Estimating observation and model error variances using multiple data sets

Anthes, Richard A.; Rieckh, Therese

doi:10.5194/amt-2017-487

Cited by 2 publications

(5 citation statements)

References 18 publications

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“…The mean value of the estimates is given by the solid line and the STD of the estimates about the mean by the shading. The 3CH estimates are considered reasonably accurate for the reasons given in Anthes and Rieckh (2018), namely that the magnitude and shape of the estimates for refractivity agree with other independent refractivity error estimates (so we assume that the method works just as well for humidity as for refractivity), and that the results are consistent for the four different RS stations studied. 15…”

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confidence: 76%

“…We use our results from the 3CH method for real data to evaluate the results of the 2CH method. Figure 11a shows the 3CH estimated error variances for specific humidity for ERA, GFS, RS and RO using three independent 10 equations ( Anthes and Rieckh, 2018). The mean value of the estimates is given by the solid line and the STD of the estimates about the mean by the shading.…”

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confidence: 99%

“…6 Estimates of error variances using 3CH and 2CH methods and real observations Anthes and Rieckh (2018) showed a large number of error variance estimates using real data and the 3CH method at four 10 radiosonde (RS) locations in the Pacific Ocean region. Here we show a few examples of how the 2CH method compares with these 3CH estimates.…”

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confidence: 99%

“…We use co-located data of radio occultation (RO), RS, NCEP Global Forecast System (GFS), and ERA-Interim (ERA) at Minamidaitojima (Mina), which is located on Okinawa at 25.6°N 131.5°W, and is one of the four RS stations studied by 15 Anthes and Rieckh (2018) and Rieckh et al (2018). We use the RO-Direct method for computing RO specific humidity (uses GFS temperature to compute specific humidity q from observed RO refractivity).…”

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Evaluating two methods of estimating error variances from multiple data sets using an error model

Rieckh

Anthes

2018

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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 10 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 15 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.

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Evaluating two methods of estimating error variances from multiple data sets using an error model

Rieckh

Anthes

2018

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“…In a companion paper (Anthes and Rieckh, 2018), these data sets are compared statistically in different ways to estimate the error variances of each data set. This method indicates that the ERA-Interim data set has the smallest errors in refractivity, temperature, specific humidity, and relative humidity from 1000 to 200 hPa.…”

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confidence: 99%

Evaluating tropospheric humidity from GPS radio occultation, radiosonde, and AIRS from high-resolution time series

et al. 2018

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Abstract. While water vapor is the most important tropospheric greenhouse gas, it is also highly variable in both space and time, and water vapor concentrations range over 3 orders of magnitude in the troposphere. These properties challenge all observing systems to accurately measure and resolve the vertical structure and variability of tropospheric humidity. In this study we characterize the humidity measurements of various observing techniques, including four separate Global Positioning System (GPS) radio occultation (RO) humidity retrievals (University Corporation for Atmospheric Research (UCAR) direct, UCAR one-dimensional variational retrieval (1D-Var), Wegener Center for Climate and Global Change (WEGC) 1D-Var, Jet Propulsion Laboratory (JPL) direct), radiosonde, and Atmospheric Infrared Sounder (AIRS) data. Furthermore, we evaluate how well the ERA-Interim reanalysis and NCEP Global Forecast System (GFS) model perform in analyzing water vapor at different levels. To investigate detailed vertical structure, we analyzed time–height cross sections over four radiosonde stations in the tropical and subtropical western Pacific for the year 2007. We found that the accuracy of RO humidity is comparable to or better than both radiosonde and AIRS humidity over 800 to 400 hPa, as well as below 800 hPa if super-refraction is absent. The various RO retrievals of specific humidity agree within 20 % in the 1000–400 hPa layer, and differences are most pronounced above 600 hPa.

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Estimating observation and model error variances using multiple data sets

Cited by 2 publications

References 18 publications

Evaluating two methods of estimating error variances from multiple data sets using an error model

Evaluating two methods of estimating error variances from multiple data sets using an error model

Evaluating tropospheric humidity from GPS radio occultation, radiosonde, and AIRS from high-resolution time series

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