ERA‐40 is a re‐analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium‐Range Weather Forecasts (ECMWF) in collaboration with many institutions. The observing system changed considerably over this re‐analysis period, with assimilable data provided by a succession of satellite‐borne instruments from the 1970s onwards, supplemented by increasing numbers of observations from aircraft, ocean‐buoys and other surface platforms, but with a declining number of radiosonde ascents since the late 1980s. The observations used in ERA‐40 were accumulated from many sources. The first part of this paper describes the data acquisition and the principal changes in data type and coverage over the period. It also describes the data assimilation system used for ERA‐40. This benefited from many of the changes introduced into operational forecasting since the mid‐1990s, when the systems used for the 15‐year ECMWF re‐analysis (ERA‐15) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re‐analysis were implemented. Several of the improvements are discussed. General aspects of the production of the analyses are also summarized.A number of results indicative of the overall performance of the data assimilation system, and implicitly of the observing system, are presented and discussed. The comparison of background (short‐range) forecasts and analyses with observations, the consistency of the global mass budget, the magnitude of differences between analysis and background fields and the accuracy of medium‐range forecasts run from the ERA‐40 analyses are illustrated. Several results demonstrate the marked improvement that was made to the observing system for the southern hemisphere in the 1970s, particularly towards the end of the decade. In contrast, the synoptic quality of the analysis for the northern hemisphere is sufficient to provide forecasts that remain skilful well into the medium range for all years. Two particular problems are also examined: excessive precipitation over tropical oceans and a too strong Brewer‐Dobson circulation, both of which are pronounced in later years. Several other aspects of the quality of the re‐analyses revealed by monitoring and validation studies are summarized. Expectations that the ‘second‐generation’ ERA‐40 re‐analysis would provide products that are better than those from the firstgeneration ERA‐15 and NCEP/NCAR re‐analyses are found to have been met in most cases. © Royal Meteorological Society, 2005. The contributions of N. A. Rayner and R. W. Saunders are Crown copyright.

In this article global estimates of 100-yr return values of wind speed and significant wave height are presented. These estimates are based on the ECMWF 40-yr Re-Analysis (ERA-40) data and are linearly corrected using estimates based on buoy data. This correction is supported by global Topographic Ocean Experiment (TOPEX) altimeter data estimates. The calculation of return values is based on the peaks-over-threshold method. The large amount of data used in this study provides evidence that the distributions of significant wave height and wind speed data belong to the domain of attraction of the exponential. Further, the effect of the space and time variability of significant wave height and wind speed on the prediction of their extreme values is assessed. This is done by performing detailed global extreme value analyses using different decadal subperiods of the 45-yr-long ERA-40 dataset.

The European Centre for Medium‐Range Weather Forecasts (ECMWF) has recently finished ERA‐40, a reanalysis covering the period September 1957 to August 2002. One of the products of ERA‐40 consists of six‐hourly global fields of wave parameters, like significant wave height and wave period. These data have been generated with the centre's WAM wave model. From these results we have derived climatologies of important wave parameters, including significant wave height, mean wave period, and extreme significant wave heights. Particular emphasis is on the variability of these parameters, both in space and time. Besides being important for scientists studying climate change, these results are also important for engineers who have to design maritime constructions. This paper describes the ERA‐40 data and gives an overview of the results derived. The results are available on a global 1.5° × 1.5° grid. They are accessible from the Web‐based KNMI/ERA‐40 wave atlas at http://www.knmi.nl/waveatlas. Copyright © 2005 Royal Meteorological Society

This paper describes the comparison of wind speed and significant wave height data from several reanalyses. The data are assessed against time-averaged altimeter and buoy measurements. The comparisons between the datasets are made in terms of description of short-scale features, monthly means, and long-scale features— namely trends and variability. The results show that although the quality of the datasets in terms of their comparisons with observations differs, most of the long-scale features are equally present in all datasets. The differences between the several wave datasets are larger than those between the wind speed datasets; moreover, differences in wave datasets exist even when the forcing winds used to produce the different wave reanalyses are the same. Most of the discrepancies between the datasets occur in the Tropics, testifying that the physics in that region is still poorly known. The data before the mid-1980s show significant discrepancies also in the Southern Hemisphere, most of ... Abstract This paper describes the comparison of wind speed and significant wave height data from several reanalyses. The data are assessed against time-averaged altimeter and buoy measurements. The comparisons between the datasets are made in terms of description of short-scale features, monthly means, and long-scale features— namely trends and variability. The results show that although the quality of the datasets in terms of their comparisons with observations differs, most of the long-scale features are equally present in all datasets. The differences between the several wave datasets are larger than those between the wind speed datasets; moreover, differences in wave datasets exist even when the forcing winds used to produce the different wave reanalyses are the same. Most of the discrepancies between the datasets occur in the Tropics, testifying that the physics in that region is still poorly known. The data before the mid-1980s show significant discrepancies also in the Southern Hemisphere, most of ...

A new nonparametric method to correct model data is proposed. At any given point in space and time the correction is determined from “analogs” in a learning dataset. The learning dataset contains model data and simultaneous observations. The method is applied to the significant wave height dataset of the 45-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40). Comparison of the corrected data with significant wave height measurements from in situ buoy and global altimeter data shows clear improvements in bias, scatter, and quantiles in the whole range of values. Temporal inhomogeneities are also removed.