scholarly journals Daily oceanographic analyses by Mediterranean Forecasting System at the basin scale

Ocean Science ◽  
2007 ◽  
Vol 3 (1) ◽  
pp. 149-157 ◽  
Author(s):  
S. Dobricic ◽  
N. Pinardi ◽  
M. Adani ◽  
M. Tonani ◽  
C. Fratianni ◽  
...  
Keyword(s):  
Positive Impact ◽  
Optimal Interpolation ◽  
Daily Cycle ◽  
Background Error ◽  
Geostrophic Balance ◽  
Error Covariance ◽  
Basin Scale ◽  
Forecasting System ◽  
First Time ◽  
Assimilation Scheme

Abstract. This study presents the upgrade of the Optimal Interpolation scheme used in the basin scale assimilation scheme of the Mediterranean Forecasting System. The modifications include a daily analysis cycle, the assimilation of ARGO float profiles, the implementation of the geostrophic balance in the background error covariance matrix and the initialisation of the analyses. A series of numerical experiments showed that each modification had a positive impact on the accuracy of the analyses: The daily cycle improved the representation of the processes with the temporal variability shorter than a week, the assimilation of ARGO floats profiles significantly improved the salinity analyses quality, the geostrophically balanced background error covariances improved the accuracy of the surface elevation analyses, and the initialisation removed the barotropic adjustment in the forecast first time steps starting from the analysis.

scholarly journals Daily oceanographic analyses by the Mediterranean basin scale assimilation system

2006 ◽  
Vol 3 (6) ◽  
pp. 1977-1998 ◽  
Author(s):  
S. Dobricic ◽  
N. Pinardi ◽  
M. Adani ◽  
M. Tonani ◽  
C. Fratianni ◽  
...  
Keyword(s):  
Positive Impact ◽  
Optimal Interpolation ◽  
Background Error ◽  
Geostrophic Balance ◽  
Error Covariance ◽  
Basin Scale ◽  
Forecasting System ◽  
The Mediterranean ◽  
First Time ◽  
Assimilation Scheme

Abstract. This study presents the upgrade of the Optimal Interpolation scheme used in the basin scale assimilation scheme of the Mediterranean Forecasting System. The modifications include a daily analysis cycle, the assimilation of ARGO float profiles, the implementation of the geostrophic balance in the background error covariance matrix and the initialisation of the analyses. A series of numerical experiments showed that each modification had a positive impact on the accuracy of the analyses: The daily cycle improved the representation of the processes with a relatively high temporal variability, the assimilation of ARGO floats profiles significantly improved the salinity analyses quality, the geostrophically balanced background error covariances improved the accuracy of the surface elevation analyses, and the initialisation removed the barotropic adjustment in the forecast first time steps starting from the analysis.

scholarly journals Background Error in WRF Model

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Data Assimilation ◽  
Physical Properties ◽  
Covariance Matrix ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Wrf Model ◽  
Background Error ◽  
Numerical Weather ◽  
Error Covariance ◽  
First Time

WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desired domain configurationare discussed and presented. The modeling was implemented in two different 3DVAR systems (WRFDA andGSI) and results were checked by pseudo observation benchmark cases using also default global and regional BEmatrixes. The mathematical and physical properties of the covariances are also reviewed.

Inhomogeneous Background Error Modeling and Estimation over Antarctica

2010 ◽  
Vol 138 (6) ◽  
pp. 2229-2252 ◽  
Author(s):  
Yann Michel ◽  
Thomas Auligné
Keyword(s):  
Wrf Model ◽  
Error Modeling ◽  
Limited Area ◽  
Error Statistics ◽  
Background Error ◽  
Recursive Filters ◽  
Error Covariance ◽  
Function Decomposition ◽  
The Antarctic ◽  
Assimilation Scheme

Abstract The structure of the analysis increments in a variational data assimilation scheme is strongly driven by the formulation of the background error covariance matrix, especially in data-sparse areas such as the Antarctic region. The gridpoint background error modeling in this study makes use of regression-based balance operators between variables, empirical orthogonal function decomposition to define the vertical correlations, gridpoint variances, and high-order efficient recursive filters to impose horizontal correlations. A particularity is that the regression operators and the recursive filters have been made spatially inhomogeneous. The computation of the background error statistics is performed with the Weather Research and Forecast (WRF) model from a set of forecast differences. The mesoscale limited-area domains of interest cover Antarctica. Inhomogeneities of background errors are shown to be related to the particular orography and physics of the area. Differences seem particularly pronounced between ocean and land boundary layers.

