Lars-Anders Breivik

Assimilation of ERS SAR wave spectra in an operational wave model

Inverted wave spectra from ERS-1 synthetic aperture radar (SAR) image and wave mode products have been assimilated in the operational wave model for the ocean wave forecast service at the Norwegian Meteorological Institute. Elements of the operational system are explained briefly, and the impact of including the SAR wave data in the operational wave model runs is shown both for individual cases and as overall statistics. Although individual cases clearly show that the satellite observations are able to influence the forecast in a generally positive way, the average improvement is minor for the areas covered by the wave model. Reasons for this are the intermittency of the data, on the average small differences between inverted SAR and model first-guess wave spectra, and to some extent, limitations in the analysis method.

Use of satellite observations for operational oceanography: recent achievements and future prospects

The paper gives an overview of the development of satellite oceanography over the past five years focusing on the most relevant issues for operational oceanography. Satellites provide key essential variables to constrain ocean models and/or serve downstream applications. New and improved satellite data sets have been developed and have directly improved the quality of operational products. The status of the satellite constellation for the last five years was, however, not optimal. Review of future missions shows clear progress and new research and development missions with a potentially large impact for operational oceanography should be demonstrated. Improvement of data assimilation techniques and developing synergetic use of high resolution satellite observations are important future priorities.

The Copernicus Marine Environment Monitoring Service Ocean State Report

ABSTRACT The Copernicus Marine Environment Monitoring Service (CMEMS) Ocean State Report (OSR) provides an annual report of the state of the global ocean and European regional seas for policy and decision-makers with the additional aim of increasing general public awareness about the status of, and changes in, the marine environment. The CMEMS OSR draws on expert analysis and provides a 3-D view (through reanalysis systems), a view from above (through remote-sensing data) and a direct view of the interior (through in situ measurements) of the global ocean and the European regional seas. The report is based on the unique CMEMS monitoring capabilities of the blue (hydrography, currents), white (sea ice) and green (e.g. Chlorophyll) marine environment. This first issue of the CMEMS OSR provides guidance on Essential Variables, large-scale changes and specific events related to the physical ocean state over the period 1993–2015. Principal findings of this first CMEMS OSR show a significant increase in global and regional sea levels, thermosteric expansion, ocean heat content, sea surface temperature and Antarctic sea ice extent and conversely a decrease in Arctic sea ice extent during the 1993–2015 period. During the year 2015 exceptionally strong large-scale changes were monitored such as, for example, a strong El Niño Southern Oscillation, a high frequency of extreme storms and sea level events in specific regions in addition to areas of high sea level and harmful algae blooms. At the same time, some areas in the Arctic Ocean experienced exceptionally low sea ice extent and temperatures below average were observed in the North Atlantic Ocean.

Objective analysis combining observation errors in physical space and observation space

A generalization of the traditional observation-error model in objective analysis is presented. It is suggested that for observations with nonlinear observation operators, the observation errors should be split up into one contribution in the measurement domain and one contribution in the domain of the model physical quantities interpolated to the measurement locations (physical space). In particular, errors of representativeness are better modelled in physical space. Analysis equations are derived for this generalized problem. The approach suggested has important applications in the assimilation of satellite observations. For special cases with a wide application this generalization does not introduce an inhibiting computational complexity. Copyright