Since 2014, space agencies have launched advanced meteorological imagers into the geostationary (GEO) orbit encircling Earth's equator, known as the GEO-Ring. JMA, NOAA, and KMA launched imagers measuring 16 spectral bands with thermal resolutions of 2 km and full-disk coverage every 10 min. China Meteorological Administration's (CMA's) Fengyun-4A (FY-4A) series, launched in 2016, observes 14 bands with 4-km thermal resolution and 15-min full-disk scans. In 2022, EUMETSAT introduced the Meteosat Third Generation (MTG) imager, offering 16 channels, 2-km thermal resolution, and 10-min full-disk coverage. Together, these satellites provide near-global coverage with improved capabilities over earlier generations. The 10-12 common channels across the latest imagers enable retrieval of diverse atmospheric variables at high temporal resolution. These data represent a substantial advance beyond the early 1980s when the International Satellite Cloud Climatology Project (ISCCP) was first developed. The challenge facing any new GEO-Ring project, such as one being planned as part of a next generation of ISCCP (ISCCP-NG), is to define a new baseline from these measurements and processing methods to extract meaningful information for the scientific community in the coming decades. This paper outlines the design of a GEO-Ring radiance project to support a future ISCCP-NG and many other applications and emphasizes the benefits compared to the B1 and B3 data used in ISCCP.

Abstract. Radiances from the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-19 satellite were successfully simulated from Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) radiances using collocated AVHRR/VIIRS datasets from 2012–2013. Spectral Band Adjustment Factors (SBAFs) were derived using linear regression and neural networks (NNs). The NN approach produced the best results, and separating daytime from night-time conditions when simulating AVHRR channel 3B at 3.7 µm was key. Furthermore, daytime radiance corrections in this channel must depend on actual surface and cloud reflectances to be realistic, which was only achieved by the NN approach. The cloud mask, cloud top height, and cloud phase products were produced from the simulated AVHRR radiances using the same retrieval methods for NOAA-19 data used to compile the CLARA-A3 climate data record (CDR). CLARA-A3 is the third edition of the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) CDR with cloud parameters, surface albedo, surface radiation, and Top of Atmosphere (TOA) radiation products from AVHRR. Products were validated using CALIPSO cloud products and agreed well with original CLARA-A3 products, with the best results provided by the NN simulation approach. The NN-based approach best reproduced the corresponding products for cloud optical thickness (COT), cloud effective radius (CRE), liquid water path (LWP), and ice water path (IWP). The CLARA-A3 CDR will be complemented and extended with VIIRS-based products to cover the period 1979–2024 (46 years). This edition will be known as CLARA-A3.5. Future extensions and editions can follow a similar approach by applying the same radiance simulation method to collocated data from the Metop-C AVHRR and the Metop-SG METimage sensors, the first version of the latter scheduled for launch in August 2025. Successful simulation of AVHRR radiances from METimage data enables the extension of the CLARA CDR for several decades using observations from VIIRS and METimage.

This paper presents the third edition of The Satellite Application Facility on Climate Monitoring's (CM SAF) cloud, albedo, and surface radiation dataset from advanced very-high-resolution radiometer (AVHRR) data, CLARA-A3. The content of earlier CLARA editions, namely cloud, surface albedo, and surface radiation products, has been extended with two additional surface albedo products (blue- and white-sky albedo), three additional surface radiation products (net shortwave and longwave radiation, and surface radiation budget), and two top of atmosphere radiation budget products (reflected solar flux and outgoing longwave radiation). The record length is extended to 42 years (1979–2020) by also incorporating results from the first version of the advanced very high resolution radiometer imager (AVHRR/1). A continuous extension of the climate data record (CDR) has also been implemented by processing an interim climate data record (ICDR) based on the same set of algorithms but with slightly changed ancillary input data. All products are briefly described together with validation results and intercomparisons with currently existing similar CDRs. The extension of the product portfolio and the temporal coverage of the data record, together with product improvements, is expected to enlarge the potential of using CLARA-A3 for climate change studies and, in particular, studies of potential feedback effects between clouds, surface albedo, and radiation. The CLARA-A3 data record is hosted by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) CM SAF and is freely available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b).

