scholarly journals Accounting for Correlated Observation Error in a Dual-Formulation 4D Variational Data Assimilation System

2017 ◽  
Vol 145 (3) ◽  
pp. 1019-1032 ◽  
Author(s):  
William F. Campbell ◽  
Elizabeth A. Satterfield ◽  
Benjamin Ruston ◽  
Nancy L. Baker
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Error Variance ◽  
Variational Data Assimilation ◽  
Advanced Technology ◽  
Radiosonde Data ◽  
Observation Error ◽  
Error Covariance ◽  
Data Assimilation System ◽  
Assimilation System

Appropriate specification of the error statistics for both observational data and short-term forecasts is necessary to produce an optimal analysis. Observation error stems from instrument error, forward model error, and error of representation. All sources of observation error, particularly error of representation, can lead to nonzero correlations. While correlated forecast error has been accounted for since the early days of atmospheric data assimilation, observation error has typically been treated as uncorrelated until relatively recently. Thinning, averaging, and/or inflation of the assigned observation error variance have been employed to compensate for unaccounted error correlations, especially for high-resolution satellite data. In this study, the benefits of accounting for nonzero vertical (interchannel) correlation for both the Advanced Technology Microwave Satellite (ATMS) and Infrared Atmospheric Sounding Interferometer (IASI) in the NRL Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) are assessed. The vertical observation error covariance matrix for the ATMS and IASI instruments was estimated using the Desroziers method. The results suggest lowering the assigned error variance and introducing strong correlations, especially in the moisture-sensitive channels. Strong positive impact on forecast skill (verified against both the ECMWF analyses and high-quality radiosonde data) is shown in both the ATMS and IASI instruments. Additionally, the convergence of the iterative solver in NAVDAS-AR can be improved by small modifications to the observation error covariance matrices, resulting in further reduction in RMS error.

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.

scholarly journals Four-Dimensional Variational Data Assimilation for the Canadian Regional Deterministic Prediction System

2012 ◽  
Vol 140 (5) ◽  
pp. 1517-1538 ◽  
Author(s):  
Monique Tanguay ◽  
Luc Fillion ◽  
Ervig Lapalme ◽  
Manon Lajoie
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Radiosonde Data ◽  
Second Step ◽  
Lateral Boundary Conditions ◽  
Analysis System ◽  
Horizontal Grid ◽  
Data Assimilation System

Abstract As a second step in the development of the Canadian Regional Data Assimilation System following Fillion et al., this study extends the approach to the four-dimensional variational data assimilation (4D-Var) context. Emphasis is first put on illustrating the importance of controlling lateral boundary conditions (LBCs). The use in the minimization of a horizontal grid over a domain exceeding the horizontal grid of the high-resolution nonlinear model is then proposed. The authors examine the performance of this 4D-Var formulation as an upcoming upgrade to the currently operational regional three-dimensional variational data assimilation (3D-Var) system. Forecast verifications against radiosonde data for 118 winter cases and 118 summer cases were performed. Results indicate a slight positive impact up to 48 h against North American radiosondes, but with a significant positive impact (especially for winds) at mid- and high latitudes during the summer. Accumulated precipitation scores over 24 h, whether during the first or second day of the forecasts, are slightly improved. The regional 4D-Var analysis system described in this study can run within current real-time “regional run” allocation for operations at the Canadian Meteorological Center (CMC). Future improvements of this system are briefly mentioned especially regarding the upcoming computer upgrade at CMC.

scholarly journals The “ABC-DA system” (v1.4): a variational data assimilation system for convective scale assimilation research with a study of the impact of a balance constraint

2020 ◽  
Author(s):  
Ross Noel Bannister
Keyword(s):  
Data Assimilation ◽  
Covariance Matrix ◽  
Variational Data Assimilation ◽  
Background Error ◽  
Error Covariance Matrix ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Convective Scale ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Following the development of the simplified atmospheric convective-scale "toy" model (the ABC model, named after its three key parameters: the pure gravity wave frequency, A, the controller of the acoustic wave speed, B, and the constant of proportionality between pressure and density perturbations, C), this paper introduces its associated variational data assimilation system, ABC-DA. The purpose of ABC-DA is to permit quick and efficient research into data assimilation methods suitable for convective scale systems. The system can also be used as an aid to teach and demonstrate data assimilation principles. ABC-DA is flexible, configurable and is efficient enough to be run on a personal computer. The system can run a number of assimilation methods (currently 3DVar and 3DFGAT have been implemented), with user configurable observation networks. Observation operators for direct observations and wind speeds are part of the system, although these can be expanded relatively easily. A key feature of any data assimilation system is how it specifies the background error covariance matrix. ABC-DA uses a control variable transform method to allow this to be done efficiently. This version of ABC-DA mirrors many operational configurations, by modelling multivariate error covariances with uncorrelated control parameters, and spatial error covariances with special uncorrelated spatial patterns separately for each parameter. The software developed (amongst other things) does model runs, calibration tasks associated with the background error covariance matrix, testing and diagnostic tasks, single data assimilation runs, multi-cycle assimilation/forecast experiments, and has associated visualisation software. As a demonstration, the system is used to tackle a scientific question concerning the role of geostrophic balance (GB) to model background error covariances between mass and wind fields. This question arises because, although GB is a very useful mechanism that is successfully exploited in larger scale assimilation systems, its use is questionable at convective scales due to the typically larger Rossby numbers where GB is not so relevant. A series of identical twin experiments is done in cycled assimilation configurations. One experiment exploits GB to represent mass-wind covariances in a mirror of an operational set-up (with use of an additional vertical regression (VR) step, as used operationally). This experiment performs badly where assimilation error accumulates over time. Two further experiments are done: one that does not use GB, and another that does but without the VR step. Turning off GB impairs the performance, and turning off VR improves the performance in general. It is concluded that there is scope to further improve the way that the background error covariance matrices are calibrated, with some directions discussed.

