Conference PaperPDF Available

METEOSAT OBSERVATIONS OF DIURNAL VARIATION OF CLOUD FRACTIONAL COVER

Authors:
  • CloudFerro / CREODIAS
  • Institute of Geodesy and Cartography

Abstract and Figures

Clouds play a key role in the Earth’s radiation budget by reflecting shortwave radiation and reducing emissions of longwave radiation. A temporal shift in diurnal cycle of cloud formation leads to significant feedbacks in the climate system. The CM SAF ClOud Fractional Cover (CFC) dataset from METeosat First and Second Generation - Edition 1 (COMET) provides high-temporal 30-minute cloud fraction estimates for the period 1991-2015. This allows to study spatial variability and trends in diurnal cycle of cloudiness. The study aims at evaluation of the COMET dataset in terms of ability to reconstruct phase and amplitude of the CFC diurnal cycle, and at analysis of trends and variability in the cloudiness diurnal cycle over last 25 years. To this end, we validate COMET CFC diurnal cycle using observations from 111 SYNOP sites and compare it with other existing datasets (CLAAS, ISCCP and ERA-Interim). On average, the COMET CFC product overestimates the SYNOP observations by 0.95%, varying from -3.37% (in winter months) to approx. 2.8% (in summer months), and from 0.11% for Meteosat Second Generation (MSG) to 1.61% for Meteosat First Generation (MFG) satellite data. The maximum CFC during the day for COMET occurs approx. 20 minutes later than for synoptic observations. COMET slightly underestimates CFC amplitude by 3%. The time series of bias for CFC, phase and amplitude are homogeneous for the period 1991-2015, but CFC and amplitude reveal statistically significant trends (below 1% per decade). We conclude that the COMET dataset is suitable for analysis of temporal changes in cloud diurnal cycle. In this context, the trends and variability calculated from the COMET in 1991-2015 ought to be reliable. The significant temporal changes in phase and amplitude show distinct spatial patterns, which should be further analysed to explain their physical origin and impact on the climate system.
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1
METEOSAT OBSERVATIONS OF DIURNAL VARIATION
OF CLOUD FRACTIONAL COVER
Jędrzej S. Bojanowski, Jan P. Musiał
Remote Sensing Centre, Institute of Geodesy and Cartography, Warsaw, Poland
Abstract
Clouds play a key role in the Earth’s radiation budget by reflecting shortwave radiation and reducing
emissions of longwave radiation. A temporal shift in diurnal cycle of cloud formation leads to significant
feedbacks in the climate system. The CM SAF ClOud Fractional Cover (CFC) dataset from METeosat
First and Second Generation - Edition 1 (COMET) provides high-temporal 30-minute cloud fraction
estimates for the period 1991-2015. This allows to study spatial variability and trends in diurnal cycle
of cloudiness. The study aims at evaluation of the COMET dataset in terms of ability to reconstruct
phase and amplitude of the CFC diurnal cycle, and at analysis of trends and variability in the
cloudiness diurnal cycle over last 25 years. To this end, we validate COMET CFC diurnal cycle using
observations from 111 SYNOP sites and compare it with other existing datasets (CLAAS, ISCCP and
ERA-Interim). On average, the COMET CFC product overestimates the SYNOP observations by
0.95%, varying from -3.37% (in winter months) to approx. 2.8% (in summer months), and from 0.11%
for Meteosat Second Generation (MSG) to 1.61% for Meteosat First Generation (MFG) satellite data.
The maximum CFC during the day for COMET occurs approx. 20 minutes later than for synoptic
observations. COMET slightly underestimates CFC amplitude by 3%. The time series of bias for CFC,
phase and amplitude are homogeneous for the period 1991-2015, but CFC and amplitude reveal
statistically significant trends (below 1% per decade). We conclude that the COMET dataset is suitable
for analysis of temporal changes in cloud diurnal cycle. In this context, the trends and variability
calculated from the COMET in 1991-2015 ought to be reliable. The significant temporal changes in
phase and amplitude show distinct spatial patterns, which should be further analysed to explain their
physical origin and impact on the climate system.
