scholarly journals Development of the Nonstationary Incremental Analysis Update Algorithm for Sequential Data Assimilation System

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yoo-Geun Ham ◽  
Hyo-Jong Song ◽  
Jaehee Jung ◽  
Gyu-Ho Lim
Keyword(s):  
Data Assimilation ◽  
Time Window ◽  
Sequential Data ◽  
Incremental Analysis ◽  
Lorenz Model ◽  
Time Step ◽  
Data Assimilation System ◽  
Forward Operator ◽  
Assimilation System ◽  
Sequential Data Assimilation

This study introduces a modified version of the incremental analysis updates (IAU), called the nonstationary IAU (NIAU) method, to improve the assimilation accuracy of the IAU while keeping the continuity of the analysis. Similar to the IAU, the NIAU is designed to add analysis increments at every model time step to improve the continuity in the intermittent data assimilation. However, unlike the IAU, the NIAU procedure uses time-evolved forcing using the forward operator as corrections to the model. The solution of the NIAU is superior to that of the forward IAU, of which analysis is performed at the beginning of the time window for adding the IAU forcing, in terms of the accuracy of the analysis field. It is because, in the linear systems, the NIAU solution equals that in an intermittent data assimilation method at the end of the assimilation interval. To have the filtering property in the NIAU, a forward operator to propagate the increment is reconstructed with only dominant singular vectors. An illustration of those advantages of the NIAU is given using the simple 40-variable Lorenz model.

scholarly journals An Approach for Filter Divergence Suppression in a Sequential Data Assimilation System and Its Application in Short-Term Traffic Flow Forecasting

2020 ◽  
Vol 9 (6) ◽  
pp. 340
Author(s):  
Xiaohua Tong ◽  
Runjie Wang ◽  
Wenzhong Shi ◽  
Zhiyuan Li
Keyword(s):  
Data Assimilation ◽  
Traffic Flow ◽  
Sequential Data ◽  
L1 Norm ◽  
Flow Measurements ◽  
Norm Constraint ◽  
Filter Divergence ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Sequential Data Assimilation

Mathematically describing the physical process of a sequential data assimilation system perfectly is difficult and inevitably results in errors in the assimilation model. Filter divergence is a common phenomenon because of model inaccuracies and affects the quality of the assimilation results in sequential data assimilation systems. In this study, an approach based on an L1-norm constraint for filter-divergence suppression in sequential data assimilation systems was proposed. The method adjusts the weights of the state-simulated values and measurements based on new measurements using an L1-norm constraint when filter divergence is about to occur. Results for simulation data and real-world traffic flow measurements collected from a sub-area of the highway between Leeds and Sheffield, England, showed that the proposed method produced a higher assimilation accuracy than the other filter-divergence suppression methods. This indicates the effectiveness of the proposed approach based on the L1-norm constraint for filter-divergence suppression.

scholarly journals Data assimilation experiments using the diffusive back and forth nudging for the NEMO ocean model

2014 ◽  
Vol 1 (2) ◽  
pp. 1073-1131
Author(s):  
G. A. Ruggiero ◽  
Y. Ourmières ◽  
E. Cosme ◽  
J. Blum ◽  
D. Auroux ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Time Window ◽  
Ocean Model ◽  
Time Step ◽  
Primitive Equation ◽  
Cpu Time ◽  
Set Up ◽  
Backward Integration ◽  
Reversed Time ◽  
Data Assimilation System

Abstract. The Diffusive Back and Forth Nudging (DBFN) is an easy-to-implement iterative data assimilation method based on the well-known Nudging method. It consists in a sequence of forward and backward model integrations, within a given time window, both of them using a feedback term to the observations. Therefore in the DBFN, the Nudging asymptotic behavior is translated into an infinite number of iterations within a bounded time domain. In this method, the backward integration is carried out thanks to what is called backward model, which is basically the forward model with reversed time step sign. To maintain numeral stability the diffusion terms also have their sign reversed, giving a diffusive character to the algorithm. In this article the DBFN performance to control a primitive equation ocean model is investigated. In this kind of model non-resolved scales are modeled by diffusion operators which dissipate energy that cascade from large to small scales. Thus, in this article the DBFN approximations and their consequences on the data assimilation system set-up are analyzed. Our main result is that the DBFN may provide results which are comparable to those produced by a 4Dvar implementation with a much simpler implementation and a shorter CPU time for convergence.

