Diurnal Cycle in Atmospheric Water over Switzerland

Hocke, Klemens; Navas-Guzmán, Francisco; Moreira, Lorena; Bernet, Leonie; Mätzler, Christian

doi:10.3390/rs9090909

Cited by 9 publications

(13 citation statements)

References 35 publications

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“…In fact, the microwave radiation measured by TROWARA during rain is strongly enhanced by the microwave emission from raindrops (d > 0.2 mm) [11]. Thus, it is raining when ILW exceeds a threshold value of about 0.4 mm.…”

Section: Ground-based Microwave Radiometer Datamentioning

confidence: 99%

“…However, this method only tested a few rain events, and whether it can estimate the rain rate in the long-term is not yet clear. The TROpospheric WAter RAdiometer (TROWARA) is a ground-based microwave radiometer that has provided long-term high-quality data of the atmospheric opacity (optical depth) every 6 s in Bern, Switzerland since 2005 [11]. Therefore, the objective of this article is to use the rain zenith opacity derived from TROWARA for the long-term rain rate using the new physical retrieval method and to perform operational processing and archiving of the rain rate estimated by the TROWARA radiometer.…”

Section: Introductionmentioning

confidence: 99%

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Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry

2021

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Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance.

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Section: Ground-based Microwave Radiometer Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry

2021

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“…The larger magnitude is possibly due to the underestimation of the daily radiosonde uncertainties. side at the surface (Holton, 2004). The colder air in the stratosphere and lower troposphere is usually drier.…”

Section: Geophysical Variabilitymentioning

confidence: 99%

A Raman lidar tropospheric water vapour climatology and height-resolved trend analysis over Payerne, Switzerland

et al. 2020

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Abstract. Water vapour is the strongest greenhouse gas in our atmosphere, and its strength and its dependence on temperature lead to a strong feedback mechanism in both the troposphere and the stratosphere. Raman water vapour lidars can be used to make high-vertical-resolution measurements on the order of tens of metres, making height-resolved trend analyses possible. Raman water vapour lidars have not typically been used for trend analyses, primarily due to the lack of long-enough time series. However, the Raman Lidar for Meteorological Observations (RALMO), located in Payerne, Switzerland, is capable of making operational water vapour measurements and has one of the longest ground-based and well-characterized data sets available. We have calculated an 11.5-year water vapour climatology using RALMO measurements in the troposphere. Our study uses nighttime measurements during mostly clear conditions, which creates a natural selection bias. The climatology shows that the highest water vapour specific-humidity concentrations are in the summer months and the lowest in the winter months. We have also calculated the geophysical variability of water vapour. The percentage of variability of water vapour in the free troposphere is larger than in the boundary layer. We have also determined water vapour trends from 2009 to 2019. We first calculate precipitable water vapour (PWV) trends for comparison with the majority of water vapour trend studies. We detect a nighttime precipitable water vapour trend of 1.3 mm per decade using RALMO measurements, which is significant at the 90 % level. The trend is consistent with a 1.38 ∘C per decade surface temperature trend detected by coincident radiosonde measurements under the assumption that relative humidity remains constant; however, it is larger than previous water vapour trend values. We compare the nighttime RALMO PWV trend to daytime and nighttime PWV trends using operational radiosonde measurements and find them to agree with each other. We cannot detect a bias between the daytime and nighttime trends due to the large uncertainties in the trends. For the first time, we show height-resolved increases in water vapour through the troposphere. We detect positive tropospheric water vapour trends ranging from a 5 % change in specific humidity per decade to 15 % specific humidity per decade depending on the altitude. The water vapour trends at five layers are statistically significant at or above the 90 % level.

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“…The diurnal cycle in IWV over Bern was described by Hocke et al (2017) using hourly data of the TROWARA radiometer.…”

Section: Introductionmentioning

confidence: 99%

Diurnal cycle of short-term fluctuations of integrated water vapour above Switzerland

Hocke¹,

Bernet²,

Hagen³

et al. 2019

Preprint

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Abstract. The TROpospheric WAter RAdiometer (TROWARA) continuously measures integrated water vapour (IWV) with a time resolution of 6 seconds at Bern in Switzerland. During summer, we often see that IWV has temporal fluctuations during daytime while the night-time 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&#8201;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 Tp. The amplitudes are proportional to Tp0.5. Another method is the computation of the moving standard deviation with time window lengths from about 1 to 100&#8201;min. Here, we get similar results as for the band pass filtering method. At all periods, the IWV fluctuations are strongest during summer while they are smallest during winter. We derive the diurnal variation of the short-term IWV fluctuations by applying a moving standard deviation with a window length of 10&#8201;min. The daily cycle is strongest during the summer season with standard deviations up to 0.22&#8201;mm at about 14:00&#8201;CET. The diurnal cycle disappears during winter time. Using the meteorological weather station at Bern, we derive the diurnal cycle of the short-term fluctuations of the specific kinetic energy ek. Since these data have a temporal resolution of 10&#8201;min, we apply a 20&#8201;min-moving standard deviation. The derived short-term ek fluctuations can be regarded as a proxy of turbulent kinetic energy (TKE). During summer time, the 20&#8201;min-moving standard deviation of ek increases during daytime and has a similar diurnal cycle like the short-term IWV fluctuations. Thus, we conclude that the diurnal cycle of the short-term IWV fluctuations is caused by turbulence associated with large convective heating during daytime in summer.

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Diurnal Cycle in Atmospheric Water over Switzerland

Cited by 9 publications

References 35 publications

Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry

Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry

A Raman lidar tropospheric water vapour climatology and height-resolved trend analysis over Payerne, Switzerland

Diurnal cycle of short-term fluctuations of integrated water vapour above Switzerland

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