A New Approach for Estimating Salinity in the Southwest Atlantic and Its Application in a Data Assimilation Evaluation Experiment

2020 ◽  
Vol 125 (9) ◽  
Author(s):  
G. S. Dorfschäfer ◽  
C. A. S. Tanajura ◽  
F. B. Costa ◽  
R. C. Santana
Keyword(s):  
Data Assimilation ◽  
Southwest Atlantic ◽  
New Approach

A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation

2018 ◽  
Vol 146 (2) ◽  
pp. 447-465 ◽  
Author(s):  
Mark Buehner ◽  
Ping Du ◽  
Joël Bédard
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Observation Error ◽  
New Approach ◽  
Regional Prediction ◽  
Observation Impact ◽  
Adjoint Approach ◽  
The Impact

Abstract Two types of approaches are commonly used for estimating the impact of arbitrary subsets of observations on short-range forecast error. The first was developed for variational data assimilation systems and requires the adjoint of the forecast model. Comparable approaches were developed for use with the ensemble Kalman filter and rely on ensembles of forecasts. In this study, a new approach for computing observation impact is proposed for ensemble–variational data assimilation (EnVar). Like standard adjoint approaches, the adjoint of the data assimilation procedure is implemented through the iterative minimization of a modified cost function. However, like ensemble approaches, the adjoint of the forecast step is obtained by using an ensemble of forecasts. Numerical experiments were performed to compare the new approach with the standard adjoint approach in the context of operational deterministic NWP. Generally similar results are obtained with both approaches, especially when the new approach uses covariance localization that is horizontally advected between analysis and forecast times. However, large differences in estimated impacts are obtained for some surface observations. Vertical propagation of the observation impact is noticeably restricted with the new approach because of vertical covariance localization. The new approach is used to evaluate changes in observation impact as a result of the use of interchannel observation error correlations for radiance observations. The estimated observation impact in similarly configured global and regional prediction systems is also compared. Overall, the new approach should provide useful estimates of observation impact for data assimilation systems based on EnVar when an adjoint model is not available.

scholarly journals An Examination of Background Error Correlations between Mass and Rotational Wind over Precipitation Regions

2010 ◽  
Vol 138 (2) ◽  
pp. 563-578 ◽  
Author(s):  
Jean-François Caron ◽  
Luc Fillion
Keyword(s):  
Linear Regression ◽  
Data Assimilation ◽  
Traditional Approach ◽  
Forecast Error ◽  
Lower Atmosphere ◽  
Forecast Errors ◽  
Background Error ◽  
New Approach ◽  
Geostrophic Balance ◽  
Analysis System

Abstract The differences in the balance characteristics between dry and precipitation areas in estimated short-term forecast error fields are investigated. The motivation is to see if dry and precipitation areas need to be treated differently in atmospheric data assimilation systems. Using an ensemble of lagged forecast differences, it is shown that perturbations are, on average, farther away from geostrophic balance over precipitation areas than over dry areas and that the deviation from geostrophic balance is proportional to the intensity of precipitation. Following these results, the authors investigate whether some improvements in the coupling between mass and rotational wind increments over precipitation areas can be achieved by using only the precipitation points within an ensemble of estimated forecast errors to construct a so-called diabatic balance operator by linear regression. Comparisons with a traditional approach to construct balance operators by linear regression show that the new approach leads to a gradually significant improvement (related to the intensity of the diabatic processes) of the accuracy of the coupling over precipitation areas as judged from an ensemble of lagged forecast differences. Results from a series of simplified data assimilation experiments show that the new balance operators can produce analysis increments that are substantially different from those associated with the traditional balance operator, particularly for observations located in the lower atmosphere. Issues concerning the implementation of this new approach in a full-fledged analysis system are briefly discussed but their investigations are left for a following study.

