Investigation of Local Weighting Filtering on Randomization Technique Estimates in a Data Assimilation System
Mainstream numerical weather prediction (NWP) centers usually estimate the standard deviations of background error by using a randomization technique to calibrate specific parameters of the background error covariance model in variational data assimilation (VAR) systems. However, the sampling size o...
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doaj-728cf2558b234507b327600d3349057c2021-09-26T01:30:36ZengMDPI AGSymmetry2073-89942021-08-01131563156310.3390/sym13091563Investigation of Local Weighting Filtering on Randomization Technique Estimates in a Data Assimilation SystemXiang Xing0Bainian Liu1Weimin Zhang2Xiaoqun Cao3Jingzhe Sun4College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaBeijing Institute of Applied Meteorology, Beijing 100029, ChinaMainstream numerical weather prediction (NWP) centers usually estimate the standard deviations of background error by using a randomization technique to calibrate specific parameters of the background error covariance model in variational data assimilation (VAR) systems. However, the sampling size of the randomization technique is typically several orders of magnitude smaller than that of model state variables, and using finite-sized estimates as a proxy for the truth can lead to sampling noise, which may contaminate the estimation of the standard deviation. The sampling noise is firstly investigated in an atmospheric model to show that the sampling noise has a symmetrical structure oscillating around the truth on a small scale. To alleviate the sampling noise, a heterogeneous local weighting filtering is proposed based on distance-weighted correlation and similarity-weighted correlation. Local weighting filtering is easy to implement in the VAR operational systems and has a low computational cost in the post-processing of reducing the sampling noise. The validity and performance of local weighting filtering method are examined in a realistic model framework to show that the proposed filtering is able to eliminate most of the sampling noise dramatically, the details of the filtered results are more visible, and the accuracy of the filtered results is almost the same as that estimated from the larger sample. The signal-to-noise ratio of the optimal filtered field is improved by nearly 20%. A comparison with the widely used spectral filtering approach in the operational system is considered, showing that the proposed filtering method is more efficient to implement in the filtering procedure and exhibits very good performance in terms of preserving the local anisotropic features of the estimates. These attractive results show the potential efficiency of the local weighting filtering method for solving the noise issue in the randomization technique.https://www.mdpi.com/2073-8994/13/9/1563data assimilationrandomization techniquesampling noiselocal weighting filtering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiang Xing Bainian Liu Weimin Zhang Xiaoqun Cao Jingzhe Sun |
spellingShingle |
Xiang Xing Bainian Liu Weimin Zhang Xiaoqun Cao Jingzhe Sun Investigation of Local Weighting Filtering on Randomization Technique Estimates in a Data Assimilation System Symmetry data assimilation randomization technique sampling noise local weighting filtering |
author_facet |
Xiang Xing Bainian Liu Weimin Zhang Xiaoqun Cao Jingzhe Sun |
author_sort |
Xiang Xing |
title |
Investigation of Local Weighting Filtering on Randomization Technique Estimates in a Data Assimilation System |
title_short |
Investigation of Local Weighting Filtering on Randomization Technique Estimates in a Data Assimilation System |
title_full |
Investigation of Local Weighting Filtering on Randomization Technique Estimates in a Data Assimilation System |
title_fullStr |
Investigation of Local Weighting Filtering on Randomization Technique Estimates in a Data Assimilation System |
title_full_unstemmed |
Investigation of Local Weighting Filtering on Randomization Technique Estimates in a Data Assimilation System |
title_sort |
investigation of local weighting filtering on randomization technique estimates in a data assimilation system |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2021-08-01 |
description |
Mainstream numerical weather prediction (NWP) centers usually estimate the standard deviations of background error by using a randomization technique to calibrate specific parameters of the background error covariance model in variational data assimilation (VAR) systems. However, the sampling size of the randomization technique is typically several orders of magnitude smaller than that of model state variables, and using finite-sized estimates as a proxy for the truth can lead to sampling noise, which may contaminate the estimation of the standard deviation. The sampling noise is firstly investigated in an atmospheric model to show that the sampling noise has a symmetrical structure oscillating around the truth on a small scale. To alleviate the sampling noise, a heterogeneous local weighting filtering is proposed based on distance-weighted correlation and similarity-weighted correlation. Local weighting filtering is easy to implement in the VAR operational systems and has a low computational cost in the post-processing of reducing the sampling noise. The validity and performance of local weighting filtering method are examined in a realistic model framework to show that the proposed filtering is able to eliminate most of the sampling noise dramatically, the details of the filtered results are more visible, and the accuracy of the filtered results is almost the same as that estimated from the larger sample. The signal-to-noise ratio of the optimal filtered field is improved by nearly 20%. A comparison with the widely used spectral filtering approach in the operational system is considered, showing that the proposed filtering method is more efficient to implement in the filtering procedure and exhibits very good performance in terms of preserving the local anisotropic features of the estimates. These attractive results show the potential efficiency of the local weighting filtering method for solving the noise issue in the randomization technique. |
topic |
data assimilation randomization technique sampling noise local weighting filtering |
url |
https://www.mdpi.com/2073-8994/13/9/1563 |
work_keys_str_mv |
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