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Application of ATOVS Microwave Radiance Assimilation to Rainfall Prediction in Summer 2004
QI Linlin,SUN Jianhua,
QI Linlin
,SUN Jianhua

大气科学进展 , 2006,
Abstract: Experiments are performed in this paper to understand the influence of satellite radiance data on the initial field of a numerical prediction system and rainfall prediction. First, Advanced Microwave Sounder Unit A (AMSU-A) and Unit B (AMSU-B) radiance data are directly used by three-dimensional variational data assimilation to improve the background field of the numerical model. Then, the detailed effect of the radiance data on the background field is analyzed. Secondly, the background field, which is formed by application of Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) microwave radiance assimilation, is employed to simulate some heavy rainfall cases.The experiment results show that the assimilation of AMSU-A (B) microwave radiance data has a certain impact on the geopotential height, temperature, relative humidity and flow fields. And the impacts on the background field are mostly similar in the different months in summer. The heavy rainfall experiments reveal that the application of AMSU-A (B) microwave radiance data can improve the rainfall prediction significantly. In particular, the AMSU-A radiance data can significantly enhance the prediction of rainfall above 10 mm within 48 h, and the AMSU-B radiance data can improve the prediction of rainfall above 50 mm within 24 h. The present study confirms that the direct assimilation of satellite radiance data is an effective way to improve the prediction of heavy rainfall in the summer in China.
Scientific design and preliminary results of three-dimensional variational data assimilation system of GRAPES
JiShan Xue,ShiYu Zhuang,GuoFu Zhu,Hua Zhang,ZhiQuan Liu,Yan Liu,ZhaoRong Zhuang
Chinese Science Bulletin , 2008, DOI: 10.1007/s11434-008-0416-0
Abstract: The scientific design and preliminary results of the data assimilation component of the Global-Regional Prediction and Assimilation System (GRAPES) recently developed in China Meteorological Administration (CMA) are presented in this paper. This is a three-dimensional variational (3DVar) assimilation system set up on global and regional grid meshes favorable for direct assimilation of the space-based remote sensing data and matching the frame work of the prediction model GRAPES. The state variables are assumed to decompose balanced and unbalanced components. By introducing a simple transformation from the state variables to the control variables with a recursive or spectral filter, the convergence rate of iteration for minimization of the cost function in 3DVar is greatly accelerated. The definition of dynamical balance depends on the characteristic scale of the circulation considered. The ratio of the balanced to the unbalanced parts is controlled by the prescribed statistics of background errors. Idealized trials produce the same results as the analytic solution. The results of real data case studies show the capability of the system to improve analysis compared to the traditional schemes. Finally, further development of the system is discussed.
Variational Assimilation of Automatic Weather Stations Rainfall in Convective Systems and its Impact on Rain Forecast
对流天气系统自动站雨量资料同化对降雨预报的影响

DING Wei-Yu,WAN Qi-Lin,YAN Jing-Hu,MENG Wei-Guang,CHEN Zi-Tong,
丁伟钰
,万齐林,闫敬华,蒙伟光,陈子通

大气科学 , 2006,
Abstract: The lack of data over the tropical regions contributes greatly to the uncertainties in the initial state of numeric weather prediction models,which in turn limits their forecast skill.Because in tropical regions the atmospheric motions are driven largely by diabatic processes,precipitation observations could be a valuable data source for improving initial fields.In Guangdong Province,there are almost 600 automatic weather stations(AWS),providing hourly rainfall records.Focusing on the convective system,this study use the extension of the KUO cumulus parameter scheme as the observation operator to assimilate the AWS rainfall records in Guangdong Province on the GRAPES(Global and Regional Assimilation and Prediction Enhanced System) 3D variational assimilation system,and compares with the assimilation of sounding data.In the case of 1 April 2004,hourly AWS rainfall records are compared with the GOES (Geostationary Operational Environmental Satellite) infrared channel temperature,TRMM(Tropical Rainfall Measuring Mission) rain rate data and flash location observations.Results show that the hourly AWS rain records can properly describe the convective system rain bands.Using WRF(Weather Research and Forecasting Model)as the forecast model,the control test shows that the initial rain bands are located in the northern part of the observation.Three assimilation experiments are designed to assimilate the AWS rain records sounding data and all the data respectively.Results show that in the regions where the initial rain bands are adjusted,assimilating the AWS rainfall and sounding data respectively both can adjust the low level atmosphere moisture convergence(or divergence),low and middle troposphere temperature and moisture increasing(or decreasing) to enhance(or weaken) the initial rainfall.The results mean that the adjustment by AWS rain assimilation scheme is consistent with the sounding data assimilation to some extent.This paper also discusses the influence of AWS rainfall assimilation on the short-range rain forecast.Results show that AWS rainfall assimilation scheme has the positive impact on the convective system short-range rain forecast.Assimilating AWS rainfall and sounding data at the same time can eliminate the deficiency of both the data,improve the rain location and structure forecast.
Variational assimilation of Lagrangian trajectories in the Mediterranean ocean Forecasting System  [PDF]
J. A. U. Nilsson,S. Dobricic,N. Pinardi,P.-M. Poulain
Ocean Science Discussions (OSD) , 2011, DOI: 10.5194/osd-8-2503-2011
Abstract: A novel method for three-dimensional variational assimilation of Lagrangian data with a primitive-equation ocean model is proposed. The assimilation scheme was implemented in the Mediterranean ocean Forecasting System and evaluated for a 4-month period. Four experiments were designed to assess the impact of trajectory assimilation on the model output, i.e. the sea-surface height, velocity, temperature and salinity fields. It was found from the drifter and Argo trajectory assimilation experiment that the forecast skill of surface-drifter trajectories improved by 15 %, that of intermediate-depth float trajectories by 20 %, and moreover, the forecasted sea-surface height fields improved locally by 5 % compared to satellite data, while the quality of the temperature and salinity fields remained at previous levels. In conclusion, the addition of Lagrangian trajectory assimilation proved to reduce the uncertainties in the model fields, thus yielding a higher accuracy of the ocean forecasts.
Variational assimilation of Lagrangian trajectories in the Mediterranean ocean Forecasting System  [PDF]
J. A. U. Nilsson,S. Dobricic,N. Pinardi,P.-M. Poulain
Ocean Science (OS) & Discussions (OSD) , 2012, DOI: 10.5194/os-8-249-2012
Abstract: A novel method for three-dimensional variational assimilation of Lagrangian data with a primitive-equation ocean model is proposed. The assimilation scheme was implemented in the Mediterranean ocean Forecasting System and evaluated for a 4-month period. Four experiments were designed to assess the impact of trajectory assimilation on the model output, i.e. the sea-surface height, velocity, temperature and salinity fields. It was found from the drifter and Argo trajectory assimilation experiment that the forecast skill of surface-drifter trajectories improved by 15 %, that of intermediate-depth float trajectories by 20 %, and moreover, that the forecasted sea-surface height fields improved locally by 5 % compared to satellite data, while the quality of the temperature and salinity fields remained at previous levels. In conclusion, the addition of Lagrangian trajectory assimilation proved to reduce the uncertainties in the model fields, thus yielding a higher accuracy of the ocean forecasts.
Radar Data Assimilation of the GRAPES Model and Experimental Results in a Typhoon Case

