The TROpospheric WAter RAdiometer (TROWARA) continuously measures integrated water vapour (IWV) with a time resolution of 6 s at Bern in Switzerland.
During summer, we often see that IWV has temporal fluctuations during daytime, while the nighttime data are without fluctuations.
The data analysis is focused on the year 2010, where TROWARA has a good data quality without data gaps.
We derive the spectrum of the IWV fluctuations in the period range from about 1 to 100 min. The FFT spectrum with a window size of 3 months leads to a serious underestimation of the spectral amplitudes of the fluctuations. Thus, we apply a band pass filtering method to derive the amplitudes as a function of period

Atmospheric water vapour is the dominant greenhouse gas and acts like a warm blanket for the Earth. Global warming due to man-made

The spatio-temporal variability of IWV on scales of less than 10 km and hours was assessed by

The TROpospheric WAter RAdiometer (TROWARA) has measured continuously IWV with a time resolution of 6 s at Bern in Switzerland since 2009. In the time from 1994 to 2008 the temporal resolution was 10 s. Thus, it is no problem to analyse the IWV variability at Bern on timescales from 1 to 100 min as a function of local time and season. The short-term IWV variability is possibly connected with the growth of the atmospheric boundary layer during daytime in summer. Convective heating and associated turbulence generate variable vertical winds and circulation cells, leading to a variable vertical water vapour flux during daytime. We expect that IWV can significantly change during daytime if the antenna beam of the radiometer transects an updraft or downdraft region in the lower troposphere.

The diurnal cycle in IWV over Bern was described by

Using observations of wind profilers, radiometers, and lidars,

The aim of the present study is to provide mean values of the amplitudes of the short-term IWV fluctuations in the period range from 1 to 100 min. These mean values may guide modelling studies about water vapour convection and circulation cells in the lower troposphere. Further, we derive the dependence of the short-term IWV variability on the season and the local time. Section 2 describes the TROWARA radiometer and the weather station of the University of Bern. Section 3 explains the data analysis to obtain the amplitudes or the moving standard deviation of the IWV variability. Section 4 presents the results on the short-term IWV variability and its relation to the latent heat flux at Bern. Concluding remarks are given in Sect. 5.

Our study is focused on the IWV observations of the TROpospheric WAter RAdiometer (TROWARA). TROWARA is a dual-channel microwave radiometer, and its design and construction were described by

TROWARA measures the vertically IWV, which is also known as precipitable water vapour. Further, TROWARA provides the vertically integrated cloud liquid water (ILW), also known as the liquid water path. The instrument is operated inside a temperature-controlled room on the roof of the building for Exakte Wissenschaften (EXWI) of the University of Bern (46.95

The antenna beam of TROWARA has a full width at half power of 4

In the following, we briefly explain the measurement principle and the retrieval. The microwave channel of TROWARA at 21.4 GHz has a bandwidth of 100 MHz, and the microwave channel at 31.5 GHz has a bandwidth of 200 MHz. The frequency channel at 31.5 GHz is more sensitive to microwaves from atmospheric liquid water, while the frequency channel at 21.4 GHz is more sensitive to microwaves from water vapour since there is a rotational transition line of water vapour centered at 22.235 GHz.

Amplitude spectra of IWV fluctuations observed at Bern in summer 2010. The solid black line is obtained by a band pass filtering method. The green line shows the inclination for

The radiative transfer equation of a non-scattering atmosphere is

Equation (

In a plane-parallel atmosphere, the opacity is linearly related to IWV and ILW:

TROWARA provides a time series of IWV since 1994 with a time resolution of 10 s until the end of 2009 and 6 s afterwards.
The IWV time series have been used for trend analysis

Amplitude spectra of IWV fluctuations observed at Bern for the four seasons of the year 2010. The green, red, blue, and black lines are obtained by a band pass filtering method for winter, spring, autumn, and summer respectively. The magenta line shows the inclination for

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) is an atmospheric reanalysis provided by NASA's Global Modeling and Assimilation Office (GMAO)

Spectra of IWV fluctuations observed at Bern for the four seasons of the year 2010. The green, red, black, and blue lines are obtained by a moving standard deviation (SD) with a variable time window length for winter, spring, summer, and autumn respectively. The magenta line shows the inclination for

The amplitude spectra of the temporal IWV fluctuations are computed by three different methods. Firstly, the fast Fourier transform (FFT) spectrum of summer 2010 is calculated. The arithmetic mean is removed from the time series of IWV. Then, the FFT spectrum is obtained by folding the IWV time series of summer 2010 with a Hamming window and by applying zero padding at the beginning and end of the time series. The FFT spectrum does not take into account the intermittency of the shortwave trains of the IWV fluctuations.

