Cloud fraction (CF), integrated liquid water (ILW) and integrated water vapour (IWV) were continuously
measured from 2004 to 2016 by the TROpospheric WAter RAdiometer (TROWARA) in Bern, Switzerland. There are
indications for interannual variations of CF and ILW.
A spectral analysis shows that IWV is dominated by an annual oscillation, leading to an IWV
maximum of 24 kg m

Time series of CF, ILW and IWV at Bern. The monthly means are given by the green lines while the red lines denote the annual means (12-month sliding average with a step of 1 month). The blue lines shows the standard deviations of the annual means.

Observation and characterization of the oscillations of
atmospheric water lead to a better understanding of the cloud processes, the
cloud-induced changes in the Earth radiative fluxes and the water cycle. In
this study, we investigate the oscillations in 12-year time series of
cloud fraction (CF), integrated liquid water (ILW) and integrated water
vapour (IWV) above Bern, Switzerland. The combined spectral analysis of
atmospheric water parameters can give hints about cloud formation and
transport processes.
The seasonal cycle of the atmospheric water parameter CF at midlatitudes has only been described in a
few articles, while the seasonal cycle in ILW remains undescribed as of now. The climatology of IWV at
Bern was presented by

Compared to these few articles about the seasonal cycle of CF at
midlatitudes, there are more articles about the seasonal change of CF over
Antarctica, Arctic and the tropics.

Normalized power spectra of CF, ILW and IWV at Bern for the time interval from January
2004 to November 2016. In addition we show the normalized power of the zonal wind

Over the Arctic ocean,

For health and environmental reasons, the weekly cycle of aerosol
concentration and precipitation is of high interest.

Annual oscillation

Our study extends the research on oscillations in atmospheric water by
analysing the continuous measurements of TROWARA in Bern, Switzerland. In Sect. 2, we describe the ground-based
microwave radiometer TROWARA, its data set and the data analysis methods
which we use in this study. Section 3 presents the seasonal cycles, the power
spectra, and the bandpass-filtered annual and semiannual oscillations in CF,
ILW and IWV. Inspired by the study of

The study is based on the measurements of TROWARA. TROWARA is a dual-channel microwave radiometer built by

The two microwave channels are at 21.4 GHz (bandwidth 100 MHz) and 31.5 GHz (bandwidth 200 MHz). The lower frequency is more sensitive to microwaves from water vapour, and the higher frequency is more sensitive to microwaves from atmospheric liquid water.

The radiative transfer equation of a non-scattering atmosphere is

From Eq. (

For a plane-parallel atmosphere, the opacity is closely related to IWV and ILW by a quasi-linear relationship

Annual oscillation

Mean seasonal behaviour of annual oscillation (blue), semiannual oscillation
(red), monthly means (green) and the sum of AO and SAO (black) derived from TROWARA measurements of
the time interval 2004 to 2016.
The top panels

An infrared radiometer channel is operated at

TROWARA has been operated since 1994, and it has delivered an almost uninterrupted time series of ILW
since 2004, with a time resolution of 11 s until the end of 2009 and 6 s afterwards.
The cloud detection in the line of sight of TROWARA is performed with the same time resolution,
and the criterion is that ILW

Thin liquid water clouds were the focus of the study by

Since TROWARA is not sensitive to ice clouds, CF of TROWARA is in general smaller compared to synoptic observations.

CF was determined in time domain. CF is the quotient of the time intervals when ILW

The power spectra are obtained by folding the time series of IWV, ILW or CF with a Hamming window and by applying zero padding at the beginning and end of the time series. After the Fourier transformation, the power spectra are normalized by the power of the strongest spectral component.

