A “dressed” Ensemble Kalman Filter using the Hybrid Coordinate Ocean Model in the Pacific

Abstract Environment and Climate Change Canada (ECCC) has recently developed an experimental high-resolution EnKF (HREnKF) regional ensemble prediction system, which it tested over the Pacific Northwest of North America for the first half of February 2011. The HREnKF has 2.5-km horizontal grid spacing and assimilates surface and upper-air observations every hour. To determine the benefits of the HREnKF over less expensive alternatives, its 24-h quantitative precipitation forecasts are compared with those from a lower-resolution (15 km) regional ensemble Kalman filter (REnKF) system and to ensembles directly downscaled from the REnKF using the same grid as the HREnKF but with no additional data assimilation (DS). The forecasts are verified against rain gauge observations and gridded precipitation analyses, the latter of which are characterized by uncertainties of comparable magnitude to the model forecast errors. Nonetheless, both deterministic and probabilistic verification indicates robust improvements in forecast skill owing to the finer grids of the HREnKF and DS. The HREnKF exhibits a further improvement in performance over the DS in the first few forecast hours, suggesting a modest positive impact of data assimilation. However, this improvement is not statistically significant and may be attributable to other factors.

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Assimilating Altimetry Data into a HYCOM Model of the Pacific: Ensemble Optimal Interpolation versus Ensemble Kalman Filter

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2009jtecho626.1 ◽

2010 ◽

Vol 27 (4) ◽

pp. 753-765 ◽

Cited By ~ 10

Author(s):

Liying Wan ◽

Laurent Bertino ◽

Jiang Zhu

Keyword(s):

Kalman Filter ◽

Ensemble Kalman Filter ◽

Nonlinear Dynamical Systems ◽

Computational Cost ◽

Optimal Interpolation ◽

Striking Difference ◽

Primary Concern ◽

Altimetry Data ◽

Ensemble Optimal Interpolation ◽

The Pacific

Abstract The ensemble Kalman filter (EnKF) has proven its efficiency in strongly nonlinear dynamical systems but is demanding in its computing power requirements, which are typically about the same as those of the four-dimensional variational data assimilation (4DVAR) systems presently used in several weather forecasting centers. A simplified version of EnKF, the so-called ensemble optimal interpolation (EnOI), requires only a small fraction of the computing cost of the EnKF, but makes the crude assumption of no dynamical evolution of the errors. How do both these two methods compare in realistic settings of a Pacific Ocean forecasting system where the computational cost is a primary concern? In this paper the two methods are used to assimilate real altimetry data via a Hybrid Coordinate Ocean Model of the Pacific. The results are validated against the independent Argo temperature and salinity profiles and show that the EnKF has the advantage in terms of both temperature and salinity and in all parts of the domain, although not with a very striking difference.

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An Ensemble Kalman Filter multi-tracer assimilation: Determining uncertain ocean model parameters for improved climate-carbon cycle projections

Ocean Modelling ◽

10.1016/j.ocemod.2012.12.012 ◽

2013 ◽

Vol 64 ◽

pp. 29-45 ◽

Cited By ~ 9

Author(s):

M. Gerber ◽

F. Joos

Keyword(s):

Kalman Filter ◽

Carbon Cycle ◽

Ensemble Kalman Filter ◽

Ocean Model ◽

Model Parameters

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Including Uncertainties of Sea Surface Temperature in an Ensemble Kalman Filter: A Case Study of Typhoon Sinlaku (2008)

Weather and Forecasting ◽

10.1175/waf-d-11-00136.1 ◽

2012 ◽

Vol 27 (6) ◽

pp. 1586-1597 ◽

Cited By ~ 16

Author(s):

Masaru Kunii ◽

Takemasa Miyoshi

Keyword(s):

Kalman Filter ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Ensemble Kalman Filter ◽

Forecast Error ◽

Ocean Model ◽

Sea Surface ◽

Ensemble Spread ◽

Error Covariance ◽

The Impact

Abstract Sea surface temperature (SST) plays an important role in tropical cyclone (TC) life cycle evolution, but often the uncertainties in SST estimates are not considered in the ensemble Kalman filter (EnKF). The lack of uncertainties in SST generally results in the lack of ensemble spread in the atmospheric states near the sea surface, particularly for temperature and moisture. In this study, the uncertainties of SST are included by adding ensemble perturbations to the SST field, and the impact of the SST perturbations is investigated using the local ensemble transform Kalman filter (LETKF) with the Weather Research and Forecasting Model (WRF) in the case of Typhoon Sinlaku (2008). In addition to the experiment with the perturbed SST, another experiment with manually inflated ensemble perturbations near the sea surface is performed for comparison. The results indicate that the SST perturbations within EnKF generally improve analyses and their subsequent forecasts, although manually inflating the ensemble spread instead of perturbing SST does not help. Investigations of the ensemble-based forecast error covariance indicate larger scales for low-level temperature and moisture from the SST perturbations, although manual inflation of ensemble spread does not produce such structural effects on the forecast error covariance. This study suggests the importance of considering SST perturbations within ensemble-based data assimilation and promotes further studies with more sophisticated methods of perturbing SST fields such as using a fully coupled atmosphere–ocean model.

