Effect of determining initial conditions by four-dimensional variational data assimilation on storm surge forecasting

Abstract A three-dimensional ocean model and its adjoint model are used to adjust the drag coefficient in the calculation of wind stress for storm surge forecasting. A number of identical twin experiments (ITEs) with different error sources imposed are designed and performed. The results indicate that when the errors come from the wind speed, the drag coefficient is adjusted to an “optimal value” to compensate for the wind errors, resulting in significant improvements of the specific storm surge forecasting. In practice, the “true” drag coefficient is unknown and the wind field, which is usually calculated by an empirical parameter model or a numerical weather prediction model, may contain large errors. In addition, forecasting errors may also come from imperfect model physics and numerics, such as insufficient resolution and inaccurate physical parameterizations. The results demonstrate that storm surge forecasting errors can be reduced through data assimilation by adjusting the drag coefficient regardless of the error sources. Therefore, although data assimilation may not fix model imperfection, it is effective in improving storm surge forecasting by adjusting the wind stress drag coefficient using the adjoint technique.

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Using adjoint sensitivity as a local structure function in variational data assimilation

Nonlinear Processes in Geophysics ◽

10.5194/npg-8-347-2001 ◽

2001 ◽

Vol 8 (6) ◽

pp. 347-355 ◽

Cited By ~ 10

Author(s):

G. Hello ◽

F. Bouttier

Keyword(s):

Data Assimilation ◽

Structure Function ◽

Local Structure ◽

Initial Conditions ◽

Storm Track ◽

Variational Data Assimilation ◽

Sensitive Area ◽

Adjoint Sensitivity ◽

Weather Events ◽

Gradient Fields

Abstract. One approach recently proposed in order to improve the forecast of weather events, such as cyclogenesis, is to increase the number of observations in areas depending on the flow configuration. These areas are obtained using, for example, the sensitivity to initial conditions of a selected predicted cyclone. An alternative or complementary way is proposed here. The idea is to employ such an adjoint sensitivity field as a local structure function within variational data assimilation, 3D-Var in this instance. Away from the sensitive area, observation increments project on the initial fields with the usual climatological (or weakly flow-dependent, in the case of 4D-Var) structure functions. Within the sensitive area, the gradient fields are projected using all the available data in the zone, conventional or extra, if any. The formulation of the technique is given and the approach is further explained by using a simple 1D scheme. The technique is implemented in the ARPEGE/IFS code and applied to 11 FASTEX (Fronts and Atlantic Storm-Track Experiment) cyclone cases, together with the targeted observations performed at the time of the campaign. The new approach is shown to allow for the desired stronger impact of the available observations and to systematically improve the forecasts of the FASTEX cyclones, unlike the standard 3D-Var.

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Simulation of Monsoon Depressions Using WRF-VAR: Impact of Different Background Error Statistics and Lateral Boundary Conditions

Monthly Weather Review ◽

10.1175/mwr-d-13-00285.1 ◽

2014 ◽

Vol 142 (10) ◽

pp. 3586-3613 ◽

Cited By ~ 20

Author(s):

A. Routray ◽

S. C. Kar ◽

P. Mali ◽

K. Sowjanya

Keyword(s):

Data Assimilation ◽

Boundary Conditions ◽

Initial Conditions ◽

Variational Data Assimilation ◽

Error Statistics ◽

Background Error ◽

Single Observation ◽

Monsoon Depressions ◽

Data Assimilation System ◽

Assimilation System

Abstract In a variational data assimilation system, background error statistics (BES) spread the influence of the observations in space and filter analysis increments through dynamic balance or statistical relationships. In a data-sparse region such as the Bay of Bengal, BES play an important role in defining the location and structure of monsoon depressions (MDs). In this study, the Indian-region-specific BES have been computed for the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation system. A comparative study using single observation tests is carried out using the computed BES and global BES within the WRF system. Both sets of BES are used in the assimilation cycles and forecast runs for simulating the meteorological features associated with the MDs. Numerical experiments have been conducted to assess the relative impact of various BES in the analysis and simulations of the MDs. The results show that use of regional BES in the assimilation cycle has a positive impact on the prediction of the location, propagation, and development of rainbands associated with the MDs. The track errors of MDs are smaller when domain-specific BES are used in the assimilation cycle. Additional experiments have been conducted using data from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) as initial and boundary conditions (IBCs) in the assimilation cycle. The results indicate that the use of domain-dependent BES and high-resolution ERA-I data as IBCs further improved the initial conditions for the model leading to better forecasts of the MDs.

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CIRA/CSU Four-Dimensional Variational Data Assimilation System

Monthly Weather Review ◽

10.1175/mwr2891.1 ◽

2005 ◽

Vol 133 (4) ◽

pp. 829-843 ◽

Cited By ~ 39

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.

