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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/amt-2018-75
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
12 Mar 2018
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Measurement Techniques (AMT).
Evaluating two methods of estimating error variances from multiple data sets using an error model
Therese Rieckh1,2 and Richard Anthes1 1COSMIC Program Office, University Corporation for Atmospheric Research, Boulder, Colorado, USA
2Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
Abstract. In this paper we compare two different methods of estimating the error variances of two or more independent data sets. One method, called the three-cornered hat (3CH) or triple co-location method, requires three data sets. Another method, which we call the two-cornered hat (2CH) method, requires only two data sets. Both methods assume that the errors of the data sets are not correlated and are unbiased. The 3CH method has been used in previous studies to estimate the error variances associated with a number of physical and geophysical data sets. Braun et al. (2001) used the two-cornered hat (2CH) method to estimate the error variances associated with two observational data sets of total atmospheric water vapor.

In this paper we compare the 3CH and 2CH methods using a simple error model to simulate three and two data sets with various error correlations and biases. With this error model, we know the exact error variances and covariances, which we use to assess the accuracy of the 3CH and 2CH estimates. We examine the sensitivity of the estimated error variances to the degree of error correlation between two of the data sets as well as the sample size. We find that the 3CH method is less sensitive to these factors than the 2CH method and hence is more accurate. We also find that biases in one of the data sets has a minimal effect on the 3CH method, but can produce large errors in the 2CH method.

Citation: Rieckh, T. and Anthes, R.: Evaluating two methods of estimating error variances from multiple data sets using an error model, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-75, in review, 2018.
Therese Rieckh and Richard Anthes
Therese Rieckh and Richard Anthes
Therese Rieckh and Richard Anthes

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Short summary
In this paper we compare two methods of estimated the error characteristics of two or three data sets, using various combinations of the data sets. The data sets can be in-situ or remotely sensed observations, or model data. We find that the method using three data sets, called the three cornered hat method, gives the more accurate results.
In this paper we compare two methods of estimated the error characteristics of two or three data...
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