scholarly journals Model-Reduced Variational Data Assimilation

2006 ◽  
Vol 134 (10) ◽  
pp. 2888-2899 ◽  
Author(s):  
P. T. M. Vermeulen ◽  
A. W. Heemink
Keyword(s):  
Diffusion Coefficient ◽  
Data Assimilation ◽  
Original Model ◽  
Variational Data Assimilation ◽  
Reduced Model ◽  
New Approach ◽  
Model Simulations ◽  
Computational Costs ◽  
Recent Estimate ◽  
Reduced Space

Abstract This paper describes a new approach to variational data assimilation that with a comparable computational efficiency does not require implementation of the adjoint of the tangent linear approximation of the original model. In classical variational data assimilation, the adjoint implementation is used to efficiently compute the gradient of the criterion to be minimized. Our approach is based on model reduction. Using an ensemble of forward model simulations, the leading EOFs are determined to define a subspace. The reduced model is created by projecting the original model onto this subspace. Once this reduced model is available, its adjoint can be implemented very easily and can be used to approximate the gradient of the criterion. The minimization process can now be solved completely in reduced space with negligible computational costs. If necessary, the procedure can be repeated a few times by generating new ensembles closer to the most recent estimate of the parameters. The reduced-model-based method has been tested on several nonlinear synthetic cases for which a diffusion coefficient was estimated.

scholarly journals OCEAN FORECASTS IN THE SOUTHWESTERN ATLANTIC: IMPACT OF DIFFERENT SOURCES OF SEA SURFACE HEIGHT IN DATA ASSIMILATION

2013 ◽  
Vol 31 (2) ◽  
pp. 243 ◽  
Author(s):  
Raquel Leite Mello ◽  
Ana Cristina Neves de Freitas ◽  
Lucimara Russo ◽  
Jean Felix de Oliveira ◽  
Clemente Augusto Souza Tanajura ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ocean Model ◽  
Sea Surface ◽  
Naval Research ◽  
Southwest Atlantic ◽  
Argo Data ◽  
Ocean Data Assimilation ◽  
Hybrid Coordinate Ocean Model ◽  
Oceanographic Data ◽  
The One

ABSTRACT. The objective in this paper is to analyze which Sea Surface Height (SSH) source applied to HYCOM (HYbrid Coordinate Ocean Model) is best suited to numerical prediction of the Southwest Atlantic Ocean. To this end two nested grids were used. One grid for the entire Atlantic Ocean (1/4◦) nesting the grid for the Southwest Atlantic (1/12◦) in the one-way mode. Three forecast experiments with different SSH data sources (Naval Research Laboratory – NRL; Archiving, Validation and Interpolation of Oceanographic Data – AVISO and MERCATOR) applied to constrain the initial conditions and a control forecast experiment without SSH constrain were compared. The comparison of forecasted temperature and salinity profiles with Argo data showed good correlation, over 0.98 for temperature and 0.87 for salinity. The NRL experiment – with SSH obtained by HYCOM+NCODA (Navy Coupled Ocean Data Assimilation System) GLOBAL 1/12◦ analysis was the one that best represented the average temperature and salinity profile with respect to the Argo data. Keywords: HYCOM, numerical modeling, ocean prediction, Argo profiler, Taylor diagram. RESUMO. O objetivo deste trabalho é avaliar qual a fonte de dados de ASM (Altura da Superfície do Mar) imposta no modelo HYCOM (HYbrid Coordinate Ocean Model) é mais adequada para a previsão numérica do Oceano Atlântico Sudoeste. Para isto foram utilizadas duas grades aninhadas, uma grade para todo o Oceano Atlântico (1/4◦) aninhada no modo one-way a outra grade para o Atlântico Sudoeste (1/12◦). Foram realizados três experimentos com diferentes campos de ASM (Naval Research Laboratory – NRL; Archiving, Validation and Interpolation of Oceanographic data – AVISO e MERCATOR) impostos na condição inicial e um experimento controle no qual não foi usada fonte de ASM externa. A comparação dos perfis de temperatura e salinidade entre os dados observados e os resultados do modelo apresentou boa correlação, maior que 0,98 para a temperatura e 0,87 para a salinidade. O experimento NRL com ASM total obtido dos resultados do HYCOM+NCODA (Navy Coupled Ocean Data Assimilation) GLOBAL 1/12◦ foi o que melhor representou o perfil médio de temperatura e salinidade observado.  Palavras-chave: HYCOM, modelagem numérica, previsão oceânica, perfiladores Argo, diagrama de Taylor.

