The importance of simulated errors in observing system simulation experiments
Observing System Simulation Experiments (OSSEs) for numerical weather prediction rely on simulated observations that should include simulated observation errors in order to realistically represent the behaviour of real data. Real observations include many types of error, such as instrument error, re...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Taylor & Francis Group
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/b0e9ff9bf69a4bc7b6b14da9676d8281 |
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| Sumario: | Observing System Simulation Experiments (OSSEs) for numerical weather prediction rely on simulated observations that should include simulated observation errors in order to realistically represent the behaviour of real data. Real observations include many types of error, such as instrument error, representativeness error, and observation operator error, with some portion of this error being correlated in time and space or possibly between data types. Data assimilation systems are designed to account for random, uncorrelated errors, but are not yet adept at handling correlated errors; as a result, the correlated errors are more readily incorporated into the analysis increment by the data assimilation system than uncorrelated errors. In this work, the role of correlated observation errors in modifying the behaviour of the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE framework is investigated. The effects on analysis increment, analysis error, forecast errors and observation impacts of including or neglecting correlated simulated errors is explored. The use of correlated observations for calibration and validation of the OSSE is also discussed. |
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