External Drift Research Articles

Noise-resilient architecture of a hybrid electron-nuclear quantum register in diamond

A hybrid quantum register consisting of nuclear spins in a solid-state platform coupled to a central electron spin is expected to combine the advantages of its elements. However, the potential to exploit long nuclear spin coherence times is severely limited by magnetic noise from the central electron spin during external interrogation. We overcome this obstacle and present protocols for addressing a decoherence-free nuclear spin subspace which was not accessible by previously existing methods. We demonstrate the efficacy of our protocols using detailed numerical simulations of a nitrogen-vacancy centre with nearby 13C nuclei, and show that the resulting hybrid quantum register is immune to electron spin noise and external magnetic field drifts. Our work takes an important step toward realising robust quantum registers that can be easily manipulated, entangled, and, at the same time, well isolated from external noise, with applications from quantum information processing and communication to quantum sensing.

Distributed hydrological modeling of floods in the Cévennes-Vivarais region, France: Impact of uncertainties related to precipitation estimation and model parameterization

The Cévennes-Vivarais (France) is a region prone to heavy precipitation events and flash floods occurring mainly during the autumn season. Due to this vulnerability, it is a well instrumented region to monitor rainfall (4 weather radars of the French ARAMIS radar network, 200 hourly raingauges) and river discharge (45 stations). A multiscale radar-raingauge rainfall re-analysis from 10 to 300 km2 and 1 h time step has been established for the period 2007–2014 by using the kriging with external drift (KED) technique. In the present work, a quantification of the isolated and joint impacts of precipitation and model uncertainties in distributed rainfall–runoff simulations is presented for 10 km2–1 h model resolution. For this purpose the following methodology is implemented: (1) Development of a distributed hydrological model based on the Curve Number and the Unit Hydrograph concepts for the Ardèche and Gardon catchments; (2) Generation of an ensemble of perturbed precipitation fields based on the KED error standard deviations and the space–time structure of the residuals to the drift. (3) Generation of so-called behavioral parameter sets for the hydrological model based on generalized sensitive analysis (GSA) and the use of discharge observations; (4) Implementation of the hydrological model for gauged and ungauged catchments with the radar-raingauge rainfall ensembles and the various parameter sets. Uncertainties in rainfall and runoff simulations are then quantified in terms of coefficient of variation. The main findings reveal that (i) the precipitation uncertainty is dampened in the hydrological simulation, especially as long as the size of the watershed increases; in the considered context, the model uncertainty dominates the precipitation uncertainty and it is shown to be independent on catchment size.

Improving copula-based spatial interpolation with secondary data

Often, the primary variable of interest can be measured only rarely and/or indirectly. Hence, it is desired to use secondary correlated data to improve the estimation of the primary variable. This paper extends the Gaussian copula-based geostatistical approach for interpolation to include both real-valued primary and secondary data. Even more, multiple types of real-valued secondary data and categorical exhaustive secondary information were incorporated and the associated benefits analysed. The performance of the proposed inclusion of secondary data and information was compared to benchmark geostatistical methods (Ordinary Kriging, co-Kriging, external drift Kriging, and copula-based interpolation of the primary variable alone) by cross-validation and split sampling, with varying compositions of primary and secondary variables. The proposed method was tested for several groundwater quality parameters and the results show that a) the joint copula-based method outperforms co-Kriging in all quality measures; (b) physically impossible estimates (i.e., negative concentrations) never occur; (c) copula-based methods outperform Kriging methods in the quantification of uncertainty and result in more realistic spatial patterns of uncertainty. Joint methods lead to better results in cases when the primary variable is available at fewer locations than the secondary variable (i.e., undersampled case) and when the variables are meaningfully related. In copula-based spatial dependence models that are constructed in probability space, detrimental effects of non-normal (here: bi-modal) marginal distributions on the joint distribution and dissimilar measurement scales of the primary and secondary variables are prevented. The proposed method offers an efficient and non-linear alternative to co-Kriging.