Inhomogeneous Background Error Modeling for WRF-Var Using the NMC Method

2014 ◽  
Vol 53 (10) ◽  
pp. 2287-2309 ◽  
Author(s):  
Hongli Wang ◽  
Xiang-Yu Huang ◽  
Juanzhen Sun ◽  
Dongmei Xu ◽  
Man Zhang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Control Variable ◽  
Variational Data Assimilation ◽  
Error Modeling ◽  
Error Statistics ◽  
Background Error ◽  
New Approach ◽  
Data Assimilation System ◽  
Assimilation System

AbstractBackground error modeling plays a key role in a variational data assimilation system. The National Meteorological Center (NMC) method has been widely used in variational data assimilation systems to generate a forecast error ensemble from which the climatological background error covariance can be modeled. In this paper, the characteristics of the background error modeling via the NMC method are investigated for the variational data assimilation system of the Weather Research and Forecasting (WRF-Var) Model. The background error statistics are extracted from short-term 3-km-resolution forecasts in June, July, and August 2012 over a limited-area domain. It is found 1) that background error variances vary from month to month and also have a feature of diurnal variations in the low-level atmosphere and 2) that u- and υ-wind variances are underestimated and their autocorrelation length scales are overestimated when the default control variable option in WRF-Var is used. A new approach of control variable transform (CVT) is proposed to model the background error statistics based on the NMC method. The new approach is capable of extracting inhomogeneous and anisotropic climatological information from the forecast error ensemble obtained via the NMC method. Single observation assimilation experiments show that the proposed method not only has the merit of incorporating geographically dependent covariance information, but also is able to produce a multivariate analysis. The results from the data assimilaton and forecast study of a real convective case show that the use of the new CVT improves synoptic weather system and precipitation forecasts for up to 12 h.

An ensemble of deep learning models with data assimilation for hydrologic forecasting

2021 ◽  
Author(s):  
Seong Jin Noh ◽  
Hyeonjin Choi ◽  
Bomi Kim
Keyword(s):  
Deep Learning ◽  
Data Assimilation ◽  
Observational Data ◽  
Statistical Approach ◽  
Bayesian Filtering ◽  
Learning Models ◽  
Time Data ◽  
New Approach ◽  
Hydrologic Systems ◽  
Real Time Data

<p>We present an approach to combine two data-centric approaches, data assimilation (DA) and deep learning (DL), from the perspective of hydrologic forecasting. DA is a statistical approach based on Bayesian filtering to produce optimal states and/or parameters of a dynamic model using observations. By extracting information from both model and observational data, DA improves not only the performance of numerical modeling, but also understanding of uncertainties in predictions. While DA complements information gaps in model and observational data, DL constructs a new modeling system by extracting and abstracting information solely from data without relying on the conventional knowledge of hydrologic systems. In a new approach, an ensemble of deep learning models can be updated by real-time data assimilation when a new observation becomes available. In the presentation, we will focus on discussing the potentials of combining two data-centric approaches.</p><p> </p>

Assimilation of Aerosol Observations in the NASA GEOS model

2020 ◽  
Author(s):  
Virginie Buchard ◽  
Arlindo da Silva ◽  
Dan Holdaway ◽  
Ricardo Todling
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Splitting Method ◽  
Real Time System ◽  
New Approach ◽  
Sensing Platforms ◽  
Aerosol Module ◽  
Data Assimilation Technique ◽  
The Impact ◽  
Assimilation System

