A three-dimensional variational data assimilation system for a climate model: basic scheme and tests

2007 ◽  
Author(s):  
Yuanhong Guan ◽  
Weisong Lu ◽  
Guangqing Zhou II ◽  
Meiying Yuan
Keyword(s):  
Data Assimilation ◽  
Climate Model ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Basic Scheme ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals A THREE-DIMENSIONAL AEROSOL VARIATIONAL DATA ASSIMILATION SYSTEM FOR AIRCRAFT AND SURFACE OBSERVATIONS

2018 ◽  
Vol XLII-3 ◽  
pp. 2215-2218
Author(s):  
Z. Zang ◽  
X. Pan ◽  
W. You ◽  
Y. Liang
Keyword(s):  
Data Assimilation ◽  
Local Time ◽  
Three Dimensional ◽  
Surface Concentration ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Aircraft Observations ◽  
Data Assimilation System ◽  
Surface Observations ◽  
Assimilation System

A three-dimensional variational data assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the three-dimensional profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, assimilating surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.

Scientific design and preliminary results of three-dimensional variational data assimilation system of GRAPES

2008 ◽  
Vol 53 (22) ◽  
pp. 3446-3457 ◽  
Author(s):  
JiShan Xue ◽  
ShiYu Zhuang ◽  
GuoFu Zhu ◽  
Hua Zhang ◽  
ZhiQuan Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Preliminary Results ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals CIRA/CSU Four-Dimensional Variational Data Assimilation System

2005 ◽  
Vol 133 (4) ◽  
pp. 829-843 ◽  
Author(s):  
Milija Zupanski ◽  
Dusanka Zupanski ◽  
Tomislava Vukicevic ◽  
Kenneth Eis ◽  
Thomas Vonder Haar
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Control Variable ◽  
Three Dimensional ◽  
Atmospheric Modeling ◽  
Variational Data Assimilation ◽  
Regional Atmospheric Modeling ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Prediction (NCEP) Eta 4DVAR system, however with added improvements addressing its use in a research environment. Preliminary results with RAMDAS are presented, focusing on the minimization performance and the impact of vertical correlations in error covariance modeling. A three-dimensional formulation of the background error correlation is introduced and evaluated. The Hessian preconditioning is revisited, and an alternate algebraic formulation is presented. The results indicate a robust minimization performance.

scholarly journals Spectral Transformation Using a Cubed-Sphere Grid for a Three-Dimensional Variational Data Assimilation System

2015 ◽  
Vol 143 (7) ◽  
pp. 2581-2599 ◽  
Author(s):  
Hyo-Jong Song ◽  
In-Hyuk Kwon
Keyword(s):  
Data Assimilation ◽  
Numerical Models ◽  
Three Dimensional ◽  
Spectral Element ◽  
Variational Data Assimilation ◽  
Specific Humidity ◽  
Spectral Transformation ◽  
Cubed Sphere ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Atmospheric numerical models using the spectral element method with cubed-sphere grids (CSGs) are highly scalable in terms of parallelization. However, there are no data assimilation systems for spectral element numerical models. The authors devised a spectral transformation method applicable to the model data on a CSG (STCS) for a three-dimensional variational data assimilation system (3DVAR). To evaluate the 3DVAR system based on the STCS, the authors conducted observing system simulation experiments (OSSEs) using Community Atmosphere Model with Spectral Element dynamical core (CAM-SE). They observed root-mean-squared error reductions: 24% and 34% for zonal and meridional winds (U and V), respectively; 20% for temperature (T); 4% for specific humidity (Q); and 57% for surface pressure (Ps) in analysis and 28% and 27% for U and V, respectively; 25% for T; 21% for Q; and 31% for Ps in 72-h forecast fields. In this paper, under the premise that the same number of grid points is set, the authors show that the use of a greater polynomial degree, np, produces better performance than use of a greater element count, ne, on equiangular coordinates in terms of the wave representation.

scholarly journals Improving Variational Data Assimilation through Background and Observation Error Adjustments

2018 ◽  
Vol 146 (2) ◽  
pp. 485-501 ◽  
Author(s):  
Jann Paul Mattern ◽  
Christopher A. Edwards ◽  
Andrew M. Moore
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Ocean Model ◽  
Variational Data Assimilation ◽  
Observation Error ◽  
Observation Window ◽  
Error Covariance ◽  
Short Term Forecasting ◽  
Data Assimilation System ◽  
Assimilation System

Abstract A procedure to objectively adjust the error covariance matrices of a variational data assimilation system is presented. It is based on popular diagnostics that utilize differences between observations and prior and posterior model solutions at the observation locations. In the application to a data assimilation system that combines a three-dimensional, physical–biogeochemical ocean model with large datasets of physical and chlorophyll a observations, the tuning procedure leads to a decrease in the posterior model-observation misfit and small improvements in short-term forecasting skill. It also increases the consistency of the data assimilation system with respect to diagnostics, based on linear estimation theory, and reduces signs of overfitting. The tuning procedure is easy to implement and only relies on information that is either prescribed to the data assimilation system or can be obtained from a series of short data assimilation experiments. The implementation includes a lognormal representation for biogeochemical variables and associated modifications to the diagnostics. Furthermore, the effect of the length of the observation window (number and distribution of observations) used to compute the diagnostics and the effect of neglecting model dynamics in the tuning procedure are examined.

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