INTRODUCTION
Clouds play a key role in the Earth’s radiation budget by reflecting shortwave radiation and reducing
emissions of longwave radiation. Incoming shortwave radiation and emitted longwave radiation have
a distinct diurnal fluctuation. Therefore, a shift in a diurnal cycle of cloudiness may cause a change in
the radiation balance, that potentially leads to significant feedbacks in the climate system.
Global satellite imagery features more than a 30-year time span which is regarded as the minimal
period to study climate changes (Rossow and Schiffer, 1999, Foster and Heidinger, 2013). In this
respect, long CFC data records were derived from the Advanced Very High Resolution Radiometer
(AVHRR) instrument mounted aboard NOAA and MetOp satellites. These data records include:
International Satellite Cloud Climatology Project (ISCCP, Rossow and Schiffer, 1999, Young et al.
2018) which also employ geostationary sensors, Pathfinder Atmospheres Extended (PATMOS-x,
Heidinger et al., 2014), CLoud, Albedo and RAdiation dataset (CLARA-A2, Karlsson et al., 2017) of
the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), and the Community
Cloud Retrieval for Climate dataset of the Cloud_cci data set (CC4CL-AVHRR, Stengel, et al. 2017)
generated in the framework of the European Space Agency Climate Change Initiative. Climatic
analyses of these datasets have mainly focused on temporal changes and variability of mean monthly
cloud cover and cloud physical properties. To date, less attention has been paid to diurnal variability of
cloudiness, and especially to its long-term changes. Meteosat-based CLAAS dataset (Benas et al.,
2
2017) derived from MSG/SEVIRI allows for studying CFC diurnal cycles, but only for the limited (non-
climatological) period of 12 years (20042015).
Figure 1: SYNOP sites used for evaluation of COMET amplitude and phase of CFC diurnal cycle.
In this context, the recently published CM SAF ClOud Fractional Cover (CFC) dataset from METeosat
First and Second Generation - Edition 1 (COMET, Stöckli et al, 2018) provides high-temporal 30-
minute CFC estimates for the period 1991-2015 that allows to study spatial variability and trends in
diurnal cycle of cloudiness. Bojanowski et al. (2018) performed comparison of the COMET CFC with
synoptic observations, CALIOP LiDAR cloud profiles, and other satellite-derived datasets. It was
shown that overall mean bias of COMET is much below 1%, however with higher negative bias in N-
hemisphere winter months, as well as with lower performance for satellite acquisitions at high sun
zenith angles and high viewing angles. Pfeifroth et al. (2018) showed that COMET CFC trends are
consistent with trends of satellite-derived top-of-atmosphere reflected radiation and surface solar
radiation. However, there were no studies so far analysing COMET performance in representing CFC
diurnal cycle and seeking for the climate signal in the temporal changes in diurnal cloud cycle.
The ultimate aim of this study is to analyse trends and variability of the cloudiness diurnal cycle over
the last 25 years. To reach this aim we first evaluate COMET dataset in terms of ability: (1) to
reconstruct phase and amplitude of the CFC diurnal cycle, and (2) to provide homogeneous climate
information on trends and variability of amplitude and phase of CFC diurnal cycle. The evaluation is
based on validation against ground-based observations, as well as on inter-comparison with existing
data records.