scholarly journals Extraction of wind and temperature information from hybrid 4D-Var assimilation of stratospheric ozone using NAVGEM

2018 ◽  
Vol 18 (4) ◽  
pp. 2999-3026 ◽  
Author(s):  
Douglas R. Allen ◽  
Karl W. Hoppel ◽  
David D. Kuhl
Keyword(s):  
Stratospheric Ozone ◽  
Vertical Axis ◽  
Background Error ◽  
Background Error Covariance ◽  
Geostrophic Balance ◽  
Error Covariance ◽  
Stratospheric Wind ◽  
The Tropics ◽  
The Impact ◽  
Static Background

Abstract. Extraction of wind and temperature information from stratospheric ozone assimilation is examined within the context of the Navy Global Environmental Model (NAVGEM) hybrid 4-D variational assimilation (4D-Var) data assimilation (DA) system. Ozone can improve the wind and temperature through two different DA mechanisms: (1) through the “flow-of-the-day” ensemble background error covariance that is blended together with the static background error covariance and (2) via the ozone continuity equation in the tangent linear model and adjoint used for minimizing the cost function. All experiments assimilate actual conventional data in order to maintain a similar realistic troposphere. In the stratosphere, the experiments assimilate simulated ozone and/or radiance observations in various combinations. The simulated observations are constructed for a case study based on a 16-day cycling truth experiment (TE), which is an analysis with no stratospheric observations. The impact of ozone on the analysis is evaluated by comparing the experiments to the TE for the last 6 days, allowing for a 10-day spin-up. Ozone assimilation benefits the wind and temperature when data are of sufficient quality and frequency. For example, assimilation of perfect (no applied error) global hourly ozone data constrains the stratospheric wind and temperature to within ∼ 2 m s−1 and ∼ 1 K. This demonstrates that there is dynamical information in the ozone distribution that can potentially be used to improve the stratosphere. This is particularly important for the tropics, where radiance observations have difficulty constraining wind due to breakdown of geostrophic balance. Global ozone assimilation provides the largest benefit when the hybrid blending coefficient is an intermediate value (0.5 was used in this study), rather than 0.0 (no ensemble background error covariance) or 1.0 (no static background error covariance), which is consistent with other hybrid DA studies. When perfect global ozone is assimilated in addition to radiance observations, wind and temperature error decreases of up to ∼ 3 m s−1 and ∼ 1 K occur in the tropical upper stratosphere. Assimilation of noisy global ozone (2 % errors applied) results in error reductions of ∼ 1 m s−1 and ∼ 0.5 K in the tropics and slightly increased temperature errors in the Northern Hemisphere polar region. Reduction of the ozone sampling frequency also reduces the benefit of ozone throughout the stratosphere, with noisy polar-orbiting data having only minor impacts on wind and temperature when assimilated with radiances. An examination of ensemble cross-correlations between ozone and other variables shows that a single ozone observation behaves like a potential vorticity (PV) “charge”, or a monopole of PV, with rotation about a vertical axis and vertically oriented temperature dipole. Further understanding of this relationship may help in designing observation systems that would optimize the impact of ozone on the dynamics.

Scale-Dependent Background Error Covariance Localization: Evaluation in a Global Deterministic Weather Forecasting System

2018 ◽  
Vol 146 (5) ◽  
pp. 1367-1381 ◽  
Author(s):  
Jean-François Caron ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Weather Forecasting ◽  
Spatial Scales ◽  
Winter Period ◽  
Wind Fields ◽  
Horizontal Scale ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Forecasting System

Abstract Scale-dependent localization (SDL) consists of applying the appropriate (i.e., different) amount of localization to different ranges of background error covariance spatial scales while simultaneously assimilating all of the available observations. The SDL method proposed by Buehner and Shlyaeva for ensemble–variational (EnVar) data assimilation was tested in a 3D-EnVar version of the Canadian operational global data assimilation system. It is shown that a horizontal-scale-dependent horizontal localization leads to implicit vertical-level-dependent, variable-dependent, and location-dependent horizontal localization. The results from data assimilation cycles show that horizontal-scale-dependent horizontal covariance localization is able to improve the forecasts up to day 5 in the Northern Hemisphere extratropical summer period and up to day 7 in the Southern Hemisphere extratropical winter period. In the tropics, use of SDL results in improvements similar to what can be obtained by increasing the uniform amount of spatial localization. An investigation of the dynamical balance in the resulting analysis increments demonstrates that SDL does not further harm the balance between the mass and the rotational wind fields, as compared to the traditional localization approach. Potential future applications for the SDL method are also discussed.