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The Four Patterns of the East Branch of the Kuroshio Bifurcation in the Luzon Strait

Water ◽

10.3390/w10121822 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1822 ◽

Cited By ~ 2

Author(s):

Ruili Sun ◽

Fangguo Zhai ◽

Yanzhen Gu

Keyword(s):

Measurement Data ◽

Reanalysis Data ◽

Ocean Model ◽

Satellite Measurement ◽

Self Organizing Map ◽

Intraseasonal Variation ◽

Sea Temperature ◽

Hybrid Coordinate Ocean Model ◽

The Pacific ◽

The Kuroshio

Based on the self-organizing map (SOM) method, a suite of satellite measurement data, and Hybrid Coordinate Ocean Model (HYCOM) reanalysis data, the east branch of the Kuroshio bifurcation is found to have four coherent patterns associated with mesoscale eddies in the Pacific Ocean: anomalous southward, anomalous eastward, anomalous northward, and anomalous westward. The robust clockwise cycle of the four patterns causes significant intraseasonal variation of 62.2 days for the east branch. Furthermore, the study shows that the four patterns of the east branch of the Kuroshio bifurcation can influence the horizontal and vertical distribution of local sea temperature.

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A Method for Assimilating Lagrangian Data into a Shallow-Water-Equation Ocean Model

Monthly Weather Review ◽

10.1175/mwr3104.1 ◽

2006 ◽

Vol 134 (4) ◽

pp. 1081-1101 ◽

Cited By ~ 48

Author(s):

H. Salman ◽

L. Kuznetsov ◽

C. K. R. T. Jones ◽

K. Ide

Keyword(s):

Kalman Filter ◽

Shallow Water ◽

Ensemble Kalman Filter ◽

Shallow Water Equation ◽

Water System ◽

Direct Consequence ◽

Ocean Model ◽

Moving Frame ◽

Time Interval ◽

Lagrangian Data

Abstract Lagrangian measurements provide a significant portion of the data collected in the ocean. Difficulties arise in their assimilation, however, since Lagrangian data are described in a moving frame of reference that does not correspond to the fixed grid locations used to forecast the prognostic flow variables. A new method is presented for assimilating Lagrangian data into models of the ocean that removes the need for any commonly used approximations. This is accomplished by augmenting the state vector of the prognostic variables with the Lagrangian drifter coordinates at assimilation. It is shown that this method is best formulated using the ensemble Kalman filter, resulting in an algorithm that is essentially transparent for assimilating Lagrangian data. The method is tested using a set of twin experiments on the shallow-water system of equations for an unsteady double-gyre flow configuration. Numerical simulations show that this method is capable of correcting the flow even if the assimilation time interval is of the order of the Lagrangian autocorrelation time scale (TL) of the flow. These results clearly demonstrate the benefits of this method over other techniques that require assimilation times of 20%–50% of TL, a direct consequence of the approximations introduced in assimilating their Lagrangian data. Detailed parametric studies show that this method is particularly effective if the classical ideas of localization developed for the ensemble Kalman filter are extended to the Lagrangian formulation used here. The method that has been developed, therefore, provides an approach that allows one to fully realize the potential of Lagrangian data for assimilation in more realistic ocean models.

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Estimation of transport at open boundaries with an ensemble Kalman filter in a coastal ocean model

Ocean Modelling ◽

10.1016/j.ocemod.2013.01.002 ◽

2013 ◽

Vol 64 ◽

pp. 56-66 ◽

Cited By ~ 4

Author(s):

Antoni Jordi ◽

Dong-Ping Wang

Keyword(s):

Kalman Filter ◽

Ensemble Kalman Filter ◽

Coastal Ocean ◽

Ocean Model ◽

Open Boundaries ◽

Coastal Ocean Model

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A Data Assimilation Case Study Using a Limited-Area Ensemble Kalman Filter

Monthly Weather Review ◽

10.1175/mwr3358.1 ◽

2007 ◽

Vol 135 (4) ◽

pp. 1455-1473 ◽

Cited By ~ 31

Author(s):

Sébastien Dirren ◽

Ryan D. Torn ◽

Gregory J. Hakim

Keyword(s):

United States ◽

Kalman Filter ◽

Pacific Northwest ◽

Surface Pressure ◽

Ensemble Kalman Filter ◽

The United States ◽

Limited Area ◽

The Pacific ◽

Northwest Region ◽

Analysis Errors

Abstract Ensemble Kalman filter (EnKF) data assimilation experiments are conducted on a limited-area domain over the Pacific Northwest region of the United States, using the Weather Research and Forecasting model. Idealized surface pressure, radiosoundings, and aircraft observations are assimilated every 6 h for a 7-day period in January 2004. The objectives here are to study the performance of the filter in constraining analysis errors with a relatively inhomogeneous, sparse-observation network and to explore the potential for such a network to serve as the basis for a real-time EnKF system dedicated to the Pacific Northwest region of the United States. When only a single observation type is assimilated, results show that the ensemble-mean analysis error and ensemble spread (standard deviation) are significantly reduced compared to a control ensemble without assimilation for both observed and unobserved variables. Analysis errors are smaller than background errors over nearly the entire domain when averaged over the 7-day period. Moreover, comparisons of background errors and observation increments at each assimilation step suggest that the flow-dependent filter corrections are accurate in both scale and amplitude. An illustrative example concerns a misspecified mesoscale 500-hPa short-wave trough moving along the British Columbia coast, which is corrected by surface pressure observations alone. The relative impact of each observation type upon different variables and vertical levels is also discussed.

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Assimilation of ice concentration in a coupled ice?ocean model, using the Ensemble Kalman filter

Ocean Dynamics ◽

10.1007/s10236-003-0049-4 ◽

2003 ◽

Vol 53 (4) ◽

pp. 368-388 ◽

Cited By ~ 74

Author(s):

Knut Arild Lis�ter ◽

Julia Rosanova ◽

Geir Evensen

Keyword(s):

Kalman Filter ◽

Ensemble Kalman Filter ◽

Ocean Model ◽

Ice Concentration

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