This paper investigates the quality of global cloud fraction and cloud-top height products provided by the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from the AVHRR data (CLARA-A3) climate data record (CDR) produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). Compared with with CALIPSO–CALIOP cloud lidar data and six other cloud CDRs, including the predecessor CLARA-A2, CLARA-A3 has improved cloud detection, especially over ocean surfaces, and improved geographical variation and cloud detection efficiency. In addition, CLARA-A3 exhibits remarkable improvements in the accuracy of its global cloud-top height measurements. For example, in tropical regions, previous underestimations for high-level clouds are reduced by more than 2 km. By taking advantage of more realistic descriptions of global cloudiness, this study attempted to estimate trends in the observable fraction of low-level clouds, acknowledging their importance in producing a net climate cooling effect. The results were generally inconclusive in the tropics, mainly due to the interference of El Nino modes during the period under study. However, the analysis found small negative trends over oceanic surfaces outside the core tropical region. Further studies are needed to verify the significance of these results.

Meteorological forecast models, notably snowfall predictions, require accurate knowledge of the properties of snow particles, such as their size, cross-sectional area, mass, shape, and fall speed. Therefore, measurements of individual snow particles’ fall speed and their cross-sectional area, from which a size parameter and area ratio can be derived, provide very useful datasets. We have compiled such a dataset from measurements with the Dual Ice Crystal Imager (D-ICI) in Kiruna during several winter seasons from 2014 to 2019. Using that data, we have previously studied shape-dependent relationships between fall speed and particle size, cross-sectional area, and particle mass. While we had used maximum dimension as the size parameter, we have found that it seems unsuitable for certain shapes like columnar particles. Here, we investigate which particle size parameter should be used depending on the shape or if one size parameter is suitable for all shapes. With a more suitable particle size parameter, we aim to improve the relationships between fall speed and particle size and mass.

Abstract Cloud ice particle effective radius in atmospheric models is usually parametrized. A widely‐used parametrization comprises a strong dependence on the temperature. Utilizing available satellite‐based estimates of both cloud ice particle effective radius and cloud‐top temperature we evaluate if a similar temperature‐dependence exists in these observations. We find that for very low cloud‐top temperatures the modeled cloud ice particle effective radius generally agrees on average with satellite observations. For high sub‐zero temperatures however, the modeled cloud ice particle effective radius becomes very large, which is not seen in the satellite observations. We conclude that the investigated parametrization for the cloud ice particle effective radius, and parametrizations with a similar temperature dependence, likely produce systematic biases at the cloud top. Supporting previous studies, our findings suggest that the vertical structure of clouds should be taken into account as factor in potential future updates of the parametrizations for cloud ice particle effective radius.

CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI (Spinning Enhanced Visible and InfraRed Imager), was released in December 2022. It is based on observations from SEVIRI, on board geostationary satellites Meteosat-8, 9, 10 and 11, which are operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). CLAAS-3 was produced and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which aims to provide high-quality satellite-based data records suitable for climate monitoring applications. Compared to previous CLAAS releases, CLAAS-3 is expanded in terms of both temporal extent and cloud properties included, and it is based on partly updated retrieval algorithms. The available data span the period from 2004 to present, covering Europe; Africa; the Atlantic Ocean; and parts of South America, the Middle East and the Indian Ocean. They include cloud fractional coverage, cloud-top height, phase (liquid or ice) and optical and microphysical properties (water path, optical thickness, effective radius and droplet number concentration), from instantaneous data (every 15 min) to monthly averages. In this study we present an extensive evaluation of CLAAS-3 cloud properties, based on independent reference data sets. These include satellite-based retrievals from active and passive sensors, ground-based observations and in situ measurements from flight campaigns. Overall results show very good agreement, with small biases attributable to different sensor characteristics, retrieval/sampling approaches and viewing/illumination conditions. These findings demonstrate the fitness of CLAAS-3 to support the intended applications, which include evaluation of climate models, cloud characterisation and process studies focusing especially on the diurnal cycle and cloud filtering for other applications. The CLAAS-3 data record is publicly available via the CM SAF website at https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V003 (Meirink et al., 2022).

