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 Computer Science , 2014, Abstract: We present bounds and a closed-form high-SNR expression for the capacity of multiple-antenna systems affected by Wiener phase noise. Our results are developed for the scenario where a single oscillator drives all the radio-frequency circuitries at each transceiver (common oscillator setup), the input signal is subject to a peak-power constraint, and the channel matrix is deterministic. This scenario is relevant for line-of-sight multiple-antenna microwave backhaul links with sufficiently small antenna spacing at the transceivers. For the 2 by 2 multiple-antenna case, for a Wiener phase-noise process with standard deviation equal to 6 degrees, and at the medium/high SNR values at which microwave backhaul links operate, the upper bound reported in the paper exhibits a 3 dB gap from a lower bound obtained using 64-QAM. Furthermore, in this SNR regime the closed-form high-SNR expression is shown to be accurate.
 Annales Geophysicae (ANGEO) , 2003, Abstract: Highly resolved precipitation forecasts are necessary in many applications, especially in mountain meteorology and flash flood forecasts for small- to medium-sized alpine watersheds. Here we present precipitation forecasts simulated by the limited area model ALADIN applying different grid resolutions (Dx = 10 km and 4 km). Target area of the investigations is the Alpine Ticino-Verzasca-Maggia watershed (total area: 2627 km2). We discuss problems of validation of high-resolution precipitation forecasts by comparison with observed precipitation fields and apply an indirect validation approach by using ALADIN forecasts as input to hydrologic simulations. These simulations are carried out with the distributed hydrologic model WaSiM-ETH (Dx = 500 m, Dt = 1 h). The time step of meteorological input to WaSiM-ETH is fixed at 1 h but spatial resolution varies. The main result of the validation experiments for three heavy precipitation events is, that coarser-scale ALADIN forecasts (in model version 11.2) provide better precipitation predictors for hydrologic modeling than higher-resolution forecasts. The experiments demonstrate that hydrologic modeling is a promising tool for the evaluation of high-resolution precipitation fields. Key words. Hydrology (floods) – Meteorology and atmospheric dynamics (mesoscale meteorology; precipitation)
 Climate of the Past Discussions , 2012, DOI: 10.5194/cpd-8-6111-2012 Abstract: Accumulation and δ18O data from Alpine ice cores provide information on past temperature and precipitation. However, their correlation with seasonal or annual mean temperature and precipitation at nearby sites is often low. Based on an example we argue that, to some extent, this is due to the irregular sampling of the atmosphere by the ice core (i.e. ice cores only record precipitation events and not dry periods) and the possible incongruity between annual layers and calendar year due to dating uncertainty. Using daily meteorological data from nearby stations and reanalyses we replicate the ice core from the Grenzgletscher (Switzerland, 4200 m a.s.l.) on a sample-by-sample basis. Over the last 15 yr of the ice core record, accumulation and δ18O variations can be well reproduced on a sub-seasonal scale. This allows a wiggle-matching approach for defining quasi-annual layers. For this period, correlations between measured and replicated quasi-annual δ18O values approach 0.8. Further back in time, the quality of the agreement deteriorates rapidly. Nevertheless, we find significant correlations for accumulation and precipitation over the entire length of the record (1938–1993), which is not the case when comparing ice core δ18O with annual mean temperature. A Monte Carlo resampling approach of long meteorological time series is used to further explore the relation, in a replicated ice core, between δ18O and annual mean temperature. Results show that meteorologically very different years can lead to quasi-identical values for δ18O. This poses limitations to the use of δ18O from Alpine ice cores for temperature reconstructions in regions with a variable seasonality in precipitation.
