Abstract. For climate change research, it is important to have multiple independent measurement techniques. The ground-based microwave radiometer at Bern has been operated since 1994 and allows the independent derivation of the linear trend of the integrated water vapour column or integrated water vapour (IWV). According to the Clausius-Clapeyron equation, the water vapour saturation increases with increase of temperature. There is also a water vapour feedback since water vapour is a natural greenhouse gas and amplifies man-made global warming. In Switzerland, climate change is stronger than at many other places in the world. We analyse observations of the tropospheric water radiometer (TROWARA) which monitored IWV above Bern from 1995 to 2025. The relative IWV increase is 5.1 %/decade. Evaluation of coincident IWV data from ERA5 (reanalysis of European Centre for Medium-Range Weather Forecasts) gives a trend of 3.7 %/decade. The ERA5 surface air temperature in Bern increased by 0.47K/decade from 1995 to 2025. Thus, we get 10.9 % more IWV for a 1 K increase in case of TROWARA and 7.8 % more IWV for a 1K increase in case of ERA5. Though the IWV trends of TROWARA and ERA5 slightly differ, both datasets agree in the fact that water vapour above the Swiss Plateau significantly increased by 11 % or 16 % from 1995 to 2025. This strong increase of water vapour certainly has an impact on weather, climate, and hydrology.

Assessing the Surface Mass Balance (SMB) of the Antarctic Ice Sheet is crucial for understanding its response to climate change. Synthetic Aperture Radar (SAR) observations from Sentinel-1 provide a potential to monitor the variability of SMB processes through changes in the scattering response of near-surface layers and internal snow layers. However, the interplay between accumulation, wind erosion, deposition and melt is complex, thereby complicating the interpretation of the changes in scattering of the microwave signal. Additionally, the lack of reliable ground truth measurements of snow surface limits our capability to relate the SMB processes to the dominant scattering processes. In this study, we focus on understanding how the surface processes relate to the changes in the dominant scattering mechanism from Sentinel-1 in a drifting snow-dominated region of East Antarctica. We introduce a new parameter, alpha_scat, derived from scattering-type and scattering entropy descriptors from Sentinel-1 SAR observations. This parameter quantifies the continuous scattering response from near-surface layers (i.e., pure scattering) and from internal snow layers (i.e., volume scattering). The changes in alpha_scat are evaluated from the repeated in-situ surface measurements acquired during Mass2Ant field campaigns. These measurements include roughness and accumulation derived from a terrestrial laser scanner, and surface densities from SnowMicroPen. At the field-scale, our analysis shows a strong correlation between surface roughness and alpha_scat (R-squared value of 0.99), thereby indicating the role of roughness on the dominant scattering mechanism. During periods associated with erosion, the vertical component of roughness (Root Mean Squared Height) is found to be more important than the horizontal component (Autocorrelation length) in changing the scattering response. This is also marked by an increase in alpha_scat value, indicating a tendency towards pure scattering. In contrast, accumulation events lead to surface smoothening with dominant scattering from internal snow layers. Looking at the long-term changes in alpha_scat (i.e., period 2017 - 2023), high surface densities are found to be associated with an increase in pure scattering. However, increasing (decreasing) accumulation rates contribute to suppressing (enhancing) the effect of surface density on dominant scattering. The analyses provide new insights into the connection between SMB processes and dominant scattering in Sentinel-1 observations, but more field data is needed from multiple locations to quantify the combined effect of roughness, surface density, and accumulation rates on dominant scattering mechanisms. Such a framework could lead into a better separation between pure scattering and volume scattering, thereby furthering our knowledge on observing the variability of SMB processes from Sentinel-1.

