The extension of the ERA5 reanalysis back to 1950 supplements the previously published segment covering 1979 to the present. It features the assimilation of additional conventional observations, as well as improved use of early satellite data. The number of observations assimilated increases from 53,000 per day in early 1950 to 570,000 per day by the end of 1978. Accordingly, the quality of the reanalysis improves throughout the period, generally joining seamlessly with the segment covering 1979 to the present. The fidelity of the extension is illustrated by the accurate depiction of the North Sea storm of 1953, and the events leading to the first discovery of sudden stratospheric warmings in 1952. Time series of ERA5 global surface temperature anomalies show temperatures to be relatively stable from 1950 until the late 1970s, in agreement with the other contemporary full‐input reanalysis covering this period and with independent data sets, although there are significant differences in the accuracy of representing specific regions, Europe being well represented in the early period but Australia less so. The variability of ERA5 precipitation from month to month agrees well with observations for all continents, with correlations above 90% for most of Europe and generally in excess of 70% for North America, Asia and Australia. The evolution of upper air temperatures, humidities and winds shows smoothly varying behaviour, including tropospheric warming and stratospheric cooling, modulated by volcanic eruptions. The Quasi‐Biennial Oscillation is well represented throughout. Aspects to be improved upon in future reanalyses include the assimilation of tropical cyclone data, the spin‐up of soil moisture and stratospheric humidity, and the representation of surface temperatures over Australia.

We provide a description and concise evaluation of the European Centre of Medium‐range Weather Forecasts Reanalysis v.5 (ERA5) global reanalysis from an additional extension back to 1940 that was released in March 2023, including its timely updates to the end of 2022. The ERA5 product from 1979 to end 2020 and a preliminary back extension from 1950 to 1978 have already been described elsewhere. The new back extension that spans 1940 to 1978 represents the official release and supersedes the preliminary product. Currently, the ERA5 data record extends over more than 83 years of hourly global three‐dimensional fields for many quantities that describe the global atmosphere, land surface, and ocean waves at a horizontal resolution of about 31 km. ERA5 relies on the ingestion of sub‐daily in‐situ and satellite observations, and the number of these increases from 17,000 per day in 1940 to 25 million per day by 2022. Accordingly, the quality of the reanalysis improves throughout the period. Over the Northern Hemisphere ERA5 generally provides a reliable representation of the synoptic situation from the early 1940s and provides long‐term variability that is in line with other datasets. Over the Southern Hemisphere, however, for the early period the description of ERA5 seems mainly statistical. Furthermore, there is a small deviation in surface temperature compared with reconstructions based on monthly aggregations of observations over land before 1946. For this period, the absence of upper air temperature observations reveals a model cold bias in the lower stratosphere. For the period from 1950 to 1978, the final release described here improves on the suboptimal treatment of International Best Track Archive for Climate Stewardship observations in the preliminary release, with, as a result, a much more homogeneous representation of tropical cyclones over the entire ERA5 record. Longer spin‐up periods also have a beneficial impact on soil moisture.

Developments in the assimilation of satellite data in numerical weather prediction (NWP), from the first experiments in the late 1960s to the present day, are presented in a two‐part review article. This part, Part II, reviews the progress in recent years, from about 2000. It includes summaries of advances in the relevant satellite remote‐sensing technologies and in methods to assimilate observations from these instruments into NWP systems. It also summarises impacts on forecast skill. Continued progress has been made on the assimilation of passive infrared (IR) sounding data and microwave (MW) sounding and imaging data. This has included data from hyperspectral IR sounders, which first became available during this period. Advances in the use of cloud‐affected radiances, from both IR and MW instruments, have been made. In support of this progress, further developments have been made in fast radiative transfer models and in bias correction techniques, and work has continued to improve understanding and representation of observation uncertainties. Continued progress has also been made on the use of wind information from satellites, including atmospheric motion vectors and scatterometer data. A new source of temperature and humidity information, from radio occultation observations, has become available during the period and has been exploited by many NWP centres. The impact of satellite data on NWP accuracy is continually assessed using a range of methods and metrics. Some results from recent Observing System Experiments (OSEs) and Forecast Sensitivity to Observation Impact (FSOI) assessment are presented and other methods are discussed. The role of satellite data in NWP‐based atmospheric reanalysis systems is also described.

