The current study analyses the cloud radiative effect during the daytime
depending on cloud fraction and cloud type at two stations in Switzerland
over a time period of 3 to 5 years. Information on fractional cloud
coverage and cloud type is retrieved from images taken by visible all-sky
cameras. Cloud-base height (CBH) data are retrieved from a ceilometer and
integrated water vapour (IWV) data from GPS measurements. The longwave cloud
radiative effect (LCE) for low-level clouds and a cloud coverage of 8 oktas
has a median value between 59 and 72 Wm
The influence of clouds on the radiation budget and radiative
transfer of energy in the atmosphere are the greatest sources of
uncertainty in simulations of climate change
Not only the cloud amount but also other cloud parameters such as cloud
type and cloud optical thickness are of importance. The physical parameters
defining the various cloud types may have distinct effects on radiation of
different wavelengths. For example, optically thin and high-level clouds have
a relatively small effect on the downward shortwave radiation, whereas
low-level and thick clouds scatter and absorb a large part of the solar
radiation and re-emit it as thermal radiation in all directions. Thus, cloud-type variations can alter both shortwave and longwave radiation fluxes due to
changes in cloud levels, water content and cloud temperatures
In detailed numerical weather and climate prediction models, cloud properties
(cloud-base height, cloud cover and cloud thickness) and the physical
processes responsible for the formation and dissipation of clouds are often
approximations and parameterisations
For several years, all-sky cloud cameras have been in use worldwide in order
to collect continuous information on clouds from the surface. Many studies
already determined cloud coverage based on all-sky camera images
The current study presents a study of cloud radiative effect at the surface
depending on cloud fraction and cloud types at two stations in Switzerland
over a time period of 3–5 years. The data and methods (including the
description of the algorithms and the models) are described in Sect.
Data are available from two stations in Switzerland. The stations are located
at two altitude levels. Payerne is located in the Central Plateau (46.49
The camera system in Davos is a Q24M from Mobotix (
The radiation data are retrieved from Kipp and Zonen CMP22 pyranometers
(shortwave; 0.3–3
For the Davos station, the cloud radiative effect (CRE) has been calculated
from 7 August 2013 to 30 April 2017 with a time resolution of 1 min.
Data have only been taken into account for daytime measurements when the sun
is located at a minimum five degrees above the horizon and the mountains. For
Payerne, the study of CRE includes data from 1 January 2013 to 30 April
2017 with a time resolution of 5 min. Data considered are during the daytime with a solar zenith angle (SZA) of maximum 78
In the current study, the cloud radiative effect (CRE) is defined as a
radiation measurement value minus a modelled cloud-free value. The total
cloud radiative effect (TCE) is divided into the shortwave cloud radiative effect
(SCE) and longwave cloud radiative effect (LCE)
For the calculation of the cloud radiative effects two cloud-free models, one
for the shortwave and the other one for the longwave range, are needed. The
cloud-free model for the longwave is an empirical model with input of
measured surface temperature and IWV values and a
climatology of the atmospheric temperature profile
The calculation of the fractional cloud coverage is based on the all-sky
cloud camera images from the aforementioned systems. Before calculating the
cloud amount the images must be preprocessed. The distortion of the images is
removed with a polynomial function. Additionally a horizon mask must be
defined, since Davos is located between two mountain ridges. For both stations
the horizon mask has been defined on the basis of an individual cloud-free
image. After the preprocessing of the images a colour ratio (the sum of the
blue to green ratio plus the blue to red ratio) is calculated per pixel
The algorithm of
The data sets for the calculation of the CRE consist
of 595 806 and 117 763 images for Davos and Payerne respectively. In Davos,
the cloud coverage is 8 oktas for 35 % of the data set. In 17 % of the
cases the cloud coverage is zero okta, which is a maximum fractional cloud
coverage of 5 %. Seven-okta cloud coverage occurs in 11 % of the
cases followed by one okta (10 %). Two to six oktas are all
equally distributed in 5 to 6 % of the cases. Also in Payerne, a cloud
coverage of 8 oktas is determined in most of the cases (41 %), followed
by zero okta in 25 % of the cases. In 10 % of the cases a cloud coverage of
1 okta is determined followed by 7 oktas (6 % of the cases) and two
oktas (5 %). A cloud coverage of three to six oktas is determined in
3–4 % of the cases. The distribution of the cloud coverage over the months
is shown for Davos and Payerne separately in Fig.
Relative frequencies (RF) of cloud coverages in 1- to 8-okta divisions (all cloud types together) for the two stations Davos (left) and Payerne (right).
In the winter half of the year (with a maximum in March and December) the sky is more often cloud-free than in the summer half of the year in Davos. In contrast, in May the sky is covered with 8 oktas in almost half of the cases. Cloud coverages of 1 to 7 oktas are quite equally distributed over the months. In Payerne the situation is the opposite for cloud-free days with more frequent 8-okta cloud coverage in wintertime, whereas cloud-free situations are more common during summertime. Also in Payerne, cloud coverages of 1 to 7 oktas are fairly equally distributed.