scholarly journals Regional Ensemble–Variational Data Assimilation Using Global Ensemble Forecasts

2017 ◽  
Vol 32 (1) ◽  
pp. 83-96 ◽  
Author(s):  
Wan-Shu Wu ◽  
David F. Parrish ◽  
Eric Rogers ◽  
Ying Lin
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Positive Impact ◽  
Relative Weight ◽  
Variational Data Assimilation ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Data Assimilation System ◽  
Assimilation System

Abstract At the National Centers for Environmental Prediction, the global ensemble forecasts from the ensemble Kalman filter scheme in the Global Forecast System are applied in a regional three-dimensional (3D) and a four dimensional (4D) ensemble–variational (EnVar) data assimilation system. The application is a one-way variational method using hybrid static and ensemble error covariances. To enhance impact, three new features have been added to the existing EnVar system in the Gridpoint Statistical Interpolation (GSI). First, the constant coefficients that assign relative weight between the ensemble and static background error are now allowed to vary in the vertical. Second, a new formulation is introduced for the ensemble contribution to the analysis surface pressure. Finally, in order to make use of the information in the ensemble mean that is disregarded in the existing EnVar in GSI, the trajectory correction, a novel approach, is introduced. Relative to the application of a 3D variational data assimilation algorithm, a clear positive impact on 1–3-day forecasts is realized when applying 3DEnVar analyses in the North American Mesoscale Forecast System (NAM). The 3DEnVar DA system was operationally implemented in the NAM Data Assimilation System in August 2014. Application of a 4DEnVar algorithm is shown to further improve forecast accuracy relative to the 3DEnVar. The approach described in this paper effectively combines contributions from both the regional and the global forecast systems to produce the initial conditions for the regional NAM system.

scholarly journals The ABC-DA system (v1.4): a variational data assimilation system for convective-scale assimilation research with a study of the impact of a balance constraint

2020 ◽  
Vol 13 (8) ◽  
pp. 3789-3816
Author(s):  
Ross Noel Bannister
Keyword(s):  
Data Assimilation ◽  
Covariance Matrix ◽  
Variational Data Assimilation ◽  
Background Error ◽  
Error Covariance Matrix ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Convective Scale ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Following the development of the simplified atmospheric convective-scale “toy” model (the ABC model, named after its three key parameters: the pure gravity wave frequency A, the controller of the acoustic wave speed B, and the constant of proportionality between pressure and density perturbations C), this paper introduces its associated variational data assimilation system, ABC-DA. The purpose of ABC-DA is to permit quick and efficient research into data assimilation methods suitable for convective-scale systems. The system can also be used as an aid to teach and demonstrate data assimilation principles. ABC-DA is flexible and configurable, and is efficient enough to be run on a personal computer. The system can run a number of assimilation methods (currently 3DVar and 3DFGAT have been implemented), with user configurable observation networks. Observation operators for direct observations and wind speeds are part of the current system, and these can, for example, be expanded relatively easily to include operators for Doppler winds. A key feature of any data assimilation system is how it specifies the background error covariance matrix. ABC-DA uses a control variable transform method to allow this to be done efficiently. This version of ABC-DA mirrors many operational configurations by modelling multivariate error covariances with uncorrelated control parameters, each with special uncorrelated spatial patterns. The software developed performs (amongst other things) model runs, calibration tasks associated with the background error covariance matrix, testing and diagnostic tasks, single data assimilation runs, and multi-cycle assimilation/forecast experiments, and it also has associated visualisation software. As a demonstration, the system is used to tackle a scientific question concerning the role of geostrophic balance (GB) to model background error covariances between mass and wind fields. This question arises because although GB is a very useful mechanism that is successfully exploited in larger-scale assimilation systems, its use is questionable at convective scales due to the typically larger Rossby numbers where GB is not so relevant. A series of identical twin experiments is done in cycled assimilation configurations. One experiment exploits GB to represent mass–wind covariances in a mirror of an operational set-up (with use of an additional vertical regression (VR) step, as used operationally). This experiment performs badly where error accumulates over time. Two further experiments are done: one that does not use GB and another that does but without the VR step. Turning off GB impairs the performance, and turning off VR improves the performance in general. It is concluded that there is scope to further improve the way that the background error covariance matrices are represented at convective scale. Ideas for further possible developments of ABC-DA are discussed.

scholarly journals Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

2021 ◽  
Author(s):  
Magnus Lindskog ◽  
Adam Dybbroe ◽  
Roger Randriamampianina
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Implementation Process ◽  
Satellite System ◽  
Limited Area ◽  
Global Navigation Satellite ◽  
Microwave Radiances ◽  
Data Assimilation System ◽  
Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

Sign in / Sign up

Export Citation Format

Share Document