DATA
Synoptic observations
Synoptic observations from the archive of the European Centre for Medium-Range Weather Forecasts
(ECMWF) were used for evaluation of COMET-derived phase and amplitude of the CFC. We selected
SYNOP sites that: (1) were not used for training of COMET’s Bayesian classifier, (2) were within 60
degree N/S and 60 degree W/E, and (3) for which the Meteosat satellite viewing angle was below 70
degrees. Further, we performed a rigorous screening to select only those sites, for which observations
were performed seamlessly every 3 hours in 1991-2015. A daily CFC mean was calculated if at least
six 3-hourly observations were available. Then, at least 20 daily means were aggregated to derive
monthly averages. Sites missing even a single monthly mean in 1991-2015 were excluded. Finally, we
evaluated time series homogeneity of monthly CFC anomalies according to the Standard Normal
Homogeneity Test (SNHT, Alexandersson,1986). Remarkably, none of the sites revealed any
inhomogeneities. In previous analyses using the same initial set of SYNOP sites (e.g. Bojanowski et
al. 2018, Bojanowski and Stöckli, 2017), SNHT was used to exclude several sites. Here we found out
that all sites where observations were performed in 1991-2015 seamlessly with a stable frequency are
of high quality. The screening procedure resulted in 111 SYNOP sites distributed within the Meteosat
disc (Fig. 1). Yet, there is a strong bias towards European stations. The relatively low number of sites
does not allow for analysis of spatial distribution of errors in COMET diurnal cycle, but guarantees best
possible reference for analysing long-term stability, which is indispensable for climate analysis.
3
Provider
Dataset
Spatial res.
Temporal res.
Coverage
CM SAF
COMET ed. 1
0.05 deg
1 h
1991-2015
CM SAF
CLAAS-2
0.25 deg
1 h
2005-2015*
NOAA/NCEI
ISCCP-HGH
1 deg
3 h
1984-2012*
ECWMF
ERA-Interim
0.75 deg
3 h
1982-2015*
ECMWF
SYNOP
111 sites
3 h
1991-2015
* only a common period 2005-2012 was used
Table 1: Datasets of CFC monthly mean diurnal cycles covering Meteosat disc used in the study.
Satellite data records and reanalysis
COMET. The CM SAF Cloud Fractional Cover dataset from Meteosat First and Second Generation
(COMET, Stöckli et al., 2018) covering 19912015 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. The COMET long-term cloud fraction
climatology features high temporal (30-minute) and spatial (0.05x0.05 deg) resolutions that allows for
studying diurnal cycle of cloudiness.
CLAAS. The CM SAF’s Cloud Property Dataset Using SEVIRI (CLAAS-2) data record (Benas et al.,
2017) is based on 12 years of MSG SEVIRI data. For the sake of inter-comparison we used the
CLAAS CFC product available as composites of monthly mean diurnal cycles generated on a regular
latitude/longitude grid with a spatial resolution of 0.25 degree.
ISCCP. The International Satellite Cloud Climatology Project (ISCCP) provides global coverage of
cloud properties over a period of more than 35 years from polar-orbiting and geostationary platforms
(Rossow and Schiffer, 1999). A new high-resolution version of the data record is denoted as ISCCP H-
Series (Young et al., 2018). It spans from July 1983 and it is under continuous production to extend to
the present. Here we used ISCCP-HGH product, i.e. CFC monthly mean diurnal cycles at 1 degree
resolution.
ERA-Interim reanalysis. ERA-Interim (Dee et al., 2011) is the reanalysis provided by the ECMWF. It
starts in 1979 and provides to date meteorological parameters in near real-time. The ERA-Interim
atmospheric model has a spatial resolution of 0.75 degree. For this study we obtained monthly means
of Total Cloud Cover combining analyses at 0,6,12 and 18UTC with forecast from 0 and 12UTC with
3 and 9 hours steps. This yielded CFC monthly mean diurnal cycles at 3 hour temporal resolution.
METHODS
Evaluation of the COMET against synoptic observations, inter-comparison between datasets, as well
as trend analyses were performed using CFC monthly mean diurnal cycle (MMDC) products. Apart
from the SYNOP, the datasets listed in Table 1 were obtained from data providers in the format of
MMDC. Only instantaneous synoptic observations were aggregated to MMDC. Further, derivatives of
the CFC diurnal cycle were generated. Amplitude of CFC diurnal cycle has been calculated as
a difference between maximum and minimum CFC during a day. Phase of CFC diurnal cycle was
calculated as the local solar time of maximum CFC during a day. To evaluate the COMET CFC,
amplitude and phase, the Meteosat pixels were collocated with synoptic observations by means of the
nearest neighbour approach. The collocations covering the entire available period of COMET climate
data record i.e. 1991-2015, were used to assess performance of the COMET-based mean CFC,
phase and amplitude by calculating mean bias error (MBE) and bias-corrected root mean square error
(bcRMSE). Further, to investigate temporal stability and homogeneity of COMET-derived
representation of CFC diurnal cycle, we analysed the time series of MBE. We tested the homogeneity
by means of the SNHT using a critical value for T statistics proposed by Khaliq and Ouarda (2007), as
well as the temporal stability revealed by a change (trend) of MBE over time.
