scholarly journals A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Hongli Fu ◽  
Jinkun Yang ◽  
Wei Li ◽  
Xinrong Wu ◽  
Guijun Han ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Density Gradient ◽  
Initial Conditions ◽  
Ocean Model ◽  
Potential Density ◽  
3 Dimensional ◽  
Ocean Data Assimilation ◽  
Variational Framework ◽  
Analysis Scheme ◽  
Density Surface

This study addresses how to maintain oceanic mixing along potential density surface in ocean data assimilation (ODA). It is well known that the oceanic mixing across the potential density surface is much weaker than that along the potential density surface. However, traditional ODA schemes allow the mixing across the potential density surface and thus may result in extra assimilation errors. Here, a new ODA scheme that uses potential density gradient information of the model background to rescale observational adjustment is designed to improve the quality of assimilation. The new scheme has been tested using a regional ocean model within a multiscale 3-dimensional variational framework. Results show that the new scheme effectively prevents the excessive unphysical projection of observational information in the direction across potential density surface and thus improves assimilation quality greatly. Forecast experiments also show that the new scheme significantly improves the model forecast skills through providing more dynamically consistent initial conditions

scholarly journals A Hybrid Global Ocean Data Assimilation System at NCEP

2015 ◽  
Vol 143 (11) ◽  
pp. 4660-4677 ◽  
Author(s):  
Stephen G. Penny ◽  
David W. Behringer ◽  
James A. Carton ◽  
Eugenia Kalnay
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Global Ocean ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ocean Data Assimilation ◽  
Data Assimilation System ◽  
Ncep Climate Forecast System ◽  
Assimilation System

Abstract Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. The Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

scholarly journals WRF-ARW Variational Storm-Scale Data Assimilation: Current Capabilities and Future Developments

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Juanzhen Sun ◽  
Hongli Wang
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Weather Research And Forecasting ◽  
Dimensional System ◽  
3 Dimensional ◽  
Future Developments ◽  
Variational Framework ◽  
Arw Model ◽  
Data Assimilation System ◽  
Scale Data

The variational radar data assimilation system has been developed and tested for the Advanced Research Weather Research and Forecasting (WRF-ARW) model since 2005. Initial efforts focused on the assimilation of the radar observations in the 3-dimensional variational framework, and recently the efforts have been extended to the 4-dimensional system. This article provides a review of the basics of the system and various studies that have been conducted to evaluate and improve the performance of the system. Future activities that are required to further improve the system and to make it operational are also discussed.

An Examination of WRF 3DVAR Radar Data Assimilation on Its Capability in Retrieving Unobserved Variables and Forecasting Precipitation through Observing System Simulation Experiments

2009 ◽  
Vol 137 (11) ◽  
pp. 4011-4029 ◽  
Author(s):  
Soichiro Sugimoto ◽  
N. Andrew Crook ◽  
Juanzhen Sun ◽  
Qingnong Xiao ◽  
Dale M. Barker
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
System Simulation ◽  
Radar Data ◽  
Wind Component ◽  
Simulation Experiments ◽  
Tangential Wind ◽  
Analysis Scheme ◽  
Radar Data Assimilation ◽  
Reflectivity Factor

Abstract The purpose of this study is to investigate the performance of 3DVAR radar data assimilation in terms of the retrievals of convective fields and their impact on subsequent quantitative precipitation forecasts (QPFs). An assimilation methodology based on the Weather Research and Forecasting (WRF) model three-dimensional variational data assimilation (3DVAR) and a cloud analysis scheme is described. Simulated data from 25 Weather Surveillance Radar-1988 Doppler (WSR-88D) radars are assimilated, and the potential benefits and limitations of the assimilation are quantitatively evaluated through observing system simulation experiments of a dryline that occurred over the southern Great Plains. Results indicate that the 3DVAR system is able to analyze certain mesoscale and convective-scale features through the incorporation of radar observations. The assimilation of all possible data (radial velocity and reflectivity factor data) results in the best performance on short-range precipitation forecasting. The wind retrieval by assimilating radial velocities is of primary importance in the 3DVAR framework and the storm case applied, and the use of multiple-Doppler observations improves the retrieval of the tangential wind component. The reflectivity factor assimilation is also beneficial especially for strong precipitation. It is demonstrated that the improved initial conditions through the 3DVAR analysis lead to improved skills on QPF.

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.

scholarly journals Estimating Forecast Error Covariances for Strongly Coupled Atmosphere–Ocean 4D-Var Data Assimilation

2017 ◽  
Vol 145 (10) ◽  
pp. 4011-4035 ◽  
Author(s):  
Polly J. Smith ◽  
Amos S. Lawless ◽  
Nancy K. Nichols
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
A Priori ◽  
Physical Structure ◽  
Ocean Model ◽  
Valuable Insight ◽  
Strongly Coupled ◽  
Ocean Data Assimilation ◽  
Different Seasons ◽  
Cross Correlations

Strongly coupled data assimilation emulates the real-world pairing of the atmosphere and ocean by solving the assimilation problem in terms of a single combined atmosphere–ocean state. A significant challenge in strongly coupled variational atmosphere–ocean data assimilation is a priori specification of the cross covariances between the errors in the atmosphere and ocean model forecasts. These covariances must capture the correct physical structure of interactions across the air–sea interface as well as the different scales of evolution in the atmosphere and ocean; if prescribed correctly, they will allow observations in one medium to improve the analysis in the other. Here, the nature and structure of atmosphere–ocean forecast error cross correlations are investigated using an idealized strongly coupled single-column atmosphere–ocean 4D-Var assimilation system. Results are presented from a set of identical twin–type experiments that use an ensemble of coupled 4D-Var assimilations to derive estimates of the atmosphere–ocean error cross correlations. The results show significant variation in the strength and structure of cross correlations in the atmosphere–ocean boundary layer between summer and winter and between day and night. These differences provide a valuable insight into the nature of coupled atmosphere–ocean correlations for different seasons and points in the diurnal cycle.