Assessing the Surface Mass Balance (SMB) of the Antarctic Ice Sheet is crucial for understanding its response to climate change. Synthetic Aperture Radar observations from Sentinel-1 provide the potential to monitor the variability of SMB processes through changes in the scattering response of near-surface and internal snow layers. However, the interplay between several factors, such as accumulation, wind erosion, deposition, and melt, complicates the interpretation of changes in the scattering of the microwave signal. Additionally, lack of reliable ground truth measurements of the snow surface limits our capability to associate the SMB processes with dominant scattering mechanism. In this study, we aim to quantify the dominant scattering in Sentinel-1 signal and evaluate the changes in scattering in drifting snow-dominated regions of East Antarctica. We introduce a scattering indicator, $\alpha _{scat,\varepsilon }$ , derived from scattering-type and entropy descriptors. This provides a measure of the dominant scattering between volume and pure scattering. By relating the field measurements to $\alpha _{scat,\varepsilon }$ , we establish that the evolution of dominant scattering in the presence of snowdrift is complex. First, $\alpha _{scat,\varepsilon }$ strongly correlates with surface roughness ( $R^{2}=0.92$ , RMSE $=2^\circ$ ). Spatially variable erosion patterns significantly increase the roughness and result in a strong affinity towards pure scattering despite net accumulation. Second, high surface densities also tend to influence pure scattering; however, the effect is dependent on the accumulation rate. With more accumulation, we observe an increasing dominance of volume scattering from internal snow layers. Long-term trends in $\alpha _{scat,\varepsilon }$ (2017/2023) further suggest that it is challenging to address the causes behind the scattering source based on a single snow surface process. We thus demonstrate the potential and limitations of $\alpha _{scat,\varepsilon }$ to infer the variability in dominant scattering from changes in surface processes.

To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains for the period of 2021–2023. The experiment objective was to improve understanding of the sensitivity of Sentinel-1 C-band backscatter radar signals to snow. The data were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The data show that scattering is present throughout the snow volume, although it is limited for low snow densities. Contrasting layer interfaces, ice features and metamorphic snow can have considerable impact on the backscatter signal. During snowmelt periods, wet snow absorbs the signal, and the soil backscatter becomes negligible. A comparison of the vertically integrated tower radar data with Sentinel-1 data shows that both systems have similar temporal behavior, and both feature an increase in backscatter during the dry-snow period in 2021–2022, even during weeks of nearly constant snow depth, likely due to morphological changes in the snowpack. The results demonstrate that C-band radar is sensitive to the dominant seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy, melt–freeze cycles and snow wetness may complicate satellite-based snow depth retrievals.

The dependence of L-band Vegetation Optical Depth (L-VOD, z) on Vegetation temperature T V is investigated for 1165 boreal forest grid cells selected for latitudes > 55 degrees and high radiometric forest fraction FFO >= 90%. SMOS Level-3 Brightness Temperatures (BT) at ascending orbits acquired from 2011 to 2022 are used. This is a spatiotemporal extension of our previous study on z(TV) made over the "Sodankyla grid cell" (Finland) in 2019. It demonstrated the Electromagnetic (EM) reasons for z(TV) reaching maximum at 0 degrees C and decreasing when T V is moving away from 0 degrees C. The parameterisation of the "L-VOD model" developed in the previous study is simplified and updated to take into account the conservation of salt in sap-water during freezing. The "forward operator" based on the Two-Stream Microwave Emission Model (2S-MEM) is inverted to retrieve z SMOS ( T V ) together with effective Ground permittivities eG during seasonal Warming Periods (WPs) determined from ERA-interim air temperatures. The "L-VOD model" parameters ( T melt WC wood ) are estimated for the boreal forest grid cells by minimizing squared differences between z SMOS ( T V ) and simulated zsim(TV). The vegetation melt-parameter T melt represents the "number of degrees below 0 degrees C" at which sap-water melts, and WC wood is the gravimetric wood- Water Content of branches. Reasonable values of ( T melt WC wood ) are achieved for a majority of the boreal forest grid cells. It is found that T melt tends to be too high over Northern Europe, a region with longer WP durations compared to other regions of the boreal forest belt. By optimising the scattering albedo used to retrieve zSMOS(TV), the correlations between zsim(TV) and z SMOS ( T V ) can be increased, thereby improving the reliability of ( T melt WC wood ). The results raise the possibility of an alternative method to parameterise the scattering albedo of boreal forests by means of the reality-based nature of z SMOS ( T V ) associated with the consistency of T melt with expected values. The study also shows that estimating the Above-Ground Biomass (AGB) of boreal forests using LVOD must take into account the reduction in L-VOD due to the EM-reasons resulting from freezing sap-water in wooden branches, which dominate the radiative transfer at L-band.