4
N
CFC (%)
Phase (h)
Amplitude (%)
bcRMSE
MBE
bcRMSE
MBE
bcRMSE
Overall
33195
9,73
0,33
5,25
-2,66
8,35
MFG
18648
9,87
0,34
5,46
-3,07
8,61
MSG
14547
9,50
0,32
4,97
-2,13
8,00
DJF
8300
10,74
0,39
7,17
-0,15
6,93
MAM
8295
8,65
0,03
5,59
-3,67
7,79
JJA
8295
8,57
0,06
5,39
-3,24
8,35
SON
8305
9,44
0,83
6,17
-3,57
7,99
N-hemisphere
32895
9,74
0,34
5,26
-2,70
8,36
S-hemisphere
300
8,72
-0,49
4,51
2,44
7,52
Table 2: Performance statistics: mean bias error (MBE) and bias-corrected root mean square error (bcRMSE), for
COMET CFC, phase and amplitude measured against synoptic observations at 111 sites.
Figure 2: Time series of mean bias error of COMET CFC, amplitude and phase validated against synoptic observations.
The Standard Normal Homogeneity Test (in red) does not reveal any inhomogeneities for T(k) critical value at 10.2. The
dashed line represents Theil-Sen linear trend.
Next, we carried out a grid-based inter-comparison of COMET CFC diurnal cycles with other data
records: CLAAS, ISCCP and ERA-Interim (Table 2). For this purpose, COMET and CLAAS were
aggregated to 0.75x0.75 degree by means of the first-order conservative remapping. ISCCP data
remained at 1 degree resolution. Inter-comparison was based on 8 observations per day (every
3 hours), which were available from all data sources for a common period 2005-2012. Finally, we
conducted a grid-based trend analysis of CFC mean, phase and amplitude. We used monotonic
trends derived with Theil-Sen estimates (Theil, 1950), and their significance was estimated with the
Mann-Kendall test (Kendall, 1938; Mann, 1945). We first calculated and compared trends from all data
sources for 2005-2012. Then, we generated trend maps for COMET only, but using the entire
available period, i.e. 1991-2015.
RESULTS
Evaluation against SYNOP
Table 2 summarizes COMET performance in relation to the SYNOP observations. Overall, COMET
CFC overestimates the reference by 0.95%. However, this mean statistic is a result of compensation
of larger negative bias in (N-hemisphere) winter months (-3.37%) with positive bias in warmer months
(approx. 2.8%). The underestimation of COMET CFC above bright and cold surfaces was reported by
Bojanowski et al. (2018). However, presented study reveals a notable difference in bias between MFG
(1.61%) and MSG (0.11%). Such a difference may result in a negative trend in MBE shown in Fig. 2a.
Although the time series of MBE features strong seasonality with large CFC underestimation (up to
15%) in winter months, it is still homogeneous in 1991-2015.
5
Figure 3: CFC diurnal cycle from COMET, CLAAS, ISCCP and ERA-Interim aggregated in 10 x 10 degree grids. Axes
used for each grid box are shown in the bottom left corner of the figure: y-axis represents 3-hourly CFC (%) divided by
daily mean, x-axis represents local solar time (h). In grey colour scale the mean COMET CFC is shown.
Figure 4: Time series of mean, amplitude and phase of CFC diurnal cycle from COMET, CLAAS, ISCCP and ERA-Interim
aggregated from all grids within Meteosat disc. Values in brackets represents the mean over the 2005-2013 period.
