Sampling Errors In Estimation Research Articles

Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands

Abstract. The Goddard Profiling algorithm (GPROF) converts radiometer observations from Global Precipitation Measurement (GPM) constellation satellites into precipitation estimates. Typically, high-quality ground-based estimates serve as reference to evaluate GPROF's performance. To provide a fair comparison, the ground-based estimates are often spatially aligned to GPROF. However, GPROF combines observations from various sensors and channels, each associated with a distinct footprint. Consequently, uncertainties related to the representativeness of the sampled areas are introduced in addition to the uncertainty when converting brightness temperatures into precipitation intensities. The exact contribution of resampling precipitation estimates, required to spatially and temporally align different resolutions when combining or comparing precipitation observations, to the overall uncertainty remains unknown. Here, we analyze the current performance of GPROF over the Netherlands during a 4-year period (2017–2020) while investigating the uncertainty related to sampling. The latter is done by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETHs) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. Spatial averaging methods (Gaussian weighting vs. arithmetic mean) minimally affect the magnitude of the precipitation estimates. Footprint size has a higher impact but cannot explain all discrepancies between the ground- and satellite-based estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETHs, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETHs. Lastly, our results do not show a clear difference between coastal and land simulations. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the discrepancies between satellite- and ground-based precipitation estimates. Hence, uncertainties related to the retrieval algorithm and environmental conditions are found to be more prominent than resampling uncertainties, in particular for shallow and light precipitation.

How Sampling Errors in Covariance Estimates Cause Bias in the Kalman Gain and Impact Ensemble Data Assimilation

Abstract Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA, and, surprisingly, we find that it depends directly on the number of independent observations but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model. Significance Statement The dampening of long-range correlations has been the key to the success of ensemble data assimilation in global numerical weather prediction. In this paper, we show how noise in covariance estimates propagates through the state estimation process and corrupts state estimates. We show that this noise results in a bias and that this bias depends on the number of observations and not, as might be expected, on the state dimension. We go on to show how dampening long-range covariances through a process referred to as “localization” helps to mitigate the detrimental effects of this sampling noise.

An analytical approach to confidence interval estimation of river microplastic sampling

Microplastics (MPs), plastic particles <5 mm in diameter, are emerging ubiquitous pollutants in natural environments, including freshwater ecosystems. As rivers facilitate efficient transport among landscapes, monitoring is crucial for elucidating the origin, dynamics, and fate of MPs. However, standardized methodologies for in situ sampling in freshwater environments remain undefined to date. Specifically, evaluating the sampling error of MP concentration estimates is crucial for comparing results among studies. This study proposes a novel method for computing confidence intervals (CIs) from a single estimate of numerical concentration (expressed in particles·m−3). MPs are expected to disperse according to purely random processes, such as turbulent diffusion, and to consequently exhibit a random distribution pattern that follows a Poisson point process. Accordingly, the present study introduced a framework based on the Poisson point process to compute CIs, which were validated using MP samples from two urban rivers in Chiba, Japan, obtained using a mesh with an opening size of 335 μm. Random number simulations revealed that the CIs were applicable when ≥10 MPs were present in a sample. Further, when ≥50 MPs were present in a sample, the sampling error (95% CI) was within ±30% of the concentration estimates. The proposed framework allows for the intercomparison of single river MP samples despite the lack of sample replicates. Further, the present study emphasizes that the volume of sampled river water is the only controllable parameter that can reduce the sampling error.

About the assessment of sampling error and representative sample in X-ray fluorescence analysis of mineral powders

The assessment of laboratory sampling errors in X-ray fluorescence analysis (XRF) of mineral powders ground to a particle size less than 75 μm is important when analyzing specimen portions less than 100–300 mg. The sampling error can be found from Poisson statistics, which allows one to estimate the variations in the number of mineral grains containing an analyte in a powder sample of a fixed mass. However, the absorption of X-ray radiation in the sample imposes a limitation on the analyzed mass due to the low penetration depth of X-rays, especially in the energy region below 4 keV.In this paper, we propose the sampling error estimation based on Poisson statistics taking into account the size of powder particles in comparison with the penetration depth of X-rays. Two limiting cases are considered, when the penetration depth is greater or less than the size of the grains containing the analyte. Zircon, ilmenite, and quartz were considered as grains containing the analyte, and boric acid and clay were taken as matrices. Measurements were carried out with wavelength dispersive XRF spectrometer. It was shown that the sampling errors obtained experimentally for different model mixtures are close to the sampling errors calculated using Poisson statistics. The described model of quartz inclusions in clay matrix was used to explain the relatively low sampling error in determining the main components Al2O3, SiO2, K2O in course-grain Neolithic ceramics.