 Hydrology and Earth System Sciences Discussions , 2012, DOI: 10.5194/hessd-9-215-2012 Abstract: The water balance in high Alpine regions is often characterized by significant variation of meteorological variables in space and time, a complex hydrogeological situation and steep gradients. The system is even more complex when the rock composition is dominated by soluble limestone, because unknown underground flow conditions and flow directions lead to unknown storage quantities. Reliable distributed modeling cannot be implemented by traditional approaches due to unknown storage processes at local and catchment scale. We present an artificial neural network extension of a distributed hydrological model (WaSiM-ETH) that allows to account for subsurface water transfer in a karstic environment. The extension was developed for the Alpine catchment of the river "Berchtesgadener Ache" (Berchtesgaden Alps, Germany), which is characterized by extreme topography and calcareous rocks. The model assumes porous conditions and does not account for karstic environments, resulting in systematic mismatch of modeled and measured runoff in discharge curves at the outlet points of neighboring high alpine sub-catchments. Various precipitation interpolation methods did not allow to explain systematic mismatches, and unknown subsurface hydrological processes were concluded as the underlying reason. We introduce a new method that allows to describe the unknown subsurface boundary fluxes, and account for them in the distributed model. This is achieved by an Artificial Neural Network approach (ANN), where three input variables are taken to calculate the unknown subsurface storage conditions. We explicitly derive the algebraic transfer function of an artificial neural net to calculate the missing boundary fluxes. The result of the ANN is then implemented in the groundwater module of the distributed model as boundary flux, and considered during the consecutive model process. The ANN was able to reproduce the observed water storage data sufficiently (r2 = 0.48). The boundary influx in the sub-catchment improved the distributed model, as performance increased from NSE = 0.34 to NSE = 0.57. This combined approach allows distributed quantification of water balance components including subsurface water transfer.
 Hydrology and Earth System Sciences (HESS) & Discussions (HESSD) , 2012, Abstract: The water balance in high Alpine regions is often characterized by significant variation of meteorological variables in space and time, a complex hydrogeological situation and steep gradients. The system is even more complex when the rock composition is dominated by soluble limestone, because unknown underground flow conditions and flow directions lead to unknown storage quantities. Reliable distributed modeling cannot be implemented by traditional approaches due to unknown storage processes at local and catchment scale. We present an artificial neural network extension of a distributed hydrological model (WaSiM-ETH) that allows to account for subsurface water transfer in a karstic environment. The extension was developed for the Alpine catchment of the river "Berchtesgadener Ache" (Berchtesgaden Alps, Germany), which is characterized by extreme topography and calcareous rocks. The model assumes porous conditions and does not account for karstic environments, resulting in systematic mismatch of modeled and measured runoff in discharge curves at the outlet points of neighboring high alpine subbasins. Various precipitation interpolation methods did not allow to explain systematic mismatches, and unknown subsurface hydrological processes were concluded as the underlying reason. We introduce a new method that allows to describe the unknown subsurface boundary fluxes, and account for them in the hydrological model. This is achieved by an artificial neural network approach (ANN), where four input variables are taken to calculate the unknown subsurface storage conditions. This was first developed for the high Alpine subbasin K nigsseer Ache to improve the monthly water balance. We explicitly derive the algebraic transfer function of an artificial neural net to calculate the missing boundary fluxes. The result of the ANN is then implemented in the groundwater module of the hydrological model as boundary flux, and considered during the consecutive model process. We tested several ANN setups in different time increments to investigate ANN performance and to examine resulting runoff dynamics of the hydrological model. The ANN with 5-day time increment showed best results in reproducing the observed water storage data (r2 = 0.6). The influx of the 20-day ANN showed best results in the hydrological model correction. The boundary influx in the subbasin improved the hydrological model, as performance increased from NSE = 0.48 to NSE = 0.57 for subbasin K nigsseetal, from NSE = 0.22 to NSE = 0.49 for subbasin Berchtesgadener Ache, and from NSE = 0.56 to NSE = 0.66 for the whole catchment within the test period. This combined approach allows distributed quantification of water balance components including subsurface water transfer.
 Mathematics , 2015, Abstract: In this paper, a novel approach for optimizing the joint deployment of small cell base stations and wireless backhaul links is proposed. This joint deployment scenario is cast as a multi-objective optimization problem under the constraints of limited backhaul capacity and outage probability. To address the problem,a novel adaptive algorithm that integrates $\epsilon$-method, Lagrangian relaxation and tabu search is proposed to obtain the Pareto optimal solution set. Simulation results show that the proposed algorithm is quite effective in finding the optimal solutions. The proposed joint deployment model can be used for planning small cell networks.
