Sea Level Forecasts Aggregated from Established Operational Systems
AbstractA system for providing routine seven-day forecasts of sea level observable at tide gauge locations is described and evaluated. Forecast time series are aggregated from well-established operational systems of the Australian Bureau of Meteorology; although following some adjustments these systems are only quasi-complimentary. Target applications are routine coastal decision processes under non-extreme conditions. The configuration aims to be relatively robust to operational realities such as version upgrades, data gaps and metadata ambiguities. Forecast skill is evaluated against hourly tide gauge observations. Characteristics of the bias correction term are demonstrated to be primarily static in time, with time varying signals showing regional coherence. This simple approach to exploiting existing complex systems can offer valuable levels of skill at a range of Australian locations. The prospect of interpolation between observation sites and exploitation of lagged-ensemble uncertainty estimates could be meaningfully pursued. Skill characteristics define a benchmark against which new operational sea level forecasting systems can be measured. More generally, an aggregation approach may prove to be optimal for routine sea level forecast services given the physically inhomogeneous processes involved and ability to incorporate ongoing improvements and extensions of source systems. View Full-Text
A printed edition of this Special Issue is available here.
Share & Cite This Article
Taylor, A.; Brassington, G.B. Sea Level Forecasts Aggregated from Established Operational Systems. J. Mar. Sci. Eng. 2017, 5, 33.
Taylor A, Brassington GB. Sea Level Forecasts Aggregated from Established Operational Systems. Journal of Marine Science and Engineering. 2017; 5(3):33.Chicago/Turabian Style
Taylor, Andy; Brassington, Gary B. 2017. "Sea Level Forecasts Aggregated from Established Operational Systems." J. Mar. Sci. Eng. 5, no. 3: 33.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.