Research article
19 Jul 2018
Research article
| 19 Jul 2018
Estimating observation and model error variances using multiple data sets
Richard Anthes and Therese Rieckh
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Cited
11 citations as recorded by crossref.
- COSMIC‐2 Radio Occultation Constellation: First Results W. Schreiner et al. 10.1029/2019GL086841
- Evaluating two methods of estimating error variances using simulated data sets with known errors T. Rieckh & R. Anthes 10.5194/amt-11-4309-2018
- Impact of Uncertainty in Precipitation Forcing Data Sets on the Hydrologic Budget of an Integrated Hydrologic Model in Mountainous Terrain A. Schreiner‐McGraw & H. Ajami 10.1029/2020WR027639
- 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 T. Rieckh et al. 10.1175/JTECH-D-20-0209.1
- Global 3D Features of Error Variances of GPS Radio Occultation and Radiosonde Observations X. Xu & X. Zou 10.3390/rs13010001
- NOAA’s Sensing Hazards with Operational Unmanned Technology (SHOUT) Experiment Observations and Forecast Impacts G. Wick et al. 10.1175/BAMS-D-18-0257.1
- The COSMIC/FORMOSAT-3 Radio Occultation Mission after 12 Years: Accomplishments, Remaining Challenges, and Potential Impacts of COSMIC-2 S. Ho et al. 10.1175/BAMS-D-18-0290.1
- Estimating GPS radio occultation observation error standard deviations over China using the three‐cornered hat method X. Xu & X. Zou 10.1002/qj.3938
- Sensitivity of Forward-Modeled Bending Angles to Vertical Interpolation of Refractivity for Radio Occultation Data Assimilation S. Gilpin et al. 10.1175/MWR-D-18-0223.1
- Evaluation and Assimilation of the COSMIC‐2 Radio Occultation Constellation Observed Atmospheric Refractivity in the WRF Data Assimilation System R. Singh et al. 10.1029/2021JD034935
- Evaluating tropospheric humidity from GPS radio occultation, radiosonde, and AIRS from high-resolution time series T. Rieckh et al. 10.5194/amt-11-3091-2018
10 citations as recorded by crossref.
- COSMIC‐2 Radio Occultation Constellation: First Results W. Schreiner et al. 10.1029/2019GL086841
- Evaluating two methods of estimating error variances using simulated data sets with known errors T. Rieckh & R. Anthes 10.5194/amt-11-4309-2018
- Impact of Uncertainty in Precipitation Forcing Data Sets on the Hydrologic Budget of an Integrated Hydrologic Model in Mountainous Terrain A. Schreiner‐McGraw & H. Ajami 10.1029/2020WR027639
- 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 T. Rieckh et al. 10.1175/JTECH-D-20-0209.1
- Global 3D Features of Error Variances of GPS Radio Occultation and Radiosonde Observations X. Xu & X. Zou 10.3390/rs13010001
- NOAA’s Sensing Hazards with Operational Unmanned Technology (SHOUT) Experiment Observations and Forecast Impacts G. Wick et al. 10.1175/BAMS-D-18-0257.1
- The COSMIC/FORMOSAT-3 Radio Occultation Mission after 12 Years: Accomplishments, Remaining Challenges, and Potential Impacts of COSMIC-2 S. Ho et al. 10.1175/BAMS-D-18-0290.1
- Estimating GPS radio occultation observation error standard deviations over China using the three‐cornered hat method X. Xu & X. Zou 10.1002/qj.3938
- Sensitivity of Forward-Modeled Bending Angles to Vertical Interpolation of Refractivity for Radio Occultation Data Assimilation S. Gilpin et al. 10.1175/MWR-D-18-0223.1
- Evaluation and Assimilation of the COSMIC‐2 Radio Occultation Constellation Observed Atmospheric Refractivity in the WRF Data Assimilation System R. Singh et al. 10.1029/2021JD034935
Latest update: 03 Jul 2022
Short summary
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 correlations among the different data sets, and we examine the consequences of this assumption.
We show how multiple data sets, including observations and models, can be combined using the...