A HIGH RESOLUTION EUROPEAN CLOUD CLIMATOLOGY FROM 15
YEARS OF NOAA/AVHRR DATA
R. Meerkötter1, G. Gesell2, V. Grewe1, C. König1, S. Lohmann1, H. Mannstein1
1
Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
Institut für Physik der Atmosphäre – 2 Deutsches Fernerkundungsdatenzentrum
Münchener Straße 20, D-82234 Weßling, Germany
ABSTRACT
A European Cloud Climatology (ECC) based on NOAA/AVHRR data has been generated at DLR, Institute of
Atmospheric Physics (IPA) and the German Remote Sensing Data Centre (DFD). Currently, the dataset
covers the period from 1989-2003. Total cloud cover is provided, as well as the coverage of low-level,
medium-level, high-level and thin clouds for entire Europe (34°N - 72°N, 11°W - 32°E). An important
characteristic of the ECC is its high spatial resolution of 0.01° x 0.0125° (~ 1.1 x 1.1 km2) in latitude and
longitude, respectively. This high resolution allows the direct comparison with surface observations on the
one hand, while the covered area is large enough to evaluate global circulation models (GCMs) on the other
hand. The cloud products are derived by our AVHRR Processing scheme Over cLouds, Land, and Ocean
(APOLLO) including a careful quality control. The scientific analysis of ECC time series is accompanied by
the comparison with ISCCP data and with synoptic surface observations in selected regions. Comparisons to
available MSG data are performed to analyse deviations in cloud detection caused by the different viewing
geometries. The first application of the ECC is the validation of the GCM ECHAM4.L39/CHEM. In this
context the cloud cover diagnosed by the model is faced to the corresponding ECC products.
1. INTRODUCTION
Clouds play an essential role in the Earth’s climate due to their large impact on the radiation budget. Cloud
climatologies are therefore important to determine the extent to which inter-annual and multi-decadal
changes of clouds affect the Earth’s radiation budget. Furthermore, cloud climatologies are required to
assess the representation of clouds in climate models and to quantify possible climate feedbacks.
Several satellite based data records meanwhile cover time periods of one or more decades and become
therefore increasingly interesting with respect to observing possible changes of cloud parameters: Well
known is the International Satellite Cloud Climatology Project (ISCCP), a global dataset containing cloud
parameters from operational satellite cloud observations since 1983 [e.g. Rossow and Schiffer ,1999;
Schiffer and Rossow, 1983]. Stowe et al. [2002] present the global PATMOS climate dataset which contains
NOAA/AVHRR derived total cloud amounts in a 19-years period from 1981-1999. A regional cloud
climatology over Scandinavia for the period 1990-2000 based on NOAA/AVHRR data has been generated
by Karlsson [2003]. For recent years, since 1999, the German Meteorological Service produces operationally
monthly averaged cloud cover products for Europe and the eastern North Atlantic, based on the so-called
‘Satellite Weather’ [Rosenow et al., 2001].
The aim of this study is to introduce the European Cloud Climatology (ECC) generated at Deutsches
Zentrum für Luft- und Raumfahrt (DLR) by use of NOAA/AVHRR data. The ECC dataset is based on
daytime as well as on night-time overpasses. Following former work by Kriebel et. al. [2003], Kästner and
Kriebel [2001], and Saunders and Kriebel [1988] we investigate a 14-years climatology (1990-2003). Here
we present monthly means of total cloud cover based on the noon overpasses mainly of NOAA-11 and -14.
For an evaluation in various regions of Europe the ECC total cloud amounts were compared with SYNOP
observations obtained over the same period and with ISCCP-D2 data. As a first attempt, we faced a mean of
the ECC total cloud cover to the cloud cover diagnosed by the global climate model ECHAM4.L39/CHEM.
Since an adaptation of the APOLLO software allows to deal with the AVHRR-similar SEVIRI channels of
MSG, we can compare cloud parameter results based on both sensors to increase the knowledge about the
influence of different viewing geometries.
2. DIURNAL COURSE OF TOTAL CLOUD COVER
A possible disadvantage of the use of noon overpasses is the shift of the overpass time of the satellites into
the afternoon, which may affect the retrieved cloud parameters, e.g. those of highly variable convective
clouds. Although the influence of the diurnal course may be big in individual scenes, Figure 1 shows that in
long time averages only a moderate or little impact on the ECC can be expected.
