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Science China Earth Sciences 2009年第11期52卷 1849-1856页

作者： tian xiangjun XIE ZhengHui ICCES/LASE Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

The concepts of sample sphere radius and sample density are proposed in this paper to help illustrate that different vector transformations result in diverse sample density with the same sample ensemble, which finally affects their assimilation performance. Several numerical experiments using a onedimensional (1-D) soil water equation and synthetic observations are conducted to evaluate this new theory in land data assimilation.

关键词： POD/SVD-E4DVAR data assimilation sample density

A land surface soil moisture data assimilation framework in consideration of the model subgrid-scale heterogeneity and soil water thawing and freezing

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Science China Earth Sciences 2008年第7期51卷 992-1000页

作者： tian xiangjun XIE ZhengHui Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China

The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent.

关键词： dual EnKF NCAR/CLM soil moisture assimilation framework subgrid-heterogeneity soil water thawing and freezing

An explicit four-dimensional variational data assimilation method based on the proper orthogonal decomposition: Theoretics and evaluation

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Science China Earth Sciences 2009年第2期52卷 279-286页

作者： tian xiangjun XIE ZhengHui Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China

The proper orthogonal decomposition (POD) method is used to construct a set of basis functions for spanning the ensemble of data in a certain least squares optimal sense. Compared with the singular value decomposition (SVD), the POD basis functions can capture more energy in the forecast ensemble space and can represent its spatial structure and temporal evolution more effectively. After the analysis variables are expressed by a truncated expansion of the POD basis vectors in the ensemble space, the control variables appear explicitly in the cost function, so that the adjoint model, which is used to de-rive the gradient of the cost function with respect to the control variables, is no longer needed. The application of this new technique significantly simplifies the data assimilation process. Several as-similation experiments show that this POD-based explicit four-dimensional variational data assimila-tion method performs much better than the usual ensemble Kalman filter method on both enhancing the assimilation precision and reducing the computation cost. It is also better than the SVD-based ex-plicit four-dimensional assimilation method, especially when the forecast model is not perfect and the forecast error comes from both the noise of the initial filed and the uncertainty of the forecast model.

关键词： POD data assimilation 4DVAR explicit method

An Adjoint-Free CNOP–4DVar Hybrid Method for Identifying Sensitive Areas in Targeted Observations: Method Formulation and Preliminary Evaluation

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Advances in Atmospheric Sciences 2019年第7期36卷 721-732页

作者： xiangjun tian Xiaobing FENG ICCES Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China University of Chinese Academy of Sciences Beijing100049China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjing210044 China Department of Mathematics University of TennesseeKnoxvilleTN37996USA

This paper proposes a hybrid method, called CNOP–4 DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation(CNOP) and four-dimensional variational assimilation(4 DVar) methods. The proposed CNOP–4 DVar method is capable of capturing the most sensitive initial perturbation(IP), which causes the greatest perturbation growth at the time of verification;it can also identify sensitive areas by evaluating their assimilation effects for eliminating the most sensitive IP. To alleviate the dependence of the CNOP–4 DVar method on the adjoint model, which is inherited from the adjoint-based approach, we utilized two adjointfree methods, NLS-CNOP and NLS-4 DVar, to solve the CNOP and 4 DVar sub-problems, respectively. A comprehensive performance evaluation for the proposed CNOP–4 DVar method and its comparison with the CNOP and CNOP–ensemble transform Kalman filter(ETKF) methods based on 10 000 observing system simulation experiments on the shallow-water equation model are also provided. The experimental results show that the proposed CNOP–4 DVar method performs better than the CNOP–ETKF method and substantially better than the CNOP method.

