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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
Advances in Atmospheric Sciences ( IF 5.8 ) Pub Date : 2020-10-09 , DOI: 10.1007/s00376-020-9252-1
Hongqin Zhang , Xiangjun Tian , Wei Cheng , Lipeng Jiang

A new forecasting system—the System of Multigrid Nonlinear Least-squares Four-dimensional Variational (NLS-4DVar) Data Assimilation for Numerical Weather Prediction (SNAP)—was established by building upon the multigrid NLS-4DVar data assimilation scheme, the operational Gridpoint Statistical Interpolation (GSI)-based data-processing and observation operators, and the widely used Weather Research and Forecasting numerical model. Drawing upon lessons learned from the superiority of the operational GSI analysis system, for its various observation operators and the ability to assimilate multiple-source observations, SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations. The multigrid NLS-4DVar assimilation framework is used for the analysis, which can adequately correct errors from large to small scales and accelerate iteration solutions. The analysis variables are model state variables, rather than the control variables adopted in the conventional 4DVar system. Currently, we have achieved the assimilation of conventional observations, and we will continue to improve the assimilation of radar and satellite observations in the future. SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments. In the case evaluation experiments, two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites. This showed that SNAP can absorb observations and improve the initial field, thereby improving the precipitation forecast. In the one-week cycling assimilation experiments, six-hourly assimilation cycles were run in one week. SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar (Four-dimensional Ensemble Variational) as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar.

中文翻译:

用于数值天气预报(SNAP)的多重网格非线性最小二乘四维变分数据同化系统:系统制定和初步评估

一个新的预测系统——用于数值天气预报 (SNAP) 的多重网格非线性最小二乘四维变分 (NLS-4DVar) 数据同化系统 – 建立在多重网格 NLS-4DVar 数据同化方案的基础上,操作网格点统计基于插值 (GSI) 的数据处理和观测算子,以及广泛使用的天气研究和预测数值模型。借鉴GSI业务分析系统的优越性,SNAP的观测算子种类繁多,具有多源观测同化能力,采用基于GSI的数据处理和观测算子模块来计算观测创新。使用多重网格 NLS-4DVar 同化框架进行分析,它可以充分纠正从大到小规模的错误并加速迭代解决方案。分析变量是模型状态变量,而不是传统4DVar系统采用的控制变量。目前,我们已经实现了常规观测的同化,未来我们将继续完善雷达和卫星观测的同化。SNAP通过病例评价实验和一周循环同化实验进行评价。在案例评价实验中,建立了两个六小时时间窗进行同化实验,并与来自2400多个国家观测点的每小时降水观测值进行了降水预报验证。这表明 SNAP 可以吸收观测并改善初始场,从而改善降水预报。在为期一周的循环同化实验中,每周进行 6 小时的同化循环。SNAP 产生的预测 RMSE 总体上略低于 GSI 4DEnVar(四维集合变分),并且从 SNAP 分析初始化的降水预报的威胁评分高于从 GSI 4DEnVar 分析获得的威胁评分。
更新日期:2020-10-09
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