 Mathematics , 2015, Abstract: The cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio system. In this paper, we consider the downlink of a CRAN formed of one central processor (the cloud) and several base-station (BS), where each BS is connected to the cloud via either a wireless or capacity-limited wireline backhaul link. The paper addresses the joint design of the hybrid backhaul links (i.e., designing the wireline and wireless backhaul connections from cloud to BSs) and the access links (i.e., determining the sparse beamforming solution from the BSs to the users). The paper formulates the hybrid backhaul and access link design problem by minimizing the total network power consumption. The paper solves the problem using a two-stage heuristic algorithm. At one stage, the sparse beamforming solution is found using a weighted mixed $\ell_1/\ell_2$ norm minimization approach; the correlation matrix of the quantization noise of the wireline backhaul links is computed using the classical rate-distortion theory. At the second stage, the transmit powers of the wireless backhaul links are found by solving a power minimization problem subject to quality-of-service constraints, based on the principle of conservation of rate by utilizing the rates found in the first stage. Simulation results suggest that the performance of the proposed algorithm approaches the global optimum solution, especially at high SINR.
 Climate of the Past Discussions , 2012, DOI: 10.5194/cpd-8-5867-2012 Abstract: Water stable isotope ratios and net snow accumulation in ice cores are usually interpreted as temperature and precipitation proxies. However, only in a few cases a direct calibration with instrumental data has been attempted. In this study we took advantage of the dense network of observations in the European Alpine region to rigorously test the relationship of the proxy data from two highly-resolved ice cores with local temperature and precipitation, respectively, on an annual basis. We focused on the time period 1961–2001 with the highest amount and quality of meteorological data and the minimal uncertainty in ice core dating (±1 yr). The two ice cores come from Fiescherhorn glacier (Northern Alps, 3900 m a.s.l.) and Grenzgletscher (Southern Alps, 4200 m a.s.l.). Due to the orographic barrier, the two flanks of the Alpine chain are affected by distinct patterns of precipitation. Therefore, the different location of the two ice cores offers the unique opportunity to test whether the precipitation proxy reflects this very local condition. We obtained a significant spatial correlation between annual δ18O and regional temperature at Fiescherhorn. Due to the pronounced intraseasonal to interannual variability of precipitation at Grenzgletscher, significant results were only found when weighting the temperature with precipitation. For this site, disentangling the temperature from the precipitation signal was thus not possible. Significant spatial correlations between net accumulation and precipitation were found for both sites but required the record from the Fiescherhorn glacier to be shifted by 1 yr (within the dating uncertainty). The study underlines that even for well-resolved ice core records, interpretation of proxies on an annual or even sub-annual basis remains critical. This is due to both, dating issues and the fact that the signal preservation intrinsically depends on precipitation.
 Natural Hazards and Earth System Sciences (NHESS) & Discussions (NHESSD) , 2005, Abstract: Northern Italy is frequently affected by severe precipitation conditions often inducing flood events with associated loss of properties, damages and casualties. The capability of correctly forecast these events, strongly required for an efficient support to civil protection actions, is still nowadays a challenge. This difficulty is also related with the complex structure of the precipitation field in the Alpine area and, more generally, over the Italian territory. Recently a new generation of non-hydrostatic meteorological models, suitable to be used at very high spatial resolution, has been developed. In this paper the performance of the non-hydrostatic Lokal Modell developed by the COSMO Consortium, is analysed with regard to a couple of intense precipitation events occurred in the Piemonte region in Northern Italy. These events were selected among the reference cases of the Hydroptimet/INTERREG IIIB project. LM run at the operational resolution of 7km provides a good forecast of the general rain structure, with an unsatisfactory representation of the precipitation distribution across the mountain ranges. It is shown that the inclusion of the new prognostic equations for cloud ice, rain and snow produces a remarkable improvement, reducing the precipitation in the upwind side and extending the intense rainfall area to the downwind side. The unrealistic maxima are decreased towards observed values. The use of very high horizontal resolution (2.8 km) improves the general shape of the precipitation field in the flat area of the Piemonte region but, keeping active the moist convection scheme, sparse and more intense rainfall peaks are produced. When convective precipitation is not parametrised but explicitly represented by the model, this negative effect is removed. Full Article (PDF, 1843 KB) Special Issue Citation: Elementi, M., Marsigli, C., and Paccagnella, T.: High resolution forecast of heavy precipitation with Lokal Modell: analysis of two case studies in the Alpine area, Nat. Hazards Earth Syst. Sci., 5, 593-602, doi:10.5194/nhess-5-593-2005, 2005. Bibtex EndNote Reference Manager XML
 Natural Hazards and Earth System Sciences (NHESS) & Discussions (NHESSD) , 2013, Abstract: The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.
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