<p>As a result of global warming, now evident also in temperate latitudes, longer periods of snow-free winters, instead with plenty of precipitation are becoming increasingly common. If the temperatures then fall below freezing point, one can, with a little luck, discover hair ice - hair-like, flexible structures reaching up to 10 cm in length without any ramifications.</p> <p>This natural phenomenon, already described in 1918 by Alfred Wegener, was a mystery for a long time. Only in the 21<sup>st</sup> century a fungus (<em>Exidiopsis effusa</em>) was discovered as the causative agent [1].</p> <p>Hair ice develops exclusively on rotten hardwoods on/in which this fungus is present, at high humidity, preferably windless, and temperatures slightly below freezing. Once infected, corresponding branches can be repeatedly elicited hair-rise growth under optimal conditions (field & climate chamber). Hair ice, unlike frost needles, arises from the base. At the onset of hair-ice melt a very thin fibre becomes apparent, which carries brownish water drops. Melting water samples show complex mass spectra similar to dissolved organic carbon e.g. from terrestrial/ marine waters, soil extracts or aerosols.</p> <p>Hair ice samples of various tree species were desalted, concentrated by solid phase extraction and subsequently analyzed by flow injection analysis in a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer, equipped with an ESI source and a 7 T supra-conducting magnet (LTQ-FT Ultra, ThermoFisher Scientific) - the key technique for the analysis of complex samples, simultaneously providing molecular level details of thousands of compounds. As main result, complex, but almost identical spectra were found. For their chemical characterization van Krevelen diagrams, typical to classify samples regarding polarity and aromaticity were plotted. By comparison with references biopolymer substance classes were derived. As result, lignin and tannin could be detected as the main hair-ice substance classes, supposed to act as freezing catalyst as well as recrystallization inhibitor.</p> <p>For the question, if and what happens in summer, we sampled in several years guttation droplets, too – of this fungus and for comparison from a fungus of another family. Both samples were carbon riche, but only the samples from <em>Exidiopsis effusa </em>show such a complex DOC-spectrum, but in contrast to hair ice peak depleted with mainly tannin assignment.</p> <p>Popular scientific publications have led to an increasing interest in hair ice and related phenomena in recent years. We have received spectacular photos of various ice structures, followed by first samples of needle ice and ice ribbons. After initial measurements for their C content, HPLC-MS investigations still with a triple quadrupole mass spectrometer have been performed. For final analyses a cooperation with a FTICRMS working group is now sought.</p> <p>[1] D. Hofmann, G. Preuss and C. Mätzler (2015) Biogeosciences 12: 4261–4273</p>