Assimilation of Global Positioning System Radio Occultation Observations into NCEP’s Global Data Assimilation System

2007 ◽  
Vol 135 (9) ◽  
pp. 3174-3193 ◽  
Author(s):  
L. Cucurull ◽  
J. C. Derber ◽  
R. Treadon ◽  
R. J. Purser
Keyword(s):  
Quality Control ◽  
Data Assimilation ◽  
Real Time ◽  
Radio Occultation ◽  
Environmental Modeling ◽  
Bending Angle ◽  
Preliminary Results ◽  
Data Assimilation System ◽  
Forward Operator ◽  
Assimilation System

Abstract The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched six small satellites in April 2006, each carrying a GPS radio occultation (RO) receiver. At final orbit, COSMIC will provide ∼2500–3000 RO soundings per day uniformly distributed around the globe in near–real time. In preparation for the assimilation of COSMIC data in an operational framework, the NCEP/Environmental Modeling Center (EMC) has successfully developed the capability of assimilating profiles of refractivity and bending angle. Each forward operator has been implemented with its own quality control and error characterization. In this paper, the infrastructure developed at NCEP/EMC to assimilate GPS RO observations, including forward models, observational and representativeness errors, and quality control procedures, is described. The advantages of using a forward operator for bending angle versus refractivity are discussed and some preliminary results on the benefits of the GPS RO in weather analysis and forecasts are presented. The different strategies adopted at NCEP/EMC to assimilate GPS RO data are aimed to select the most appropriate forward operator in the operational data assimilation system when COSMIC products are stable and routinely available to the Numerical Weather Centers. In the meantime, data from the Challenging Minisatellite Payload (CHAMP) satellite is available in non–real time and has been used in the assimilation tests to examine the potential benefits of the GPS RO–derived products. In the preliminary results presented in this study, the use of GPS RO observations slightly improves anomaly correlation scores for temperature (by ∼0.01–0.03) in the Southern Hemisphere and Tropics throughout the depth of the atmosphere while a slight degradation is found in the upper troposphere and stratosphere in the Northern Hemisphere. However, significant reduction of the temperature and humidity biases is found for all latitudes. The benefits from assimilating GPS RO data also extend to other fields, such as 500-hPa geopotential heights and tropical winds, demonstrating the potential use of GPS RO data in operational forecasting.

scholarly journals Towards operational assimilation of surface based remote sensing temperature and humidity profiler data at MeteoSwiss

2021 ◽  
Author(s):  
Bas Crezee ◽  
Claire Merker ◽  
Jasmin Vural ◽  
Daniel Leuenberger ◽  
Alexander Haefele ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Weather Prediction ◽  
Good Representation ◽  
Raman Lidar ◽  
Temperature And Humidity ◽  
Microwave Radiometers ◽  
Data Assimilation System ◽  
Forward Operator ◽  
Assimilation System

<p>The current atmospheric observing systems fail to provide observations of temperature and humidity in the planetary boundary layer (PBL) with satisfactory spatial and temporal resolutions despite their potential positive impact on numerical weather prediction (NWP). This is particularly critical for humidity, which exhibits a very high variability in space and time, and for the vertical profile of temperature, which determines the atmospheric stability. Therefore, the analyzed thermodynamic structure of the PBL can be prone to errors, leading to poor forecasts of warnings for relevant phenomena, such as severe storms due to intense summer convection or winter fog and low stratus.</p><p>One approach to improve the model’s representation of the PBL is to include novel, ground-based remote sensing profiler observations in the data assimilation system to improve the forecast initial conditions. This also improves the quality of downstream applications relying on a good representation of the PBL in the model, such as dispersion modelling for emergency response after nuclear, chemical or biological incidents.</p><p>In this contribution, we present results of the MeteoSwiss effort to include observations from Raman lidar and microwave radiometers into the 1km mesh-size ensemble data assimilation system KENDA-1. To this end, we have developed a forward operator for water vapor mixing ratio and temperature to assimilate profiles from the Raman lidar. Brightness temperatures from the microwave radiometers are assimilated using the RTTOV-gb forward operator. We produced extensive O-B statistics to validate the observations with respect to the model and to derive the error covariance matrices of the observations. Furthermore, we will present results of several data assimilation cycling experiments during summer-time convective situations.</p>