The High- Latitude Measurement of Snowfall (HiLaMS) campaign explored variability in snowfall properties and processes at meteorologically distinct field sites located in Haukeliseter, Norway, and Kiruna, Sweden, during the winters of 2016/17 and 2017/18, respectively. Campaign activities were founded upon the sensitivities of a low-cost, core instrumentation suite consisting of Micro Rain Radar, Precipitation Imaging Package, and Multi-Angle Snow Camera. These instruments are highly portable to remote field sites and, considered together, provide a unique and complementary set of snowfall observations including snowflake habit, particle size distributions, fall speeds, surface snowfall accumulations, and vertical profiles of radar moments and snow water content. These snow-specific parameters, used in combination with existing observations from the field sites such as snow gauge accumulations and ambient weather conditions, allow for advanced studies of snowfall processes. HiLaMS observations were used to 1) successfully develop a combined radar and in situ microphysical property retrieval scheme to estimate both surface snowfall accumulation and the vertical profile of snow water content, 2) identify the predominant snowfall regimes at Haukeliseter and Kiruna and characterize associated macrophysical and microphysical properties, snowfall production, and meteorological conditions, and 3) identify biases in the HARMONIE-AROME numerical weather prediction model for forecasts of snowfall accumulations and vertical profiles of snow water content for the distinct snowfall regimes observed at the mountainous Haukeliseter site. HiLaMS activities and results suggest value in the deployment of this enhanced snow observing instrumentation suite to new and diverse high-latitude locations that may be underrepresented in climate and weather process studies.

Weather forecast and climate models require good knowledge of the microphysical properties of atmospheric snow particles. For example, particle cross-sectional area and shape are especially important parameters that strongly affect the scattering properties of ice particles and consequently their response to remote sensing techniques. The fall speed and mass of ice particles are other important parameters. The fall speed affects the rate of removal of ice from numerical models. The particle mass is a key quantity that connects the cloud microphysical properties to radiative properties. Measurements of snow particles using the ground-based in-situ instrument Dual Ice Crystal Imager (D-ICI) have been carried out in Kiruna, Sweden, during several winter seasons. The D-ICI takes high-resolution side- and top-view images of falling hydrometeors, from which maximum dimension (describing particle size), cross-sectional area, and fall speed of individual particles are determined. Images from 2014 to 2018 form the dataset that is analysed for relationships between the different microphysical properties. The analysis is performed as a function of snow particle shape after sorting particles into 15 different shape groups. While particle mass can be easily estimated geometrically from the image data for the simpler shapes such as columns and plates, mass for particles with more complex shapes cannot. Thus, particle mass of all snow particles in our dataset is derived from the direct measurements of particle size, cross-sectional area, and fall speed. For this we use an approach that connects mass to fall speed using an empirical relationship between the dimensionless Reynolds and Best numbers. Consequently, the relation between mass and the other microphysical properties can be studied as a function of shape. In addition, by evaluating these relationships and comparison to relationships from literature, we can study the usability of this Reynolds-to-Best-number-approach for the different shapes. In general, our results show, depending on shape, varying but moderately to strongly correlated relationships among particle size, cross-sectional area, and fall speed that also compare favourably with many previous studies. There are a few discrepancies that can be linked to certain shapes, in particular column- and needle-like shapes, which show poor correlations between fall speed and particle size. We speculate that maximum dimension is not suitable to represent particle size for these shapes. Inconsistencies between the different relationships found for the same shapes corroborate our hypothesis as they indicate that maximum dimension is not suitable to determine Reynolds number. Thus, the Reynolds-to-Best-number-approach works poorly for these shapes and mass cannot be determined accurately. However, column width, where available, is a better representative particle size. Using a selection of columns, for which the simple geometry allows the verification of the empirical Best number vs. Reynolds number relationship, we show that Reynolds number and fall speed are more closely related to the diameter of the basal facet (i.e. the column width) than the maximum dimension. The agreement with the empirical relationship is further improved using a modified Best number, a function of an area ratio based on the falling particle seen in the vertical direction.

