II: Evaluating radio occultation and radiosonde observations, global model forecasts, and reanalyses

We use a large number and combinations of datasets to obtain insights into the impact of the error correlations among different datasets that affect 3CH estimates.

Error correlations may be caused by actual correlations of errors, representativeness differences, or imperfect collocation of the datasets. We show that the 3CH method discriminates among the datasets and how error statistics of observations compare to state-of-the-art reanalyses and forecasts, as well as reanalyses that do not assimilate satellite data. We explore results for October and November 2006 and 2019 over different latitudinal regions and show error growth of the NCEP forecasts with time.