Figure 1: Long-term average of the diurnal variation of cloud cover for selected months based on
SYNOP data. Indicated is the time interval of temporal drifts of the NOAA noon overpasses.
3. SPATIAL DISTRIBUTION OF ECC TOTAL CLOUD COVER IN EUROPE
Figure 2 shows the spatial distribution of total cloud cover in full AVHRR-resolution for the entire ECC area.
Monthly means presented here are obtained from averaging over eleven/ten years (1991-2001/2000) for
selected months representing the four seasons.
Figure 2: Means of total cloud cover in Europe from NOAA/AVHRR noon overpass data. The January
(upper-left), April (upper-right) and July (lower-left) products are eleven year means, the October
product (lower-right) is a ten year mean.
4. TIME SERIES OF ECC AND SYNOP TOTAL CLOUD COVER
Figure 3 shows the areas investigated in this study and, as examples, the time series of monthly means of
total cloud cover from ECC and SYNOP data and the mean differences (d) (Satellite minus SYNOP),
standard deviations of these differences (s), and correlation coefficients (c). Figure 4 shows the time series
of monthly means of high- medium- and low-level cloud cover from the ECC for the Germany area.
Figure 3: Investigated areas and examples of time series of monthly means of total cloud cover from
the ECC and the SYNOP data.
Figure 4: Time series of ECC cloud cover for high-level (< 400 hPa, upper plot), medium-level (< 700
hPa and > 400 hPa, middle plot) and low-level clouds (> 700 hPa, lower plot).
5. SEASONAL COMPARISON ECC VERSUS SYNOP
For all investigated areas the total cloud cover from ECC data has been compared with SYNOP
observations for the four seasons. Figure 5 shows four scatter plots, each including three months of the
according season. Additionally given are mean differences (d), standard deviations of differences (s), and
correlation coefficients (c). Due to convective clouds and different observation geometries satellite derived
cloud coverage is systematically lower than the SYNOP observations especially in summer.
Figure 5: Total cloud cover from ECC data vs. SYNOP observations for the four seasons.
6. TIME SERIES OF TOTAL CLOUD COVER: A COMPARISON BETWEEN ISCCP,
ECC AND SYNOP
For the Germany area, as one example, spatially averaged monthly means of total cloud cover from ISCCPD2 data have been compared to corresponding data of surface SYNOP observations and ECC data. Figure
6 shows that the ISCCP-based monthly means are systematically higher than the means based on SYNOP
observations and on ECC data.
Figure 6: Comparison of spatially averaged monthly means of total cloud cover from ISCCP-D2,
SYNOP observations and ECC data.
7. ECC TOTAL CLOUD COVER IN COMPARISON TO RESULTS OF
ECHAM4.L39/CHEM
For the entire ECC area an eleven year mean of ECC total cloud cover distribution in July has been
compared to a sixteen year mean from time-slice experiments of the ECHAM.L39/CHEM model. This is
shown in Figure 7 together with a comparison between ECC time series of total cloud cover and the longterm transient simulations of ECHAM4.L39/CHEM for the Germany area.
Figure 7: Eleven year July mean of total cloud cover from ECC (upper-left) vs. sixteen year mean
obtained from ECHAM4.L39/CHEM (upper-right) and comparison of ECC time series with
ECHAM4.L39/CHEM simulations (lower).
8. COMPARISON OF NOAA- AND MSG-PRODUCTS
APOLLO has been adapted to deal with AVHRR-like SEVIRI channels to provide cloud products for other
projects DLR is involved in. We use this opportunity to cross-check AVHRR- and SEVIRI-based APOLLO
cloud products. Deviations in the cloud detection caused by different viewing geometries and the impact of
different spatial resolution can be analysed and a diurnal course of cloud cover from SEVIRI can be used to
assess ECC weaknesses. In Figure 8 color composites and color images of the APOLLO cloud coverage
product from AVHRR and SEVIRI data of the same time are compared. Cloud detection results look quite
similar in lower latitudes but more differences appear towards higher latitudes approaching the edge of the
earth disk. Especially the amount of detected thin clouds increases with increasing SEVIRI zenith angle
compared to the AVHRR-based result.
