scholarly journals Data assimilation of Argo profiles in a northwestern Pacific model

2017 ◽  
Vol 17 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Zhaoyi Wang ◽  
Andrea Storto ◽  
Nadia Pinardi ◽  
Guimei Liu ◽  
Hui Wang
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Three Dimensional ◽  
Predictive Performance ◽  
Ocean Model ◽  
Northwestern Pacific ◽  
Background Error ◽  
Salinity Data ◽  
Argo Profiles ◽  
Assimilation Scheme

Abstract. Based on a novel estimation of background-error covariances for assimilating Argo profiles, an oceanographic three-dimensional variational (3DVAR) data assimilation scheme was developed for the northwestern Pacific Ocean model (NwPM) for potential use in operational predictions and maritime safety applications. Temperature and salinity data extracted from Argo profiles from January to December 2010 were assimilated into the NwPM. The results show that the average daily temperature (salinity) root mean square error (RMSE) decreased from 0.99 °C (0.10 psu) to 0.62 °C (0.07 psu) in assimilation experiments throughout the northwestern Pacific, which represents a 37.2 % (27.6 %) reduction in the error. The temperature (salinity) RMSE decreased by  ∼  0.60 °C ( ∼  0.05 psu) for the upper 900 m (1000 m). Sea level, temperature and salinity were in better agreement with in situ and satellite datasets after data assimilation than before. In addition, a 1-month experiment with daily analysis cycles and 5-day forecasts explored the performance of the system in an operational configuration. The results highlighted the positive impact of the 3DVAR initialization at all forecast ranges compared to the non-assimilative experiment. Therefore, the 3DVAR scheme proposed here, coupled to ROMS, shows a good predictive performance and can be used as an assimilation scheme for operational forecasting.

scholarly journals Analysis and Forecasting of Sea Ice Conditions with Three-Dimensional Variational Data Assimilation and a Coupled Ice–Ocean Model

2010 ◽  
Vol 27 (2) ◽  
pp. 353-369 ◽  
Author(s):  
Alain Caya ◽  
Mark Buehner ◽  
Tom Carrieres
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Three Dimensional ◽  
Ocean Model ◽  
Variational Data Assimilation ◽  
Error Statistics ◽  
Background Error ◽  
Gaussian Shape ◽  
Ice Conditions ◽  
Image Analyses

Abstract A three-dimensional variational data assimilation (3DVAR) system has been developed to provide analyses of the ice–ocean state and to initialize a coupled ice–ocean numerical model for forecasting sea ice conditions. This study focuses on the estimation of the background-error statistics, including the spatial and multivariate covariances, and their impact on the quality of the resulting sea ice analyses and forecasts. The covariances are assumed to be horizontally homogeneous and fixed in time. The horizontal correlations are assumed to have a Gaussian shape and are modeled by integrating a diffusion equation. A relatively simple implementation of the ensemble Kalman filter is used to produce ensembles of the ice–ocean model state that are representative of background error and from which the 3DVAR covariance parameters are estimated. Data assimilation experiments, using various configurations of 3DVAR and simpler assimilation approaches, are conducted over a 7-month period during the winter of 2006/07 for the Canadian east coast region. The only data assimilated are the gridded daily ice charts and RADARSAT image analyses produced by the Canadian Ice Service. All of the data assimilation experiments produce significantly improved short-term forecasts as compared with persistence. When assimilating the same data, the forecast quality from the experiments employing either the 3DVAR, direct insertion, or nudging is quite similar. However, assimilation of both the daily ice charts and RADARSAT image analyses in 3DVAR results in significant improvements to the sea ice concentration forecasts. This result supports the use of a data assimilation approach, such as 3DVAR, for combining multiple sources of observational data together with a sophisticated forecast model to provide analyses and forecasts of sea ice conditions.

scholarly journals Data Assimilation of Argos profiles in North-west Pacific Model

2016 ◽  
Author(s):  
Zhaoyi Wang ◽  
Andrea Storto ◽  
Nadia Pinardi ◽  
Guimei Liu ◽  
Hui Wang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Background Error ◽  
West Pacific ◽  
North West ◽  
Background Estimation ◽  
In Situ Data ◽  
The North ◽  
Salinity Data ◽  
Set Up

