The utilisation of past, current, and future geostationary observations for climate monitoring is a challenge as up to 50 geostationary satellite missions operated by many agencies are part of the record with a variety of instrumentation since the late 1970s. Data from the geostationary ‘ring’ are essential for the provision of many geophysical variables via retrieval methods or data assimilation schemes. The data are used in many climate service applications and climate system analysis such as undertaken by the WCRP core project GEWEX. EUMETSAT is engaged in data rescue, uncertainty characterisation, recalibration, harmonisation and reprocessing of geostationary satellite data and aims at the provision of the geostationary ‘ring’ data to users in the near future on its joint EUMETSAT-ECMWF cloud infrastructure the so called European Weather Cloud and the EUMETSAT Data Store.

The presentation describes the application of an analysis chain for geostationary satellite data from the detection and correction of artefacts and other long-term issues in the time series of the instrument data to the estimation of uncertainties according to metrological principles and the efficient production and provision of climate data sets.

Processing of operational geostationary radiances comes with the possibility of unforeseen radiometric, geometric and metadata anomalies, which introduce unintended errors making data useless for data assimilation and prevent retrieving quantities. These anomalies may not always be instrument-related and rather could be related to data processing errors as well. EUMETSAT designed a system that performs an automatic anomaly analysis on Earth Observation images, applicable to all Meteosat and JMA satellite measurements. The automated anomaly detection system employs a dedicated detection algorithm for each of 30 anomalies deducted from manual inspections of large subsets of the images. The result is a system with a high probability of detection and low false alarm rate. Furthermore, most of these algorithms are able to pinpoint the anomalies to the specific pixels affected in the image, allowing the maximum use of the data available. The anomalies are stored in a data base and inform downstream processing.

The MVIRI and SEVIRI infrared channel measurements have been recalibrated using IASI, AIRS, and HIRS measurements as references. The improvements in the radiometric accuracy of MVIRI measurements are up to 3 K while that for SEVIRI is less than 0.5 K. Such improvements allow now the seamless use of Meteosat first and second generation observation in downstream retrievals providing ~40 year long time series of ECVs from geostationary orbit. This re-calibration approach has also been applied to the instruments operated on JMA geostationary satellites, i.e., VISSR/JAMI/IMAGER on the GMS/MTSAT series. Significant seasonal changes in radiometric bias are present in some of the original data of old JMA satellites’ (GMS-1 to GMS-5) IR channel measurements that could be corrected. Other JMA satellites (MTSAT, MTSAT-1R,MTSAT-2) performance is closer to EUMETSAT SEVIRI and could be corrected as well.
While IASI and AIRS can be used as references, the usage of HIRS is an issue that requires first the derivation of a consistent time series of observations from the HIRS instrument starting in 1979. EUMETSAT has produced a new HIRS radiance data record by reprocessing all available data from three generations of the HIRS instrument on-board sixteen satellites, TIROS-N (HIRS/2), NOAA-6—14 (HIRS/2), NOAA-15—17 (HIRS/3), and NOAA-18—19 and Metop A/B (HIRS/4) applying a consistent calibration to the whole time series. The latest HIRS calibration algorithm (version 4) contained in the ATOVS and AVHRR Processing Package (AAPP) software was adapted for application to the HIRS/2 instrument data. The modified version of AAPP calibration algorithm takes into account the self-emission characteristics of the HIRS instruments, which was not considered in the HIRS/2 original calibration procedure. For the HIRS/2 instruments, a discontinuity in the middle of two calibration cycles was found in the original data, but this has been mitigated in the new data record by considering multiple calibration cycles. For HIRS/3 instruments, original data were calibrated assuming static instrument gain over a period of 24 h, which can cause a significant bias, while the new calibration considers more dynamic values for instrument gain.

Regarding the quantitative side of geostationary satellite data quality, it has been shown that the application of uncertainty analysis developed in the European FIDUCEO project adds another dimension of quality information that is broken down to individual effects causing errors in the measurements. This goes hand in hand with other analysis methods that use radiative transfer simulations with reanalyses as input.

The methods presented here are now being applied to the US geostationary sensor data to complete the geostationary ‘ring’. A next step is to apply methods developed for ISCPP-NG to convert the individual geostationary satellite time series, including calibration information, into simpler formats and present them to users. EUMETSAT intends to host the ISCCP-NG test data on its cloud infrastructure and data store, followed by the entire geostationary ‘ring’ data set in the near future.

Through a massive investment by the Meteorological Space Agencies, almost all of the geostationary imagers measuring the non-polar globe are far more capable than those flown 10 years ago. We now have a ring of advanced geostationary imagers (GEO-RING) providing data at fine spatial and temporal resolutions from roughly 18 different channels of which approximately 12 are common across all sensors. The data from these imagers are available at various locations, in different formats and on different projections. These factors coupled with the vast size of the data volume make a coherent use of the GEO-RING data difficult for a large number of users.