Meteorological forecast and climate models require good knowledge of the microphysical properties of hydrometeors and the atmospheric snow and ice crystals in clouds, for instance, their size, cross-sectional area, shape, mass, and fall speed. Especially shape is an important parameter in that it strongly affects the scattering properties of ice particles and consequently their response to remote sensing techniques. The fall speed and mass of ice particles are other important parameters for both numerical forecast models and the representation of snow and ice clouds in climate models. In the case of fall speed, it is responsible for the rate of removal of ice from these models. The particle mass is a key quantity that connects the cloud microphysical properties to radiative properties. Using an empirical relationship between the dimensionless Reynolds and Best numbers, fall speed and mass can be derived from each other if particle size and cross-sectional area are also known. In this study, ground-based in situ measurements of snow particle microphysical properties are used to analyse mass as a function of shape and the other properties particle size, cross-sectional area, and fall speed. The measurements for this study were done in Kiruna, Sweden, during snowfall seasons of 2014 to 2019 and using the ground-based in situ Dual Ice Crystal Imager (D-ICI) instrument, which takes high-resolution side- and top-view images of natural hydrometeors. From these images, particle size (maximum dimension), cross-sectional area, and fall speed of individual particles are determined. The particles are shape-classified according to the scheme presented in our previous study, in which particles sort into 15 different shape groups depending on their shape and morphology. Particle masses of individual ice particles are estimated from measured particle size, cross-sectional area, and fall speed. The selected dataset covers sizes from about 0.1 to 3.2 mm, fall speeds from 0.1 to 1.6 m s−1, and masses from 0.2 to 450 µg. In our previous study, the fall speed relationships between particle size and cross-sectional area were studied. In this study, the same dataset is used to determine the particle mass, and consequently, the mass relationships between particle size, cross-sectional area, and fall speed are studied for these 15 shape groups. Furthermore, the mass relationships presented in this study are compared with the previous studies. For certain crystal habits, in particular columnar shapes, the maximum dimension is unsuitable for determining Reynolds number. Using a selection of columns, for which the simple geometry allows the verification of an empirical Best-number-to-Reynolds-number relationship, we show that Reynolds number and fall speed are more closely related to the diameter of the basal facet than the maximum dimension. The agreement with the empirical relationship is further improved using a modified Best number, a function of an area ratio based on the falling particle seen in the vertical direction.

Improved snowfall predictions require accurate knowledge of the properties of ice crystals and snow particles, such as their size, cross-sectional area, shape, and fall speed. The fall speed of ice particles is a critical parameter for the representation of ice clouds and snow in atmospheric numerical models, as it determines the rate of removal of ice from the modelled clouds. Fall speed is also required for snowfall predictions alongside other properties such as ice particle size, cross-sectional area, and shape. For example, shape is important as it strongly influences the scattering properties of these ice particles and thus their response to remote sensing techniques. This work analyzes fall speed as a function of particle size (maximum dimension), cross-sectional area, and shape using ground-based in situ measurements. The measurements for this study were done in Kiruna, Sweden, during the snowfall seasons of 2014 to 2019, using the ground-based in situ instrument Dual Ice Crystal Imager (D-ICI). The resulting data consist of high-resolution images of falling hydrometeors from two viewing geometries that are used to determine particle size (maximum dimension), cross-sectional area, area ratio, orientation, and the fall speed of individual particles. The selected dataset covers sizes from about 0.06 to 3.2 mm and fall speeds from 0.06 to 1.6 m s−1. Relationships between particle size, cross-sectional area, and fall speed are studied for different shapes. The data show in general low correlations to fitted fall speed relationships due to large spread observed in fall speed. After binning the data according to size or cross-sectional area, correlations improve, and we can report reliable parameterizations of fall speed vs. particle size or cross-sectional area for part of the shapes. For most of these shapes, the fall speed is better correlated with cross-sectional area than with particle size. The effects of orientation and area ratio on the fall speed are also studied, and measurements show that vertically oriented particles fall faster on average. However, most particles for which orientation can be defined fall horizontally.