scholarly journals Incremental Analysis Update Implementation into a Sequential Ocean Data Assimilation System

2006 ◽  
Vol 23 (12) ◽  
pp. 1729-1744 ◽  
Author(s):  
Y. Ourmières ◽  
J-M. Brankart ◽  
L. Berline ◽  
P. Brasseur ◽  
J. Verron
Keyword(s):  
Data Assimilation ◽  
High Frequency ◽  
General Circulation ◽  
Circulation Model ◽  
Low Rank ◽  
Incremental Analysis ◽  
High Frequency Oscillations ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract This study deals with the enhancement of a sequential assimilation method applied to an ocean general circulation model (OGCM). A major drawback of sequential assimilation methods is the time discontinuity of the solution resulting from intermittent corrections of the model state. The data analysis step can induce shocks in the model restart phase, causing spurious high-frequency oscillations and data rejection. A method called Incremental Analysis Update (IAU) is now recognized to efficiently tackle these problems. In the present work, an IAU-type method is implemented into an intermittent data assimilation system using a low-rank Kalman filter [Singular Evolutive Extended Kalman (SEEK)] in the case of an OGCM with a 1/3° North Atlantic grid. A 1-yr (1993) experiment has been conducted for different setups in order to evaluate the impact of the IAU scheme. Results from all of the different tests are compared with a specific interest in high-frequency output behaviors and solution consistency. The improvements brought up by the IAU implementation, such as the disappearance of spurious high-frequency oscillations and the time continuity of the solution, are shown. An overall assessment of the impact of this new approach on the assimilated runs is discussed. Advantages and drawbacks of the IAU method are pointed out.

scholarly journals Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model

2015 ◽  
Vol 22 (2) ◽  
pp. 233-248 ◽  
Author(s):  
G. A. Ruggiero ◽  
Y. Ourmières ◽  
E. Cosme ◽  
J. Blum ◽  
D. Auroux ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Time Window ◽  
Ocean Model ◽  
Time Step ◽  
Sensitivity Tests ◽  
Set Up ◽  
The Impact ◽  
Backward Integration ◽  
Reversed Time ◽  
Data Assimilation System

Abstract. The diffusive back-and-forth nudging (DBFN) is an easy-to-implement iterative data assimilation method based on the well-known nudging method. It consists of a sequence of forward and backward model integrations, within a given time window, both of them using a feedback term to the observations. Therefore, in the DBFN, the nudging asymptotic behaviour is translated into an infinite number of iterations within a bounded time domain. In this method, the backward integration is carried out thanks to what is called backward model, which is basically the forward model with reversed time step sign. To maintain numeral stability, the diffusion terms also have their sign reversed, giving a diffusive character to the algorithm. In this article the DBFN performance to control a primitive equation ocean model is investigated. In this kind of model non-resolved scales are modelled by diffusion operators which dissipate energy that cascade from large to small scales. Thus, in this article, the DBFN approximations and their consequences for the data assimilation system set-up are analysed. Our main result is that the DBFN may provide results which are comparable to those produced by a 4Dvar implementation with a much simpler implementation and a shorter CPU time for convergence. The conducted sensitivity tests show that the 4Dvar profits of long assimilation windows to propagate surface information downwards, and that for the DBFN, it is worth using short assimilation windows to reduce the impact of diffusion-induced errors. Moreover, the DBFN is less sensitive to the first guess than the 4Dvar.

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