The MSG-SEVIRI-based cloud property data record CLAAS-2

Benas, Nikos; Finkensieper, Stephan; Stengel, Martin; Zadelhoff, Gerd-Jan van; Hanschmann, Timo; Hollmann, Rainer; Meirink, Jan Fokke

doi:10.5194/essd-9-415-2017

Cited by 83 publications

(119 citation statements)

References 43 publications

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“…Cloud properties derived from geostationary measurements of SEVIRI have been used in this study, that is, data of the CLoud property dAtAset using SEVIRI dataset edition 2 (CLAAS-2; Benas et al, 2017;Stengel et al, 2014). In particular, cloud mask, cloud top temperature, cloud top phase, cloud effective radius, and cloud optical thickness, in pixel resolution of 3 × 3 km 2 at subsatellite point and approximately 4 × 5 km 2 over Europe, are used with a temporal resolution of 15 min.…”

Section: Observationsmentioning

confidence: 99%

“…Further information can be found in Derrien and Le Gléau (2005) and Derrien (2013). This results in one of the following cloud types: liquid, supercooled, opaque ice, cirrus, overlap, or overshooting, which are then further converted to a binary cloud phase (Benas et al, 2017). For cloud top pressure retrieval, the measurements in SEVIRI infrared channels are matched to clear-sky and cloudy simulations of these using the Radiative Transfer for TOVS (Matricardi et al, 2004;Saunders et al, 1999) and ERA-Interim (Dee et al, 2011) as source for all required ancillary data.…”

Section: Observationsmentioning

confidence: 99%

“…3. It is important to note that, even if the 10.8-m channel is used to retrieve the cloud top phase and temperature, the two cloud products are mostly independent (Benas et al, 2017). This approach is complemented with H 2 O-IRW (infrared window) intercept method (Schmetz et al, 1993) and the radiance rationing method (Menzel et al, 1983).…”

Section: Observationsmentioning

confidence: 99%

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Detection of Mixed‐Phase Convective Clouds by a Binary Phase Information From the Passive Geostationary Instrument SEVIRI

2019

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Between −37 and 0 °C, clouds are liquid, ice, or mixed phase. Nearly all retrieval algorithms for passive instruments provide binary phase information—ice or liquid—making it difficult to retrieve mixed‐phase cloud properties. Based on measurements from the geostationary space‐based instrument Spinning Enhanced Visible and InfraRed Imager (SEVIRI), we show that the retrieved ice crystal effective radius is smaller than the liquid droplet effective radius for 48% of 230 analyzed cloud thermodynamic phase transitions—phase transition from liquid to ice of rising convective clouds—while ice crystals are expected to be larger than cloud droplets. We simulate mixed‐phase cloud radiances with the numerical model Santa Barbara DISORT Atmospheric Radiative Transfer for which we compare simulated effective radius retrievals with observations. The phase retrieval algorithm from SEVIRI does not represent well mixed‐phase clouds, and categorizing clouds by only ice and liquid is not enough to accurately represent mixed‐phase cloud optical properties. We conclude that the mixed‐phase nature of clouds explains that retrieved cloud droplet radii are larger than ice crystal radii directly before and after the phase transition. However, from a cloud tracking algorithm perspective, the variation of the effective radius enables the detection of mixed‐phase convective clouds from binary phase information.

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Section: Observationsmentioning

confidence: 99%

Section: Observationsmentioning

confidence: 99%

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Detection of Mixed‐Phase Convective Clouds by a Binary Phase Information From the Passive Geostationary Instrument SEVIRI

2019

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“…The CM SAF cLouds, Albedo and Radiation dataset from the AVHRR (CLARA-A2) data record [10] is the second edition of the CM SAF's global data record of cloud, surface albedo and surface radiation products derived from homogenized measurements of AVHRR on board the polar orbiting NOAA (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) and Metop (A) satellites. Herein, we used CFC monthly means compiled from all AVHRR sensors over a time period 1982-2015 and provided by CM SAF on a global regular latitude-longitude grid with 0.25 degree resolution.…”

Section: Clara-a2mentioning

confidence: 99%

“…The Cloud Property Dataset Using SEVIRI (CLAAS-2) data record [15] is based on 12 years of SEVIRI data. We employed the CFC product available as monthly composites on a regular latitude/longitude grid with a spatial resolution of 0.05 • × 0.05 • .…”

