In ISOPE’2019, the first intercomparison of wave reanalysis products (ERA5 and CFSR-W) was presented (Stefanakos, 2019). In the present work, this intercomparison is further extended by including global wave reanalysis product WAVERYS, which comes from Copernicus Marine Service (CMEMS) and it is based on the third-generation wave MFWAM model. WAVERYS takes into account oceanic currents (GLORYS12) and assimilates wave height from altimetry missions and wave spectra from Sentinel 1. In the present analysis, several statistical features are assessed, such as seasonal variability, probability structure, several error metrics, as well as extreme-value statistics and trends.
In a previous work (Stefanakos, 2019), an intercomparison of two wave reanalysis products, namely ERA5 and CFSR-W, has been presented for the first time. Datasets of significant wave height were assessed by means of several statistical features, covering a period of 31 years (1979-2009). Then, this work has been further extended to the wind speed and the results have been presented in Stefanakos (2021).
In addition, in these works, a survey of previous reanalysis products have been given. Moreover, it has been stressed the vital importance of wind and wave reanalysis databases to wind and wave climate studies, which, subsequently, play an instrumental role to the design, operation and maintenance of a large number of structures floating at seas (like ships, platforms, aquaculture facilities, renewable energy installations, floating or submerged bridges/tunnels etc).
Further, the generation of new reanalyses has brought up several studies dealing with the sensitivity and/or comparison against other sources of data such as buoy in situ and satellite altimeter measurements. For a survey of past studies, see, e.g., Stefanakos (2021).
Since then, several new studies have been published, shedding more light in the performance and the accuracy of various reanalysis products, as well as their use to various aspects of wave climate description, such as variability, trends, probability structure, extreme values etc.
For example, Patra et al. (2020) examine the influences of major climate variability modes on global extreme significant wave height (SWH) during 1992–2016 using merged satellite altimeter records and ERA5 reanalysis data set. Various climatic indices (ENSO: El Niño-Southern Oscillation, NAO: North Atlantic Oscillation, NPO: North Pacific Oscillation, and SAM: Southern Annular mode) are considered to identify the regions with significant impacts in Hmax.