Forecast Sensitivity-based Observation Impact (FSOI) in an analysis-forecast system of the California Current Circulation

Forecast Sensitivity-based Observation Impacts (FSOI) in an analysis-forecast system of the California Current System (CCS) are quantified using an adjoint-based approach. The analysis-forecast system is based on the Regional Ocean Modeling System (ROMS) and a 4-dimensional variational (4D-Var) data assimilation approach. FSOI was applied to four different metrics of forecast skill that target important features of the CCS circulation along the central California coast. A particular focus of the FSOI analysis is the impact of assimilation of measurements of the radial component of surface currents from a network of high frequency (HF) radars since this is a new data stream in the near-real-time system considered here. On average, similar to 50-60% of all observations assimilated into the model yielded improvements in the forecast skill. Conversely, the remaining similar to 40-50% of data degrade the forecasts, in line with similar findings in numerical weather prediction systems. Much of the improvement in forecast skill arises from remotely sensed observations, including HF radar data; on average only similar to 50% of in situ measurements contribute to a reduction in forecast error. This is partly due to the large volume of remote sensing observations compared to in situ observations. However, in situ observations are an order of magnitude more impactful than remotely sensed data when viewed in terms of the average impact per observation.

Automated summary

In this study, the impact of observations on the forecast skill in the California Current System (CCS) analysis-forecast system was quantified using a sensitivity-based approach. The results showed that assimilation of observations improved the forecast skill by about 50-60% on average, while approximately 40-50% of the data had a negative impact on the forecasts.