The time of maximum CFC during the day (phase) occurs approx. 20 minutes later in COMET than in
synoptic observations, with no difference for MFG and MSG. Unexpectedly, the largest bias is
revealed for September-November and not for winter months (December-February). Yet, the lowest
precision (according to bcRMSE) is for the latter. In general, relatively large bcRMSE for phase can
originate from regions with no distinct CFC diurnal cycle. In these regions, a slight bias in CFC can
cause large errors in phase. The MBE of phase shows no significant trend and no inhomogeneities
(Fig. 2c). The range between maximum and minimum diurnal CFC (amplitude) is 3% lower in COMET
than in synoptic observations. Despite the lowest accuracy and precision of mean CFC estimates for
winter months, for these months the CFC amplitude is the most accurate (-0.15%). Figure 2b shows
a significant positive bias in amplitude MBE, but no inhomogeneities. Thus, it is unlikely that is caused
by the difference in cloud detection accuracy between MFG and MSG sensors.
Inter-comparisons
The course of CFC diurnal cycle reveal noticeable spatial variation among analysed datasets (Fig. 3).
For regions of relatively stable CFC across a day, all data sources do agree. This can be seen over
the ocean at mid-high latitudes at both hemispheres (approx. > 40 deg). Yet, over bright desserts with
relatively stable low cloud cover, ISCCP reveals large overestimations around noon (i.e. for low sun
zenith angles). In Western and Central Europe, all sources reveal similar diurnal cycles, but ISCCP
again detects larger CFC amplitude. Concurrently, according to ERA-Interim, the maximum cloudiness
occurs earlier during the day than in rest of the datasets. COMET detects a distinct CFC minimum
around noon over the Indian Ocean, which is not revealed by other products. Finally, it must be noted
that all of the datasets disagree over South America. These discrepancies can be however related to
lower performance of CFC derived from geostationary satellites at high viewing angles.
6
Mean CFC
Amplitude
Phase
COMET
CLAAS
ISCCP
ERA-I
Figure 5: Comparison of Theil-Sen monotonic trends in CFC, amplitude and phase calculated for all intercompared
datasets in a common 2005-2012 period. Statistical significance (p-val < 0.05) according to the Mann-Kendall test is
shown by black dots.
Figure 4 shows time series of CFC, phase and amplitude averaged from all grids. COMET and ERA-
Interim reveal similar mean CFC range (Fig. 4a). Both sources are expected to have CFC values close
to synoptic observations, because COMET’s Bayesian classifier was trained against SYNOP data,
and these were also used in the data assimilation scheme of ERA-Interim reanalysis. CFC of CLAAS
and ISCCP show similar values. These COMET-ERA-Interim and CLAAS-ISCCP similarities are no
longer visible for amplitude (Fig. 4b). Especially ERA-Interim outlies with lower CFC amplitude and
less distinct amplitude seasonality. Also for phase (Fig. 4c), ERA-Interim differs from other data
sources detecting the highest CFC during the day, on average at least half an hour earlier.
7
Remarkably, COMET and CLAAS show some single outlying months, for instance for winter 2007 and
winter 2009, respectively.
Mean CFC
Amplitude
Phase
COMET
Figure 6: COMET CFC, amplitude and phase of Theil-Sen monotonic trend calculated for 1991-2015. Statistical
significance (p-val < 0.05) according to the Mann-Kendall test is shown by black dots. Please note the different colour
bars than in Fig.5.
Trend analysis
COMET and CLAAS show similar trends in mean CFC (Fig. 5). ISCCP agrees in most areas, however
trend values and statistical significance differ. ISCCP also shows, unconfirmed by other sources,
significant positive trends in southern high latitudes, and in the southern edges of Sahara Dessert.
ERA-Interim shows similar patterns than other datasets, but with stronger trends and larger areas of
their significance. Trends in amplitude are spatially more heterogenous. Still, the datasets agree at
several regions of significant positive trend over the Atlantic, as well as at Horn of Africa, where
significant negative amplitude trend was detected. Phase does not reveal significant trends, which is
showed by all the sources. Distinct climate signals are revealed while analysing COMET trends in
1991-2015 (Fig. 6). Clear spatial patterns of significant trends in mean CFC can be observed, e.g.
negative trend around Black Sea and in the Southern Atlantic, as well as positive trends in the
Northern and Central Atlantic and the Indian Ocean. Amplitude tends to significantly decrease over the
ocean within the whole Meteosat disc. Phase reveals only a few separate spots of positive trends
again only over the ocean.
