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A Berne - One of the best experts on this subject based on the ideXlab platform.
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Rainfall nowcasting by combining radars microwave links and Rain Gauges
arXiv: Atmospheric and Oceanic Physics, 2018Co-Authors: B Bianchi, Peter Jan Van Leeuwen, Robin J Hogan, A BerneAbstract:The objective of this work is to provide high-resolution Rain rate maps at short lead-time forecasts (nowcasts) necessary to anticipate flooding and properly manage sewage systems in urban areas by combining radars, Rain Gauges, and operational microwave links, and taking into account their respective uncertainties. A variational approach (3D-Var) is used to find the best estimate for the Rain rate, and its error covariance, from the different Rain sensors. Short-term Rain rate forecasts are then produced by assuming Lagrangian persistence. A velocity field is obtained from the operational radar-derived Rain fields, and the Rain rate field is advected using the Total Variance Diminishing (TVD) scheme. The error covariance associated to the estimated Rain rate is also propagated, and we use these two in the 3D-Var at the next observation time step. This approach can be seen as a Variational Kalman Filter (VKF), in which the covariance of the prior is not constant but dependent on time. The proposed approach has been tested using data from 14 Rain Gauges, 14 microwave links and the operational radar Rain product from MeteoSwiss in the area of Zurich (Switzerland). During the applications the assumption of the Lagrangian persistence appears to be valid up to 20 min (a bit longer for stratiform events). During convective events, the algorithm is less powerful and shorter lead times should be considered (i.e., 15 min). Although such lead times are short, they are still useful to various hydrological and outdoor applications.
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measurement of precipitation in the alps using dual polarization c band ground based radars the gpm spaceborne ku band radar and Rain Gauges
Remote Sensing, 2017Co-Authors: Marco Gabella, Peter J Speirs, Ulrich Hamann, Urs Germann, A BerneAbstract: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.
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a variational approach to retrieve Rain rate by combining information from Rain Gauges radars and microwave links
Journal of Hydrometeorology, 2013Co-Authors: B Bianchi, Peter Jan Van Leeuwen, Robin J Hogan, A BerneAbstract:Accurate and reliable Rain rate estimates are important for various hydrometeorological applications. Consequently, Rain sensors of different types have been deployed in many regions. In this work, measurements from different instruments, namely, Rain gauge, weather radar, and microwave link, are combined for the first time to estimate with greater accuracy the spatial distribution and intensity of Rainfall. The objective is to retrieve the Rain rate that is consistent with all these measurements while incorporating the uncertainty associated with the different sources of information. Assuming the problem is not strongly nonlinear, a variational approach is implemented and the Gauss–Newton method is used to minimize the cost function containing proper error estimates from all sensors. Furthermore, the method can be flexibly adapted to additional data sources. The proposed approach is tested using data from 14 Rain Gauges and 14 operational microwave links located in the Zurich area (Switzerland) to correct the prior Rain rate provided by the operational radar Rain product from the Swiss meteorological service (MeteoSwiss). A cross-validation approach demonstrates the improvement of Rain rate estimates when assimilating Rain gauge and microwave link information.
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Quality control of Rain gauge measurements using telecommunication microwave links
Journal of Hydrology, 2013Co-Authors: B Bianchi, J Rieckermann, A BerneAbstract:Accurate Rain rate measurements are essential for many hydrological applications. Although Rain gauge remains the reference instrument for the measurement of Rain rate, the strong spatial and temporal variability of Rainfall makes it difficult to spot faulty Rain Gauges. Due to the poor spatial representativeness of the point Rainfall measurements, this is particularly difficult where their density is low. Taking advantage of the high density of telecommunication microwave links in urban areas, a consistency check is proposed to identify faulty Rain Gauges using nearby microwave links. The methodology is tested on a data set from operational Rain Gauges and microwave links, in Zurich (Switzerland). The malfunctioning of Rain Gauges leading to errors in the occurrence of dry/Rainy periods are well identified. In addition, the gross errors affecting quantitative Rain gauge measurements during Rainy periods, such as blocking at a constant value, random noise and systematic bias, can be detected. The proposed approach can be implemented in real time.