Figure 8: Comparison of two scenes observed with MSG/SEVIRI (left) and NOAA/AVHRR (right) at
almost the same time (11:57 UT) on April 24th, 2004. Upper: Color composites generated with
channels in the visible, the near and the thermal infrared. Lower: Cloud classification with the
APOLLO scheme for both sensors. Low-level clouds are displayed in yellow, medium-level clouds in
green, high-level clouds in blue and thin clouds in violet.
9. CONCLUSION AND OUTLOOK
The exploitation of the ECC dataset done so far shows that
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the spatial distribution of mean total cloud cover is reasonable over Europe
annual courses are reasonable, but the ECC- and ECHAM-results show larger amplitudes than the
SYNOP- and ISCCP-results
comparison of ECC with SYNOP total cloud cover shows an agreement in the order of one octa with
the tendency of systematically lower ECC data
ISCCP provides the highest total cloud cover compared to the other datasets
no significant long-term trend of total cloud cover can be detected
single events like the hot and dry summer 2003 can be seen
a cross-check between AVHRR- and SEVIRI-based cloud products can improve the accuracy
assessment of the ECC
The ECC dataset is constantly complemented with newer data, but also extended into the past (before
1989). Further plans include the analysis of night-time data and the further investigation of other ECC
parameters as the microphysical and optical cloud parameters. From such studies we expect more insight
into yet unexplained features in the time series of total cloud cover.
It is under development to put the experience we gathered up to now from exploiting the ECC dataset into a
post-processor, which reprocesses all NOAA-passes in the ECC. This will lead to a reliability mask for all
passes which contains not just the differences in the cloud detection due to what we have learned during the
years of ECC-processing. It will also contain ancillary information on critical geometry constellations, e.g.
where scattering effects etc. can affect cloud detection. This reliability masks, one for each pass, will be
used to quantify the impact of e.g. sun/sensor zenith and azimuth angles (BRDF) on the cloud detection.
In parallel it is envisaged to compare and supplement the ECC cloud products obtained from the polar
orbiting NOAA satellites with those from the new European geostationary satellite Meteosat Second
Generation (MSG) providing data in a very high temporal resolution of 15 minutes.
ACKNOWLEDGEMENTS
We would like to thank P. Bissolli (German Meteorological Service) for making available the SYNOP data
and K.T. Kriebel for his many years of engagement in the development of the program package APOLLO.
REFERENCES
Kästner, M., K. T. Kriebel, Alpine cloud climatology using long-term NOAA-AVHRR satellite data, Theor. and
Appl. Clim., 57, 181-185, 2001.
Karlsson, K.-G., A 10 year cloud climatology over Scandinavia derived from NOAA Advanced Very High
Resolution Radiometer imagery, Int. J. of Climatol., 23, 1023-1044, 2003.
Kriebel, K.T., G. Gesell, M. Kästner, and H. Mannstein, The cloud analysis tool APOLLO: improvements and
validations, Int. J. Remote Sensing, Vol. 24, No. 12, 2389-2408, 2003.
Kriebel, K.T., R. W. Saunders, G. Gesell, Optical properties of clouds derived from fully cloudy pixels, Beitr.
Phys. Atmosph., 62, 165-171, 1989.
Rosenow, W., J. Güldner, D. Spänkuch, The “Satellite Weather” of the German Weather Service – an
assimilation procedure with a spectral component, Proceedings of the 2001 EUMETSAT Met. Data User’s
Conf., EUMETSAT Publ. EUM P 33, 541-545, 2001.
Rossow, W. B., R. A. Schiffer, Advances in understanding clouds from ISCCP, Bull. Am. Met. Soc., 80,
2261-2287, 1999.
Saunders, R. W., K. T. Kriebel, An improved method for detecting clear sky and cloudy radiances from
AVHRR data, Int. J. Remote Sensing, 9, 123-150, 1988.
Schiffer, R. A., W. B. Rossow, The international satellite cloud climatology project (ISCCP): the first project of
the World Climate Research Programme, Bull. Am. Met. Soc., 64, No. 7, 779-784, 1983.
Stowe, L. L., H. Jacobowitz, G. Ohring, K. R. Knapp, N. R. Nalli, The advanced Very High Resolution
Radiometer (AVHRR) Pathfinder Atmosphere (PATMOS) Climate Dataset: Initial Analyses and Evaluations,
J. of Climate, 15, 1243-1260, 2001.