关键词： CNOP 4DVar NLS-4DVar targeted observations sensitive area identification

Handling non-linearity in radar data assimilation using the non-linear least squares enhanced POD-4DVar

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Science China Earth Sciences 2017年第3期60卷 478-490页

作者： ZHANG Bin tian xiangjun ZHANG LiFeng SUN JianHua International Center for Climate and Environment Sciences(ICCES) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Meteorology and Oceanography PLA University of Science and TechnologyNanjing 211101China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science&TechnologyNanjing 210044China Ley Laboratory of Cloud-Precipitation Physics and Severe Storms(LACS) Institute of Atmospheric PhysicsChinese Academy of Sciences Beijing 100029China

The Proper Orthogonal Decomposition(POD)-based ensemble four-dimensional variational(4DVar) assimilation method(POD4DEnVar) was proposed to combine the strengths of EnKF(i.e.,the ensemble Kalman filter) and 4DVar assimilation ***,a POD4DEnVar-based radar data assimilation scheme(PRAS) was built and its effectiveness was ***4 DEnVar is based on the assumption of a linear relationship between the model perturbations(MPs)and the observation perturbations(OPs);however,this assumption is likely to be destroyed by the highly non-linear forecast model or observation *** address this issue,using the Gauss-Newton iterative method,the nonlinear least squares enhanced POD4 DEnVar algorithm(referred to as NLS-4DVar) was ***,the PRAS was upgraded to form the NLS-4DVar-based radar data assimilation scheme(NRAS).To evaluate the performance of NRAS against PRAS,observing system simulation experiments(OSSEs) were conducted to assimilate reflectivity and radial velocity individually,with one,two,and three *** results demonstrated that the NRAS outperformed PRAS in improving the initial condition and the forecasting of model variables and *** NRAS,with a smaller number of iterations,can yield a convergent *** contrast to the situation when assimilating radial velocity,the advantages of NRAS over PRAS were more obvious when assimilating reflectivity.

关键词： Data assimilation Non-linearity Gauss-Newton NRAS PRAS Radar reflectivity Radial velocity

The impacts of modeling global CO2 concentrations with GEOS-Chem using different ocean carbon fluxes

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Atmospheric and Oceanic Science Letters 2019年第5期12卷 343-348页

作者： ZHANG Shan tian xiangjun International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijingChina Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjingChina

The rise in atmospheric carbon dioxide(C02)concentrations caused by human activities is leading to global climate change,which poses a threat to human development and *** study analyzed the distribution of the ocean carbon flux with interannual changes and compared it with the climatological ocean carbon flux to deepen our understanding of carbon sources and *** simulate global CO2 concentrations for the years2008-2010,the ocean carbon flux with interannual changes and the climatological ocean carbon flux were used to drive the GEOS-Chem model,an atmospheric chemical transport *** simulated values were compared with the CO2 concentrations at nine observation stations to explore the influence of interannual changes in the ocean carbon fluxes on the simulated CO2 *** authors found that the difference between the two simulation results was greater in the Southern Hemisphere all year,and the difference in autumn was the *** with the observations,the simulated CO2 concentration of the ocean carbon flux with interannual changes is closer to the observations,indicating that this simulation is more accurate.

关键词： Carbon sources and sinks CO2 concentration GEOS-Chem model Ocean carbon fluxes

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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Advances in Atmospheric Sciences 2020年第11期37卷 1267-1284页

作者： Hongqin ZHANG xiangjun tian Wei CHENG Lipeng JIANG International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjing 210044China Beijing Institute of Applied Meteorology Beijing 100029China National Meteorological Information Center China Meteorological AdministrationBeijing 100081China

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 *** 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 *** multigrid NLS-4DVar assimilation framework is used for the analysis,which can adequately correct errors from large to small scales and accelerate iteration *** analysis variables are model state variables,rather than the control variables adopted in the conventional 4DVar ***,we have achieved the assimilation of conventional observations,and we will continue to improve the assimilation of radar and satellite observations in the *** was evaluated by case evaluation experiments and one-week cycling assimilation *** 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 *** showed that SNAP can absorb observations and improve the initial field,thereby improving the precipitation *** the one-week cycling assimilation experiments,six-hourly assimilation cycles were run in one *** 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

关键词： data assimilation numerical weather prediction NLS-4DVar multigrid GSI

Impacts of Multigrid NLS-4DVar-based Doppler Radar Observation Assimilation on Numerical Simulations of Landfalling Typhoon Haikui (2012)