Spatial modeling of daily concentrations of ground-level ozone in Montreal, Canada: A comparison of geostatistical approaches

Ground-level ozone (O3) is a powerful oxidizing agent and a harmful pollutant affecting human health, forests and crops. Estimating O3 exposure is a challenge because it exhibits complex spatiotemporal patterns. The aim in this study was to provide high-resolution maps (100 × 100 m) of O3 for the metropolitan area of Montreal, Canada. We assessed the kriging with external drift (KED) model to estimate O3 concentration by synoptic weather classes for 2010. We compared these results with ordinary kriging (OK), and a simple average of 12 monitoring stations. We also compared the estimates obtained for the 2010 summer with those from a Bayesian maximum entropy (BME) model reported in the literature (Adam-Poupart et al., 2014). The KED model with road and vegetation density as covariates showed good performance for all six synoptic classes (daily R2 estimates ranging from 0.77 to 0.92 and RMSE from 2.79 to 3.37 ppb). For the summer of 2010, the model using KED demonstrated the best results (R2 = 0.92; RMSE = 3.14 ppb), followed by the OK model (R2 = 0.85, RMSE = 4 ppb). Our results showed that errors appear to be substantially reduced with the KED model. This may increase our capacity of linking O3 levels to health problems by means of improved assessments of ambient exposures. However, future work integrating the temporal dependency in the data is needed to not overstate the performance of the KED model.

Comparison of Spatial Interpolation Methods of Precipitation and Temperature Using Multiple Integration Periods

Eight spatial interpolation methods are used to interpolate precipitation and temperature over several integration periods in a local scale. The methods used are inverse distance weighting (IDW), Thiessen polygons (TP), trend surface analysis, local polynomial interpolation, thin plate spline, and three Kriging methods: ordinary, universal, and simple (OK, UK, and SK). Daily observations from 17 stations in the Seyhan Basin, Turkey, between 1987 and 1994 are used. A variety of parameters and models are used in each method to interpolate surfaces for several integration periods, namely, daily, monthly and annual total precipitation; monthly and annual average precipitation; and daily, monthly and annual average temperature. The performance is assessed using independent validation based on four measurements: the root mean squared error, the mean squared relative error, the coefficient of determination (r2), and the coefficient of efficiency. Based on these validation measurements, the method with smallest errors for most of the integration periods concerning both precipitation and temperature is IDW with a power of 3, whereas TP has the highest errors. The Gaussian model is found superior than other models with less errors in the three Kriging methods for interpolating precipitation, but no specific model is better than another for modeling temperature. UK with elevation as the external drift and SK with the mean as an additional parameter show no superiority over OK. For precipitation, annual average and monthly totals are found to be the worst and best modeled integration periods respectively, with the monthly average the best for temperature.

A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data

Deriving high quality precipitation estimates at high spatial resolution is of prime importance for many hydrological, meteorological, and environmental investigations. Rain gauge observations and satellite-derived precipitation data are two main sources of precipitation estimates. Gauge observations are accurate and reliable, but are heavily point-based and sparse in areas of rugged or complex terrains. Satellite-derived precipitation products can cover large areas, but they are generally characterized by inherent bias. To optimize the use of both datasets, we propose in this paper, a downscaling-integration framework to generate high quality monthly precipitation datasets at 1 km spatial resolution by merging rain gauge observations and TRMM 3B43 products. Firstly, an area-to-point kriging (ATPK) approach is used to downscale the original TRMM product to 1 km, so as to ensure a fair comparison with rain gauge data. Then, the downscaled TRMM precipitation datasets are integrated with the gauge observations using geographically weighted regression kriging (GWRK). The geographical factors (i.e. longitude, latitude and elevation) are also used as auxiliary variables in the GWRK model. Applying this approach to an experiment conducted at the middle and lower reaches of the Yangtze River in China from 2001 to 2014 shows that: (1) the downscaled monthly TRMM precipitation data by ATPK are more accurate than the original TRMM estimates; (2) the GWRK model employing the downscaled TRMM precipitation data and geographical factors provides better monthly precipitation estimates than the conventional ordinary kriging (OK) interpolation and the commonly used merging methods (i.e. geographical difference analysis, GDA and kriging with external drift, KED); (3) the GWRK method reduces the influence of the inaccuracy (bias) of satellite-derived precipitation data on the precipitation estimates compared to GDA. The approach presented in this study has provided an efficient alternative for solving the scale mismatch problem between point-based gauge data and low resolution satellite data, and producing improved precipitation data at high spatial resolution.

