Abstract. There are numerous networks and initiatives concerned with the non-satellite-observing segment of Earth observation. These are owned and operated by various entities and organisations often with different practices, norms, data policies, etc. The Horizon 2020 project GAIA-CLIM is working to improve our collective ability to use an appropriate subset of these observations to rigorously characterise satellite observations. The first fundamental question is which observations from the mosaic of non-satellite observational capabilities are appropriate for such an application. This requires an assessment of the relevant, quantifiable aspects of the measurement series which are available. While fundamentally poor or incorrect measurements can be relatively easily identified, it is metrologically impossible to be sure that a measurement series is "correct". Certain assessable aspects of the measurement series can, however, build confidence in their scientific maturity and appropriateness for given applications. These are aspects such as that it is well documented, well understood, representative, updated, publicly available and maintains rich metadata. Entities such as the Global Climate Observing System have suggested a hierarchy of networks whereby different subsets of the observational capabilities are assigned to different layers based on such assessable aspects. Herein, we make a first attempt to formalise both such a system-of-systems networks concept and a means by which to, as objectively as possible, assess where in this framework different networks may reside. In this study, we concentrate on networks measuring primarily a subset of the atmospheric Essential Climate Variables of interest to GAIA-CLIM activities. We show assessment results from our application of the guidance and how we plan to use this in downstream example applications of the GAIA-CLIM project. However, the approach laid out should be more widely applicable across a broad range of application areas. If broadly adopted, the system-of-systems approach will have potential benefits in guiding users to the most appropriate set of observations for their needs and in highlighting to network owners and operators areas for potential improvement.
Climate services are largely supported by satellite Climate Data Records (CDRs). This paper demonstrates how CDR development and uptake benefit from simulations. We identify three classes of application. Using global reanalyses and an offline radiance simulator, we provide three examples for each application class. The first application is to validate assumptions. Hereto we establish the order of channels in the U.S. Defense Meteorological Satellite Program (DMSP) Special Sensor H (SSH) data record, rescued from oblivion thanks to inter-agency collaboration. We then show the value of applying advanced quality controls to geostationary European (Meteosat) and Japanese (Himawari) images. We also investigate the corrections applied during reprocessing of DMSP Special Sensor Microwave Temperature sounder (SSM/T-1) data. The second application is to assess the coherence between and observations. Hereto we show the capability of reanalyses to reconstruct spectra observed by the Spektrometer Interferometer (SI-1) flown on Soviet satellites before 1980. We also explore the coherence of the impact of two major volcanic eruptions between Advanced Very High Resolution Radiometer (AVHRR) and the High-resolution Infrared Radiation Sounder (HIRS) data. Finally, we investigate how advanced bias correction can help improve coherence between reanalysis and Nimbus-3 Medium-Resolution Infrared Radiometer (MRIR) in 1969. The third application is to inform CDR users about particular quality aspects. Hereto we show, with the Nimbus-7 Scanning Multichannel Microwave Radiometer (SSMR), the Meteosat Second Generation imager, and the DMSP Special Sensor Microwave Water Vapor Profiler (SSM/T-2), how simulations bring information that should help to make a better-informed use of the corresponding CDR.
<p>Mesoscale convective systems (MCs) are central to the water and energy cycle of the tropical region. Geostationary satellite observations can provide a useful resource to constraint theoretical and modelling perspectives of the convective systems. Thus, the MCS life cycle information can only be readily obtained using high frequency imagery available from the geostationary orbit. The METEOSAT series of satellites operated by EUMETSAT observe continuously the African and Atlantic region since more than 40 years and offer us the opportunity to improve our understanding of the MCS and to analyze their climatological trends over the region.</p><p>We will introduce a MCS database over the African and Atlantic regions built from the long-term thermal infrared METEOSAT first and second-generation archive and from a cloud tracking algorithm called TOOCAN spanning the 1981-2020 period.</p><p>The METEOSAT first and second-generation imagers exhibit some spectral window channels disparities, different temporal resolutions, and slight variability in the spatial resolution of the sensors. Moreover, the imagers of the early METEOSAT satellites were designed for qualitative analyses of weather patterns, and the quality of their data do not comply with climate requirements. Finally, the calibration procedure of each instrument is also performed at the individual level with instruments specifics mode of operation. The cloud tracking can be impacted by these various sources of inhomogeneity, and some technical specifications are then required to ensure the validity of the cloud-tracking and to build a 39-year homogenous MCS dataset.</p><p>First, by using the multi-sensor infrared channel calibration (MSICC) algorithm relied on the IASI, AIRS and HIRS/2 as reference observations, an intercalibration and spectral band adjustment of the IR long-term database has been performed to reduce the METEOSAT sensors differences. The spatial resolution has been homogenized by remapping each METEOSAT native projection to a 0.04&#176; longitude-latitude equal-angle grid. A final effort has been performed to correct the limb darkening effect, and a careful quality control has been applied on each infrared image. The TOOCAN cloud tracking algorithm has then been applied to this homogenous long-term METEOSAT infrared dataset at a 30-min temporal frequency to build a 39-year tropical convective systems database giving an access to the morphological parameters of around 14&#215;10<sup>6</sup> MCS along their life cycles.</p><p>Finally, we will present our preliminary analyses showing significant trends in MCS occurrence for different geographical regions.</p>
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