CONCLUSIONS
We conclude that COMET dataset is suitable for analysis of temporal changes in diurnal cloud
formation. This is proven by temporal stability of CFC amplitude and phase measured against the
referential SYNOP observations. Moreover, the inter-comparison of trends in CFC phase and
amplitude within overlapping period (2005-2012) also shows good agreement among analysed
datasets. In this context, the trends and variability calculated from COMET in 1991-2015 ought to be
reliable. The significant temporal changes in phase and amplitude show distinct spatial patterns.
These are recommended for further climate analysis to explain the phenomena and assess their
impact on climate system.
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ACKNOWLEDGEMENTS
This work was supported by the National Science Centre, Poland under the POLONEZ
grant No 2015/19/P/ST10/03990 that received funding from the European Union’s Horizon
2020 research and innovation programme under the Marie Skłodowska-Curie grant
agreement No 665778.
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... Within the study, the cloud fraction diurnal cycles were extracted from the COMET Monthly Mean Diurnal Cycle (MMDC) product. The COMET MMDC has been already validated against the SYNOP cloud observations, and it was proven to be suitable for the analysis of climatic trends and variability in the cloudiness diurnal cycle (Bojanowski and Musiał, 2018). Such an accurate dataset was used for the generation of the AVHRR-like synthetic dataset (i.e. ...
... Secondly, the objective of the study is to analyse spurious trends in the AVHRR CFC CDRs caused by orbital drift and sampling issues and not to analyse climatic trends in the CFC diurnal cycles revealed by the COMET dataset. For this please refer to Bojanowski and Musiał (2018). ...
... Hence, we expect that a real negative trend in CFC for Europe is hidden by the effect of the undersampling of the CFC diurnal cycle and satellite orbital drift. This trend would be in line with other findings, for instance of Bojanowski and Musiał (2018) and Pfeifroth et al. (2018). For the afternoon (PM) NOAA satellites, we can expect that observed positive trend in the central Atlantic Ocean is lower than in reality due to the negative spurious trend. ...
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... We have successfully applied COMET for downstream LST retrieval [61]. The homogeneity and temporal stability of COMET and phase and amplitude of COMET diurnal cycles have been proven to be realistic [62]. As a next step, we are going to extend the algorithm framework behind COMET called "GeoSatClim" for a joint retrieval of all four Surface Radiation Balance (SRB) components. ...
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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.
Preprint
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Abstract. Radiometers such as the AVHRR mounted aboard a series of the NOAA and MetOp polar-orbiting satellites provide 4-decade-long global climate data records (CDRs) of cloud fractional cover. Generation of such long datasets requires combining data from consecutive satellite platforms. A varying number of satellites operating simultaneously in the morning and afternoon orbits, together with the satellite orbital drift cause the uneven sampling of the cloudiness diurnal cycle along a course of CDR. This in turn leads to significant biases, spurious trends and inhomogeneities in the data records of climate variables featuring the distinct diurnal cycle (such as clouds). To quantify the uncertainty and magnitude of spurious trends in the AVHRR-based cloudiness CDRs, we sampled the 30-minute reference CM SAF Cloud Fractional Cover dataset derived from Meteosat First and Second Generation (COMET) at times of the NOAA and MetOp satellites overpasses. The sampled cloud fractional cover (CFC) time series were aggregated to monthly means and compared with the reference COMET dataset covering the Meteosat disc (up to 60 degrees N/S/W/E). For individual NOAA/MetOp satellites the errors in mean monthly CFC reach ± 10 % (bias) and ± 7 % per decade (spurious trends). For the combined data record consisting of several NOAA/MetOp satellites, the CFC bias is 3 % and the spurious trends are 1 % per decade. This study proves that before 2002 the AVHRR-derived CFC CDRs do not comply with the GCOS temporal stability requirement of 1 % CFC per decade just due to the satellite orbital drift effect. After this date the requirement is fulfilled due to the numerous NOAA/MetOp satellites operating simultaneously. Yet, the time series starting in 2003 is shorter than 30 years that voids climatological analyses. We expect that the error estimates provided in this study will allow for a correct interpretation of the AVHRR-based CFC CDRs and ultimately will contribute to the development of a novel satellite orbital drift correction methodology widely accepted by the AVHRR-based CDRs providers.