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detection of faulty Rain Gauges using telecommunication microwave links
2011Co-Authors: B Bianchi, J Rieckermann, A BerneAbstract:For urban hydrology, Rainfall intensity must be monitored at a high temporal and spatial resolution, which is often achieved using Rain gauge networks. The strong spatial and temporal variability of Rainfall makes it difficult to spot faulty Rain Gauges, in particular when their density is limited. Measurements from telecommunication microwave links can be used to monitor Rainfall. Taking advantage of the high density of microwave links in urban areas, we propose a method to identify faulty Rain Gauges using neighboring telecommunication microwave links. This method is applied to data collected in Zurich, Switzerland, and is shown to correctly detect Rain Gauges which provide erroneous dry/Rainy periods, which provide random values, and which are blocked at a constant value. It performs less satisfactorily to detect systematic relative bias.
B Bianchi - One of the best experts on this subject based on the ideXlab platform.
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Rainfall nowcasting by combining radars microwave links and Rain Gauges
arXiv: Atmospheric and Oceanic Physics, 2018Co-Authors: B Bianchi, Peter Jan Van Leeuwen, Robin J Hogan, A BerneAbstract:The objective of this work is to provide high-resolution Rain rate maps at short lead-time forecasts (nowcasts) necessary to anticipate flooding and properly manage sewage systems in urban areas by combining radars, Rain Gauges, and operational microwave links, and taking into account their respective uncertainties. A variational approach (3D-Var) is used to find the best estimate for the Rain rate, and its error covariance, from the different Rain sensors. Short-term Rain rate forecasts are then produced by assuming Lagrangian persistence. A velocity field is obtained from the operational radar-derived Rain fields, and the Rain rate field is advected using the Total Variance Diminishing (TVD) scheme. The error covariance associated to the estimated Rain rate is also propagated, and we use these two in the 3D-Var at the next observation time step. This approach can be seen as a Variational Kalman Filter (VKF), in which the covariance of the prior is not constant but dependent on time. The proposed approach has been tested using data from 14 Rain Gauges, 14 microwave links and the operational radar Rain product from MeteoSwiss in the area of Zurich (Switzerland). During the applications the assumption of the Lagrangian persistence appears to be valid up to 20 min (a bit longer for stratiform events). During convective events, the algorithm is less powerful and shorter lead times should be considered (i.e., 15 min). Although such lead times are short, they are still useful to various hydrological and outdoor applications.
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a variational approach to retrieve Rain rate by combining information from Rain Gauges radars and microwave links
Journal of Hydrometeorology, 2013Co-Authors: B Bianchi, Peter Jan Van Leeuwen, Robin J Hogan, A BerneAbstract:Accurate and reliable Rain rate estimates are important for various hydrometeorological applications. Consequently, Rain sensors of different types have been deployed in many regions. In this work, measurements from different instruments, namely, Rain gauge, weather radar, and microwave link, are combined for the first time to estimate with greater accuracy the spatial distribution and intensity of Rainfall. The objective is to retrieve the Rain rate that is consistent with all these measurements while incorporating the uncertainty associated with the different sources of information. Assuming the problem is not strongly nonlinear, a variational approach is implemented and the Gauss–Newton method is used to minimize the cost function containing proper error estimates from all sensors. Furthermore, the method can be flexibly adapted to additional data sources. The proposed approach is tested using data from 14 Rain Gauges and 14 operational microwave links located in the Zurich area (Switzerland) to correct the prior Rain rate provided by the operational radar Rain product from the Swiss meteorological service (MeteoSwiss). A cross-validation approach demonstrates the improvement of Rain rate estimates when assimilating Rain gauge and microwave link information.
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Quality control of Rain gauge measurements using telecommunication microwave links
Journal of Hydrology, 2013Co-Authors: B Bianchi, J Rieckermann, A BerneAbstract:Accurate Rain rate measurements are essential for many hydrological applications. Although Rain gauge remains the reference instrument for the measurement of Rain rate, the strong spatial and temporal variability of Rainfall makes it difficult to spot faulty Rain Gauges. Due to the poor spatial representativeness of the point Rainfall measurements, this is particularly difficult where their density is low. Taking advantage of the high density of telecommunication microwave links in urban areas, a consistency check is proposed to identify faulty Rain Gauges using nearby microwave links. The methodology is tested on a data set from operational Rain Gauges and microwave links, in Zurich (Switzerland). The malfunctioning of Rain Gauges leading to errors in the occurrence of dry/Rainy periods are well identified. In addition, the gross errors affecting quantitative Rain gauge measurements during Rainy periods, such as blocking at a constant value, random noise and systematic bias, can be detected. The proposed approach can be implemented in real time.