One of the primary purposes of satellite simulators is to emulate the inability of retrievals, based on visible and infrared sensors, to detect subvisible clouds from space by removing them from the model. The current simulators in the COSP rely on a single visible cloud optical depth (τ)-threshold (τ=0.3) applied globally to delineate cloudy and cloud-free conditions. However, in reality, the cloud sensitivity of a retrieval varies regionally.This presentation describes the satellite simulator for the CLARA-A2 climate data record (CDR). The CLARA simulator takes into account the variableskill in cloud detection of the CLARA-A2 CDR using long/lat-gridded values separated by daytime and nighttime, which enable it to filter out clouds fromclimate models that would be undetectable by observations. We introduce two methods of cloud mask simulation, one that depends on a spatially variableτ-threshold and one that uses the cloud probability of detection (POD) as a function of the model τ and long/lat. The gridded POD values are from theCLARA-A2 validation study by Karlsson and Hakansson (2018).Both methods replicate the relative ease or difficulty for cloud retrievals, depending on the region and illumination. They increase the cloud sensitivity where the cloud retrievals are relatively straightforward, such as over mid-latitude oceans, and they decrease the sensitivity where cloud retrievals arenotoriously tricky, such as where thick clouds may be inseparable from cold, snow-covered surfaces, as well as in areas with an abundance of broken andsmall-scale cumulus clouds such as the atmospheric subsidence regions over the ocean.The CLARA simulator, together with the International Satellite Cloud Climatology Project (ISCCP) simulator of the COSP, is used to assess Arctic clouds in the EC-Earth climate model compared to the CLARA-A2 and ISCCP H-Series CDRs. Compared to CLARA-A2, EC-Earth generally underestimates cloudiness in the Arctic. However, compared to ISCCP and its simulator, the opposite conclusion is reached. Based on EC-Earth, this paper shows that the simulated cloud mask of CLARA-A2 is more representative of the CDR than using a global optical depth threshold, such as used by the ISCCP simulator.The simulator substantially improves the simulation of the CLARA-A2-detected clouds compared to a global optical depth threshold, especially in the polar regions, by accounting for the variable cloud detection skill over the year.

We present ground-based in situ snow measurements in Kiruna, Sweden, using the ground-based in situ instrument Dual Ice Crystal Imager (D-ICI). D-ICI records dual high-resolution images from above and from the side of falling natural snow crystals and other hydrometeors with particle sizes ranging from 50 μ m to 4 mm. The images are from multiple snowfall seasons during the winters of 2014/2015 to 2018/2019, which span from the beginning of November to the middle of May. From our images, the microphysical properties of individual particles, such as particle size, cross-sectional area, area ratio, aspect ratio, and shape, can be determined. We present an updated classification scheme, which comprises a total of 135 unique shapes, including 34 new snow crystal shapes. This is useful for other studies that are using previous shape classification schemes, in particular the widely used Magono–Lee classification. To facilitate the study of the shape dependence of the microphysical properties, we further sort these individual particle shapes into 15 different shape groups. Relationships between the microphysical properties are determined for each of these shape groups.

Cloud screening in satellite imagery is essential for enabling retrievals of atmospheric and surface properties. For climate data record (CDR) generation, cloud screening must be balanced, so both false cloud-free and false cloudy retrievals are minimized. Many methods used in recent CDRs show signs of clear-conservative cloud screening leading to overestimated cloudiness. This study presents a new cloud screening approach for Advanced Very-High-Resolution Radiometer (AVHRR) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) imagery based on the Bayesian discrimination theory. The method is trained on high-quality cloud observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The method delivers results designed for optimally balanced cloud screening expressed as cloud probabilities together with information on for which clouds (minimum cloud optical thickness) the probabilities are valid. Cloud screening characteristics over 28 different Earth surface categories were estimated. Using independent CALIOP observations (including all observed clouds) in 2010 for validation, the total global hit rates for AVHRR data and the SEVIRI full disk were 82% and 85%, respectively. High-latitude oceans had the best performance, with a hit rate of approximately 93%. The results were compared to the CM SAF cLoud, Albedo, and surface RAdiation dataset from AVHRR data–second edition (CLARA-A2) CDR and showed general improvements over most global regions. Notably, the Kuipers’ Skill Score improved, verifying a more balanced cloud screening. The new method will be used to prepare the new CLARA-A3 and CLAAS-3 (CLoud property dAtAset using SEVIRI, Edition 3) CDRs in the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project.

