Assessing the impact of observations on ocean forecasts and reanalyses: Part 2, Regional applications

The value of global (e.g., altimetry, satellite sea-surface temperature, Argo) and regional (e.g., radars, gliders, instrumented mammals, airborne profiles, biogeochemical) observation-types for monitoring the mesoscale ocean circulation and biogeochemistry is demonstrated using a suite of global and regional prediction systems and remotely-sensed data. A range of techniques is used to demonstrate the value of different observation-types to regional systems and the benefit of high- resolution and adaptive sampling for monitoring the mesoscale circulation. The techniques include Observing System Experiments, Observing System Simulation Experiments, adjoint sensitivities, representer matrix spectrum, observation footprints, information content and spectral analysis. It is shown that local errors in global and basin-scale systems can be significantly reduced when assimilating observations from regional observing systems

Barents-2.5km v2.0: An operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard

An operational ocean and sea ice forecast model, Barents-2.5, is implemented at MET Norway for short-term forecasting at the coast off Northern Norway, the Barents Sea, and waters around Svalbard. Primary forecast parameters are the sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model is also a substantial input for drift modeling of pollutants, ice berg, and in search-and-rescue pertinent applications in the Arctic domain. Barents-2.5 has recently been upgraded to include an Ensemble Prediction System with 24 daily realizations of the model state. SIC, SST and in-situ hydrography are constrained through the Ensemble Kalman Filter (EnKF) data assimilation scheme executed in daily forecast cycles with lead time up to 66 hours. While the ocean circulation is not directly constrained by assimilation of ocean currents, the model ensemble represents the given uncertainty in the short-term current field by retaining the current state for each member throughout forecast cycles. Here we present the model setup and a validation in terms of SIC, SST and in-situ hydrography. The performance of the ensemble to represent the models uncertainty, and the performance of the EnKF to constrain the model state are discussed, in addition to the model&rsquo;s forecast capabilities for SIC and SST.</p

Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model

Operational forecasting systems routinely assimilate daily means of sea ice concentration (SIC) from microwave radiometers in order to improve the accuracy of the forecasts. However, the temporal and spatial averaging of the individual satellite swaths into daily means of SIC entails two main drawbacks: (i) the spatial resolution of the original product is blurred (especially critical in periods with strong sub-daily sea ice movement), and (ii) the sub-daily frequency of passive microwave observations in the Arctic are not used, providing less temporal resolution in the data assimilation (DA) analysis and, therefore, in the forecast. Within the SIRANO (Sea Ice Retrievals and data Assimilation in NOrway) project, we investigate how challenges (i) and (ii) can be avoided by assimilating individual satellite swaths (level 3 uncollated) instead of daily means (level 3) of SIC. To do so, we use a regional configuration of the Barents Sea (2.5 km grid) based on the Regional Ocean Modeling System (ROMS) and the Los Alamos Sea Ice Model (CICE) together with the ensemble Kalman filter (EnKF) as the DA system. The assimilation of individual swaths significantly improves the EnKF analysis of SIC compared to the assimilation of daily means; the mean absolute difference (MAD) shows a 10 % improvement at the end of the assimilation period and a 7 % improvement at the end of the 7 d forecast period. This improvement is caused by better exploitation of the information provided by the SIC swath data, in terms of both spatial and temporal variance, compared to the case when the swaths are combined to form a daily mean before assimilation.</p

Surface currents in operational oceanography: Key applications, mechanisms, and methods

This paper reviews physical mechanisms, observation techniques and modelling approaches dealing with surface currents on short time scales (hours to days) relevant for operational oceanography. Key motivations for this article include fundamental difficulties in reliable measurements and the persistent lack of a widely held consensus on the definition of surface currents. These problems are augmented by the fact that various methods to observe and model ocean currents yield very different representations of a surface current. We distinguish between four applicable definitions for surface currents; (i) the interfacial surface current, (ii) the direct wind-driven surface current, (iii) the surface boundary layer current, and (iv) an effective drift current. Finally, we discuss challenges in synthesising various data sources of surface currents - i.e. observational and modelling – and take a view on the predictability of surface currents concluding with arguments that parts of the surface circulation exhibit predictability useful in an operational context

Constraining energetic slope currents through assimilation of high-frequency radar observations

Assimilation of high-frequency (HF) radar current observations and CTD hydrography is performed with the 4D-Var analysis scheme implemented in the Regional Ocean Modeling System (ROMS). We consider both an idealized case, with a baroclinic slope current in a periodic channel, and a realistic case for the coast of Vesterålen in northern Norway. In the realistic case, the results of the data assimilation are compared with independent data from acoustic profilers and surface drifters. Best results are obtained when background error correlation scales are small (10 km or less) and when the data assimilation window is short, i.e. about 1 day. Furthermore, we find that the impact of assimilating HF radar currents is generally larger than the impact of CTD hydrography. However, combining the HF radar currents with a few hydrographic profiles gives significantly better results, which demonstrates the importance of complementing surface observations with observations of the vertical structure of the ocean