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detection of faulty Rain Gauges using telecommunication microwave links
2011Co-Authors: B Bianchi, J Rieckermann, A BerneAbstract:For urban hydrology, Rainfall intensity must be monitored at a high temporal and spatial resolution, which is often achieved using Rain gauge networks. The strong spatial and temporal variability of Rainfall makes it difficult to spot faulty Rain Gauges, in particular when their density is limited. Measurements from telecommunication microwave links can be used to monitor Rainfall. Taking advantage of the high density of microwave links in urban areas, we propose a method to identify faulty Rain Gauges using neighboring telecommunication microwave links. This method is applied to data collected in Zurich, Switzerland, and is shown to correctly detect Rain Gauges which provide erroneous dry/Rainy periods, which provide random values, and which are blocked at a constant value. It performs less satisfactorily to detect systematic relative bias.
L G Lanza - One of the best experts on this subject based on the ideXlab platform.
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performance of post processing algorithms for Rainfall intensity using measurements from tipping bucket Rain Gauges
Atmospheric Measurement Techniques, 2016Co-Authors: Mattia Stagnaro, L G Lanza, Matteo Colli, Pak Wai ChanAbstract:Abstract. Eight Rainfall events recorded from May to September 2013 at Hong Kong International Airport (HKIA) have been selected to investigate the performance of post-processing algorithms used to calculate the Rainfall intensity (RI) from tipping-bucket Rain Gauges (TBRGs). We assumed a drop-counter catching-type gauge as a working reference and compared Rainfall intensity measurements with two calibrated TBRGs operated at a time resolution of 1 min. The two TBRGs differ in their internal mechanics, one being a traditional single-layer dual-bucket assembly, while the other has two layers of buckets. The drop-counter gauge operates at a time resolution of 10 s, while the time of tipping is recorded for the two TBRGs. The post-processing algorithms employed for the two TBRGs are based on the assumption that the tip volume is uniformly distributed over the inter-tip period. A series of data of an ideal TBRG is reconstructed using the virtual time of tipping derived from the drop-counter data. From the comparison between the ideal gauge and the measurements from the two real TBRGs, the performances of different post-processing and correction algorithms are statistically evaluated over the set of recorded Rain events. The improvement obtained by adopting the inter-tip time algorithm in the calculation of the RI is confirmed. However, by comparing the performance of the real and ideal TBRGs, the beneficial effect of the inter-tip algorithm is shown to be relevant for the mid–low range (6–50 mm h−1) of Rainfall intensity values (where the sampling errors prevail), while its role vanishes with increasing RI in the range where the mechanical errors prevail.
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metrological analysis of a gravimetric calibration system for tipping bucket Rain Gauges
Meteorological Applications, 2015Co-Authors: Marcio Antonio Aparecido Santana, Patricia L O Guimaraes, L G Lanza, Emanuele VuerichAbstract:The monitoring of hydro-meteorological variables for operational and research purposes requires accurate measurements. The reliability of such measurements varies depending on the need to meet different requirements in several sectors, including aviation, agriculture, civil protection, entertainment and weather forecast. In the case of Rain Gauges, many factors and variables affect the measurement of liquid and solid precipitation in the field. Calibration is a primary tool for quality control and requires suitable infrastructure to perform the tests, and is done by using properly evaluated testing methods and procedures. The current study presents the analysis of a typical calibration system for tipping-bucket Rain Gauges, using the gravimetric method, in accordance with the recommendations and requirements of both meteorology and metrology. As a result, the uncertainty contribution of each component of the system and an assessment of the resulting overall uncertainty budget are obtained.
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high resolution performance of catching type Rain Gauges from the laboratory phase of the wmo field intercomparison of Rain intensity Gauges
Atmospheric Research, 2009Co-Authors: L G Lanza, Luigi StagiAbstract:Abstract The WMO Field Intercomparison of Rainfall Intensity (RI) Gauges started on October, 1st 2007 at Vigna di Valle (Italy) and was concluded in May 2009. Those catching type instruments, out of the selected Rain Gauges based on various measuring principles, and the four Rain Gauges selected as reference instruments to be installed in a pit, were preliminarily calibrated in the laboratory before their final installation at the Field Intercomparison site. The recognized WMO laboratory at the University of Genoa was involved in this task, using the same standard tests adopted for the previously held WMO Laboratory Intercomparison of RI Gauges. Further tests were performed to investigate the one-minute performance of the involved instruments. The present paper deals with basically Tipping-Bucket Rain Gauges (TBRs) and Weighing Gauges (WGs), using results from tests performed under constant flow rates in laboratory conditions. The objective of this initial phase of the Intercomparison was to single out the counting errors associated with each instrument, so as to help the understanding of the measured differences between instruments in the field during the second phase. Results and comments on the preliminary laboratory calibration exercise are reported in this paper together with their implications for the analysis of the outcome of the Intercomparison in the Field.
