Application of leeway drift data to predict the drift of panga skiffs

Case study of maritime search and rescue in the tropical pacific

Ben A. Brushett, Arthur A. Allen, Brian A. King, Charles J. Lemckert

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The use of Search and Rescue (SAR) drift forecasting in an operational capacity is demonstrated through two SAR case studies, each predicting the drift of a panga skiff for 120 h (Case 1) and 72 h (Case 2). The leeway characteristics of panga skiffs were previously unknown, until a leeway field study was undertaken in mid-2012 to empirically determine the influence of wind and waves on their drift. As part of the two case studies described herein, four ocean models were used as environmental forcing for a stochastic particle trajectory model, to forecast the drift and resulting search areas for the panga skiffs. Each of the four ocean models were tested individually, and then combined into a consensus forecast to ascertain which ocean model was the most accurate in terms of distance error of modelled positions compared to actual panga skiff locations. Additionally, a hit analysis was undertaken to determine whether the panga skiff was located within the forecast search areas for each ocean model, and for consensus search areas. Finally, an assessment of the search area sizes was carried out to assess the single ocean model forecast search area sizes, and how they compared with the consensus search area size. In both of the case studies, all four ocean model forecast search areas contained the panga skiff at the time intervals tested, indicating a 100% hit rate and general consensus between the ocean models. The consensus search area, where all four ocean models overlapped, was approximately one third the size of the average single model search area. This demonstrates that the consensus search areas provide a more efficient search area compared to individual ocean model search area forecasts.

Original languageEnglish
Pages (from-to)109-124
Number of pages16
JournalApplied Ocean Research
Volume67
DOIs
Publication statusPublished - 1 Sep 2017

    Fingerprint

Cite this