scholarly journals Transports and budgets in a 1/4 ° global ocean reanalysis 1989–2010

Ocean Science ◽  
2012 ◽  
Vol 8 (3) ◽  
pp. 333-344 ◽  
Author(s):  
K. Haines ◽  
M. Valdivieso ◽  
H. Zuo ◽  
V. N. Stepanov
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Ocean Model ◽  
Global Ocean ◽  
Ocean Reanalysis ◽  
Ocean Data Assimilation ◽  
Ocean Reanalyses ◽  
Atmospheric Observations ◽  
Atmospheric Heat ◽  
Regional Budgets

Abstract. Large-scale ocean transports of heat and freshwater have not been well monitored, and yet the regional budgets of these quantities are important to understanding the role of the oceans in climate and climate change. In contrast, atmospheric heat and freshwater transports are commonly assessed from atmospheric reanalysis products, despite the presence of non-conserving data assimilation based on the wealth of distributed atmospheric observations as constraints. The ability to carry out ocean reanalyses globally at eddy-permitting resolutions of 1/4 ° or better, along with new global ocean observation programs, now makes a similar approach viable for the ocean. In this paper we examine the budgets and transports within a global high resolution ocean model constrained by ocean data assimilation, and compare them with independent oceanic and atmospheric estimates.

Evaluation of Global Ocean Data Assimilation Experiment products on South Florida nested simulations with the Hybrid Coordinate Ocean Model

2008 ◽  
Vol 59 (1) ◽  
pp. 47-66 ◽  
Author(s):  
Vassiliki H. Kourafalou ◽  
Ge Peng ◽  
HeeSook Kang ◽  
Patrick J. Hogan ◽  
Ole-Martin Smedstad ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ocean Model ◽  
South Florida ◽  
Global Ocean ◽  
Assimilation Experiment ◽  
Ocean Data Assimilation ◽  
Hybrid Coordinate Ocean Model

scholarly journals Transports and budgets in a 1/4° global ocean reanalysis 1989–2010

2012 ◽  
Vol 9 (1) ◽  
pp. 261-290 ◽  
Author(s):  
K. Haines ◽  
M. Valdivieso ◽  
H. Zuo ◽  
V. N. Stepanov
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Ocean Model ◽  
Global Ocean ◽  
Ocean Reanalysis ◽  
Ocean Data Assimilation ◽  
Ocean Reanalyses ◽  
Atmospheric Observations ◽  
Atmospheric Heat ◽  
Regional Budgets

Abstract. Large scale ocean transports of heat and freshwater have not been well monitored, and yet the regional budgets of these quantities are vital to understanding the role of the oceans in climate and climate change. In contrast atmospheric heat and freshwater transports are commonly assessed from atmospheric reanalysis products, despite the presence of non-conserving data assimilation based on the wealth of distributed atmospheric observations as constraints. The ability to carry out ocean reanalyses globally at eddy permitting resolutions of 1/4° or better, along with new global ocean observation programs, now make a similar approach viable for the ocean. In this paper we examine the budgets and transports within a global high resolution ocean model constrained by ocean data assimilation, and compare them with independent ocean and atmospheric estimates.

scholarly journals Impact of Data Assimilation on Forecasting Convection over the United Kingdom Using a High-Resolution Version of the Met Office Unified Model

2009 ◽  
Vol 137 (5) ◽  
pp. 1562-1584 ◽  
Author(s):  
Mark Dixon ◽  
Zhihong Li ◽  
Humphrey Lean ◽  
Nigel Roberts ◽  
Sue Ballard
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Unified Model ◽  
High Resolution Data ◽  
Surface Precipitation ◽  
Analysis Scheme ◽  
Satellite Radar ◽  
The Individual ◽  
Grid Length

Abstract A high-resolution data assimilation system has been implemented and tested within a 4-km grid length version of the Met Office Unified Model (UM). A variational analysis scheme is used to correct larger scales using conventional observation types. The system uses two nudging procedures to assimilate high-resolution information: radar-derived surface precipitation rates are assimilated via latent heat nudging (LHN), while cloud nudging (CN) is used to assimilate moisture fields derived from satellite, radar, and surface observations. The data assimilation scheme was tested on five convection-dominated case studies from the Convective Storm Initiation Project (CSIP). Model skill was assessed statistically using radar-derived surface-precipitation hourly accumulations via a scale-dependent verification scheme. Data assimilation is shown to have a dramatic impact on skill during both the assimilation and subsequent forecast periods on nowcasting time scales. The resulting forecasts are also shown to be much more skillful than those produced using either a 12-km grid length version of the UM with data assimilation in place, or a 4-km grid-length UM version run using a 12-km state as initial conditions. The individual contribution to overall skill attributable to each data-assimilation component is investigated. Up until T + 3 h, LHN has the greatest impact on skill. For later times, VAR, LHN, and CN contribute equally to the skill in predicting the spatial distribution of the heaviest rainfall; while VAR alone accounts for the skill in depicting distributions corresponding to lower accumulation thresholds. The 4-km forecasts tend to overpredict both the intensity and areal coverage of storms. While it is likely that the intensity bias is partially attributable to model error, both VAR and LHN clearly contribute to the overestimation of the areal extent. Future developments that may mitigate this problem are suggested.

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