scholarly journals Harmonization and Diagnostics of MIPAS ESA CH<sub>4</sub> and N<sub>2</sub>O Profiles Using Data Assimilation

2016 ◽  
Author(s):  
Quentin Errera ◽  
Simone Ceccherini ◽  
Yves Christophe ◽  
Simon Chabrillat ◽  
Michaela I. Hegglin ◽  
...  
Keyword(s):  
Lower Stratosphere ◽  
Michelson Interferometer ◽  
Calibration Method ◽  
Background Error ◽  
Error Covariance ◽  
Independent Observations ◽  
Ch4 And N2o ◽  
Using Data ◽  
The Tropics ◽  
First Time

Abstract. This paper discusses assimilation experiments of methane (CH4) and nitrous oxide (N2O) profiles observed by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). Here we focus on data versions 6 and 7 retrieved by the ESA processor. These datasets have been assimilated by the Belgian Assimilation System for Chemical ObsErvations (BASCOE). The CH4 and N2O profiles can be noisy especially in the tropical lower stratosphere. Using the averaging kernels of the observations and a background error covariance matrix – the B matrix, which has been previously calibrated, allows the system to partly remedy this issue and provide assimilated fields that are more regular vertically. In general, there is a good agreement between the BASCOE analyses and independent observations demonstrating the general good quality of these two retrievals provided by MIPAS ESA. Nevertheless, this study also identifies two issues in these datasets. First, time-series of the observations show unexpected discontinuities, due to the calibration method used for the level-1 data. Second, the correlations between BASCOE analyses and independent observations are poor in the lower stratosphere, especially in the tropics, probably due to the presence of outliers in the assimilated data. In this region, we recommend using MIPAS CH4 and N2O observations with caution.

A new method for computing horizontally anisotropic background error covariance matrices for data assimilation in ocean models.

2020 ◽  
Author(s):  
Jose M Gonzalez-Ondina ◽  
Lewis Sampson ◽  
Georgy Shapiro
Keyword(s):  
Data Assimilation ◽  
Observational Data ◽  
Matrix Decomposition ◽  
New Method ◽  
Estimation Methods ◽  
Optimal Interpolation ◽  
Background Error ◽  
Diffusion Operator ◽  
Background Error Covariance ◽  
Error Covariance

&lt;p&gt;Current operational ocean modelling systems often use variational data assimilation (DA) to improve the skill of the ocean predictions by combining the numerical model with observational data. Many modern methods are derivatives of objective (optimal) interpolation techniques developed by L. S. Gandin in the 1950s, which requires computation of the background error covariance matrix (BECM), and much research has been devoted into overcoming the difficulties surrounding its calculation and improving its accuracy. In practice, due to time and memory constraints, the BECM is never fully computed. Instead, a simplified model is used, where the correlation at each point is modelled using a simple function while the variance and length scales are computed using error estimation methods such as the Hollingsworth-Lonnberg&amp;#160; or the NMC (National Meteorological Centre). Usually, the correlation is assumed to be horizontally isotropic, or to have a predefined anisotropy based on latitude. However, observations indicate that horizontal diffusion is sometimes anisotropic, hence this has to be propagated into BECM. It is suggested that including these anisotropies would improve the accuracy of the model predictions.&lt;/p&gt;&lt;p&gt;We present a new method to compute the BECM which allows to extract horizontal anisotropic components from observational data. Our method, unlike current techniques, is fundamentally multidimensional and can be applied to 2D or 3D sets of un-binned data. It also works better than other methods when observations are sparse, so there is no penalty when trying to extract the additional anisotropic components from the data.&lt;/p&gt;&lt;p&gt;Data Assimilation tools like NEMOVar use a matrix decomposition technique for the BECM in order to minimise the cost function. Our method is well suited to work with this type of decomposition, producing the different components of the decomposition which can be readily used by NEMOVar.&lt;/p&gt;&lt;p&gt;We have been able to show the spatial stability of our method to quantify anisotropy in areas of sparse observations. While also demonstrating the importance of including anisotropic representation within the background error. Using the coastal regions of the Arabian Sea, it is possible to analyse where improvements to diffusion can be included. Further extensions of this method could lead to a fully anisotropic diffusion operator for the calculation of BECM in NEMOVar. However further testing and optimization are needed to correctly implement this into operational assimilation systems.&lt;/p&gt;

scholarly journals Background error covariance with balance constraints for aerosol species and applications in data assimilation