Abstract. Based on a novel specification of the background error covariance applying to Argos profiles assimilation, an oceanographic three-dimensional variational (3DVAR) data assimilation scheme is set up in the Regional Oceanic Model system (ROMs). Temperature and salinity data extracted from Argos profiles in 2006 have been assimilated into the North-West Pacific Model (NWPM). The quality control is done by comparing background estimation with observations in 2006.Firstly, the assimilated results are compared with merged in-situ data, Sea Surface Temperature (SST) derived from satellite data and reanalysis salinity data. It is found that assimilation of Argos profiles can improve the model results of SST and salinity. Secondly, the Root Mean Square (RMS) difference between model and Argos profiles is analyzed. For the tropic Pacific, the range of RMS temperature (salinity) error are less than 0.83 ℃ (0.11 PSU), decreasing ~ 23.2 % (~ 18.8 %) by comparing with the experiments without data assimilation. For the sub-tropic Pacific Ocean, the RMS of temperature (salinity) is less than 1.43 ℃ (0.135 PSU) and it also shows a decreasing trend after assimilation. It's indicated that the 3DVAR method works well in ROMs and can be used for the operational forecasting systems.

Application of the Sequential Three-Dimensional Variational Method to Assimilating SST in a Global Ocean Model

2008 ◽  
Vol 25 (6) ◽  
pp. 1018-1033 ◽  
Author(s):  
Zhongjie He ◽  
Yuanfu Xie ◽  
Wei Li ◽  
Dong Li ◽  
Guijun Han ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Ocean Model ◽  
Global Ocean ◽  
Background Error ◽  
Recursive Filters ◽  
Error Covariance ◽  
Observation Network ◽  
Isotropic Functions ◽  
Dependent Error

Abstract A recursive filter or parameterized curve fitting technique is usually used in a three-dimensional variational data assimilation (3DVAR) scheme to approximate the background error covariance, which can only represent the errors of an ocean field over a predetermined scale. Without an accurate flow-dependent error covariance that is also local and time dependent, a 3DVAR system may not provide good analyses because it is optimal only under the assumption of an accurate covariance. In this study, a sequential 3DVAR (S3DVAR) is formulated in model grid space to examine if there is useful information that can be extracted from the observation. This formulation is composed of a series of 3DVARs, each of which uses recursive filters with different length scales. It can provide an inhomogeneous and anisotropic analysis for the wavelengths that can be resolved by the observation network, just as with the conventional Barnes analysis or successive corrections. Being a variational formulation, S3DVAR can deal with data globally with an explicit specification of the observation errors; explicit physical balances or constraints; and advanced datasets, such as satellite and radar. Even though the S3DVAR analysis can be viewed as a set of isotropic functions superpositioned together, this superposition is not prespecified as in a single 3DVAR approach but is determined by the information that can be resolved by observation. The S3DVAR is adopted in a global sea surface temperature (SST) data assimilation system, into which the shipboard SSTs and the 4-km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder daily SSTs are assimilated, respectively. The results demonstrate that the proposed S3DVAR works better in practice than a single 3DVAR.

scholarly journals A GSI-Based Coupled EnSRF–En3DVar Hybrid Data Assimilation System for the Operational Rapid Refresh Model: Tests at a Reduced Resolution

2014 ◽  
Vol 142 (10) ◽  
pp. 3756-3780 ◽  
Author(s):  
Yujie Pan ◽  
Kefeng Zhu ◽  
Ming Xue ◽  
Xuguang Wang ◽  
Ming Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hybrid System ◽  
Control Variable ◽  
Three Dimensional ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
Noticeable Improvement ◽  
Forecasting System

Abstract A coupled ensemble square root filter–three-dimensional ensemble-variational hybrid (EnSRF–En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at ⅓ of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

Improving the Estimation of Temperature and Salinity by Assimilation of Observed Sound Speed Profiles

2021 ◽  
Author(s):  
Jiali Zhang ◽  
Liang Zhang ◽  
Anmin Zhang ◽  
Lianxin Zhang ◽  
Dong Li ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sound Speed ◽  
Field Experiments ◽  
Underwater Acoustics ◽  
Ocean Model ◽  
Speed Profile ◽  
Sound Speed Profile ◽  
Coastal Sea ◽  
Short Time ◽  
Assimilation Scheme