Characterising the Seasonal Variations and Spatial Distribution of Ambient PM10 in Urban Ankara, Turkey

The urban airshed of Turkey is under the influence of both traffic and heating emissions. Periodic monitoring and management of air pollution is a must for an up-to-date exposure assessment for effective health and environmental management. The main objective of this paper is to assess the spatial and temporal variability of particulate matter (PM10) in urban areas of Turkey using the metropolitan area of Ankara as a case study, exploring the seasonal variation of PM10 concentrations resulting from indoor heating and transport. This study employed spatiotemporal kriging with external drift to examine the space-time variability and distribution of PM10 concentrations in Ankara for year 2015. The measurements of PM10 in and around metropolitan Ankara were carried out at nine stations. Measurements for the period December 2014 – November 2015 were used and averaged using a 6-h interval for each season: Winter (Dec, Jan, Feb), Spring (Mar, Apr, May), Summer (Jun, Jul, Aug) and Autumn (Sep, Oct, Nov). Population distribution, used as a proxy for indoor heating, and proximity to roads were used as auxiliary variables for kriging analysis. PM10 concentration levels were observed to be highest during autumn and winter. Summer was identified as the “cleanest” period although, in some regions of the city, pollution levels remained high. The southeast region of metropolitan Ankara was identified to be the most polluted region of the city after the city centre.

Kriging with external drift in a Birnbaum–Saunders geostatistical model

Spatial models to describe dependent georeferenced data are applied in different fields and, particularly, are used to analyze earth and environmental data. Most of these applications are carried out under Gaussian spatial models. The Birnbaum–Saunders distribution is a unimodal and positively skewed model which has received considerable attention in several areas, including earth and environmental sciences. In addition, theoretical arguments have been provided to justify its usage in the data modeling from these sciences, at least in the same settings where the lognormal distribution can be employed. This paper presents kriging with external drift based on a Birnbaum–Saunders spatial model. The maximum likelihood method is considered to estimate its parameters. The results obtained in the paper are illustrated by an experimental data set related to agricultural management. Specifically, in this illustration, the spatial variability of magnesium content in the soil as a function of calcium content is analyzed.

A polynomial approximation of the traffic contributions for kriging-based interpolation of urban air quality model

The European directives for ambient air quality require to assess areas where air pollutant concentrations exceed a regulatory threshold. As the spatial distribution of the pollutant is not exactly known, deterministic atmospheric dispersion models are commonly used to supplement the observational network. To reduce the computational time, the simulations are made on irregular grids, especially in urban areas where the grid is refined close to the roads. An interpolation method is then necessary to map the dispersion model at any location. We propose a new geostatistical approach based on kriging with external drift to distinguish the information along and across the roads. An exponential function is introduced to describe the decrease of the concentrations across the roads. Its series expansion is used to build a set of polynomial auxiliary predictors with unknown coefficients. This framework leads to a drift that is more generic and flexible in the kriging system.