The time series of the AO and the SAO are derived by means of bandpass filtering.
The time series
are filtered with a digital non-recursive, finite impulse response bandpass filter performing zero-phase filtering by
processing the time series in forward and reverse directions. The number of filter coefficients corresponds to a time window
of three times the central period, and a Hamming window has been selected for the filter. Thus, the bandpass filter has a fast
response time to temporal changes in the data series. The variable choice of the filter order permits the analysis of wave trains
with a resolution that matches their scale. The bandpass cut-off frequencies are at

The mean seasonal behaviour of the time series is obtained by sorting the data for the month and taking the mean and the standard error of the mean.

Mean seasonal behaviour of zonal wind

Mean amplitude spectra of CF, ILW and IWV from TROWARA at Bern for the
time interval from January 2004 to November 2016. In addition, we show the coincident amplitude
spectrum of zonal wind

The time series of CF, ILW and IWV are shown in Fig.

Figure

Climatologies of the short-term variability of CF, ILW and IWV from
TROWARA at Bern for the time interval from January 2004 to November 2016. In addition, we show
the coincident climatology of zonal wind

Weekly cycle of CF, ILW and IWV at Bern for the June to September observations of TROWARA during the time interval from January 2004 to November 2016. Weekday 1 corresponds to Sunday, weekday 2 corresponds to Monday, and so on. The vertical lines indicate the error of the mean of the averaged values.

Since lower-tropospheric wind is a major player for cloud formation and
transport processes, we suggest that the spectral components in the zonal wind
spectrum could be one cause for the annual and semiannual oscillations in
the power spectra of CF and ILW. In addition, the periodicities of 97 and 85
days (close to the fourth harmonic) are strong in the spectra of

During winter, the Swiss plateau often has low stratus which develops from
condensation of atmospheric water vapour near to the cold Earth surface.
Turbulence spreads the fog or cloud droplets up to the inversion layer in
about 1.5 km altitude.

Figure

Figure

Figure

For the investigation of the short-term variability, we change from the time
series of monthly means to the time series of daily means. It can be assumed
that the short-term oscillations with periods of a few days to weeks only
persist over time intervals of three wave cycles. Thus a Fourier transform over
the time interval from 2004 to 2016 is not adequate to address the role of
the short-term variability. Instead, we determine the mean amplitudes with a
bandpass filter with a fast response time. As described in the data analysis
section, the number of filter coefficients corresponds to each central
frequency to a time interval of three wave cycles. Thus short-term variations
existing over a short time interval contribute to the mean amplitude spectra,
which are shown in Fig.

The bandpass-filtered data sets are also appropriate for the derivation of
the climatologies of CF, ILW, IWV and

We investigate whether the 7-day oscillation is phase-locked to a
weekly cycle which is found in aerosol concentration as induced by manmade
air pollution

TROWARA continuously measured CF, ILW and IWV in Bern, Switzerland, from 2004 to 2016. We find indications for
interannual variations of CF and ILW.
Fourier transformation and bandpass filtering give the result that IWV is dominated by an annual oscillation, leading to
an IWV maximum of 24 kg m

The normalized power spectra of ILW and CF show statistically significant
spectral components with periods of 76, 85, 97 and 150 days. We find a
similarity between the power spectra of ILW and CF with those of zonal wind
at 830 hPa (1.5 km) above Bern. The occurrence of higher harmonics in the CF
and ILW spectra is possibly forced by the behaviour of the lower-tropospheric
wind and the occurrence rate of weather types. This observational result
emphasizes the role of the lower-tropospheric wind for generation and
transport of clouds over the Swiss plateau. The climatology of CF shows a
maximum in winter when the eastward wind is maximal. The mean amplitude
spectra of CF, ILW and

Routines for data analysis and visualization are
available upon request by Klemens Hocke. Hourly measurements of IWV and ILW
from the radiometer TROWARA are available at the data centre STARTWAVE
(

KH carried out the spectral analysis. FNG and CM took care on the radiometer. All authors contributed to the interpretation of the data set.

The authors declare that they have no conflict of interest.

The study was supported by Swiss National Science Foundation under grant number 200021-165516. We thank the reviewers for their valuable and helpful comments. Edited by: Martina Krämer Reviewed by: two anonymous referees