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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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Solar radiation is the main driver of the Earth's climate. Measuring solar radiation and analysing its interaction with clouds are essential for the understanding of the climate system. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates satellite-based, high-quality climate data records, with a focus on the energy balance and water cycle. Here, multiple of these data records are analyzed in a common framework to assess the consistency in trends and spatio-temporal variability of surface solar radiation, top-of-atmosphere reflected solar radiation and cloud fraction. This multi-parameter analysis focuses on Europe and covers the time period from 1992 to 2015. A high correlation between these three variables has been found over Europe. An overall consistency of the climate data records reveals an increase of surface solar radiation and a decrease in top-of-atmosphere reflected radiation. In addition, those trends are confirmed by negative trends in cloud cover. This consistency documents the high quality and stability of the CM SAF climate data records, which are mostly derived independently from each other. The results of this study indicate that one of the main reasons for the positive trend in surface solar radiation since the 1990's is a decrease in cloud coverage even if an aerosol contribution cannot be completely ruled out.
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Generating a cloud climate data record (CDR) from observations acquired by a set of polar orbiting satellites requires dealing with potential artefacts caused by lack of long-term stability, degradation of satellite sensors, radiometric and geometric calibration problems, and satellite orbital drift. The majority of these aspects have been resolved within the scope of the ESA Cloud_cci project's retrieval algorithms used to produce a new 30y+ (1982-2014) cloud CDR from the AVHRR sensor onboard NOAA satellites (CC4CL-AVHRR). Yet, orbital drift and varying equatorial crossing times of consecutive satellites have not been accounted for. This may inhibit the use of the original dataset for investigating the cloud variability and change over the last 3 decades. We propose and apply a simple statistical method for correcting the original dataset over Europe by a bias removal using quality-checked synoptic observations from 158 sites. The approach builds on kriging with external drift in which ground-based observations are interpolated using satellite data as explanatory variable. Interpolation is applied to monthly means, i.e. not explicitly resolving the diurnal cycle of cloudiness. Averaged over 68 evaluation sites, the corrected (debiased) dataset (MBE=-0.68%, bcRMSE=6.42%) significantly outperforms the original one (MBE=4.05%, bcRMSE=14.90%). The correction also decreases the performance differences among NOAA satellites, and implicitly removes the inhomogeneity in cloud fractional cover (CFC) time series due to changing overpass times. We also show the correction reduces the magnitude of trends in CFC but keeps their sign unchanged.
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This paper describes the new global long-term, International Satellite Cloud Climatology Project (ISCCP) H-Series climate data record (CDR). The H-Series data contains a suite of level 2 and level 3 products for monitoring the distribution and variation of cloud and surface properties to better understand the effects of clouds on climate, the radiation budget, and the global hydrologic cycle. This product is currently available for public use and is derived from both geostationary and polar orbiting satellite imaging radiometers with common visible and infrared (IR) channels. The H-Series data spans from July 1983 to Dec 2009 with plans for continued production to extend the record to the present with regular updates. The H-series data are the longest combined geostationary and polar orbiter satellite based CDR of cloud properties. Access to the data is provided in network Common Data Form (netCDF) and archived by NOAA's National Centers for Environmental Information (NCEI) under the satellite Climate Data Record Program (https://doi.org/10.7289/V5QZ281S). The basic characteristics, history, and evolution of the dataset are presented herein with particular emphasis on and discussion of product changes between the H-Series and the widely used predecessor D-Series product which spans from July 1983 through December 2009. Key refinements included to the ISCCP H-Series CDR are based on improved quality control measures, modified ancillary inputs, higher spatial resolution input and output products, calibration refinements, and updated documentation and metadata to bring the H-Series product into compliance with existing standards for climate data records.