A better method is the band pass filtering of the IWV time series with a digital non-recursive, finite-impulse response (FIR) band pass 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 3 times the central period, and a Hamming window of equal length has been selected for the filter. Thus, the band pass 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 cutoff frequencies are at

The third method for the estimation of the strength of the IWV fluctuations is a moving standard deviation where the time window length is subsequently changed from 0.5 to 90 min.

Diurnal cycle of IWV fluctuations observed at Bern for the four seasons of the year 2010 (moving standard deviation SD with a time window length of 10 min). The green, red, blue, and black lines are for winter, spring, autumn, and summer respectively.
CET stands for Central European Time (Universal Time

Diurnal cycle of latent heat flux near Bern for different seasons as derived from MERRA-2 reanalysis data. The error of the mean (for hourly averages) is shown for the summer season (JJA) by the dashed black lines.

The main effect investigated in the present study is that short-term IWV fluctuations occur at daytime in summer (June–August), while they disappear at night.
Figure

Figure

Figure

Another method to characterize the IWV fluctuations is the moving standard deviation with a variable time window length. The time window length is a bit similar to the period. The four seasonal spectra of the standard deviation SD are shown as a function of the time window length in Fig.

Scatter plot of SD(IWV) and the latent heat flux measured by TROWARA at Bern in 2010.
The parameters are described in detail in Fig.

Figure

The seasonal variation of the diurnal cycle of short-term IWV fluctuations is possibly related to the annual variation in solar heating of the Earth's surface.
Surface heating leads to increased turbulence, convection, and upward water vapour flux during daytime. Figure

These observations and simulations suggest that the increase in short-term IWV fluctuations during daytime in summer is due to the diurnal cycle of latent heat flux, which is a precondition for an increase in strong and variable convection cells in the afternoon. The updraft and downdraft regions of the convection cells pass the antenna beam of the TROWARA radiometer, which consequently measures larger or smaller values of IWV at time distances of about 10 min. In addition, a convection cell is itself time-variable: the lifetime of a convection cell is roughly between 30 and 60 min

It remains an open question why the IWV fluctuations do not show a maximum around noon in winter in Fig.

Figure

During summer, we often see that IWV has temporal fluctuations during daytime, while the nighttime data have smaller fluctuations.
We derive the spectrum of the IWV fluctuations in the period range from about 1 to 100 min. The FFT spectrum with a window size of 3 months leads to a serious underestimation of the spectral amplitudes of the fluctuations. Thus, we apply a band pass filtering method to derive the amplitudes as a function of period

Thus, we suggest that the diurnal cycle of the short-term IWV fluctuations is mainly caused by the diurnal cycle of latent heat flux, which is a precondition for strong and variable convection cells in the afternoon during summer. The spatio-temporal variability of the convection cells induces the diurnal cycle of short-term IWV fluctuations as observed by the TROWARA radiometer at Bern in summer. However, other sources such as eddies in the lower troposphere may also contribute to the short-term variability of IWV. High-resolution modelling of the diurnal cycle of short-term IWV fluctuations could be compared to the TROWARA observations so that one can estimate how well convective processes are represented by the model. Generally, we think that the high-resolution IWV observations of TROWARA can contribute to research on the atmospheric boundary layer.

Programs are available from KH upon request.

High-resolution IWV data of TROWARA are available upon request. Data of the EXWI weather station are provided by the startwave

KH provided the data analysis of the short-term IWV fluctuations. CM took care of the radiometer and provided the description of the radiometer. AM and MR discussed the influence of turbulence and the atmospheric boundary layer on IWV. LB and JH took care of the MySQL databases. All the authors contributed to the discussion and interpretation of the results.

The authors declare that they have no conflict of interest.

We thank the reviewers and editor for their efforts. The study is supported by SNSF 200021-165516.

This research has been supported by the Swiss National Science Foundation (grant no. 200021-165516).

This paper was edited by Farahnaz Khosrawi and reviewed by three anonymous referees.