AbstractSound Speed Profile (SSP) affecting underwater acoustics is closely related to the temperature and the salinity fields. It is of great value to obtain the temperature and the salinity information through the high-precision sound speed profiles. In this paper, a data assimilation scheme by introducing sound speed profiles as a new constraint is proposed within the framework of 3DVAR data assimilation (referenced as SSP-constraint 3DVAR (SSPC-3DVAR) ), which aims at improving the analysis accuracy of initial fields of the temperature and salinity in coastal sea areas. In order to validate the performance of the new assimilation scheme, ideal experiments are firstly carried out to show the advantages of the new proposed SSPC-3DVAR. Then the temperature, the salinity, and the SSP observations from field experiments in a coastal area are assimilated into the Princeton Ocean Model to validate the performance of short-time forecasts, adopting the SSPC-3DVAR scheme. Results show that it is efficient to improve the estimate accuracy by as much as 14.6% (11.1%) for the temperature (salinity), compared with the standard 3DVAR. It demonstrates that the proposed SSPC-3DVAR approach works better in practice than the standard 3DVAR and will primarily benefit from variously and widely distributed observations in the future.

scholarly journals A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

2019 ◽  
Vol 12 (9) ◽  
pp. 4031-4051 ◽  
Author(s):  
Shizhang Wang ◽  
Zhiquan Liu
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
Poor Quality ◽  
Variational Data Assimilation ◽  
Radar Reflectivity ◽  
Background Error ◽  
Background Fields ◽  
Ice Phase

Abstract. A reflectivity forward operator and its associated tangent linear and adjoint operators (together named RadarVar) were developed for variational data assimilation (DA). RadarVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3D-Var) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadarVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50–100 iterations in each loop were needed to obtain a converged 3-D analysis of reflectivity, rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. It is shown that the deficiencies in the analysis using this operator, caused by the poor quality of the background fields and the use of the static background error covariance, can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using RadarVar also improved the short-term (2–5 h) precipitation forecasts compared to those of the experiment without DA.

scholarly journals Data Assimilation of Cloud-Affected Radiances in a Cloud-Resolving Model

2011 ◽  
Vol 139 (3) ◽  
pp. 755-773 ◽  
Author(s):  
Rosanne Polkinghorne ◽  
Tomislava Vukicevic
Keyword(s):  
Data Assimilation ◽  
Near Infrared ◽  
Positive Impact ◽  
Microwave Radiometer ◽  
Atmospheric Modeling ◽  
Separate Experiment ◽  
Background Error ◽  
Cloud Resolving Model ◽  
Infrared Radiances ◽  
Cloud Mask

Abstract Assimilation of cloud-affected infrared radiances from the Geostationary Operational Environmental Satellite-8 (GOES-8) is performed using a four-dimensional variational data assimilation (4DVAR) system designated as the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). A cloud mask is introduced in order to limit the assimilation to points that have the same type of cloud in the model and observations, increasing the linearity of the minimization problem. A series of experiments is performed to determine the sensitivity of the assimilation to factors such as the maximum-allowed residual in the assimilation, the magnitude of the background error decorrelation length for water variables, the length of the assimilation window, and the inclusion of other data such as ground-based data including data from the Atmospheric Emitted Radiance Interferometer (AERI), a microwave radiometer, radiosonde, and cloud radar. In addition, visible and near-infrared satellite data are included in a separate experiment. The assimilation results are validated using independent ground-based data. The introduction of the cloud mask where large residuals are allowed has the greatest positive impact on the assimilation. Extending the length of the assimilation window in conjunction with the use of the cloud mask results in a better-conditioned minimization, as well as a smoother response of the model state to the assimilation.

The Met. Office global three-dimensional variational data assimilation scheme

2000 ◽  
Vol 126 (570) ◽  
pp. 2991-3012 ◽  
Author(s):  
A. C. Lorenc ◽  
S. P. Ballard ◽  
R. S. Bell ◽  
N. B. Ingleby ◽  
P. L. F. Andrews ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

A Parallel Three-dimensional Variational Data Assimilation Scheme

2011 ◽  
Author(s):  
L. D’Amore ◽  
R. Arcucci ◽  
L. Marcellino ◽  
A. Murli ◽  
Theodore E. Simos ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme
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