Towards the Development of an Operational Forecast System for the Florida Coast

Paramygin, Vladimir A.; Sheng, Y. Peter; Davis, Justin R.

doi:10.3390/jmse5010008

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Paramygin et al. (2017) applied the CH3D model nested in a large‐scale ADCIRC (Luettich et al., 1992) grid and identified that enhanced resolution in coastal zones is possible using this approach; however, the large‐scale grid simulations require significant computational time. Recently, Dietrich et al.…”

Section: Introductionmentioning

confidence: 99%

“…An early study by Robinson et al (1996) applied the Harvard ocean prediction system in a forecast configuration and highlighted the importance of effective data assimilation for accurate model results. Paramygin et al (2017) applied the CH3D model nested in a large-scale ADCIRC (Luettich et al, 1992) grid and identified that enhanced resolution in coastal zones is possible using this approach; however, the large-scale grid simulations require significant computational time. Recently, Dietrich et al (2018) found that atmospheric forecasts produce more accurate coastal forecasts compared to parametric hurricane wind models.…”

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confidence: 99%

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Influence of Hurricane Wind Field Variability on Real‐Time Forecast Simulations of the Coastal Environment

Rey

Mulligan

2021

JGR Oceans

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Tropical cyclones are a significant and increasing natural hazard for human life and infrastructure along many coastlines throughout the world. Atlantic Ocean hurricanes deliver powerful conditions to the east and Gulf coasts of North America annually and are the most destructive natural disaster in the United States (Grinsted et al., 2019). The frequency and intensity of these storms are projected to increase with future climate warming and longer storm formation periods (Knutson et al., 2010). During these storms, large waves, high storm surge, and strong currents can combine to create a multihazard marine environment, making understanding the impacts of these events in coastal areas a vital research area (Mulligan & Hanson, 2016). Since wind forcing acts as a critical driver for coastal hydrodynamic conditions, the selection of an atmospheric forcing model represents a critical decision and several atmospheric models can be used to forecast wind conditions during a storm. Moreover, large-scale ocean models can provide predictions of surface waves, water levels, and currents. However, these forecasts lack the high resolution needed to resolve local Abstract Dynamic conditions occur in the coastal ocean during severe storms. Forecasting these conditions is challenging, and large-scale numerical models require significant computing power. In this paper, we describe a real-time modeling system (DUNEX-RT), developed in support of the During Nearshore Event experiment (DUNEX) off the coast of North Carolina, United States of America. The model is run with wave, current, and water level boundary conditions from larger-scale models, and provides 36-h forecasts of significant wave height, depth-averaged velocity, and water levels every 6-h using Delft3D-SWAN. Observations and forecasts run at different times are compared and communicated via an interactive website to verify model performance in real-time and to visualize uncertainty from changing inputs. Here, we evaluate model sensitivity to inputs from seven different atmospheric hindcasts and two atmospheric forecasts for Hurricane Dorian in September 2019. The results emphasize the importance of accurate wind forcing, with significant differences observed between the output model results for different input atmospheric forcing models and forecasts produced at different times. The best results were achieved using atmospheric forcing from the high resolution rapid refresh model, and overall, DUNEX-RT had low errors at 33 wave, water level, and current sites across the system. The model results for water levels and significant wave heights were also accurate over a longer period of 49 days. Overall, the good forecast skill achieved for the wide range of conditions over this time results suggest that this high-resolution regional approach could be applied to forecast conditions in other coastal areas. Plain Language Summary Large waves and fast flowing currents occur in the coastal ocean during severe storm events, including hurricanes. Forecasting these conditions is ...

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Influence of Hurricane Wind Field Variability on Real‐Time Forecast Simulations of the Coastal Environment

Rey

Mulligan

2021

JGR Oceans

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“…Inevitably the foundations of operational global forecasts are being leveraged for localised downscaled dynamic operational ocean models: e.g., [15][16][17][18].…”

Section: Routine Sea Level and Operationsmentioning

confidence: 99%

Sea Level Forecasts Aggregated from Established Operational Systems

Taylor

Brassington

2017

JMSE

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Abstract:A 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.

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