This article experimentally investigates relationships between copol backscattering at a wide range of frequencies (L- to Ka-bands) and snow-ground state parameters (SPs) in different evolution phases during the full winter cycle of 2019/2020. Backscattering coefficients from 1 to 40 GHz, in situ snow-ground SPs, and meteorological data are measured at the Davos-Laret Remote Sensing Field Laboratory (Switzerland). Relative strengths of the snow-ground system's three primary scattering elements (air-snow interface, snow volume, and snow-ground interface) on backscattering are assessed. An anticorrelation between reasonably high snow wetness and backscattering coefficient is found, especially at higher microwave frequencies. For small amounts of snow wetness, backscatter coefficients at L- and S-bands are intensified via increasing snow volume and snow surface scattering. Snow-ground SPs influence backscattering according to their characteristic time scales of temporal evolution. Under dry snow conditions and at low and intermediate frequencies, ground permittivity is the major influencer of backscatter at a time scale of roughly two weeks. Snowfall is the major influencer of backscatter at a time scale of a few hours to a few days. The findings of this article are valuable to the development of retrieval algorithms using machine learning while maintaining a grasp on the ongoing physical processes. Another key message is that multifrequency active microwave measurements are critical to maximize the number of retrievable SPs and their estimation accuracy. For example, while Ka-band performs well in the detection of snow cover, L-band measurements are more responsive to changes of snow water equivalent (SWE) under moist or wet snow conditions.

Microwave remote sensing of the cryosphere demands a formulation of the scattering coefficient which can be applied over the entire range of relevant densities, from fresh snow to bubbly ice, at all frequencies and for any grain size and snow type. Most challenging are intermediate densities (450–550 kg m−3) and high frequencies (or coarse-grained snow) where current scattering formulations break down. In this brief communication we demonstrate that the strong contrast expansion method recently developed for heterogeneous, dielectric media can be applied to microwave scattering in snow, firn and ice to solve these problems.

This study reports on four consecutive winter campaigns (2016–2020) at the “Davos-Laret Remote Sensing Field Laboratory” in the Swiss Alps to gain insight into the L-band microwave emission of ground covered with seasonal snow. Close-range L-band Brightness temperatures $T_\mathrm{{B}}^{p,\phi }(\theta)$ were measured over the site scanning different observation nadir angles $\theta$ and azimuth angles $\phi$ at horizontal and vertical polarization p = {H,V}. State parameters (SPs) of the snowpack (e.g., height, density, and snow water equivalent) and the subnivean soil (permittivity, temperature) were measured quasi-simultaneously using in-situ sensors and sampling, as well as meteorological data. In each campaign, $T_\mathrm{{B}}^{p,\phi }(\theta)$ were measured over a “natural area” and a “reflector area” with a metal mesh reflector laid on the ground before snow accumulation. The radiometer measurements over “reflector area” allowed to retrieve the time-series of Snow liquid Water-content $W_\mathrm{{S}}$ and Snow liquid Water-Column ${WC}_\mathrm{{S}}$ , which are employed as “derived measurements” to support interpretation of $T_\mathrm{{B}}^{p,\phi }(\theta)$ measured over “natural areas” during different winter phases. The detailed approach for the estimation of $W_\mathrm{{S}}$ and ${WC}_\mathrm{{S}}$ using L-band radiometer data is presented. The data and analyses in this article address the following major points: 1) determination of the characteristic features of measured $T_\mathrm{{B}}^{p,\phi }(\theta)$ during different periods in each of the four winter campaigns; 2) effects of dry and wet snow precipitation on L-band radiometer data compared to corresponding simulations; 3) effect of removal and compression of the snowpack on $T_\mathrm{{B}}^{p,\phi }(\theta)$ ; 4) effects of spatial heterogeneity on brightness temperatures. Finally, the study is concluded with recommendations relevant for future close-range remote sensing campaigns.

