Distant effect of assimilation of moored currents into a model of coastal wind-driven circulation off Oregon

An optimal interpolation (OI) sequential algorithm is implemented for a three-dimensional primitive equation model to assimilate current measurements from acoustic Doppler profilers moored on the Oregon shelf as a part of the Coastal Ocean Advances in Shelf Transport (COAST) upwelling experiment (May–August 2001). A stationary estimate of the forecast error covariance required by the OI is computed based on the error covariance in the model solution not constrained by data assimilation. Lagged model error covariances are used to account for the effect of previously assimilated data. The forecast error covariance has a shorter alongshore spatial scale than the model error covariance unconstrained by the data, as an effect of propagating dynamical modes. Assimilation of currents from one or two of the moorings located on the path of the upwelling jet helps to improve the model data rms error and correlation at the mooring sites located at an alongshore distance of 90 km, south or north from the assimilation sites. The coastal jet is deflected offshore over Heceta Bank, and assimilation of data from an inner-shelf mooring in the jet separation zone does not help to improve prediction in the far field. Larger improvements are obtained for the first part of the study period (yeardays 146–190). In the second part (days 191–237) the geometry of our limited area model possibly limits prediction accuracy. In numerical experiments involving assimilation of data from only one mooring the actual and expected rms error improvements are compared, providing a consistency test for the forecast error covariance.Keywords: upwelling, coastal ocean prediction, data assimilationKeywords: upwelling, coastal ocean prediction, data assimilatio

How Efficient IsModel-to-Model Data Assimilation atMitigating Atmospheric Forcing Errors in a Regional Ocean Model?

This paper examines the efficiency of a recently developed Nesting with Data Assimilation (NDA) method at mitigating errors in heat and momentum fluxes at the ocean surface coming from external forcing. The analysis uses a set of 19 numerical simulations, all using the same ocean model and exactly the same NDA process. One simulation (the reference) uses the original atmospheric data, and the other eighteen simulations are performed with intentionally introduced perturbations in the atmospheric forcing. The NDA algorithm uses model-to-model data assimilation instead of assimilating observations directly. Therefore, it requires a good quality, although a coarser resolution data assimilating parent model. All experiments are carried out in the South East Arabian Sea. The variables under study are sea surface temperature, kinetic energy, relative vorticity and enstrophy. The results show significant improvement in bias, root-mean-square-error, and correlation coefficients between the reference and the perturbed models when they are run in the data assimilating configurations. Residual post-assimilation uncertainties are similar or lower than uncertainties of satellite based observations. Different length of DA cycle within a range from 1 to 8 days has little effect on the accuracy of results

Model error and sequential data assimilation. A deterministic formulation

Data assimilation schemes are confronted with the presence of model errors arising from the imperfect description of atmospheric dynamics. These errors are usually modeled on the basis of simple assumptions such as bias, white noise, first order Markov process. In the present work, a formulation of the sequential extended Kalman filter is proposed, based on recent findings on the universal deterministic behavior of model errors in deep contrast with previous approaches (Nicolis, 2004). This new scheme is applied in the context of a spatially distributed system proposed by Lorenz (1996). It is found that (i) for short times, the estimation error is accurately approximated by an evolution law in which the variance of the model error (assumed to be a deterministic process) evolves according to a quadratic law, in agreement with the theory. Moreover, the correlation with the initial condition error appears to play a secondary role in the short time dynamics of the estimation error covariance. (ii) The deterministic description of the model error evolution, incorporated into the classical extended Kalman filter equations, reveals that substantial improvements of the filter accuracy can be gained as compared with the classical white noise assumption. The universal, short time, quadratic law for the evolution of the model error covariance matrix seems very promising for modeling estimation error dynamics in sequential data assimilation

Influence of assimilating rainfall derived from WSR-88D radar on the rainstorm forecasts over the southwestern United States

In this study, the impact of rainfall assimilation on the forecasts of convective rainfall over the mountainous areas in the southwestern United States is investigated. The rainfall is derived from the U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network, and the fifth-generation Mesoscale Model (MM5) Four-Dimensional Variational (4DVAR) system is employed in the study. We evaluate the rainfall assimilation skill through two rainstorm events (5-6 August and 11-12 September 2002) that occurred over the southwestern United States in 2002. A series of experiments for the two cases is conducted. The results show that the minimization process in the 4DVAR is sensitive to the length of assimilation window and error variance in the observation data. Assimilation of rainfall can produce a better short-range precipitation forecast. However, the time range of improved forecasts is limited to about 15 hours with the model resolution of 20 km. It is indicated that rainfall assimilation produces more realistic moisture divergence and temperature fields in the initial conditions for the two cases. Therefore the forecast of rainstorms is closer to observations in both quantity and pattern. Copyright 2006 by the American Geophysical Union