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New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude-longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named: AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. Together with the ensured spectral consistency and rigorous uncertainty propagation though all processing levels, the Cloud_cci datasets approach new benchmarks for climate data records of cloud properties based on passive imaging sensors. For each dataset a Digital Object Identifier has been issued: Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002 Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002 Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002 Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002 Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002 Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002
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Clouds play a central role in the Earth's atmosphere, and satellite observations are crucial to monitor clouds and understand their impact on the energy budget and water cycle. Within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) a new cloud property data record was derived from geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements for the time frame 2004–2015. The resulting CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) data record is publicly available via the CM SAF website (doi:10.5676/EUM_SAF_CM/CLAAS/V002). In this paper we present an extensive evaluation of the CLAAS-2 cloud products, which include cloud fractional coverage, thermodynamic phase, cloud top properties, liquid/ice cloud water path and corresponding optical thickness and particle effective radius. Validation and comparisons were performed on both level 2 (native SEVIRI grid and repeat cycle) and level 3 (daily and monthly averages and histograms) data, with reference data sets derived from lidar, microwave and passive imager measurements. The evaluation results show a very good overall agreement, with matching spatial distributions and temporal variability, and small biases attributed mainly to differences in sensor characteristics, retrieval approaches, spatial/temporal samplings and viewing geometries. No major discrepancies were found. Underpinned by the good evaluation results, CLAAS-2 proves its fitness for the envisaged applications such as process studies, e.g. studies of the diurnal cycle of clouds, and the evaluation of regional climate models. The data record is planned to be extended and updated in the future.
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The second edition of the satellite-derived climate data record CLARA ("The CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data" – second edition denoted CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail as well as some major validation results. Some of the main improvements of the data record come from a major effort in cleaning and homogenising the basic AVHRR level 1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Polar Summer surface albedo and cloud conditions, as well as global cloud redistribution patterns, are provided.
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The Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres-Extended (PATMOS-x) dataset offers over three decades of global observations from the NOAA Polar-orbiting Operational Environmental Satellite (POES) project and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) [Meteorological Operational (MetOp)] satellite series. The AVHRR PATMOS-x data provide calibrated AVHRR observations in addition to properties about tropospheric clouds and aerosols, Earth's surface, Earth's radiation budget, and relevant ancillary data. To provide three decades of data in a convenient format, PATMOS-x generates mapped and sampled results with a spatial resolution of 0.1° on a global latitude-longitude grid. AVHRR PATMOS-x is composed of data from 17 different sensors. An examination of cloud amount and total-sky time series demonstrates that intersatellite biases are less than 2%. AVHRR PATMOS-x data are hosted by the National Climatic Data Center (NCDC).
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Satellite drift is a historical issue affecting the consistency of those few satellite records capable of being used for studies on climate time scales. Here, the authors address this issue for the Pathfinder Atmospheres Extended (PATMOS-x)/Advanced Very High Resolution Radiometer (AVHRR) cloudiness record, which spans three decades and 11 disparate sensors. A two-harmonic sinusoidal function is fit to a mean diurnal cycle of cloudiness derived over the course of the entire AVHRR record. The authors validate this function against measurements from Geostationary Operational Environmental Satellite (GOES) sensors, finding good agreement, and then test the stability of the diurnal cycle over the course of the AVHRR record. It is found that the diurnal cycle is subject to some interannual variability over land but that the differences are somewhat offset when averaged over an entire day. The fit function is used to generate daily averaged time series of ice, water, and total cloudiness over the tropics, where it is found that the diurnal correction affects the magnitude and even the sign of long-term cloudiness trends. A statistical method is applied to determine the minimum length of time required to detect significant trends, and the authors find that only recently have they begun generating satellite records of sufficient length to detect trends in cloudiness.