The difference in cloud-free and overcast situations can be explained by the location and the topography of the two stations. In the Central Plateau, where Payerne is located, in the autumn and winter months a common meteorological condition is an inversion, which leads to fog and thus to an overcast sky. Whereas in Davos, located in the Alps, the weather is rather dominated by thermal lift, which occurs more often in summer than in winter.
Regarding the distribution of the cloud coverages in oktas throughout the day, no real pattern can be observed in Davos. In Payerne there are more cloud-free conditions in the early morning than later in the day. The other okta cloud coverages are also equally distributed throughout the day.
In the 595 806 cases from Davos, St–As is the cloud type that is most detected in the studied time period, with 37 % of the analysed cases. The second and third most detected sky conditions in Davos are Cf and Cc–Ac with 17 % and 14 % respectively, followed by Sc (13 %), Cu (12 %), Ci–Cs (5 %) and Cb–Ns (2 %). In the 117 763 sky images from Payerne, the cloud type Sc is detected in 31 % of the cases. This is followed by Cf in around 25 % of cases, Cb–Ns, Cc–Ac and Ci–Cs (each 11 %), St–As (7 %) and Cu (4 %).
Figure
Relative frequencies (RF) of all cloud classes per month (all cloud coverages together) for the two stations Davos (left) and Payerne (right). Cf: cloud-free; Ci–Cs: cirrus–cirrostratus; Cc–Ac: cirrocumulus–altocumulus; Cb–Ns: cumulonimbus–nimbostratus; St–As: stratus–altostratus; Cu: cumulus; Sc: stratocumulus.
In Davos, as determined by our algorithm, from October to May St–As is present in at least 30 % of the cases per month. This fraction of St–As is rather too high and might be due to a limitation of the cloud-type algorithm. The limitation is that the algorithm applied to Davos is trained with images from Payerne. Therefore, it might be more difficult to distinguish between low-level cloud classes (e.g. St–As and Sc) in Davos. This limitation might also be responsible for the rather infrequent determination of Cu in Davos. The cloud class Cc–Ac is more often present in summertime than in wintertime. Ci–Cs is almost absent in the months August to October. This absence of the cloud class Ci–Cs in the late summer months does not match with the visual analysis of images and might be explained by the fact that the cloud detection algorithm is not sensitive enough to detect thin high-level clouds. The largest fraction of cloud type in Payerne is Sc for all months. The cloud classes Cb–Ns and St–As are both more often observed during wintertime than during summertime. The larger frequency of these two cloud types agree with the fact that there is more often fully covered sky in wintertime than summertime.
Regarding the distribution of the cloud classes throughout the day, there are no large differences in the occurrence of cloud types per time of day. The distribution is quite flat for both stations.
By applying Eq. (
Dependence of the LCE on cloud coverage for Davos for cloud classes stratocumulus (Sc), cumulus (Cu), stratus–altostratus (St–As), cumulonimbus–nimbostratus (Cb–Ns), cirrocumulus–altocumulus (Cc–Ac) and cirrus–cirrostratus (Ci–Cs). Data points (yellow dots) and box plots per okta with median (red line), interquartile range (blue box) and spread without outliers.
Figure
In Davos, the highest median LCE for a cloud coverage of 8 oktas is observed
for the low-level cloud classes Cb–Ns, St–As, Cu and Sc with a maximum
influence on the downward longwave radiation at the surface for Cb–Ns
(67 Wm
Median and interquartile range of longwave cloud radiative effect
values [Wm
Although the numbers differ between the two stations, the same pattern also holds
for Payerne, namely that the lower the cloud, the higher the LCE value.
Thus for Payerne, the four low-level cloud types (Sc, Cu, St–As and Cb–Ns)
and 8-okta cloud coverages have median LCE values of
59–72 Wm
The difference in the median LCE values between the two stations increases
with decreasing cloud coverage. Except Sc and Cb–Ns, the LCE values are
generally larger for the station Payerne in comparison with Davos. The
difference might be partly due to a higher underestimation of the calculated
LW cloud-free irradiances at Payerne. Another explanation for this difference
might be that Payerne is located at a lower altitude level and thus the cloud-base
temperature is higher, which leads to a larger emission of LW radiation.
Some of the differences might also occur due to a limited number of cases in
the specific groups (see Tables
Table
Median and interquartile range of relative shortwave cloud radiative effect values [%] per okta for the two stations Davos (DAV) and Payerne (PAY) and six cloud classes stratocumulus (Sc), cumulus (Cu), stratus–altostratus (St–As), cumulonimbus–nimbostratus (Cb–Ns), cirrocumulus–altocumulus (Cc–Ac) and cirrus–cirrostratus (Ci–Cs).
In Davos, the cloud type Cb–Ns, with
In Payerne, a different order is observed in the lowest to the highest
SCE
The differences in SCE
For the calculation of the values in Table
Figure
Density distribution of the dependence of SCE
If we define a cloud radiative enhancement with a minimum SCE
In Davos, 2238 cases (0.5 % of the cloud data) are observed with
SCE
In Payerne, in 10 % of the 88 155 cloud cases a cloud enhancement of more
than 5 % SCE
A cloud enhancement of at least 40 % SCE
The manual analysis of the cloud camera images with cloud enhancement leads
to the result that in most of the cases there is a low solar zenith angle.