One solution to this challenge is to provide software to satellite data users to process the GEO-RING data onto a common grid. This approach has been inspired by the 1980s project developed for the first generation GEO-RING called the International Satellite Cloud Climatology Project (ISCCP). To promote the evolution of this project to an ISCPP next generation (ISCPP-NG) application, the Global Energy and Water Exchanges Project (GEWEX) Data and Analysis Panel (GDAP) has designed a collaborative effort among the Meteorological Space Agencies and other relevant international bodies. The first ISCCP-NG Workshop was held at EUMETSAT in late 2019. The first goal of ISCCP-NG working group is to create an easy to use Level-1 dataset from all relevant sensors placed onto a common grid. This data is referred to as L1g. Calibration efforts for L1g are leveraged from the Global Space-based Inter-Calibration System (GSICS). At this point in the project, prototype ISCCP-NG L1g data are publicly available at 0.05 and 0.1 degree resolutions along with ancillary data.

This presentation will summarize the progress of the ISCCP-NG project in making prototype L1g. Information for accessing prototype data and code repository will be provided along with how to contribute to ISCCP-NG L1g development. Several efforts are underway to demonstrate the utility of ISCCP-NG L1g for making L2 products. These efforts will be summarized as well as the plans to finish the first phase of ISCCP-NG development.

Cloud properties are critical for understanding the Earth’s radiation budget and cloud feedbacks. At NASA Langley Research Center, the Satellite ClOud and Radiative Property retrieval System (SatCORPS) provides real-time and historical analyses of clouds derived from Geostationary satellite (GEOsat) data for weather and climate applications. For the Clouds and the Earth’s Radiant Energy System (CERES) program, the global constellation of GEOsats has been analyzed since 2000 to help characterize and account for the diurnal cycle of clouds and their radiative impacts in the CERES climate data record. Obtaining consistent cloud properties over the GEOsat data record at all times of day during the CERES era is a major objective but a significant challenge considering the diversity of imaging capabilities deployed during that time. A brief overview of the current CERES/GEO cloud property data record constructed from three generations of GEOsat imagers for CERES Edition-4 is presented. In this version, different algorithms were applied to different satellites to take advantage of as much spectral information as possible for each satellite. Under this approach, the derived cloud properties were found to become accurate (and more similar to MODIS) for the GEOsat imagers with more spectral information. However, this approach also led to some discontinuities in cloud properties across platforms. Significant differences were also found between daytime and nighttime cloud properties. This paper will highlight the major issues in the Edition-4 record and discuss new strategies and early results from the next version of the SatCORPS GEOsat cloud property time series in development for CERES Edition-5 with the goal of improving day-night and cross-platform consistency during the CERES record.

The retrieval of physical quantities such as cloud properties from passive satellite measurements requires accurate knowledge of both the instrument calibration – the process of transforming digital counts into a radiance quantity - and the uncertainty associated with each measurement. Typically, the calibration is supplied by the satellite operator based on-orbit calibration methodologies. The uncertainty, meanwhile, is expressed as the Signal to Noise Ratio (SNR) for visible wavelength channels and the Noise Equivalent delta Temperature (NeDT) for infrared channels: Values that are typically calculated in pre-launch testing against known radiance sources.
However, both the calibration and uncertainty can vary over time due to factors such as instrument age and temperature, contamination of sensor optics and interference along the optical path due to stray light. Calibration accuracy varies with sensor, as some instruments have onboard calibration sources while others – like Meteosat’s SEVIRI – rely on vicarious calibration against external targets such as bright desert surfaces or the Moon. For many satellites numerous additional calibration coefficients exist, such as the GSICS approach that is applied to the visible and infrared bands of many geostationary satellite sensors. Uncertainty, meanwhile, is typically measured at a specific visible reflectance value or infrared brightness temperature, such as 0.5K at 300K. In reality, though, this uncertainty will vary with the measured radiance.
Here we aim to highlight the importance of accurately considering both calibration and uncertainty within retrieval algorithms, using the example of the ORAC/CC4CL algorithm that can retrieve cloud properties from a wide variety of passive instruments. We show that the application of different calibration parameters can have a significant effect on the retrieved optical properties of clouds and we also demonstrate that the GSICS thermal calibration can improve cloud top height retrievals, but that the GSICS coefficients are problematic in some situations such as the extremely low brightness temperatures associated with tropical deep convection.
Going further, we implement a technique that considers sensor noise as a fractional component of the measured radiance, rather than as a fixed quantity, and highlight the improvement this makes to cloud properties retrievals that show smoother optical and thermal properties across cloud tops and show fewer unphysical values near cloud edges.

The ISCCP-NG initiative generates global composites of VIS/IR measurements (called L1g) from the latest geostationary imagers, providing a powerful source for near-global retrievals of geophysical properties on high temporal resolution. Recently, cloud and radiative flux properties have been prototyped based on L1g applying the Community Cloud Retrieval for CLimate (CC4CL) which was developed in the ESA Cloud_cci project.

CC4CL is a very flexible retrieval system capable of handling multiple satellite sensors. It contains state-of-the-art artificial neural networks for cloud detection and cloud phase determination as well as a physical retrieval core for cloud optical thickness, effective radius and cloud-top pressure based on optimal estimation theory. Furthermore, consistent radiative fluxes are computed at TOA and surface in a post-processing step that facilitate the investigation of cloud radiative effects on pixel basis.

In this presentation we will show first results of CC4CL-based, near-global cloud and radiative flux properties using ISCCP-NG L1g data as input. This will include elaborating on the utilization of spectral band adjustments to emulate the same spectral characteristics from all satellite sensors included. We will further discuss the advantages of the ISCCP-NG approach and the complementarity of ISCCP-NG cloud products to exiting cloud property records.