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 apply the three-cornered hat (3CH) method to estimate refractivity, bending angle, and specific humidity error variances for a number of datasets widely used in research and/or operations: radiosondes, radio occultation (COSMIC, COSMIC-2), NCEP global forecasts, and nine 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. Because of the importance of tropospheric water vapor to weather and climate, we compare error estimates of refractivity for dry and moist atmospheric conditions.