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the wmo field intercomparison of Rain intensity Gauges
Atmospheric Research, 2009Co-Authors: L G Lanza, E VuerichAbstract:Abstract The first Field Intercomparison of Rainfall Intensity (RI) Gauges was organised by WMO (the World Meteorological Organisation) from October 2007 to April 2009 in Vigna di Valle, Rome (Italy). The campaign is held at the Centre of Meteorological Experimentations (ReSMA) of the Italian Meteorological Service. A group of 30 previously selected Rain Gauges based on different measuring principles are involved in the Intercomparison. Installation of the instruments in the field was preceded by the laboratory calibration of all submitted catching-type Rain Gauges at the University of Genoa. Additional meteorological sensors (ancillary information) and the observations and measurements performed by the Global Climate Observing System/Global Atmosphere Watch (GCOS/GAW) meteorological station of Vigna di Valle were analyzed as metadata. All catching-type Gauges were tested after installation using a portable calibration device specifically developed at the University of Genoa, simulating an ordinary calibration inspection in the field. This paper is dedicated to the summary of preliminary results of the Intercomparison measurements. It offers a view on the main achievements expected from the Intercomparison in evaluating the performance of the instruments in field conditions. Comparison of several Rain Gauges demonstrated the possibility to evaluate the performance of RI Gauges at one-minute resolution in time, as recommended by the WMO Commission for Instruments and Methods of Observations (WMO-CIMO). Results indicate that synchronised tipping-bucket Rain Gauges (TBR), using internal correction algorithms, and weighing Gauges (WG) with improved dynamic stability and short step response are the most accurate Gauges for one-minute RI measurements, since providing the lowest measurement uncertainty with respect to the assumed working reference.
Henrik Madsen - One of the best experts on this subject based on the ideXlab platform.
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probabilistic online runoff forecasting for urban catchments using inputs from Rain Gauges as well as statically and dynamically adjusted weather radar
Journal of Hydrology, 2014Co-Authors: Roland Lowe, Soren Liedtke Thorndahl, Peter Steen Mikkelsen, Michael R Rasmussen, Henrik MadsenAbstract:Summary We investigate the application of Rainfall observations and forecasts from Rain Gauges and weather radar as input to operational urban runoff forecasting models. We apply lumped Rainfall runoff models implemented in a stochastic grey-box modelling framework. Different model structures are considered that account for the spatial distribution of Rainfall in different degrees of detail. Considering two urban example catchments, we show that statically adjusted radar Rainfall input improves the quality of probabilistic runoff forecasts as compared to input based on Rain gauge observations, although the characteristics of these radar measurements are rather different from those on the ground. Data driven runoff forecasting models can to some extent adapt to bias of the Rainfall input by model parameter calibration and state-updating. More detailed structures in these models provide improved runoff forecasts compared to the structures considering mean areal Rainfall only. A time-dynamic adjustment of the radar data to Rain gauge data provides improved Rainfall forecasts when compared with Rainfall observations on the ground. However, dynamic adjustment reduces the potential for creating runoff forecasts and in fact also leads to reduced cross correlation between radar Rainfall and runoff measurements. We conclude that evaluating the performance of radar Rainfall adjustment against Rain Gauges may not always be adequate and that adjustment procedure and online runoff forecasting should ideally be considered as one unit.
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quantification of the spatial variability of Rainfall based on a dense network of Rain Gauges
Atmospheric Research, 2010Co-Authors: Lisbeth Pedersen, Niels Einar Jensen, L Christensen, Henrik MadsenAbstract:Abstract The spatial variability of Rainfall within a single Local Area Weather Radar (LAWR) pixel of 500 × 500 m is quantified based on data from two locations. The work was motivated by the need to quantify the variability on this scale in order to provide an estimate of the uncertainty of using a single Rain gauge for calibrating the LAWR. A total of nine Rain Gauges were used, each representing one-ninth of the 500 × 500 m area. The analysis was carried out based on a dataset obtained using tipping bucket Gauges during the summer and fall of 2007 and 2008, and the results were compared with results from an earlier campaign in 2003. The fact that the 2007–2008 dataset was almost four times larger than the original dataset from 2003 motivated this extended study. Two methods were used to describe the variability: the coefficient of variation and the spatial correlation structure of the Rainfall field. Despite the small area of 0.25 km 2 , accumulated Rainfall was found to vary significantly within individual events with durations ranging from 5 min to 13 h. The coefficient of variation was found to range from 1–26% in the 2007–2008 dataset and in some special cases even higher. The 95% prediction interval for a given Rainfall depth is estimated and can be used to address the uncertainty of using a single Rain gauge to represent the Rainfall within a 500 × 500 m area.
