The Impact of Length-Scale Variation When Diagnosing the Standard Deviations of Background Error in a 4D-Var System and Filtering Method Investigation Article Swipe
Xiang Xing
,
Bainian Liu
,
Weimin Zhang
,
Xiaoqun Cao
,
Hongze Leng
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1155/2020/8885607
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1155/2020/8885607
The four-dimensional variational data assimilation (4D-Var) method has been widely employed as an operational scheme in mainstream numerical weather prediction (NWP) centers. In addition to the ensemble data assimilation method, the randomization technique is still used to diagnose the standard deviations of background error in variational data assimilation (VAR) systems; however, such randomization techniques induce sampling noise, which may contaminate the quality of the standard deviations. First, this paper studies the properties of the sampling noise induced by the randomization technique. The results show that the sampling noise is on a small scale displaying high-frequency oscillations around the estimate compared with the estimate and this difference motivates the use of filtering techniques to eliminate the sampling noise effects. The characteristics of the standard deviation field of the control variables are also investigated, and the standard deviation fields of different model parameters have different scales and vary with the vertical model levels. To eliminate such sampling noise, the spectral filtering method used widely in the operational system and a modified spatial averaging approach are investigated. Although both methods have splendid performance in eliminating sampling noise, the spatial averaging approach is more efficient and easier to implement in operational systems. In addition, the optimal filtered results from the spatial averaging approach are dependent on model parameters and vertical levels, which is consistent with the variation in the standard deviation field. Finally, the spatial averaging approach is tested on the operational system at the global scale based on the YH4DVAR and the global NWP system, and the results indicate that the spatial averaging approach has positive effects on both analysis and forecast quality.
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- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1155/2020/8885607
- https://downloads.hindawi.com/journals/amete/2020/8885607.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3090655705
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- OpenAlex ID
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https://openalex.org/W3090655705Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2020/8885607Digital Object Identifier
- Title
-
The Impact of Length-Scale Variation When Diagnosing the Standard Deviations of Background Error in a 4D-Var System and Filtering Method InvestigationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-05Full publication date if available
- Authors
-
Xiang Xing, Bainian Liu, Weimin Zhang, Xiaoqun Cao, Hongze LengList of authors in order
- Landing page
-
https://doi.org/10.1155/2020/8885607Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/amete/2020/8885607.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/amete/2020/8885607.pdfDirect OA link when available
- Concepts
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Data assimilation, Standard deviation, Sampling (signal processing), Noise (video), Algorithm, Statistics, Mathematics, Scale (ratio), Computer science, Meteorology, Filter (signal processing), Geography, Artificial intelligence, Computer vision, Image (mathematics), CartographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
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18Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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