System of Multigrid Nonlinear Least-squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction (SNAP): System Formulation and Preliminary Evaluation

Zhang, Hongqin; Tian, Xiangjun; Cheng, Wei; Jiang, Lipeng

doi:10.1007/s00376-020-9252-1

Cited by 10 publications

(10 citation statements)

References 55 publications

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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%

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%

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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“…The equitable threat score (ETS) described in Z20 and the Far score were used as the verification metrics. The Far metric was computed as follows:

Far = \frac{b}{a + b}

where a is the number of correct hits and b is the number of false alarms (Zhang,Tian, Cheng, & Jiang, 2020).…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…where a is the number of correct hits and b is the number of false alarms (Zhang,Tian, Cheng, & Jiang, 2020).…”

Section: Verification Scoresmentioning

confidence: 99%

“…The multigrid NLS‐4DVar assimilation scheme was developed by combining the NLS‐4DVar method with a multigrid technique (Zhang & Tian, 2018a). Recently, the System of Multigrid NLS‐4DVar Data Assimilation for Numerical Weather Prediction (SNAP) was established by building upon the multigrid NLS‐4DVar data assimilation scheme, operational Gridpoint Statistical Interpolation (GSI)‐based data‐processing and observation operators, and the widely used Weather Research and Forecasting (WRF) numerical model (Zhang,Tian, Cheng, & Jiang, 2020, Z20 hereafter). The multigrid NLS‐4DVar assimilation framework is used in the analysis and provides high assimilation efficiency and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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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)

Zhang

Tian

2021

J Adv Model Earth Syst

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The regional System of Multigrid Nonlinear Least Squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction (SNAP) was recently established based on the multigrid NLS-4DVar assimilation scheme, Weather Research and Forecasting numerical model, and Gridpoint Statistical Interpolation (GSI)-based observation quality control and observation operator modules. The analysis variables are model state variables, rather than the control variables adopted in the conventional 4DVar system. The regional SNAP adopts the multigrid NLS-4DVar, which can correct errors from large to small scales and accelerate iteration solutions, to minimize the cost function and obtain the optimal analysis. Therefore, the regional SNAP has a higher assimilation efficiency and accuracy. In addition, the assimilation performance of the regional SNAP for conventional and radar observations had been evaluated. The main goal of this study is to achieve the direct assimilation of satellite radiation data using the regional SNAP. In this study, 1-week cycle assimilation experiments assimilating Advanced TIROS Operational Vertical Sounder (ATOVS) data were designed to fully evaluate the performance of regional SNAP compared with the GSI Ensemble Four-dimensional Variational (4DEnVar) scheme. First, a rainstorm was selected to illustrate the performance of regional SNAP. The cumulative precipitation distribution of SNAP was closer to reality and the higher equitable threat score and lower far score indicate that the regional SNAP improves precipitation forecast. In the 1-week numerical experiments, for the u/v wind and temperature variables, the regional SNAP outperforms GSI and there was an improvement in GSI for the humidity variable.

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“…At present, there are plenty of studies on the prediction of nonlinear systems in the atmosphere [22][23][24][25]. The main research methods are the traditional numerical model and machine learning prediction method, and there are plenty of defects of traditional mathematical statistical methods like autoregression (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) in the forecasting of weather phenomena and various indexes [26][27][28][29][30][31][32][33].…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting

et al. 2021

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The intensity variation of the South Asian high (SAH) plays an important role in the formation and extinction of many kinds of mesoscale systems, including tropical cyclones, southwest vortices in the Asian summer monsoon (ASM) region, and the precipitation in the whole Asia Europe region, and the SAH has a vortex symmetrical structure; its dynamic field also has the symmetry form. Not enough previous studies focus on the variation of SAH daily intensity. The purpose of this study is to establish a day-to-day prediction model of the SAH intensity, which can accurately predict not only the interannual variation but also the day-to-day variation of the SAH. Focusing on the summer period when the SAH is the strongest, this paper selects the geopotential height data between 1948 and 2020 from NCEP to construct the SAH intensity datasets. Compared with the classical deep learning methods of various kinds of efficient time series prediction model, we ultimately combine the Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which has the ability to deal with the nonlinear and unstable single system, with the Permutation Entropy (PE) method, which can extract the SAH intensity feature of IMF decomposed by CEEMDAN, and the Convolution-based Gated Recurrent Neural Network (ConvGRU) model is used to train, test, and predict the intensity of the SAH. The prediction results show that the combination of CEEMDAN and ConvGRU can have a higher accuracy and more stable prediction ability than the traditional deep learning model. After removing the redundant features in the time series, the prediction accuracy of the SAH intensity is higher than that of the classical model, which proves that the method has good applicability for the prediction of nonlinear systems in the atmosphere.

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System of Multigrid Nonlinear Least-squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction (SNAP): System Formulation and Preliminary Evaluation

Cited by 10 publications

References 55 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)

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

Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting

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