Assimilation of Doppler radar current data into numerical ocean models

Lewis, James K.; Shulman, Igor; Blumberg, Alan F.

doi:10.1016/s0278-4343(98)00006-5

Cited by 74 publications

(52 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite this level of quality control on the data, physically unrealistic currents have been recorded during calm periods Shay et al, 1995;Paduan and Rosenfeld, 1996], and regions in which there are physically unrealistic convergences or divergences in the surface currents have also been reported [Lewis et al, 1998]. Similar physically unrealistic flows will be shown here.…”

Section: Introductionsupporting

confidence: 64%

A detailed comparison of measured and modeled wind‐driven currents in the North Channel of the Irish Sea

Davies¹,

Hall²,

Howarth³

et al. 2001

J. Geophys. Res.

View full text Add to dashboard Cite

Abstract. The wind-induced flow in the North Channel of the Irish Sea is examined using an acoustic doppler current profiler (ADCP), to determine current profile, together with ocean surface current radar (OSCR) measurements of surface current and numerical models. The period considered is a major outflow event which took place during February 3-5, 1994. Although OSCR has been used previously to examine the spatial variability of tidal currents and long-term wind-driven flows, no data sets exist, and comparisons with models have not been performed before during a major storm event. Such an event is considered here. A three-dimensional shelf-wide coarse grid (resolution -12 km) model is used to take account of large-scale wind events. A more limited area model of the North Channel (of higher resolution, -1 km) with boundary forcing from the large-area model is used to determine current profiles in the vertical for comparison with ADCP measurements and surface currents in the area for comparison with OSCR measurements. The grid size of this model is the same as the bin size over which the OSCR system measures the current. This combination of models and measurements gives significant insight into the temporal and spatial variability of the shelf-wide winds, which produced the major outflow event, and the spatial variability of the flow in the North Channel. A comparison between along-channel flows computed with the shelf-wide model and ADCP measurements shows that this model overestimates the flow.However, a similar comparison with the higher-resolution model shows that this model slightly underestimates the flow. Since both models contain the same physics, namely, solve the same three-dimensional equations with identical vertical resolution, these differences can be attributed to the differences in horizontal resolution. These give rise to differences in the magnitudes of elevation gradients and local wind-forced currents computed with the models. •), (taking into account errors in HF Radar measurements) in the computed and observed spatial and temporal variability of the surface current. However, the variability (in both space and time) of the computed current is significantly smoother than that determined by the radar. Also, away from the transmitter sites the signal to noise ratio in the radar signal is so large that accurate measurements (to within 0.3 m s '•) could not be made during this storm event. Calculations showed that the value of the computed surface current was sensitive to the assumed roughness length, although below the surface layer (of the order of 3 m) the current was not affected by the surface roughness value. Comparison between computed currents determined with the high-resolution model and those measured by the radar showed a similar bias to that found in the ADCP comparison.

show abstract

Section: Introductionsupporting

confidence: 64%

A detailed comparison of measured and modeled wind‐driven currents in the North Channel of the Irish Sea

Davies¹,

Hall²,

Howarth³

et al. 2001

J. Geophys. Res.

View full text Add to dashboard Cite

show abstract

“…The first work assimilating HF radar surface data into an ocean model was done by Lewis et al (1998) using a nudging technique to correct the model surface current towards the HF radar estimates. Since then, and driven by the continuous expansion of the network of HF radar systems, different data assimilation approaches have been used to assimilate HF radar currents into nonlinear, high-resolution ocean models: nudging (Lewis et al, 1998;Wilkin et al, 2005;Gopalakrishnan and Blumberg, 2012), sequential assimilation (Breivik and Saetra, 2001;Oke et al, 2002;Paduan and Shulman, 2004;Kurapov et al, 2005a;Oke et al, 2009) and 4DVAR assimilation schemes (Hoteit et al, 2009;Zhang et al, 2010;Yu et al, 2012).…”

Section: Data Assimilation Of Ocean Currentsmentioning

confidence: 99%

“…The first work aiming to assimilate HF radar currents into a regional model of the Monterey Bay (California, US) was published by Lewis et al (1998). The HF radar observations, u o , were assimilated by adding a fictitious surface wind stress term that nudged the model solution u 1 (uppermost layer) towards the observed values:…”

Section: Nudgingmentioning

confidence: 99%

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

Isern‐Fontanet

Ballabrera-Poy

Turiel

et al. 2017

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

Abstract. Ocean currents play a key role in Earth's climate -they impact almost any process taking place in the ocean and are of major importance for navigation and human activities at sea. Nevertheless, their observation and forecasting are still difficult. First, no observing system is able to provide direct measurements of global ocean currents on synoptic scales. Consequently, it has been necessary to use sea surface height and sea surface temperature measurements and refer to dynamical frameworks to derive the velocity field. Second, the assimilation of the velocity field into numerical models of ocean circulation is difficult mainly due to lack of data. Recent experiments that assimilate coastal-based radar data have shown that ocean currents will contribute to increasing the forecast skill of surface currents, but require application in multidata assimilation approaches to better identify the thermohaline structure of the ocean. In this paper we review the current knowledge in these fields and provide a global and systematic view of the technologies to retrieve ocean velocities in the upper ocean and the available approaches to assimilate this information into ocean models.

show abstract

“…Since then, the method has been successfully applied to several oceanographic numerical problems such as the estimation of boundary conditions (Marchesiello et al, 2001;Chen et al, 2013), downscaling (Li et al, 2012), and other DA problems (Verron, 1992;Haines et al, 1993;Blayo et al, 1994;Lewis et al, 1998;Killworth et al, 2001;Thompson et al, 2006). Concerning applications to DA problems, the weights given to the model and the observations are generally not based on any optimality condition, but are rather scalars or Gaussian-like functions constructed based on physical assumptions or empirical considerations.…”

Section: G a Ruggiero Et Al: Numerical Experiments With The Dbfnmentioning

confidence: 99%

Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model

Ruggiero

Ourmiéres

Blum

et al. 2015

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

Abstract. The diffusive back-and-forth nudging (DBFN)is an easy-to-implement iterative data assimilation method based on the well-known nudging method. It consists of a sequence of forward and backward model integrations, within a given time window, both of them using a feedback term to the observations. Therefore, in the DBFN, the nudging asymptotic behaviour is translated into an infinite number of iterations within a bounded time domain. In this method, the backward integration is carried out thanks to what is called backward model, which is basically the forward model with reversed time step sign. To maintain numeral stability, the diffusion terms also have their sign reversed, giving a diffusive character to the algorithm. In this article the DBFN performance to control a primitive equation ocean model is investigated. In this kind of model non-resolved scales are modelled by diffusion operators which dissipate energy that cascade from large to small scales. Thus, in this article, the DBFN approximations and their consequences for the data assimilation system set-up are analysed. Our main result is that the DBFN may provide results which are comparable to those produced by a 4Dvar implementation with a much simpler implementation and a shorter CPU time for convergence. The conducted sensitivity tests show that the 4Dvar profits of long assimilation windows to propagate surface information downwards, and that for the DBFN, it is worth using short assimilation windows to reduce the impact of diffusioninduced errors. Moreover, the DBFN is less sensitive to the first guess than the 4Dvar.

show abstract

Assimilation of Doppler radar current data into numerical ocean models

Cited by 74 publications

References 8 publications

A detailed comparison of measured and modeled wind‐driven currents in the North Channel of the Irish Sea

A detailed comparison of measured and modeled wind‐driven currents in the North Channel of the Irish Sea

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model

Contact Info

Product

Resources

About