Extreme High Swell Events on the Moroccan Atlantic Coast

1.0 Introduction » 1.2 About the Lesson

This lesson aims to improve the ability of marine forecasters to forecast extreme marine events related to high swells. It does so by providing background information on winds and waves, and presenting a process for monitoring and forecasting high swell events using a variety of data. These include ASCAT scatterometer wind data and the ECMWF Extreme Forecast Index (EFI) product, which is useful for verifying model output and improving the quality of heavy swell forecasts. The forecast process is applied to two cases that occurred on the Moroccan Atlantic coast. The first case goes through the forecast process in detail while the second case highlights particular aspects.

Target audience

The lesson is intended for marine operational forecasters, be they beginners or experts. The lesson can be used by forecasters as a self-paced learning program and by instructors as a lecture aid in marine meteorology and forecasting courses, particularly those with a remote sensing component.

Prerequisite knowledge

Before taking the lesson, learners should be familiar with the basics of general and marine forecasting techniques as well as marine forecast models and observational and remote sensing tools.

The lesson uses ASCAT scatterometer wind data, which may be new to some learners. Regardless of whether you have used the data before, it is strongly recommended you take the COMET lesson Using Scatterometer Wind and Altimeter Wave Estimates in Marine Forecasting before starting this lesson.

ASCAT

Lesson objectives

After going through the lesson, learners should be able to:

  • Differentiate high swell and wind waves
  • Describe the characteristics of high swells caused by synoptic events
  • Describe the impact of swell events
  • Monitor and forecast high swell events using a marine forecast process that involves the use of observational data, including ASCAT scatterometer wind data, and model output from WAVEWATCH III and the WAveModel (WAM) along with its Extreme Forecast Index