Section: Claas-2mentioning

confidence: 99%

Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations

et al. 2018

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Abstract:The CM SAF Cloud Fractional Cover dataset from Meteosat First and Second Generation (COMET, https://doi.org/10.5676/EUM_SAF_CM/CFC_METEOSAT/V001) covering 1991-2015 has been recently released by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF). COMET is derived from the MVIRI and SEVIRI imagers aboard geostationary Meteosat satellites and features a Cloud Fractional Cover (CFC) climatology in high temporal (1 h) and spatial (0.05 • × 0.05 • ) resolution. The CM SAF long-term cloud fraction climatology is a unique long-term dataset that resolves the diurnal cycle of cloudiness. The cloud detection algorithm optimally exploits the limited information from only two channels (broad band visible and thermal infrared) acquired by older geostationary sensors. The underlying algorithm employs a cyclic generation of clear sky background fields, uses continuous cloud scores and runs a naïve Bayesian cloud fraction estimation using concurrent information on cloud state and variability. The algorithm depends on well-characterized infrared radiances (IR) and visible reflectances (VIS) from the Meteosat Fundamental Climate Data Record (FCDR) provided by EUMETSAT. The evaluation of both Level-2 (instantaneous) and Level-3 (daily and monthly means) cloud fractional cover (CFC) has been performed using two reference datasets: ground-based cloud observations (SYNOP) and retrievals from an active satellite instrument (CALIPSO/CALIOP). Intercomparisons have employed concurrent state-of-the-art satellite-based datasets derived from geostationary and polar orbiting passive visible and infrared imaging sensors (MODIS, CLARA-A2, CLAAS-2, PATMOS-x and CC4CL-AVHRR). Averaged over all reference SYNOP sites on the monthly time scale, COMET CFC reveals (for 0-100% CFC) a mean bias of −0.14%, a root mean square error of 7.04% and a trend in bias of −0.94% per decade. The COMET shortcomings include larger negative bias during the Northern Hemispheric winter, lower precision for high sun zenith angles and high viewing angles, as well as an inhomogeneity around 1995/1996. Yet, we conclude that the COMET CFC corresponds well to the corresponding SYNOP measurements, and it is thus useful to extend in both space and time century-long ground-based climate observations.

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Spatio‐temporal variability of warm rain events over southern West Africa from geostationary satellite observations for climate monitoring and model evaluation

Young

Williams

Stein

et al. 2018

Quart J Royal Meteoro Soc

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This article presents the spatio‐temporal variability of warm rain events over southern West Africa (SWA) during the summer monsoon season for the first time, using Spinning Enhanced Visible Infrared Radiometer (SEVIRI) observations on the Meteosat geostationary satellites. The delineation of warm rain events is based on the principle that precipitating low‐level clouds are associated with either sufficient water content or large cloud droplet size. Capitalising on the ability of space‐borne radar to resolve vertical cloud structures and detect the presence of precipitation, the delineation is trained by collocated SEVIRI and CloudSat observations. The resulting 12 years of observations from SEVIRI are used to examine the spatial, diurnal, seasonal and interannual variability of warm rain events over SWA. Warm rain events predominate during the monsoon in August, with little interannual variability, and persist over orography in the morning and the coasts after midday, likely enhanced by orographic lifting and land–sea breeze effects. Warm clouds have a much higher probability of precipitation along the coastlines of Liberia and Nigeria compared to the central SWA coastline and further inland. Finally, when evaluating an 8‐day yet high‐spatial resolution model simulation, we find that warm rain frequencies from the simulation agree well with SEVIRI near the coast but simulated warm cloud cover and thus warm rain frequencies are too low over the Gulf of Guinea. The probability of precipitation of warm clouds is also too low inland. The newly developed observations of warm rain create opportunities to further investigate the diurnal cycle of warm rain, study aerosol–cloud–precipitation interactions, and assess the role of warm rain in the water cycle across Africa and beyond.

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The MSG-SEVIRI-based cloud property data record CLAAS-2

Cited by 83 publications

References 43 publications

Detection of Mixed‐Phase Convective Clouds by a Binary Phase Information From the Passive Geostationary Instrument SEVIRI

Detection of Mixed‐Phase Convective Clouds by a Binary Phase Information From the Passive Geostationary Instrument SEVIRI

Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations

Spatio‐temporal variability of warm rain events over southern West Africa from geostationary satellite observations for climate monitoring and model evaluation

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