Detailed characterisation of AVHRR global cloud detection performance of the CM SAF CLARA-A2 climate data record based on CALIPSO-CALIOP cloud information

Karlsson, Karl-Göran; Håkansson, Nina

doi:10.5194/amt-2017-307

Cited by 5 publications

(6 citation statements)

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“…Ackerman et al (2008) estimated the MODIS limit for τ to be approximately 0.4. A similar improvement in agreement with CALIOP as a consequence of increasing τ was observed by Karlsson and Håkansson (2017) for the AVHRR instrument. The latter study demonstrated that the imager's probability of detection changed in the range 0.0<τ<1.0.…”

Section: Discussionsupporting

confidence: 78%

Calibration of global MODIS cloud amount using CALIOP cloud profiles

Kotarba¹

2020

Preprint

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Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOV) as either confident cloudy, probably cloudy, probably clear, or confident clear. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by NASA assumes that confident clear and probably clear IFOV are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this best guess approach is unreliable, especially on a regional/ local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with MODIS/ Aqua IFOV. Based on 33,793,648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in the polar regions at night, and in selected locations over the northern hemisphere. Applications of MODIS data at ~10 degrees resolution (or finer) should first assess the extent of the error. Uncertainties were related to the efficiency of the cloud masking algorithm. Until the algorithm can be significantly modified, our method is a robust way to calibrate (correct) MODIS estimates. It can be also used for MODIS/ Terra data, and other missions where the footprint is collocated with CALIPSO.

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Section: Discussionsupporting

confidence: 78%

Calibration of global MODIS cloud amount using CALIOP cloud profiles

Kotarba¹

2020

Preprint

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“…The algorithm should then be improved to solve the limitations pinpointed in this study (e.g., inhomogeneity 1995/1996, cloud detection over snow and at coastal zones). In addition, a training set which consisted of synoptic observations on land, could be enhanced with global data of CALIPSO/CALIOP [47]. Further, the new release should follow the metrological norms on providing uncertainties of climate variables [48].…”

Section: Discussionmentioning

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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“…The CM SAF cloud detection algorithm for AVHRR and SEVIRI results in three cloud classes: cloud filled, cloud contaminated and cloud free (Dybbroe et al , ; Stengel et al , ). The first two classes are considered 100% cloudy (Dybbroe et al , ; Karlsson, ; Kniffka et al , ), which in reality, is probably only correct for the cloud filled class. As the CM SAF climate product was in the form of a mean monthly cloud amount, only the data provider's cloud mask was considered.…”

Section: Discussionmentioning

confidence: 99%

“…After cloud detection, results are georeferenced onto a 0.25° × 0.25° grid (∼25 × 25 km). For further details on the AVHRR cloud detection methodology, see Karlsson ().…”

Section: Methodsmentioning

confidence: 99%

Regional high‐resolution cloud climatology based on MODIS cloud detection data

Kotarba

2015

Intl Journal of Climatology

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Most satellite cloud climatologies come in the form of global, low-resolution datasets: so-called 'gridded' Level 3 products, resulting from the reprojection and spatio-temporal aggregation of swath (Level 2) data. Their coarse resolution means that global datasets are of limited usefulness in regional studies. In this paper we develop and evaluate a new, regional cloud climatology over Poland and its neighbouring countries (∼10% of the area covered by Europe), based on observations performed with the state-of-the-art cloud imager, the moderate resolution imaging spectroradiometer (MODIS). In contrast to the operational, global MODIS cloud climatology, which is delivered as a Level 3 product at a spatial resolution of 1 ∘ × 1 ∘ , this regional climatology maintains the MODIS nadir spatial resolution of 1 km/pixel. The resulting high-spatial-resolution climatology is compared with AVHRR and SEVIRI datasets, and surface-based (SYNOP) observations at the level of monthly and annual means. The results shows that the standard MODIS Level 2 cloud mask product MOD35/MYD35 can be successfully used to develop a regional, high-resolution cloud climatology. MODIS provides reliable estimates of cloud amount at the national scale (annual mean: 64.0% or 70.8%, depending on the MODIS data interpretation scheme), and correctly reproduces the annual cloud amount cycle (correlation between monthly means with SEVIRI/AVHRR >0.98). A comparison with monthly mean surface observations reveals a bias ranging from −1.1% up to 5.9%, and a root mean square error of 4.2% − 6.6%. MODIS data also correctly indicates the spatial distribution of clouds. However, local anomalies were detected that were identified as artifacts of the MODIS cloud detection algorithm. Those artifacts covered 9% of the study area, but had no impact on spatially-averaged metrics.

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Detailed characterisation of AVHRR global cloud detection performance of the CM SAF CLARA-A2 climate data record based on CALIPSO-CALIOP cloud information

Cited by 5 publications

References 0 publications

Calibration of global MODIS cloud amount using CALIOP cloud profiles

Calibration of global MODIS cloud amount using CALIOP cloud profiles

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

Regional high‐resolution cloud climatology based on MODIS cloud detection data

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