Multivariate estimation for accurate and logically consistent forest-attributes maps at macroscales

Spatially explicit wall-to-wall forest-attributes information is critically important for designing management strategies resilient to climate-induced uncertainties. Multivariate estimation methods that link forest attributes and auxiliary variables at full-information locations can be used to estimate the forest attributes for locations with only auxiliary variables information. However, trade-offs between estimation accuracies versus logical consistency among estimated attributes may occur. This is particularly likely for macroscales (i.e., ≥1 Mha) with large forest-attributes variances and wide spacing between full-information locations. We examined these trade-offs for ∼390 Mha of Canada’s boreal zone using variable-space nearest-neighbours imputation versus two modelling methods (i.e., a system of simultaneous nonlinear models and kriging with external drift). We found logical consistency among estimated forest attributes (i.e., crown closure, average height and age, volume per hectare, species percentages) using (i) k ≤ 2 nearest neighbours or (ii) careful model selection for the modelling methods. Of these logically consistent methods, kriging with external drift was the most accurate, but implementing this for a macroscale is computationally more difficult. This extra cost is justified given the importance of assessing strategies under expected climate changes in Canada’s boreal forest and in other forest regions.

Accounting for non-stationary variance in geostatistical mapping of soil properties

Simple and ordinary kriging assume a constant mean and variance of the soil variable of interest. This assumption is often implausible because the mean and/or variance are linked to terrain attributes, parent material or other soil forming factors. In kriging with external drift (KED) non-stationarity in the mean is accounted for by modelling it as a linear combination of covariates. In this study, we applied an extension of KED that also accounts for non-stationary variance. Similar to the mean, the variance is modelled as a linear combination of covariates. The set of covariates for the mean may differ from the set for the variance. The best combinations of covariates for the mean and variance are selected using Akaike's information criterion. Model parameters of the selected model are then estimated by differential evolution using the Restricted Maximum Likelihood (REML) in the objective function. The methodology was tested in a small area of the Hunter Valley, NSW Australia, where samples from a fine grid with gamma K measurements were treated as measurements of the variable of interest. Terrain attributes were used as covariates. Both a non-stationary variance and a stationary variance model were calibrated. The mean squared prediction errors of the two models were somewhat comparable. However, the uncertainty about the predictions was much better quantified by the non-stationary variance model, as indicated by the mean and median of the standardized squared prediction error and by accuracy plots. We conclude that the non-stationary variance model is more flexible and better suited for uncertainty quantification of a mapped soil property. However, parameter estimation of the non-stationary variance model requires more attention due to possible singularity of the covariance matrix.

Threshold contour production of rainfall intensity that induces landslides in susceptible regions of northern Turkey

This study is aimed at developing rainfall intensity contour lines to illustrate upper and lower rainfall intensity limits that trigger landslides. The first phase began by developing precipitation empirical thresholds as follows: (i) the relationship between intensity (I) and accumulative rainfall (E) and date (I-date and E-date, respectively); (ii) the antecedent rainfall up to 3, 5, 10, 15, and 30 days prior to landslide occurrence; (iii) the relationship between the I and the duration (D); and (iv) the relationship between the cumulative rainfall event and the D (ID and ED, respectively). The data recorded by two rain gauges (Rize and Rize–Pazar) in the province of Rize in Northwest Turkey were used to analyze 24 previous landslide events during the period 1985 to 2006. The second phase began by developing surface interpolation maps after deriving threshold and assumption of normality, thereby producing contours of the rainfall intensity data of the rain gauges. All thresholds were verified by no-rainfall events, and the ID threshold successfully distinguished 97 and 95% (in Rize and Rize–Pazar, respectively) of false alarm days with the highest accuracy among the thresholds (antecedent rainfall days, I-date, and E-date thresholds). Thus, a descriptive analysis was conducted using the rainfall data of selected rain gauges to test the normality of rainfall between the selected rain gauges in the study area. The linear correlation coefficient was found at 0.75, and other tests verified the normality with optimistic results. A universal or collaborative kriging interpolation (UK) in a geographic information systems’ environment was used to estimate the spatial distribution of rainfall intensity because rainfall is controlled robustly by topography and other factors. This distribution corresponds to the landslide day event and hole-effect mathematical models that are used to forecast un-sampled values in the study area. Topographic elevation, slope, and normalized difference vegetation index (NDVI) maps were integrated into the landslide day events by using rainfall intensity. The UK map was validated by a cross-validation procedure using root-mean-square error. The highest interpolated surface map accuracy was reached without using an external drift (using rainfall intensity values only) and when using an NDVI as the external drift. The optimal model was selected by comparing the measured and predicted values at approximately 78% accuracy. Ultimately, the rainfall intensity contour lines were used to illustrate the upper and lower rainfall intensity limits that trigger landslides. This study used well-documented spatio-temporal data of landslide events and introduced a primary threshold that can be validated for any future event.