Additionally, it has been observed that in cloud enhancement cases the sun is
either in the vicinity of the cloud or covered with a thin cloud layer.
Several studies
The total cloud radiative effect (TCE) is calculated as the sum of the LCE
and SCE (Eq.
The median and interquartile range of the total cloud radiative
effect [Wm
During the daytime, the SCE values are the main contribution to the TCE for all
cloud classes and cloud coverages of 6 to 8 oktas and the two stations, Davos
and Payerne. For the low-level cloud type Cb–Ns, the TCE values are negative
for all okta cloud coverages. Thus, during the daytime, the SCE is the main
contributor to TCE for this cloud class. The smaller the cloud coverage is,
the less negative the TCE values are. This behaviour can be seen for all
cloud types and both stations. One reason for these
positive values with smaller cloud coverages might be the cloud enhancement
events as described in Sect.
As described in Sect.
Dependence of the LCE on integrated water vapour (IWV) for Davos and cloud coverage of 8 oktas for low-level clouds (Sc, Cu, St–As, Cb–Ns) shown as a density plot.
Figure
The observed relationship between the LCE and IWV was analysed by modelling a
standard situation with the moderate resolution atmospheric transmission
model MODTRAN5
Dependence of the LCE on integrated water vapour (IWV) modelled for cumulus (blue) and stratocumulus
(red) clouds. Solid line: summer standard atmosphere (SSA) and cloud-base height (CBH) of 1 km.
Dotted line: SSA, CBH
The mean values of the observed dependence of the LCE on IWV (Fig.
Another parameter which might explain the large spread in the LCE within one
cloud cover range is the CBH. This analysis has only been
performed for the data set in Payerne, because it is only at this location
that we measure the CBH with a ceilometer. The observed mean dependence of
LCE on CBH and IWV is shown in Fig.
Dependence of the LCE on cloud-base height (CBH) for Payerne and linear regression lines of the following measured
IWV ranges: red:
Figure
Another important parameter in the LCE discussion for thin clouds is the
optical depth of clouds
In Sect.
Distribution of SCE
If the cases are now divided when the measured direct radiation
value is below 120 Wm
The current study analyses the cloud radiative effect depending on cloud type and cloud fraction at two stations in Switzerland over a time period of 3 to 5 years.
We have shown that low-level cloud types like cumulus, stratocumulus,
stratus–altostratus and cumulonimbus–nimbostratus have greater longwave cloud radiative effect values with median values of
59–72 Wm
Our study confirmed that the cloud-base height and the fractional cloud coverage have an influence on the range of the LCE. The higher the cloud coverage, the greater the LCE and the lower the cloud-base height, the larger the LCE.
We also showed that there is a negative dependence of the LCE on integrated
water vapour. A similar trend was observed using radiative transfer modelling
studies, as well as by
Low-level clouds have a greater effect on the SCE
Our data show that in 14 and 10 % of the cases in Davos and Payerne respectively a shortwave cloud radiative enhancement of at least 5 % is observed. We show that Cc–Ac is the cloud type that is responsible for at least one-third of the cloud enhancement cases in Davos and Payerne.
In the current analysis, only one cloud type per cloud camera image is defined. A step forward would be to distinguish between different cloud types per image. This detection of different cloud types per image is already an intermediate step in our algorithm. At the current state the cloud type with most of the hits is determined. A further step would be to not only get the most probable cloud type per image but also to obtain the different cloud types per image as output. Thereafter, a more accurate analysis considering the influence of the cloud type on the cloud radiative effect would be possible.
To further minimise the number of misclassifications, for a future study it might be enough to distinguish between low-, mid- and high-level clouds instead of cloud types. This would also increase the number of cases per cloud type and cloud fraction and might decrease the uncertainty of the cloud-type detection algorithm. However, it would also decrease the variety in the cloud information.
Another step forward might be to combine different cloud detection instruments. A new observing system (thermal infrared cloud camera) has been developed in order to collect all-sky cloud information from day- and nighttime measurements. This expansion of the data set to nighttime information is necessary for climate-monitoring applications.
All data are available from the corresponding author on request.
Number of cases per okta for Davos and six cloud classes stratocumulus (Sc), cumulus (Cu), stratus–altostratus (St–As), cumulonimbus–nimbostratus (Cb–Ns), cirrocumulus–altocumulus (Cc–Ac) and cirrus–cirrostratus (Ci–Cs).
Number of cases per okta for Payerne and six cloud classes stratocumulus (Sc), cumulus (Cu), stratus–altostratus (St–As), cumulonimbus–nimbostratus (Cb–Ns), cirrocumulus–altocumulus (Cc–Ac) and cirrus–cirrostratus (Ci–Cs).
The authors declare that they have no conflict of interest.
This research was carried out within the framework of the project