LIU Hongy,XUE Jishan,GU Jianfeng,XU Haiming,

大气科学进展 , 2012,
Abstract: Constructing β-mesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction (NWP) model. Without vertical velocity matching the β-mesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrometeors. In this study, a method, basing on the three-dimensional variational (3DVAR) assimilation technique, was developed to obtain reasonable structures of β-mesoscale weather systems by assimilating radar data in a next-generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System) of China. Single-point testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity (taking Richardson’s equation as the observational operator) can greatly improve the vertical motion. Experiments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further in-depth study.
On Variational Data Assimilation in Continuous Time  [PDF]
Jochen Br?cker
Physics , 2010, DOI: 10.1002/qj.695
Abstract: Variational data assimilation in continuous time is revisited. The central techniques applied in this paper are in part adopted from the theory of optimal nonlinear control. Alternatively, the investigated approach can be considered as a continuous time generalisation of what is known as weakly constrained four dimensional variational assimilation (WC--4DVAR) in the geosciences. The technique allows to assimilate trajectories in the case of partial observations and in the presence of model error. Several mathematical aspects of the approach are studied. Computationally, it amounts to solving a two point boundary value problem. For imperfect models, the trade off between small dynamical error (i.e. the trajectory obeys the model dynamics) and small observational error (i.e. the trajectory closely follows the observations) is investigated. For (nearly) perfect models, this trade off turns out to be (nearly) trivial in some sense, yet allowing for some dynamical error is shown to have positive effects even in this situation. The presented formalism is dynamical in character; no assumptions need to be made about the presence (or absence) of dynamical or observational noise, let alone about their statistics.
Implicit particle methods and their connection with variational data assimilation  [PDF]
Ethan Atkins,Matthias Morzfeld,Alexandre J. Chorin
Physics , 2012,
Abstract: The implicit particle filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability regions via a sequence of steps that includes minimizations. We present a new and more general derivation of this approach and extend the method to particle smoothing as well as to data assimilation for perfect models. We show that the minimizations required by implicit particle methods are similar to the ones one encounters in variational data assimilation and explore the connection of implicit particle methods with variational data assimilation. In particular, we argue that existing variational codes can be converted into implicit particle methods at a low cost, often yielding better estimates, that are also equipped with quantitative measures of the uncertainty. A detailed example is presented.
Variational Data Assimilation via Sparse Regularization  [PDF]
A. M. Ebtehaj,M. Zupanski,G. Lerman,E. Foufoula-Georgiou
Physics , 2013, DOI: 10.3402/tellusa.v66.21789
Abstract: This paper studies the role of sparse regularization in a properly chosen basis for variational data assimilation (VDA) problems. Specifically, it focuses on data assimilation of noisy and down-sampled observations while the state variable of interest exhibits sparsity in the real or transformed domain. We show that in the presence of sparsity, the $\ell_{1}$-norm regularization produces more accurate and stable solutions than the classic data assimilation methods. To motivate further developments of the proposed methodology, assimilation experiments are conducted in the wavelet and spectral domain using the linear advection-diffusion equation.
Variational assimilation of Lagrangian data in oceanography  [PDF]
Ma?lle Nodet
Mathematics , 2008, DOI: 10.1088/0266-5611/22/1/014
Abstract: We consider the assimilation of Lagrangian data into a primitive equations circulation model of the ocean at basin scale. The Lagrangian data are positions of floats drifting at fixed depth. We aim at reconstructing the four-dimensional space-time circulation of the ocean. This problem is solved using the four-dimensional variational technique and the adjoint method. In this problem the control vector is chosen as being the initial state of the dynamical system. The observed variables, namely the positions of the floats, are expressed as a function of the control vector via a nonlinear observation operator. This method has been implemented and has the ability to reconstruct the main patterns of the oceanic circulation. Moreover it is very robust with respect to increase of time-sampling period of observations. We have run many twin experiments in order to analyze the sensitivity of our method to the number of floats, the time-sampling period and the vertical drift level. We compare also the performances of the Lagrangian method to that of the classical Eulerian one. Finally we study the impact of errors on observations.
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