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

2001 ◽  
Vol 8 (6) ◽  
pp. 347-355 ◽  
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.

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

2014 ◽  
Vol 142 (10) ◽  
pp. 3586-3613 ◽  
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.

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

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.

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

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.

scholarly journals Inclusion of Linearized Moist Physics in NASA’s Goddard Earth Observing System Data Assimilation Tools

2014 ◽  
Vol 142 (1) ◽  
pp. 414-433 ◽  
Author(s):  
Daniel Holdaway ◽  
Ronald Errico ◽  
Ronald Gelaro ◽  
Jong G. Kim
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Weather Forecast ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Adjoint Sensitivity ◽  
Earth Observing System ◽  
Sensitivity Studies ◽  
System Data ◽  
Simple Conversion

Abstract Inclusion of moist physics in the linearized version of a weather forecast model is beneficial in terms of variational data assimilation. Further, it improves the capability of important tools, such as adjoint-based observation impacts and sensitivity studies. A linearized version of the relaxed Arakawa–Schubert (RAS) convection scheme has been developed and tested in NASA’s Goddard Earth Observing System data assimilation tools. A previous study of the RAS scheme showed it to exhibit reasonable linearity and stability. This motivates the development of a linearization of a near-exact version of the RAS scheme. Linearized large-scale condensation is included through simple conversion of supersaturation into precipitation. The linearization of moist physics is validated against the full nonlinear model for 6- and 24-h intervals, relevant to variational data assimilation and observation impacts, respectively. For a small number of profiles, sudden large growth in the perturbation trajectory is encountered. Efficient filtering of these profiles is achieved by diagnosis of steep gradients in a reduced version of the operator of the tangent linear model. With filtering turned on, the inclusion of linearized moist physics increases the correlation between the nonlinear perturbation trajectory and the linear approximation of the perturbation trajectory. A month-long observation impact experiment is performed and the effect of including moist physics on the impacts is discussed. Impacts from moist-sensitive instruments and channels are increased. The effect of including moist physics is examined for adjoint sensitivity studies. A case study examining an intensifying Northern Hemisphere Atlantic storm is presented. The results show a significant sensitivity with respect to moisture.

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

2018 ◽  
Vol 22 (11) ◽  
pp. 5759-5779 ◽  
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.

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.

Multi-parametric Variational Data Assimilation of MODIS snow cover data through HBV Model in Mountainous Upper Euphrates River Catchment

2020 ◽  
Author(s):  
Aynur Sensoy ◽  
Gokcen Uysal ◽  
Rodolfo Alvarado Montero
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Initial Conditions ◽  
Variational Data Assimilation ◽  
Parametric Modelling ◽  
Multiple Sources ◽  
Modeling And Forecasting ◽  
Novel Method ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation

<p>Modeling streamflows is challenging in snow dominated high altitude regions due to limited observations, harsh topographic conditions and complex snow physics. Different uncertainties arise from multiple sources in modeling and forecasting. The uncertainties of the initial conditions are mainly tackled with data assimilation techniques. On the other hand, the uncertainty of the model structure should also be considered since assimilation techniques can only use same model and parameter sets in each implementation. Generally, this uncertainty can be taken into account using multi-modelling methods that can produce ensemble set of parameters. In order to make use of this approach, this study aims the realization of a novel method that generates a probabilistic estimate of initial states using a multi-parametric modelling method with deterministic Variational Data Assimilation, as referred to the multi-parametric variational data assimilation, MP-VarDA.  The study is accomplished for runoff predictions over the mountainous Eastern part of Turkey concerning the importance of snowmelt and the limited availability of observed data. The model pool is generated with Generalized Likelihood Uncertainty Estimation (GLUE) method with a calibrated hydrological model using HBV. The implementation of MP-VarDA assimilates both discharge and satellite snow observations on snow cover. The preliminary results having 3 model instances are promising to set a model pool for MP-VarDA method which can reduce model uncertainty. The model is also tested via hindcast experiments under close-loop mode in order to assimilate discharge and satellite snow data, and model results showed that runoff and snow state predictions are improved compared to conditional assimilation techniques.</p>

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