Indonesian Geomagnetic Maps for Epoch 2015.0 to cover of Indonesian Regions

In compliance with the resolutions of IAGA (International Association of Geomagnetism and Aeronomy), Since 1960’s, every five years BMKG or Meteorology, Climatology and Geophysics Agency of Indonesia made geomagnetic field maps based on actual measurements in 53 repeat stations. It’s the map for more accurate result of Geomagnetic maps Epoch 2015.0, the number of repeat stations has been increased to 68 locations. Analysis data was conducted by spatial analyses using collocated co-kriging and kriging with external drift to map the observation data in five components, such as Declination (D), Inclination (I), Vertical (Z), Horizontal (H), and Total Geomagnetic Field (F). The data reduction used one permanent observatory i.e., Kupang Geophysical Observatory, as a reference standard. The results of this Geomagnetic Maps, that the contour lines of Indonesian geomagnetic declination in range -1 to 4.5 degree, Inclination component are -5 to -37 degree, Vertical component are -4000 to -28000 nT, Horizontal component are 36000 to 42000 nT, and Total Geomagnetic Field are 39000 to 46000 nT. In conclusion, Indonesian Geomagnetic Maps for Epoch 2015.0 can be used to compute geomagnetic data around Indonesian regions until next 5 years.

The Significance of the Spatial Variability of Rainfall on the Numerical Simulation of Urban Floods

The growth of urban population, combined with an increase of extreme events due to climate change call for a better understanding and representation of urban floods. The uncertainty in rainfall distribution is one of the most important factors that affects the watershed response to a given precipitation event. However, most of the investigations on this topic have considered theoretical scenarios, with little reference to case studies in the real world. This paper incorporates the use of spatially-variable precipitation data from a long-range radar in the simulation of the severe floods that impacted the city of Hull, U.K., in June 2007. This radar-based rainfall field is merged with rain gauge data using a Kriging with External Drift interpolation technique. The utility of this spatially-variable information is investigated through the comparison of computed flooded areas (uniform and radar) against those registered by public authorities. Both results show similar skills at reproducing the real event, but differences in the total precipitated volumes, water depths and flooded areas are illustrated. It is envisaged that in urban areas and with the advent of higher resolution radars, these differences will be more important and call for further investigation.

Evaluation of a Data Fusion Approach to Estimate Daily PM2.5 Levels in North China

Background/Aim: PM2.5 air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM2.5 exposure spatiotemporally in China, but all these have limitations. This study was to develop a new spatiotemporal model with data fusion approach based on Kriging and Chemistry Module models. Methods: Two techniques were applied to create daily spatial cover of PM2.5 in grid cells with a resolution of 10 km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF-Chem). A data fusion technique was developed by fusing PM2.5 concentration predicted by KED and WRF-Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF-Chem and observations. Model performance was evaluated by comparing them with ground observations and the spatial prediction errors. Results: KED and data fusion performed better with a daily model R2 of 0.95 and 0.94, respectively and the PM2.5 was overestimated by WRF-Chem (R2=0.51). The annual mean PM2.5 concentration of the 365 ground monitors was 90.9 μg/m3, similar with the PM2.5 fields modeled by KED and data fusion at monitoring sites, but lower than that simulated by WRF-Chem. KED and the data fusion model performed better around the ground monitors (15-35 μg/m3), WRF-Chem performed relative worse with high prediction errors in northern Henan, southeastern Hebei and southern Shanxi (65-75 μg/m3). Conclusions: Both KED and data fusion technique provided highly accurate PM2.5. Current monitoring network in North China was dense enough to provide a reliable PM2.5 prediction by interpolation technique. Data fusion is an effective approach to improve the accuracy of WRF-Chem simulation spatiotemporally.