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Advances in Atmospheric Sciences 2020年第8期37卷 873-892页

作者： Lu ZHANG xiangjun tian Hongqin ZHANG Feng CHEN International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjing 210044China Zhejiang Institute of Meteorological Sciences Hangzhou 310008China

We applied the multigrid nonlinear least-squares four-dimensional variational assimilation(MG-NLS4DVar)method in data assimilation and prediction experiments for Typhoon Haikui(2012)using the Weather Research and Forecasting(WRF)*** data included radial velocity(Vr)and reflectivity(Z)data from a single Doppler radar,quality controlled prior to *** prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar *** with a forecast that began with NCEP analysis data,our radar data assimilation results were clearly improved in terms of structure,intensity,track,and precipitation prediction for Typhoon Haikui(2012).The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method,but that the latter was more *** assimilation of Vr alone and Z alone each improved predictions of typhoon intensity,track,and precipitation;however,the impacts of Vr data were significantly greater that those of Z *** window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals.

关键词： MG-NLS4DVar NLS-4DVar radar data assimilation typhoon forecast

A soil moisture assimilation scheme based on the ensemble Kalman filter using microwave brightness temperature

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Science China Earth Sciences 2009年第11期52卷 1835-1848页

作者： BingHao Jia ZhengHui Xie xiangjun tian ChunXiang Shi ICCES/LASG Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Graduate University of Chinese Academy of Sciences Beijing 100049 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China

This study presents a soil moisture assimilation scheme, which could assimilate microwave brightness temperature directly, based on the ensemble Kalman filter and the shuffled complex evolution method (SCE-UA). It uses the soil water model of the land surface model CLM3.0 as the forecast operator, and a radiative transfer model (RTM) as the observation operator in the assimilation system. The assimilation scheme is implemented in two phases: the parameter calibration phase and the pure soil moisture assimilation phase. The vegetation optical thickness and surface roughness parameters in the RTM are calibrated by SCE-UA method and the optimal parameters are used as the final model parameters of the observation operator in the assimilation phase. The ideal experiments with synthetic data indicate that this scheme could significantly improve the simulation of soil moisture at the surface layer. Further- more, the estimation of soil moisture in the deeper layers could also be improved to a certain extent. The real assimilation experiments with AMSR-E brightness temperature at 10.65 GHz (vertical polariza- tion) show that the root mean square error (RMSE) of soil moisture in the top layer (0―10 cm) by as- similation is 0.03355 m3·m-3, which is reduced by 33.6% compared with that by simulation (0.05052 m3·m-3). The mean RMSE by assimilation for the deeper layers (10―50 cm) is also reduced by 20.9%. All these experiments demonstrate the reasonability of the assimilation scheme developed in this study.

关键词： land data assimilation soil moisture ensemble Kalman filter SCE-UA method radiative transfer model AMSR-E

Application of multigrid NLS-4DVar in radar radial velocity data assimilation with WRF-ARW

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Atmospheric and Oceanic Science Letters 2019年第6期12卷 409-416页

作者： ZHANG Lu tian xiangjun ZHANG Hongqin International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijingChina Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjingChina

The nonlinear least-squares four-dimensional variational assimilation(NLS-4DVar)method intro-duced here combines the merits of the ensemble Kalman lter and 4DVar assimilation *** multigrid NLS-4DVar method can be implemented without adjoint models and also corrects small-to large-scale errors with greater *** this paper,the multigrid NLS-4DVar method is used in radar radial velocity data *** system simulation experiments were conducted to determine the capability and efficiency of multigrid NLS-4DVar for assimilating radar radial velocity with WRF-ARW(the Advanced Research Weather Research and Forecasting model).The results show signi cant improvement in 24-h cumulative precipitation prediction due to improved initial conditions after assimilating the radar radial ***,the multigrid NLS-4DVar method reduces computational cost.

关键词： Heavy rainfall multigrid scheme NLS-4DVar method radar radial velocity data assimilation