ElectroMagnetic (EM) reasons resulting in temperature dependence of L-band Vegetation Optical Depth (L-VOD) are currently overlooked in remote sensing products. Discrepancies in retrievals of geophysical surface properties over vegetated areas can result from this incompleteness. This perception motivated to explore EM considerations in how temperature drives L-VOD of a boreal forest. Thereto, a novel physics-based model is developed and evaluated to assess L-VOD sensitivities to canopy temperature and some other model parameters. The L-VOD model is compared to L-VOD derived from close-range L-band brightness temperatures measured through the tree canopy at the Finnish Meteorological Institute's Arctic Research Center (FMI-ARC) in Sodankyla center dot (Finland) during a 4-week and a 1-day period in 2019. Furthermore, the model's ability to explain L-VOD retrieved from brightness temperatures of the "Soil Moisture and Ocean Salinity" (SMOS) satellite over the "Sodankyla center dot grid cell" is investigated. Experimental L-VOD are maximal at around 0 degrees C and decrease when canopy temperature is moving away from zero degree Celsius. This temperature response, observed at different temporal- and spatial scales, is captured by the proposed L-VOD model and explained by freezing tree sap-water and the dependence of water permittivity on temperature. The demonstrated EM-induced temperature dependence suggest caution with interpreting satellite-based L-VOD, because increased L-VOD around the freezing point is not solely due to increased biomass or rehydration of the vegetation. Further, our study can find future application to compensate L-VOD for EM-induced temperature sensitivity. This potentially leads to improved explanatory power of temperature normalized L-VOD for characterization of forest phenology. Furthermore, we suggest examining the presence and strength of the demonstrated L-VOD temperature response as a practical L-VOD retrieval quality assessment method under steady forest phenology.

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 R-2 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.

The modular Snow Microwave Radiative Transfer (SMRT) model simulates microwave scattering behavior in snow via different selectable theories and snow microstructure representations, which is well suited to intercomparisons analyses. Here, five microstructure models were parameterized from X-ray tomography and thin-section images of snow samples and evaluated with SMRT. Three field experiments provided observations of scattering and absorption coefficients, brightness temperature, and/or backscatter with the increasing complexity of snowpack. These took place in Sodankylä, Finland, and Weissfluhjoch, Switzerland. Simulations of scattering and absorption coefficients agreed well with observations, with higher errors for snow with predominantly vertical structures. For simulation of brightness temperature, difficulty in retrieving stickiness with the Sticky Hard Sphere microstructure model resulted in relatively poor performance for two experiments, but good agreement for the third. Exponential microstructure gave generally good results, near to the best performing models for two field experiments. The Independent Sphere model gave intermediate results. New Teubner–Strey and Gaussian Random Field models demonstrated the advantages of SMRT over microwave models with restricted microstructural geometry. Relative model performance is assessed by the quality of the microstructure model fit to micro-computed tomography (CT) data and further improvements may be possible with different fitting techniques. Careful consideration of simulation stratigraphy is required in this new era of high-resolution microstructure measurement as layers thinner than the wavelength introduce artificial scattering boundaries not seen by the instrument.

Water vapor column density, or vertically-integrated water vapor (IWV), is monitored by ground-based microwave radiometers (MWR) and ground-based receivers of the Global Navigation Satellite System (GNSS). For rain periods, the retrieval of IWV from GNSS Zenith Wet Delay (ZWD) neglects the atmospheric propagation delay of the GNSS signal by rain droplets. Similarly, it is difficult for ground-based dual-frequency single-polarisation microwave radiometers to separate the microwave emission of water vapor and cloud droplets from the rather strong microwave emission of rain. For ground-based microwave radiometry at Bern (Switzerland), we take the approach that IWV during rain is derived from linearly interpolated opacities before and after the rain period. The intermittent rain periods often appear as spikes in the time series of integrated liquid water (ILW) and are indicated by ILW ≥ 0.4 mm. In the present study, we assume that IWV measurements from radiosondes are not affected by rain. We intercompare the climatologies of IWV(rain), IWV(no rain), and IWV(all) obtained by radiosonde, ground-based GNSS atmosphere sounding, ground-based MWR, and ECMWF reanalysis (ERA5) at Payerne and Bern in Switzerland. In all seasons, IWV(rain) is 3.75 to 5.94 mm greater than IWV(no rain). The mean IWV differences between GNSS and radiosonde at Payerne are less than 0.26 mm. The datasets at Payerne show a better agreement than the datasets at Bern. However, the MWR at Bern agrees with the radiosonde at Payerne within 0.41 mm for IWV(rain) and 0.02 mm for IWV(no rain). Using the GNSS and rain gauge measurements at Payerne, we find that IWV(rain) increases with increase of the precipitation rate during summer as well as during winter. IWV(rain) above the Swiss Plateau is quite well estimated by GNSS and MWR though the standard retrievals are limited or hampered during rain periods.