Fusion Based on Geostatistics to Improve the Altimetry Accuracies of Digital Elevation Models

Fusions based on geostatistical methods are used in this article to improve the accuracy of the altimetry attribute of Digital Elevation Models (DEMs). Ordinary kriging, kriging with external drift, regression kriging and cokriging procedures, are applied to assess uncertainty representations from which it is possible to get altimetry predictions and other information. The fusion data modeling is performed from existing DEMs, mainly available for free in the internet, and additional high accurate set of 3D sample points. Although the freeware DEMs are dense and generally have good spatial distributions, the accuracy of their altimetry informationmight not be suitable for many applications. A way of mitigating this problem is to combine, in the data modeling processes, the available DEM data along with additional information coming from various other sources and having better quality. Usually, high accurate altimetry data are collected in field works, with higher cost,at specific point locations inside the spatial region of interest. In short, this work aims to integrate, through geostatistical methods, spatial elevation information of different sources, data structures and elevation accuracies to obtain better accurate DEMs. The methodology addressed in this research was applied to a casestudy in a Brazilian Southeast geographical region. Quantitative and qualitative validations were performed using an independent high accurate data set and comparisons based on DEM differences and drainage network automatic extraction. For the considered study area, the kriging with external drift and the regression kriging have led to similar quantitative and qualitative improvements, better than the co-kriging approach.

Radar Quantitative Precipitation Estimation improvement in Champagne vineyard.

Rainfall has a crucial importance in viticulture, especially in Champagne vineyards, where irrigation is prohibited. Rainfall directly influences the phytosanitary pressure, nitrogen mineralization, flowering conditions, parcel practicability, soil erosion etc… In these conditions, implementing a weather stations network is the solution that the Comité Champagne chose to monitor rainfall all over the Champagne appellation since the 1990's. This networks is actually composed of 42 weather stations implemented in order to have the best spatial coverage as possible. The Comité Champagne also obtain some weather stations data from Météo France, the French national weather service. Even with that network, capturing all rainfall events accurately is difficult, especially in convective cases. Therefore, the interest in radar data has increased, to capture rainfall everywhere. Some tests have been previously made with PANTHERE radar data from Météo France with a resolution of 1 km2, results were promising, but presented inaccuracies particularly in convective events. In this article, we use a radar merging technique similar to the ANTILOPE method from Météo France, with a higher resolution network. The tool employed is the Estimages toolbox merger, based on krigine with external drift (KED) which has been demonstrated to give good results in quantitative precipitation estimation (QPE) improvement.

PROPOSAL OF A GEOSTATISTICAL PROCEDURE FOR TRANSPORTATION PLANNING FIELD

Abstract: The main objective of this study is to estimate variables related to transportation planning, in particular transit trip production, by proposing a geostatistical procedure. The procedure combines the semivariogram deconvolution and Kriging with External Drift (KED). The method consists of initially assuming a disaggregated systematic sample from aggregate data. Subsequently, KED was applied to estimate the primary variable, considering the population as a secondary input. This research assesses two types of information related to the city of Salvador (Bahia, Brazil): an origin-destination dataset based on a home-interview survey carried out in 1995 and the 2010 census data. Besides standing out for the application of Geostatistics in the field of transportation planning, this paper introduces the concepts of semivariogram deconvolution applied to aggregated travel data. Thus far these aspects have not been explored in the research area. In this way, this paper mainly presents three contributions: 1) estimating urban travel data in unsampled spatial locations; 2) obtaining the values of the variable of interest deriving out of other variables; and 3) introducing a simple semivariogram deconvolution procedure, considering that disaggregated data are not available to maintain the confidentiality of individual data.