<p>In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASA’s Global Modeling Assimilation Office (GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. The prognostic model is based on the GEOS model radiatively coupled to GOCART aerosol module and includes assimilation of bias-corrected Aerosol Optical Depth (AOD) at 550 nm from various space-based remote sensing platforms.</p><p>Along with the progress made in the JCSDA-Joint Effort for Data Assimilation Integration (JEDI) framework, we have developed a prototype including GEOS aerosols as a component of the JEDI framework. Using members produced by the GEOS hybrid meteorological data assimilation system, we are updating the aerosol component of our assimilation system to a variational ensemble type of scheme. In this talk we will examine the impact of replacing the current analysis splitting scheme with this new approach. By including the assimilation of satellite-based single and multi-channel retrievals; we will discuss the impact of this aerosol data assimilation technique on the 3D aerosol distributions by means of innovation statistics and verification against independent datasets such as the Aerosol Robotic Network (AERONET) and surface PM<sub>2.5</sub>.</p>

scholarly journals A Statistical Measurement of Randomness Based on Pattern Vectors

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Data Assimilation ◽  
Failure Rate ◽  
Kalman Filters ◽  
Binary Sequence ◽  
Statistical Testing ◽  
New Approach ◽  
New Device ◽  
Statistical Measurement ◽  
Pattern Vector

Randomness of data or signals has been applied and studied in various theoretical and industrial fields. There are many ways to define and measure randomness. The most popular one probably is the statistical testing for randomness. Among the approaches adopted, Runs Test is a highly used technique in testing the randomness. In this article, we demonstrate the inefficient aspects of Runs Test and put forward a new approach, or pattern-vector-based statistic, based on pattern vectors that could effectively enhance the precision of testing randomness. A random binary sequence is supposedly to have less or no patterns. Based on this, we put forward our randomness-testing statistic. We also run an experiment to demonstrate how to apply this statistic and compare the efficiency or failure rate with Runs Test in dealing with a set of randomly generated input sequences. Moreover, we devise a statistically-justifiable measure of randomness for any given binary sequence. In the end, we demonstrate a way to combine this new device with Kalman filters to enhance the data assimilation.

scholarly journals Ensemble Riemannian data assimilation over the Wasserstein space

2021 ◽  
Vol 28 (3) ◽  
pp. 295-309
Author(s):  
Sagar K. Tamang ◽  
Ardeshir Ebtehaj ◽  
Peter J. van Leeuwen ◽  
Dongmian Zou ◽  
Gilad Lerman
Keyword(s):  
Data Assimilation ◽  
Evolutionary Dynamics ◽  
Probability Distributions ◽  
Wasserstein Metric ◽  
New Approach ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Integrable Probability ◽  
Wasserstein Space ◽  
Non Gaussian

Abstract. In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Euclidean distance used in classic data assimilation methodologies, the Wasserstein metric can capture the translation and difference between the shapes of square-integrable probability distributions of the background state and observations. This enables us to formally penalize geophysical biases in state space with non-Gaussian distributions. The new approach is applied to dissipative and chaotic evolutionary dynamics, and its potential advantages and limitations are highlighted compared to the classic ensemble data assimilation approaches under systematic errors.

scholarly journals Ensemble Riemannian Data Assimilation over the Wasserstein Space

2021 ◽  
Author(s):  
Sagar K. Tamang ◽  
Ardeshir Ebtehaj ◽  
Peter J. van Leeuwen ◽  
Dongmian Zou ◽  
Gilad Lerman
Keyword(s):  
Data Assimilation ◽  
Evolutionary Dynamics ◽  
Probability Distributions ◽  
Random Errors ◽  
Wasserstein Metric ◽  
New Approach ◽  
Integrable Probability ◽  
Wasserstein Space ◽  
Non Gaussian ◽  
Background State

Abstract. In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Eulerian penalization of error in the Euclidean space, the Wasserstein metric can capture translation and difference between the shapes of square-integrable probability distributions of the background state and observations – enabling to formally penalize geophysical biases in state-space with non-Gaussian distributions. The new approach is applied to dissipative and chaotic evolutionary dynamics and its potential advantages and limitations are highlighted compared to the classic variational and filtering data assimilation approaches under systematic and random errors.

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