Understanding surface melt over the Greenland Ice Sheet (GrIS) is important for evaluating the effect of the changing global climate on humanity, and for forecasting sea level rise. We demonstrate a recently developed L-band passive microwave algorithm for the retrieval of snow liquid water and snow and firn density over the GrIS. The retrievals are performed using brightness temperatures from the ESA SMOS satellite. The density retrievals show potential for mapping the equilibrium line altitude of the ice sheet by determining where retrieved densities exceed typical seasonal snowpack density, thus indicating the presence of firn. We integrate snow liquid water retrievals over area and time to obtain water column maps and daily estimates of the surface liquid water present over Greenland. The integrated annual surface liquid water retrieved over the entire ice sheet compares on the same order as modeled estimates of total meltwater available in current literature.

Reliable microstructure measurement of snow is a requirement for microwave radiative transfer model validation. Snow specific surface area (SSA) can be measured using stereological methods, in which snow samples are cast in the field and photographed in the laboratory. Processing stereology photographs manually by counting intersections of test cycloids with air–ice boundaries reduces the problems in binary segmentation. This paper is a case study to evaluate the repeatability of the manually stereology interpretation by two independent research groups. We further assessed how uncertainty in snow SSA influences simulated brightness temperature (TB) driven by the Microwave Emission Model of Layered Snowpacks (MEMLS), and how stereology compares to Near Infrared (NIR) camera and hand lens. Data was obtained from two alpine snow profiles from Steamboat Springs, Colorado. Results showed that stereological SSA values measured by two groups are highly consistent, and the ground radiometer measured TB at 19 and 37 GHz was successfully predicted (RMSE<3.8 K); simulations using NIR SSA and hand-lens geometric grain size (Dg) measurements have larger errors. This conclusion was not sensitive to uncertainty in the free parameters of TB modeling.

Vertically integrated water vapour (IWV) is expected to increase globally in a warming climate. To determine whether IWV increases as expected on a regional scale, we present IWV trends in Switzerland from ground-based remote sensing techniques and reanalysis models, considering data for the time period 1995 to 2018. We estimate IWV trends from a ground-based microwave radiometer in Bern, from a Fourier transform infrared (FTIR) spectrometer at Jungfraujoch, from reanalysis data (ERA5 and MERRA-2) and from Swiss ground-based Global Navigation Satellite System (GNSS) stations. Using a straightforward trend method, we account for jumps in the GNSS data, which are highly sensitive to instrumental changes. We found that IWV generally increased by 2 % per decade to 5 % per decade, with deviating trends at some GNSS stations. Trends were significantly positive at 17 % of all GNSS stations, which often lie at higher altitudes (between 850 and 1650 m above sea level). Our results further show that IWV in Bern scales to air temperature as expected (except in winter), but the IWV–temperature relation based on reanalysis data in the whole of Switzerland is not clear everywhere. In addition to our positive IWV trends, we found that the radiometer in Bern agrees within 5 % with GNSS and reanalyses. At the Jungfraujoch high-altitude station, we found a mean difference of 0.26 mm (15 %) between the FTIR and coincident GNSS data, improving to 4 % after an antenna update in 2016. In general, we showed that ground-based GNSS data are highly valuable for climate monitoring, given that the data have been homogeneously reprocessed and that instrumental changes are accounted for. We found a response of IWV to rising temperature in Switzerland, which is relevant for projected changes in local cloud and precipitation processes.