Impact of Spatiotemporal Characteristics of Rainfall Inputs on Integrated Catchment Dissolved Oxygen Simulations

Integrated Catchment Modelling aims to simulate jointly urban drainage systems, wastewater treatment plant and rivers. The effect of rainfall input uncertainties in the modelling of individual urban drainage systems has been discussed in several studies already. However, this influence changes when simultaneously simulating several urban drainage subsystems and their impact on receiving water quality. This study investigates the effect of the characteristics of rainfall inputs on a large-scale integrated catchment simulator for dissolved oxygen predictions in the River Dommel (The Netherlands). Rainfall products were generated with varying time-aggregation (10, 30 and 60 min) deriving from different sources of data with increasing spatial information: (1) Homogeneous rainfall from a single rain gauge; (2) block kriging from 13 rain gauges; (3) averaged C-Band radar estimation and (4) kriging with external drift combining radar and rain gauge data with change of spatial support. The influence of the different rainfall inputs was observed at combined sewer overflows (CSO) and dissolved oxygen (DO) dynamics in the river. Comparison of the simulations with river monitoring data showed a low sensitivity to temporal aggregation of rainfall inputs and a relevant impact of the spatial scale with a link to the storm characteristics to CSO and DO concentration in the receiving water.

Measurement of Precipitation in the Alps Using Dual-Polarization C-Band Ground-Based Radars, the GPM Spaceborne Ku-Band Radar, and Rain Gauges

The complex problem of quantitative precipitation estimation in the Alpine region is tackled from four different points of view: (1) the modern MeteoSwiss network of automatic telemetered rain gauges (GAUGE); (2) the recently upgraded MeteoSwiss dual-polarization Doppler, ground-based weather radar network (RADAR); (3) a real-time merging of GAUGE and RADAR, implemented at MeteoSwiss, in which a technique based on co-kriging with external drift (CombiPrecip) is used; (4) spaceborne observations, acquired by the dual-wavelength precipitation radar on board the Global Precipitation Measuring (GPM) core satellite. There are obviously large differences in these sampling modes, which we have tried to minimize by integrating synchronous observations taken during the first 2 years of the GPM mission. The data comprises 327 “wet” overpasses of Switzerland, taken after the launch of GPM in February 2014. By comparing the GPM radar estimates with the MeteoSwiss products, a similar performance was found in terms of bias. On average (whole country, all days and seasons, both solid and liquid phases), underestimation is as large as −3.0 (−3.4) dB with respect to RADAR (GAUGE). GPM is not suitable for assessing what product is the best in terms of average precipitation over the Alps. GPM can nevertheless be used to evaluate the dispersion of the error around the mean, which is a measure of the geographical distribution of the error inside the country. Using 221 rain-gauge sites, the result is clear both in terms of correlation and in terms of scatter (a robust, weighted measure of the dispersion of the multiplicative error around the mean). The best agreement was observed between GPM and CombiPrecip, and, next, between GPM and RADAR, whereas a larger disagreement was found between GPM and GAUGE. Hence, GPM confirms that, for precipitation mapping in the Alpine region, the best results are obtained by combining ground-based radar with rain-gauge measurements using a geostatistical approach. The GPM mission is adding significant new coverage to mountainous areas, especially in poorly instrumented parts of the world and at latitudes not previously covered by the Tropical Rainfall Measuring Mission (TRMM). According to this study, one could expect an underestimation of the precipitation product by the dual-frequency precipitation radar (DPR) also in other mountainous areas of the world.