Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

Abstract

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991-2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations-the diurnal cycle of reflectance and brightness temperature-is fully exploited instead of just accounting for single "imagery". The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

On the Methods for Recalibrating Geostationary Longwave Channels Using Polar Orbiting Infrared Sounders

“…First, Govaerts et al, (2018) [22] demonstrated the improved identification of deep convective clouds by using the recalibrated IR radiances. Second, Bojanowski et al, (2018) [23] and Stockli et al, (2019) [24] used the recalibrated radiances for deriving cloud fractional cover climatology. Finally, Duguay-Tetzlaf et al, (2017) [25] demonstrated the impact of reduced bias and better temporal stability of the recalibrated radiances on their land surface temperature data record.…”

“…Further, radiances recalibrated with the methods presented in this paper were used in several studies [22][23][24][25] that showed their superiority to operational calibrated radiances. Finally, Tabata et al, (2019) [8] presented a comprehensive validation a full time-series, covering the years 1978-2016, of recalibrated IR and WV radiances from the VISSR, JAMI, IMAGER and Imager instruments onboard JMA's geostationary satellites (paper has been submitted to the same special issue as this paper).…”

Climate Data Records from Meteosat First Generation Part III: Recalibration and Uncertainty Tracing of the Visible Channel on Meteosat-2–7 Using Reconstructed, Spectrally Changing Response Functions

“…For those targets the spectral band adjustment functions can be derived using SCIAMACHY measurements. In order to control the cloudiness of the considered SCIAMACHY spectra, the SEVIRI cloud product is used [2] that also provides an estimate of the cloud-top pressure. To be accepted for the analysis, the footprint of a SCIAMACHY measurement has to be entirely cloud-free (Algeria-3 and Atlantic-1) or homogeneously covered by a cloud with the defined cloud-top-pressure (High-, Mid-and Low-cloud).…”

Dissecting effects of orbital drift of polar-orbiting satellites on accuracy and trends of climate data records of cloud fractional cover

“…The lower and upper hinges correspond to the 25th and 75th percentiles, while whiskers extend from the hinge to the largest and lowest values within 1.5 times the interquartile range. derived using the Theil-Sen estimates (Theil, 1950), and their significance was estimated with the Mann-Kendall test (Kendall, 1938;Mann, 1945). For multiple comparisons of the statistical significance of each grid, we applied the adjustment of the p value using the method of Benjamini and Hochberg (1995).…”