Water vapour in the atmosphere is not only a strong greenhouse gas, but also affects many atmospheric processes such as the formation of clouds and precipitation. With increasing temperature, Integrated Water Vapour (IWV) is expected to increase. Analysing how atmospheric water vapour changes in time is therefore important to monitor ongoing climate change. To determine whether IWV increases in Switzerland as expected, we asses IWV trends from a tropospheric water radiometer (TROWARA) in Bern, from a Fourier transform infrared (FTIR) spectrometer at Jungfraujoch and from the Swiss network of ground-based Global Navigation Satellite System (GNSS) stations. In addition, trends are assessed from reanalysis data, using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) and the Modern-Era Retrospecitve Analysis for Research and Applications (MERRA-2). Ground-based GNSS data are well suited for IWV trends due to their high temporal resolution and the spatially dense networks. However, they are highly sensitvie to instrumental changes and care has to be taken when determining GNSS based trends. We therefore use a straightforward trend method to account for jumps in the GNSS data when instrumental changes were performed. Our data show mostly positive IWV trends between 2 and 5% per decade in Switzerland. GNSS trends are significant for some stations and the significance has the tendency to increase with altitude. Further, we found that IWV scales on average to lower tropospheric temperatures as expected, except in winter. However, the correlation between IWV and temperature based on reanalysis data is spatially incoherent. Besides our positive IWV trends, we found a good agreement of radiometer, GNSS and reanalysis data in Bern. Further, we found a dry bias of the FTIR compared to GNSS data at Jungfraujoch, due to the restriction of FTIR to clear-sky conditions. Our results are generally consistent with the positive water vapour feedback in a warming climate. We show that ground-based GNSS networks provide a valuable source for regional climate monitoring with high spatial and temporal resolution, but homogeneously reprocessed data and advanced trend techniques are needed to account for data jumps.

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 T-p. The amplitudes are proportional to T-p(0.5). Another method is the calculation of the moving standard deviation with time window lengths from about 1 to 100 min Here, we get similar results to those with 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 min The daily cycle is strongest during the summer season, with standard deviations up to 0 22 mm at about 14:00 CET. The diurnal cycle disappears during wintertime. A similar seasonal behaviour is observed in the diurnal cycle of latent heat flux as provided by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2 reanalysis) at Bern. Further, the 3 d averages of the latent heat flux and the magnitude of the short-term IWV variability show a strong correlation at Bern in 2010 (r = 0.82 with a 95 % confidence interval from 0.75 to 0.87). Thus, we suggest that the diurnal cycle of short-term IWV fluctuations at Bern is mainly caused by large convective heating during daytime in summer.

An Emission Models (EM) adequate for a retrieval algorithm requires being simple while still capturing the responses of brightness temperatures T B p,θ to the retrieval parameters. The objective of this study is to explore the benefits of the multiple-scattering Two-Stream (2S) EM over the "Tau-Omega" (TO) EM to retrieve soil Water Content WC and vegetation optical depth τ from L-band T B p,θ . For sparse and low-scattering vegetation T B,EM p,θ simulated with EM = TO and EM = 2S converge, which is not the case for dense and strongly scattering vegetation. WC RC and τ RC are retrieved with Retrieval Configurations RC = {TO, 2S} from T B p,θ : i) from a tower within a deciduous forest, and ii) by the "Soil Moisture and Ocean Salinity" (SMOS) mission. Using 2S EM instead of TO EM resulted in marginally lower WC RC retrievals while τ RC retrievals are reduced more considerably. With respect to in-situ WC in-situ , retrievals WC 2S derived from tower-based T B p,θ performed better than forest soil water-content WC TO retrieved via the inversion of the "reference" TO EM. Likewise, SMOS based WC 2S retrievals revealed better agreement with ECMWF WC simulations than WC TO achieved with the "reference" RC = TO. In short, our study provides clear evidence that it is meaningful to replace TO EM used for current SMOS and SMAP land retrieval with 2S EM.Further advantages of the 2S EM over the TO EM are outlined in this study.