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

Hongqin ZHANG; Xiangjun TIAN; Wei CHENG; Lipeng JIANG

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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

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摘要

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.

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《大气科学进展：英文版》 |2020年第11期|1267-1284|共18页
作者
Hongqin ZHANG; Xiangjun TIAN; Wei CHENG; Lipeng JIANG;
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作者单位

International Center for Climate and Environment Sciences;

Institute of Atmospheric Physics;

Chinese Academy of Sciences;

Beijing 100029;

China;

University of Chinese Academy of Sciences;

Beijing 100049;

China;

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters;

Nanjing University of Information Science and Technology;

Nanjing 210044;

China;

Beijing Institute of Applied Meteorology;

Beijing 100029;

China;

National Meteorological Information Center;

China Meteorological Administration;

Beijing 100081;

China;

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原文格式 PDF
正文语种 chi
中图分类计算数学;
关键词
data assimilation; numerical weather prediction; NLS-4DVar; multigrid; GSI;

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

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