Evaluation of Environmental Data for Search and Rescue II

Roarty, Hugh; Allen, A A; Glenn, Scott; Kohut, Josh; Nazzaro, Laura; Fredj, Erick

doi:10.1109/oceanskobe.2018.8559228

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…An effective way to display this information could be by means of a model ranking based on the averaged model performance over the area of interest. This can be addressed by computing the spatiotemporal average of SS considering all available drifter observations during a specific period of observations (as in Röhrs et al, 2012;De Dominicis et al, 2014;Roarty et al, 2016;Sotillo et al, 2016;French-McCay et al, 2017;Phillipson and Toumi, 2017;Roarty et al, 2018). However, we show in this article that averages of the SS should be applied and interpreted with caution.…”

Section: Introductionmentioning

confidence: 93%

“…After being applied in the context of the Deepwater Horizon oil spill (Liu and Weisberg, 2011;Mooers et al, 2012;Halliwell et al, 2014), this metric has been one of the most widely used statistics for trajectory evaluation. The SS has been used to evaluate different parameterizations in operational oil spill trajectory models (Ivichev et al, 2012;Röhrs et al, 2012;De Dominicis et al, 2014;Berta et al, 2015;Wang et al, 2016;French-McCay et al, 2017;Janeiro et al, 2017;Chen et al, 2018;Zhang et al, 2018;Tamtare et al, 2019), to assess the impact of data assimilation in the model's Lagrangian predictability (Sperrevik et al, 2015;Phillipson and Toumi, 2017), to estimate the accuracy of the gap-filled method for HFR data (Fredj et al, 2017), to test the ability of ocean models in simulating surface transport (Sotillo et al, 2016), and to evaluate the relative performance of ocean models and HFR surface currents in predicting trajectories for SAR operations (Roarty et al, 2016(Roarty et al, , 2018.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications

et al. 2021

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Search and rescue (SAR) modeling applications, mostly based on Lagrangian tracking particle algorithms, rely on the accuracy of met-ocean forecast models. Skill assessment methods are therefore required to evaluate the performance of ocean models in predicting particle trajectories. The Skill Score (SS), based on the Normalized Cumulative Lagrangian Separation (NCLS) distance between simulated and satellite-tracked drifter trajectories, is a commonly used metric. However, its applicability in coastal areas, where most of the SAR incidents occur, is difficult and sometimes unfeasible, because of the high variability that characterizes the coastal dynamics and the lack of drifter observations. In this study, we assess the performance of four models available in the Ibiza Channel (Western Mediterranean Sea) and evaluate the applicability of the SS in such coastal risk-prone regions seeking for a functional implementation in the context of SAR operations. We analyze the SS sensitivity to different forecast horizons and examine the best way to quantify the average model performance, to avoid biased conclusions. Our results show that the SS increases with forecast time in most cases. At short forecast times (i.e., 6 h), the SS exhibits a much higher variability due to the short trajectory lengths observed compared to the separation distance obtained at timescales not properly resolved by the models. However, longer forecast times lead to the overestimation of the SS due to the high variability of the surface currents. Findings also show that the averaged SS, as originally defined, can be misleading because of the imposition of a lower limit value of zero. To properly evaluate the averaged skill of the models, a revision of its definition, the so-called SS∗, is recommended. Furthermore, whereas drifters only provide assessment along their drifting paths, we show that trajectories derived from high-frequency radar (HFR) effectively provide information about the spatial distribution of the model performance inside the HFR coverage. HFR-derived trajectories could therefore be used for complementing drifter observations. The SS is, on average, more favorable to coarser-resolution models because of the double-penalty error, whereas higher-resolution models show both very low and very high performance during the experiments.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications

et al. 2021

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“…The analysis presented here was conducted as part of a validation experiment of the radar network in conjunction with the Coast Guard Office of Search and Rescue. Three clusters of Coast Guard surface drifters (Allen, 1996) were released: one cluster along the 30 m isobath in the northern area of the 5 MHz network, one along the 70 m isobath in the northern area of the 5 MHz network and one along the 30 m isobath in the central region of the 5 and 13 MHz network (Roarty et al, 2018). The average surface drift is towards the southwest so the hope was that the drifters deployed in the northern region of the network would drift through the majority of the network coverage.…”

Section: Resultsmentioning

confidence: 99%

“…variables derived from multiple measurements. The types of products that are generated include daily, seasonal and annual means of the Mid Atlantic surface currents (Gong et al, 2010;Roarty et al, 2020), virtual Lagrangian drifter trajectories (Roarty et al, 2016a;Roarty et al, 2018) and Eulerian velocity time series at any point in the field of coverage. For daily, monthly or yearly maps of the surface currents we typically require 50% temporal coverage and the OI normalized velocity uncertainty to be below 0.6 (Kohut et al, 2012) at a grid point in order for a vector to be displayed.…”

Section: Level 4derived Productsmentioning

confidence: 99%

Real-time quality assurance and quality control for a high frequency radar network

Roarty,

Updyke,

Nazzaro

et al. 2024

Front. Mar. Sci.

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This paper recommends end to end quality assurance methods and quality control tests for High Frequency Radar Networks. We focus on the network that is operated by the Mid Atlantic Regional Association Coastal Ocean Observing System (MARACOOS). The network currently consists of 38 radars making real-time measurements of the surface currents over the continental shelf for a variety of applications including search and rescue planning, oil spill trajectory modelling and providing a transport context for marine biodiversity observing networks. MARACOOS has been delivering surface current measurements to the United States Coast Guard (USCG) since May 2009. Data quality is important for all applications; however, since the USCG uses this surface current information to plan life-saving missions, delivery of the best quality data is crucial. We have mapped the components of the HF radar data processing chain onto the data levels presented in the NASA Earth Science Reference Handbook and have applied quality assurance and quality control techniques at each data level to achieve the highest quality data. There are approximately 400 High Frequency radars (HFRs) deployed globally and the presented techniques can provide a foundation for data quality checks and standardization of the data collected by the large number of systems operating today.

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