Confidence in the use of Earth observations for monitoring essential climate variables (ECVs) relies on the validation of satellite calibration accuracy to within a well-defined uncertainty. The gap analysis for integrated atmospheric ECV climate monitoring (GAIA-CLIM) project investigated the calibration/validation of satellite data sets using non-satellite reference data. Here, we explore the role of numerical weather prediction (NWP) frameworks for the assessment of several meteorological satellite sensors: the advanced microwave scanning radiometer 2 (AMSR2), microwave humidity sounder-2 (MWHS-2), microwave radiation imager (MWRI), and global precipitation measurement (GPM) microwave imager (GMI). We find departures (observation-model differences) are sensitive to instrument calibration artefacts. Uncertainty in surface emission is identified as a key gap in our ability to validate microwave imagers quantitatively in NWP. The prospects for NWP-based validation of future instruments are considered, taking as examples the microwave sounder (MWS) and infrared atmospheric sounding interferometer-next generation (IASI-NG) on the next generation of European polar-orbiting satellites. Through comparisons with reference radiosondes, uncertainties in NWP fields can be estimated in terms of equivalent top-of-atmosphere brightness temperature. We find NWP-sonde differences are consistent with a total combined uncertainty of 0.15 K for selected temperature sounding channels, while uncertainties for humidity sounding channels typically exceed 1 K.

Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper, we analyse the uncertainty budget of the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where each of the error sources are identified in an uncertainty tree diagram and briefly discussed. Atmospheric correction algorithms depend on ancillary variables (such as meteorological properties and column densities of gases), yet the uncertainties in these variables were not studied previously in detail. To analyse these uncertainties for the first time, the spread in the ERA5 ensemble is used as an estimate for the uncertainty in the ancillary data, which is then propagated to uncertainties in remote sensing reflectances using a Monte Carlo approach and the SeaDAS atmospheric correction algorithm. In an example data set, wind speed and relative humidity are found to be the main contributors (among the ancillary parameters) to the remote sensing reflectance uncertainties.

Climate services are largely supported by climate reanalyses and by satellite Fundamental (Climate) Data Records (F(C)DRs). This paper demonstrates how the development and the uptake of F(C)DR benefit from radiance simulations, using reanalyses and radiative transfer models. We identify three classes of applications, with examples for each application class. The first application is to validate assumptions during F(C)DR development. Hereto we show the value of applying advanced quality controls to geostationary European (Meteosat) images. We also show the value of a cloud mask to study the spatio‐temporal coherence of the impact of the Mount Pinatubo volcanic eruption between Advanced Very High Resolution Radiometer (AVHRR) and the High‐resolution Infrared Radiation Sounder (HIRS) data. The second application is to assess the coherence between reanalyses and observations. Hereto we show the capability of reanalyses to reconstruct spectra observed by the Spektrometer Interferometer (SI‐1) flown on a Soviet satellite in 1979. We also present a first attempt to estimate the random uncertainties from this instrument. Finally, we investigate how advanced bias correction can help to improve the coherence between reanalysis and Nimbus‐3 Medium‐Resolution Infrared Radiometer (MRIR) in 1969. The third application is to inform F(C)DR users about particular quality aspects. We show how simulations can help to make a better‐informed use of the corresponding F(C)DR, taking as examples the Nimbus‐7 Scanning Multichannel Microwave Radiometer (SMMR), the Meteosat Second Generation (MSG) imager, and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Water Vapor Profiler (SSM/T‐2).