Witold F Krajewski - One of the best experts on this subject based on the ideXlab platform.
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empirically based modeling of spatial sampling uncertainties associated with Rainfall measurements by Rain Gauges
Advances in Water Resources, 2008Co-Authors: Gabriele Villarini, Witold F KrajewskiAbstract:Abstract In the quantitative evaluation of radar-Rainfall products (maps), Rain gauge data are generally used as a good approximation of the true ground Rainfall. However, Rain Gauges provide accurate measurements for a specific location, while radar estimates represent areal averages. Because these sampling discrepancies could introduce noise into the comparisons between these two sensors, they need to be accounted for. In this study, the spatial sampling error is defined as the ratio between the measurements by a single Rain gauge and the true areal Rainfall, defined as the value obtained by averaging the measurements by an adequate number of Gauges within a pixel. Using a non-parametric scheme, the authors characterize its full statistical distribution for several spatial (4, 16 and 36 km2) and temporal (15 min and hourly) scales. To accomplish this task, a large dataset (more than six years) of Rain gauge measurements obtained through a highly dense Rain gauge network deployed in the Brue catchment in southwest England is used. The authors show that the standard deviation of the spatial sampling error decreases with increasing Rainfall intensity and accumulation time and increases with increasing pixel size. Additionally, the authors show how the Laplace distribution could be used to model the distribution of spatial sampling errors for the spatial and temporal scales considered in this study.
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simulation of airflow around Rain Gauges comparison of les with rans models
Advances in Water Resources, 2007Co-Authors: George Constantinescu, Witold F Krajewski, Celalettin E Ozdemir, Talia TokyayAbstract:Abstract Wind is responsible for systematic errors that affect Rain gauge measurements. The authors investigate the use of computational fluid dynamics (CFD) to calculate airflow around Rain Gauges by applying a high-resolution large eddy simulation (LES) model to determine the flow fields around a measuring system of two Rain Gauges. The simulated air flow field is characterized by the presence of massive separation which induces the formation and shedding of highly unsteady eddies in the detached shear layers and wakes. Parts of these detached structures occur over the orifice of the Rain Gauges and may substantially affect the dynamics of the Raindrops in this critical region. Non-dissipative LES methods used with fine enough meshes can successfully predict these eddies and their associated fluctuations. The authors compare statistics from LES with steady-state Reynolds averaged Navier–Stokes (RANS) simulations using the k – e and shear stress transport k – ω turbulence models. They find that both RANS and LES models predict similar mean velocity distributions around the Rain Gauges. However, they determine the distribution of the resolved turbulent kinetic energy (TKE) to be strongly dependent on the RANS model used. Neither RANS model predictions of TKE are close to those of LES. The authors conclude that the failure of RANS to predict TKE is an important limitation, as TKE is needed to scale the local velocity fluctuations in stochastic models used to calculate the motion of Raindrops in the flow field.
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uncertainty of monthly Rainfall estimates from Rain Gauges in the global precipitation climatology project
Water Resources Research, 1998Co-Authors: Jeffrey R Mccollum, Witold F KrajewskiAbstract:The Global Precipitation Climatology Project (GPCP), initiated by the World Climate Research Program, has as a main objective the production of monthly global Rainfall estimates on a 2.5° × 2.5° longitude/latitude grid by combining different sources of information such as satellite remote sensing and Rain Gauges. It is important to understand the accuracy of the Rain gauge estimates of mean area Rainfall because these are considered the most reliable estimates for regions with high numbers of Gauges. Methods to model the error resulting from using Rain gauge data to estimate the spatially averaged Rainfall accumulation have been developed previously. Three of these methods are investigated in this study: an empirical equation derived from Rain gauge data, an analytical equation derived from statistical concepts, and an empirical equation derived from theory with parameters determined from calibration. Simulations are performed to determine the utility of these three approaches in estimating the error of the Rain gauge mean in the context of the GPCP.