The Cloud Climate Change Initiative (Cloud_cci) satellite simulator has been developed to enable comparisons between the Cloud_cci climate data record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth global climate model as well as the Regional Atmospheric Climate Model (RACMO) regional climate model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100% error in liquid water path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces total cloud fraction (TCF) globally by about 10% for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian subcontinent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean but lower than retrieved over the European continent, where in addition the cloud top pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modeled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

An evaluation of the ERA-Interim clouds using satellite observations is presented. To facilitate such an evaluation in a proper way, a simplified satellite simulator has been developed and applied to 6-hourly ERA-Interim reanalysis data covering the period of 1982 to 2014. The simulator converts modelled cloud fields, for example those of the ERA-Interim reanalysis, to simulated cloud fields by accounting for specific characteristics of passive imaging satellite sensors such as the Advanced Very High Resolution Radiometer (AVHRR), which form the basis of many long-term observational datasets of cloud properties. It is attempted to keep the simulated cloud fields close to the original modelled cloud fields to allow a quality assessment of the latter based on comparisons of the simulated clouds fields with the observations. Applying the simulator to ERA-Interim data, this study firstly focuses on the spatial distribution and frequency of clouds (total cloud fraction) and on their vertical position, using cloud-top pressure to express the cloud fraction of high-level, mid-level and low-level clouds. Furthermore, the cloud-top thermodynamic phase is investigated. All comparisons incorporate knowledge of systematic uncertainties in the satellite observations and are further stratified by accounting for the limited sensitivity of the observations to clouds with very low cloud optical thickness (COT). The comparisons show that ERA-Interim cloud fraction is generally too low nearly everywhere on the globe except in the polar regions. This underestimation is caused by a lack of mid-level and/or low-level clouds, for which the comparisons only show a minor sensitivity to the cloud optical thickness thresholds applied. The amount of ERA-Interim high-level clouds, being higher than in the observations, agrees with the observations within their estimated uncertainties. Removing the optically very thin clouds (COT <0.15) from the model fields improves the agreement with the observations for high-level cloud fraction locally (e.g. in the tropics), while for the mid-latitude regions, the best agreement for high-level cloud fraction is found when removing all clouds with COT <1.0. Comparisons of the cloud thermodynamic phase at the cloud top reveal a too high relative ice cloud frequency in ERA-Interim, being most pronounced in the higher latitudes. Indications are found that this is due to the suppression of liquid cloud occurrence for temperatures below −23 ∘C in ERA-Interim. The application of this simulator facilitates a more effective use of passive satellite observations of clouds in the evaluation of modelled cloudiness, for example in reanalyses.

The microwave radiometers (MWRs) on board the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat provide a continuous time series of brightness temperature observations between 1991 and 2012. Here we report on a new total column water vapour (TCWV) and wet tropospheric correction (WTC) dataset that builds on this time series. We use a one-dimensional variational approach to derive TCWV from MWR observations and ERAInterim background information. A particular focus of this study lies on the intercalibration of the three different instruments, which is performed using constraints on liquid water path (LWP) and TCWV. Comparing our MWR-derived time series of TCWV against TCWV derived from Global Navigation Satellite System (GNSS) we find that the MWR-derived TCWV time series is stable over time. However, observations potentially affected by precipitation show a degraded performance compared to precipitation-free observations in terms of the accuracy of retrieved TCWV. An analysis of WTC shows further that the retrieved WTC is superior to purely ERA-Interim-derived WTC for all satellites and for the entire time series. Even compared to the European Space Agency's (ESA) operational WTC retrievals, which incorporate in addition to MWR additional observational data, the here-described dataset shows improvements in particular for the mid-latitudes and for the two earlier satellites, ERS-1 and ERS-2. The dataset is publicly available under doi: 10.5676/DWD_EMIR/V001 (Bennartz et al., 2016).