Evaluating the Forecast Impact of Assimilating ATOVS Radiance With the Regional System of Multigrid NLS‐4DVar Data Assimilation for Numerical Weather Prediction (SNAP)

Zhang, Hongqin; Tian, Xiangjun

doi:10.1029/2020ms002407

Cited by 6 publications

(15 citation statements)

References 92 publications

(201 reference statements)

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“…The SN-i4DVar Zhang et al (2020) constructed the regional data assimilation system SNAP to carry out research on NWP (named as SN-4DVar), which directly optimizes the model state variables by assimilating the observations, rather than the control variables adopted in some other 4DVar systems. SN-4DVar runs a six-hourly assimilation cycle when assimilating conventional (Zhang et al, 2020) and satellite observations (Zhang and Tian, 2021), using the GSI-based data-processing and observation operators. Radar data assimilation was realized based on the improved radar observation operator (Zhang et al, 2020).…”

Section: 2mentioning

confidence: 99%

“…(2) The IAU-based 4DVar used the same background error covariance B in the conventional 4DVar system (Zhang et al, 2015). i4DVar adopted very similar to B in the traditional 4DVar but with different (smaller) standard deviation (Tian et al, 2021).…”

Section: Appendix A: the Differences Between I4dvar And Iaumentioning

confidence: 99%

“…The assimilation window [t 0 , t S ] is divided into (t S − t 0 ) ∕𝝉 sub-windows, where 𝝉 is the length of each sub-window-for more details, see Tian et al (2021). x ′ is the averaged integral correction term, and x * t 0 = x b , where x b is the background vector.…”

Section: Brief Review Of Nls-i4dvarmentioning

confidence: 99%

“…B and R k are the background and observation error covariances, respectively. It is worth noting that the structure of B should be very similar but with different (smaller) standard deviation to B in the traditional 4DVar (Tian et al, 2021). When i4DVar only adds the correction term at the initial time, the solving process of i4DVar is the same as that of 4DVar:…”

Section: Brief Review Of Nls-i4dvarmentioning

confidence: 99%

“…In this study, we incorporated i4DVar into the System of Multigrid Nonlinear Least‐squares (NLS)‐4DVar Data Assimilation for Numerical Weather Prediction (SNAP), hereafter referred to as SN‐i4DVar (Zhang et al ., 2020; Zhang and Tian, 2021), which borrows the data processing and observation operator modules of operational Gridpoint Statistical Interpolation (GSI) and has the ability to assimilate multisource and multiscale observations due to the multigrid NLS‐4DVar data assimilation scheme, and the Weather Research and Forecasting (WRF) model is used to forecast. At the same time, the performance of SNAP in assimilating conventional (Zhang et al ., 2020), satellite (Zhang and Tian, 2021), and Doppler radar (Zhang et al ., 2020) observations was evaluated. The multigrid NLS‐4DVar assimilation framework provides high assimilation accuracy and computational efficiency while avoiding the complexity and computational costs of the tangent linear and adjoint models.…”

Section: Introductionmentioning

confidence: 99%

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Integral correction of initial and model errors in system of multigrid NLS‐4DVar data assimilation for numerical weather prediction (SNAP)

Zhang

Tian

2022

Quart J Royal Meteoro Soc

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Model error has a marked influence on forecast error, and integral correction of the initial and model errors in a novel four-dimensional variational data assimilation (4DVar) method (i4DVar) has recently been developed. Unlike traditional 4DVar, in which the analysis increment focuses only on the initial time point, i4DVar introduces an averaged integral correction term to correct the error evolution at multiple times with the same time interval in the assimilation window; that is, the assimilation window is divided into several sub-windows.In this study, we incorporated i4DVar into the System of Multigrid Nonlinear Least-squares-4DVar Data Assimilation for Numerical Weather Prediction (NWP) (SNAP), hereafter referred to as SN-i4DVar, and explored its impacts on NWP. The effectiveness and performance of SN-i4DVar were evaluated using two extreme rainstorm cases assimilating conventional and satellite observations, respectively. The results indicated that integral correction of the initial and model errors reduced analysis error and forecast error growth rates. SN-i4DVar performed better in precipitation forecasting than the original SNAP and exhibited generally better forecasting capability in terms of the horizontal wind speed (u and v), temperature, and humidity variables, indicating that SN-i4DVar simultaneously countered initial model errors to improve NWP capability. Meanwhile, the 4DEnVar method, which considers the optimal analysis in the ensemble space, benefits from the improved ensemble space generated according to the formulas of i4DVar, and the observation information can be more effectively absorbed and a more accurate analysis can be obtained by SN-i4DVar.

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Section: 2mentioning

confidence: 99%

Section: Appendix A: the Differences Between I4dvar And Iaumentioning

confidence: 99%

Section: Brief Review Of Nls-i4dvarmentioning

confidence: 99%

Section: Brief Review Of Nls-i4dvarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integral correction of initial and model errors in system of multigrid NLS‐4DVar data assimilation for numerical weather prediction (SNAP)

Zhang

Tian

2022

Quart J Royal Meteoro Soc

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Introducing large‐scale analysis constraints in regional hybrid EnVar data assimilation for the prediction of triple typhoons

Wang,

Qian,

Chen

et al. 2024

Quart J Royal Meteoro Soc

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Large‐scale environment fields play an important role in the accurate prediction of typhoons. However, regional predictions for typhoons often suffer from inadequate representation of large‐scale flow pattern such as those from global models due to limited domain size and observations employed in regional models, especially when multiple typhoons that interact concurrently occur in a regional domain. This study merges the large‐scale information from global model forecasts with the mesoscale information from regional model forecasts in the hybrid ensemble‐variational (EnVar) data assimilation by adding an analysis constraint in the EnVar cost function, which is defined by the departure of the regional model EnVar analysis from the global model fields and takes advantage of flow‐dependent ensemble background error covariance for the introduction of large scales using data assimilation. The EnVar assimilation impacts of the large‐scale fields on predictions of triple typhoons are assessed by conducting cycling assimilation and forecast experiments for a 13‐day‐long period in July 2015 when three typhoons concurrently occurred. Results show that the large‐scale constraint for EnVar can clearly improve the triple‐typhoons' track and intensity forecasts of the regional model. The large‐scale information introduced by the proposed method is also shown to reduce forecast errors of wind, temperature and humidity, respectively. Predictions of the rainfall caused by typhoons are also ameliorated. Besides, the analysis‐constrained regional predictions provide better model dynamic fields in terms of sea surface pressure, geopotential height, and water vapor transport, as well as developed typhoon structures. In addition, the adaptive bias correction for radiance assimilation presents a stable performance under the influence of introducing extra background large‐scale fields. The results indicate that the large‐scale analysis constraint introduced in the hybrid EnVar takes advantages of the multiscale information from the global model and the regional model respectively, thus improving the final results of the predictions of multiple typhoons.

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Recent Advances in China on the Predictability of Weather and Climate

Duan

Yang

et al. 2023

Adv. Atmos. Sci.

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Evaluating the Forecast Impact of Assimilating ATOVS Radiance With the Regional System of Multigrid NLS‐4DVar Data Assimilation for Numerical Weather Prediction (SNAP)

Cited by 6 publications

References 92 publications

Integral correction of initial and model errors in system of multigrid NLS‐4DVar data assimilation for numerical weather prediction (SNAP)

Integral correction of initial and model errors in system of multigrid NLS‐4DVar data assimilation for numerical weather prediction (SNAP)

Introducing large‐scale analysis constraints in regional hybrid EnVar data assimilation for the prediction of triple typhoons

Recent Advances in China on the Predictability of Weather and Climate

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