A three-dimensional variational data assimilation system for the South China Sea: preliminary results from observing system simulation experiments

Peng, Shiqiu; Zeng, Xuezhi; Li, Zhijin

doi:10.1007/s10236-016-0946-y

Cited by 9 publications

(5 citation statements)

References 62 publications

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“…More specifically, the distortion of the T-S large-scale variation within the model led to the deviation of the density layer position [57]. Peng et al [58] pointed out that by adjusting the temperature and salinity of the entire water column, the variability of density layer and spatial height can be constrained, which indirectly affects the simulation of sea surface height. In the SPN1 experiment, the SSH simulation of the Luzon Strait and the northeastern South China Sea had indeed been improved de facto (Figure 7b,e).…”

Section: Indirect Impactmentioning

confidence: 99%

Impact of Correction Target Selection on Long-Term Spectral Nudging in Luzon Strait and Its Adjacent Regions

Fu,

He,

Zhao

et al. 2023

JMSE

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Previous studies have pointed out that spectral nudging is still insufficient in improving the long-term simulation ability of numerical models. In response to this problem, this study started with the Luzon Strait and its adjacent areas and discussed the influence of the selection of correction targets on its long-term spectral nudging. We established two sets of numerical experiments with the same parameter configuration except for the correction target: one was the monthly climatological target, and the other was the monthly real-time. The results showed that, compared with the climatology, the real-time target improved the consistency with the observations in large-scale variability on the premise of ensuring the correction of the climatological bias of the model. Further verification of the real-time scheme better simulated the meso- and small-scale characteristics, especially more accurately reproducing the position, intensity, and movement trend of eddies when the Kuroshio intrusion event occurred. Multi-scale energy analysis revealed the significance of adjusting large-scale potential energy to improve the overall simulation ability. The premise is that the correction target needs to fully contain these effective large-scale signals and non-stationary features, and then introduce them into the numerical integration of the regional model through appropriate band-pass filter parameter settings, driving a more reasonable large-scale background state thereby.

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Section: Indirect Impactmentioning

confidence: 99%

Impact of Correction Target Selection on Long-Term Spectral Nudging in Luzon Strait and Its Adjacent Regions

Fu,

He,

Zhao

et al. 2023

JMSE

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“…Data assimilation modules are constructed separately for the atmospheric component and the oceanic component of the NG-RFSSME: in addition to the built-in WRF-3DVAR data assimilation system, a “scale-selective data assimilation” (SSDA) scheme 49–52 is incorporated in the WRF model, while a multi-scale 3DVAR (MS-3DVAR) data assimilation scheme 27,53–56 is applied to the POM model. In the SSDA scheme, a low pass filter is employed to perform scale separation on the wind fields from both GFS (6 hourly 1° × 1°) and WRF outputs, and then the large-scale component of GFS wind field is assimilated to adjust the large-scale component of the WRF wind field using 3DVAR method; the adjusted large-scale component of the WRF wind field is recombined with the unchanged small scale component of the WRF wind field to be a new wind field for the initial conditions at each forecast cycle.…”

Section: Field Observation Experiments and T/s Profile Observationsmentioning

confidence: 99%

Improving the Real-time Marine Forecasting of the Northern South China Sea by Assimilation of Glider-observed T/S Profiles

Peng

Zhu

Li³

et al. 2019

Sci Rep

Self Cite

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Prediction of marine conditions is notoriously challenging in the northern South China Sea (NSCS) due to inadequate observations in the region. The underwater gliders that were developed during the past decade may provide observing platforms that could produce required observations. During a field experiment, temperature/salinity (T/S) profiles from a set of underwater gliders were assimilated into a real-time marine forecasting system, along with the assimilation of climatological monthly mean Argo data to constrain the basin-wide model biases. The results show that, in addition to the reduction of the basin-wide model biases by the assimilation of the climatological monthly mean Argo data, the assimilation of glider-observed T/S profiles is efficient to reduce the local biases of the NSCS marine forecasting by as much as 28–31% (19–36%) in 24 h to 120 h forecasts for temperature (salinity) from sea surface to a depth of 1000 m. Our results imply that the real-time marine forecasting for the NSCS can largely benefit from a sustainable glider observing network of the NSCS in the future.

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“…2017, 9, 1195 3 of 16 variational (3DVAR) to assimilate observation information (temperature, humidity, intensity of pressure, wind speed, etc.) and satellite-borne detected data to improve the accuracy of background field generated by reanalysis data in the field of weather research and forecast (WRF) model to increase the reliability of meteorological forecasting results [31,32]. Therefore, this study proposes a scheme to estimate spatial snow depth in order to obtain the optimal estimation of the snow depth with a higher accuracy than shown in previous methods, which combines remote sensing with in situ data.…”

Section: Study Areamentioning

confidence: 99%

“…In related studies, researchers have used three dimensional variational (3DVAR) to assimilate observation information (temperature, humidity, intensity of pressure, wind speed, etc.) and satellite-borne detected data to improve the accuracy of background field generated by reanalysis data in the field of weather research and forecast (WRF) model to increase the reliability of meteorological forecasting results [31,32].…”

Section: Introductionmentioning

confidence: 99%

Estimating Snow Depth Using Multi-Source Data Fusion Based on the D-InSAR Method and 3DVAR Fusion Algorithm

Liu

Yang

et al. 2017

Remote Sensing

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Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the accuracy of snow depth inversion to a certain extent. This study proposes a scheme to estimate spatial snow depth that combines remote sensing with site observation. On the one hand, this scheme adopts the Sentinel-1 C-band of the European Space Agency (ESA), making use of the two-pass method of differential interferometry for inversion of spatial snow depth. On the other hand, the 3DVAR (three dimensional variational) fusion algorithm is used to integrate actual snow depth data of virtual stations and real-world observation stations into the snow depth inversion results. Thus, the accuracy of snow inversion will be improved. This scheme is applied in the study area of Bayanbulak Basin, which is located in the central hinterland of Tianshan Mountains in Xinjiang, China. Observation data from stations in different altitudes are selected to test the fusion method. According to the results, most of the obtained snow depth values using interferometry are lower than the observed ones. However, after the fusion using the 3DVAR algorithm, the snow depth accuracy is slightly higher than it was in the inversion results (R 2 = 0.31 vs. R 2 = 0.50, RMSE = 2.51 cm vs. RMSE = 1.96 cm; R 2 = 0.27 vs. R 2 = 0.46, RMSE = 4.04 cm vs. RMSE = 3.65 cm). When compared with the inversion results, the relative error (RE) improved by 6.97% and 3.59%, respectively. This study shows that the scheme can effectively improve the accuracy of regional snow depth estimation. Therefore, its future application is of great potential.

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A three-dimensional variational data assimilation system for the South China Sea: preliminary results from observing system simulation experiments

Cited by 9 publications

References 62 publications

Impact of Correction Target Selection on Long-Term Spectral Nudging in Luzon Strait and Its Adjacent Regions

Impact of Correction Target Selection on Long-Term Spectral Nudging in Luzon Strait and Its Adjacent Regions

Improving the Real-time Marine Forecasting of the Northern South China Sea by Assimilation of Glider-observed T/S Profiles

Estimating Snow Depth Using Multi-Source Data Fusion Based on the D-InSAR Method and 3DVAR Fusion Algorithm

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