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Data Assimilation and Nested Hydrodynamic Modelling in Storm Surge Forecasting

Coastal Engineering 1998 ◽

10.1061/9780784404119.102 ◽

1999 ◽

Author(s):

Rafael Cañizares ◽

Henrik Madsen

Keyword(s):

Data Assimilation ◽

Storm Surge ◽

Hydrodynamic Modelling ◽

Storm Surge Forecasting

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Strong Solvability of a Variational Data Assimilation Problem for the Primitive Equations of Large-Scale Atmosphere and Ocean Dynamics

Journal of Nonlinear Science ◽

10.1007/s00332-021-09707-3 ◽

2021 ◽

Vol 31 (3) ◽

Author(s):

Peter Korn

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Initial Conditions ◽

Strong Solutions ◽

Variational Data Assimilation ◽

Ocean Dynamics ◽

Primitive Equations ◽

Cost Functional ◽

Strong Solvability ◽

Gradient Based

AbstractFor the primitive equations of large-scale atmosphere and ocean dynamics, we study the problem of determining by means of a variational data assimilation algorithm initial conditions that generate strong solutions which minimize the distance to a given set of time-distributed observations. We suggest a modification of the adjoint algorithm whose novel elements is to use norms in the variational cost functional that reflects the $$H^1$$ H 1 -regularity of strong solutions of the primitive equations. For such a cost functional, we prove the existence of minima and a first-order adjoint condition for strong solutions that provides the basis for computing these minima. We prove the local convergence of a gradient-based descent algorithm to optimal initial conditions using the second-order adjoint primitive equations. The algorithmic modifications due to the $$H^1$$ H 1 -norms are straightforwardly to implement into a variational algorithm that employs the standard $$L^2$$ L 2 -metrics.

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Hybridizing Bayesian and variational data assimilation for robust high-resolution hydrologic forecasting

10.5194/hess-2017-431 ◽

2017 ◽

Author(s):

Felipe Hernández ◽

Xu Liang

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Predictive Accuracy ◽

Initial Conditions ◽

Time Estimation ◽

Variational Data Assimilation ◽

Streamflow Forecasting ◽

Infiltration Capacity ◽

Computational Overhead ◽

Capacity Model

Abstract. The success of real-time estimation and forecasting applications based on geophysical models has been possible thanks to the two main frameworks for the determination of the models’ initial conditions: Bayesian data assimilation and variational data assimilation. However, while there have been efforts to unify these two paradigms, existing attempts struggle to fully leverage the advantages of both in order to face the challenges posed by modern high-resolution models – mainly related to model indeterminacy and steep computational requirements. In this article we introduce a hybrid algorithm called OPTIMISTS (Optimized PareTo Inverse Modeling through Integrated STochastic Search) which is targeted at non-linear high-resolution problems and that brings together ideas from particle filters, 4-dimensional variational methods, evolutionary Pareto optimization, and kernel density estimation in a unique way. Streamflow forecasting experiments were conducted to test which specific parameterizations of OPTIMISTS led to higher predictive accuracy. The experiments analysed two watersheds, one with a low resolution using the VIC (Variable Infiltration Capacity) model and one with a high-resolution using the DHSVM (Distributed Hydrology Soil Vegetation Model). By selecting kernel-based non-parametric sampling, non-sequential evaluation of candidate particles, and through the multi-objective minimization of departures from the streamflow observations and from the background states, OPTIMISTS was shown to outperform a particle filter and a 4D variational method. Moreover, the experiments demonstrated that OPTIMISTS scales well in high-resolution cases without imposing a significant computational overhead and that it was successful in mitigating the harmful effects of overfitting. With these combined advantages, the algorithm shows the potential to increase the accuracy and efficiency of operational prediction systems for the improved management of natural resources.

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On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations

Atmospheric Environment ◽

10.1016/j.atmosenv.2011.09.066 ◽

2012 ◽

Vol 46 ◽

pp. 318-328 ◽

Cited By ~ 22

Author(s):

J. Vira ◽

M. Sofiev

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Variational Data Assimilation ◽

Short Term ◽

Emission Fluxes ◽

Short Term Forecasting

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Hybridizing Bayesian and variational data assimilation for high-resolution hydrologic forecasting

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5759-2018 ◽

2018 ◽

Vol 22 (11) ◽

pp. 5759-5779 ◽

Cited By ~ 5

Author(s):

Felipe Hernández ◽

Xu Liang

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Predictive Accuracy ◽

Initial Conditions ◽

Time Estimation ◽

Variational Data Assimilation ◽

Streamflow Forecasting ◽

Infiltration Capacity ◽

Probabilistic Forecasts ◽

Non Linear

Abstract. The success of real-time estimation and forecasting applications based on geophysical models has been possible thanks to the two main existing frameworks for the determination of the models' initial conditions: Bayesian data assimilation and variational data assimilation. However, while there have been efforts to unify these two paradigms, existing attempts struggle to fully leverage the advantages of both in order to face the challenges posed by modern high-resolution models – mainly related to model indeterminacy and steep computational requirements. In this article we introduce a hybrid algorithm called OPTIMISTS (Optimized PareTo Inverse Modeling through Integrated STochastic Search) which is targeted at non-linear high-resolution problems and that brings together ideas from particle filters (PFs), four-dimensional variational methods (4D-Var), evolutionary Pareto optimization, and kernel density estimation in a unique way. Streamflow forecasting experiments were conducted to test which specific configurations of OPTIMISTS led to higher predictive accuracy. The experiments were conducted on two watersheds: the Blue River (low resolution) using the VIC (Variable Infiltration Capacity) model and the Indiantown Run (high resolution) using the DHSVM (Distributed Hydrology Soil Vegetation Model). By selecting kernel-based non-parametric sampling, non-sequential evaluation of candidate particles, and through the multi-objective minimization of departures from the streamflow observations and from the background states, OPTIMISTS was shown to efficiently produce probabilistic forecasts with comparable accuracy to that obtained from using a particle filter. Moreover, the experiments demonstrated that OPTIMISTS scales well in high-resolution cases without imposing a significant computational overhead. With the combined advantages of allowing for fast, non-Gaussian, non-linear, high-resolution prediction, the algorithm shows the potential to increase the efficiency of operational prediction systems.

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