2015 ◽  
Vol 8 (11) ◽  
pp. 10053-10088
Author(s):  
Z. Zang ◽  
Z. Hao ◽  
Y. Li ◽  
X. Pan ◽  
W. You ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Elemental Carbon ◽  
Statistical Regression ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Balance Analysis ◽  
Forecasting System ◽  
Cross Correlations ◽  
The Impact

Abstract. Balance constraints are important for a background error covariance (BEC) in data assimilation to spread information between different variables and produce balance analysis fields. Using statistical regression, we develop the balance constraint for the BEC of aerosol variables and apply it to a data assimilation and forecasting system for the WRF/Chem model. One-month products from the WRF/Chem model are employed for BEC statistics with the NMC method. The cross-correlations among the original variables are generally high. The highest correlation between elemental carbon and organic carbon without balance constraints is approximately 0.9. However, the correlations for the unbalanced variables are less than 0.2 with the balance constraints. Data assimilation and forecasting experiments for evaluating the impact of balance constraints are performed with the observations of the surface PM2.5 concentrations and speciated concentrations along an aircraft flight track. The speciated increments of the experiment with balance constraints are more coincident than the speciated increments of the experiment without balance constraints, for the observation information can spread across variables by balance constraints in the former experiment. The forecast results of the experiment with balance constraints show significant and durable improvements from the 3rd hour to the 18th hour compared with the forecast results of the experiment without the balance constraints. However, the forecasts of these two experiments are similar during the first 3 h. The results suggest that the balance constraint is significantly positive for the aerosol assimilation and forecasting.

On the modification of operational oceanography forecasting system for South China Sea in National Marine Environmental Forecasting Center of China

2020 ◽  
Author(s):  
Ziqing Zu ◽  
Xueming Zhu ◽  
Hui Wang
Keyword(s):  
South China Sea ◽  
South China ◽  
Optimal Interpolation ◽  
Physical Parameters ◽  
Background Error ◽  
Pass Filter ◽  
China Sea ◽  
Operational Oceanography ◽  
Forecasting System ◽  
Marine Environmental

&lt;p&gt;Based on ROMS and Ensemble Optimal Interpolation (EnOI) method, the South China Sea operational Oceanography Forecasting System (SCSOFS) is implemented in National Marine Environmental Forecasting Center (NMEFC), to provide the forecast of the currents, temperature and salinity in South China Sea for the future 5 days. Recently, a systematic modification has been carried out to SCSOFS to improve its forecast skill.&lt;/p&gt;&lt;p&gt;For the data assimilation system, new methods have been implemented, such as using Increment Analysis Update (IAU) and First Guess at Appropriate Time (FGAT), using a high-pass filter to evaluate the background error, assimilating multi-source observations, using non-uniform localization radius. In addition, the respective contribution of each method will also be discussed.&lt;/p&gt;&lt;p&gt;An&amp;#160;optimization system is implemented for evaluating the values of physical parameters in ROMS, to remove the long-term bias of simulation. Argo temperature profiles is assimilated in the first half of 2017, to obtain the optimal coefficients of horizontal/vertical viscosity/diffusion&amp;#160;and linear bottom drag. An independent validation from July of 2017 to December of 2018 shows that the simulation is improved using the optimal values.&lt;/p&gt;

scholarly journals A Kalman filter application to a spectral wave model

2005 ◽  
Vol 12 (6) ◽  
pp. 775-782 ◽  
Author(s):  
J. P. Pinto ◽  
M. C. Bernadino ◽  
A. Pires Silva
Keyword(s):  
Kalman Filter ◽  
Wave Model ◽  
Theory Model ◽  
Optimal Interpolation ◽  
Advection Equation ◽  
Small Surface ◽  
Dynamic Constraints ◽  
Error Covariance ◽  
Spectral Wave Model ◽  
Assimilation Scheme

Abstract. A sequential time dependent data assimilation scheme based on the Kalman filter is applied to a spectral wave model. Usually, the first guess covariance matrices used in optimal interpolation schemes are exponential spreading functions, which remain constant. In the present work the first guess correlation errors evolve in time according to the dynamic constraints of the wave model. A system error noise is deduced and used to balance numerical errors. The assimilation procedure is tested in a standard situation of swell propagation, where the Kalman filter is used to assimilate the significant wave height. The evolution of the wave field is described by a linear two-dimensional advection equation and the propagation of the error covariance matrix is derived according to Kalman's linear theory. Model simulations were performed in a 2-dimensional domain with deep-water conditions, a relatively small surface area and without wind forcing or dissipation. A true state simulation and a first guess simulation were used to illustrate the assimilation outcome, showing a reasonable performance of the Kalman filter.

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