A 2.5° × 2.5° gridded drought/flood grades dataset for eastern China during the last millennium

Hydroclimate reconstruction for the last millennium is essential to understand the differences in hydroclimate extremes and their causes under cold/warm conditions. In this study, the first gridded drought/flood grades (D/F grades) dataset in eastern China (EC) during the last millennium was generated. This D/F grades dataset mainly consisted of two components. The first section was created by interpolating drought/flood grades from 1500 to 2000 using the angular distance weight method. Sampling error estimates were employed to assess the effects of the interpolated dataset. The second section for the D/F grades dataset during 960–1500 was generated by constructing best subset regression models using selected tree-ring chronologies in the United States through atmospheric teleconnection. The validation parameters of the calibration equations were also derived, including the adjusted R2, predicted R2, RE, and CE. This dataset provides critical support for investigating the characteristics and causes of hydroclimate extremes in EC at various spatiotemporal scales, as well as the relationship with climate modes, such as El Niño-Southern Oscillation, Pacific Decadal Oscillation, and East Asia Summer Monsoon.

Assessment of greenhouse emissions of the green bean through the static enclosure technique

Urban green installations are extensively promoted to increase sustainable and accessible food production and simultaneously improve the environmental performance and liveability of city buildings. In addition to the multiple benefits of plant retrofitting, these installations may lead to a consistent increase in biogenic volatile organic compounds (BVOCs) in the urban environment, especially indoors. Accordingly, health concerns could limit the implementation of building-integrated agriculture. In a building-integrated rooftop greenhouse (i-RTG), throughout the whole hydroponic cycle, green bean emissions were dynamically collected in a static enclosure. Four representative BVOCs, α-pinene (monoterpene), β-caryophyllene (sesquiterpene), linalool (oxygenated monoterpene) and cis-3-hexenol (LOX derivate), were investigated in the samples collected from two equivalent sections of a static enclosure, one empty and one occupied by the i-RTG plants, to estimate the volatile emission factor (EF). Throughout the season, extremely variable BVOC levels between 0.04 and 5.36 ppb were found with occasional but not significant (P > 0.05) variations between the two sections. The highest emission rates were observed during plant vegetative development, with EFs equivalent to 78.97, 75.85 and 51.34 ng g−1 h−1 for cis-3-hexenol, α-pinene, and linalool, respectively; at plant maturity, all volatiles were either close to the LLOQ (lowest limit of quantitation) or not detected. Consistent with previous studies significant relationships (r ≥ 0.92; P < 0.05) were individuated within volatiles and temperature and relative humidity of the sections. However, correlations were all negative and were mainly attributed to the relevant effect of the enclosure on the final sampling conditions. Overall, levels found were at least 15 folds lower than the given Risk and LCI values of the EU-LCI protocol for indoor environments, suggesting low BVOC exposure in the i-RTG. Statistical outcomes demonstrated the applicability of the static enclosure technique for fast BVOC emissions survey inside green retrofitted spaces. However, providing high sampling performance over entire BVOCs collection is recommended to reduce sampling error and incorrect estimation of the emissions.

Impact of the High-Emission Event Duration and Sampling Frequency on the Uncertainty in Emission Estimates

Short duration remote sensing measurements of methane emissions from oil and gas operations are being deployed at a large scale, but interpretation of these snapshot measurements is complex due to the spatial and temporal variability of methane emissions. The accuracy and precision of annual emission estimates extrapolated from short duration measurements depend on the measurement frequency and complexity of temporal emission patterns. This work examines sampling uncertainties associated with short duration measurements of varying frequencies for methane emissions from a group of sites representing the Barnett Shale region. Routine, frequent emissions are accurately captured with minimal bias through semiannual sampling; however, infrequent high-emission rate events increase the error associated with annual emission estimates, even under the assumption of no measurement uncertainty. If emission events have a duration of ≤1 week, monthly sampling has an estimated sampling error of >15%. For quarterly sampling with emission events that persist for ≤1 month, the sampling error is >30%. There is also negative bias associated with quarterly, semiannual, and annual sampling, which suggests infrequent campaigns may be systemically underestimating emissions. The sampling error increases as the duration of the high-emission events becomes shorter, making the temporal persistence of emission events an important factor in designing measurement protocols.

Client Racial Composition in First-Episode Psychosis Programs Compared With Compositions in Program Service Areas.

The authors examined the extent to which clients served by first-episode psychosis programs reflected the racial composition of the surrounding service area and, to the extent that they did not, explored possible explanatory factors. As part of a national study of coordinated specialty care (CSC) sites in the United States, 35 programs documented race for 772 clients. Programs identified a geographic service area for their clients. Using Census data, the authors identified the proportion of clients in this service area who were Black and then examined the extent of disproportionality, calculated as a risk ratio and as a relative difference in racial composition between CSC programs and their service areas. Overall, 71% of CSC programs had a disproportionately greater proportion of Black clients than Black residents within the service area. This disproportionality was still evident after conducting sensitivity analyses that included adjusting for sampling error in the service area population estimates; however, smaller study sites displayed greater fluctuations in disproportionality in the sensitivity analyses. Using data from diverse CSC programs, the authors illustrate that the odds of Blacks receiving services through a CSC program are much higher than would be expected on the basis of the population living in the area being served by the program. Multiple reasons may explain this finding, but in the absence of clear explanatory factors, this result may be ripe for discussion and further investigation.

Uncertainty and Consistency Assessment in Multiple Microplastic Observation Datasets in the Baltic Sea

This paper aims to quantify data uncertainties in marine microplastic measurements, including spatiotemporal sampling error and sample volume estimation error, identify impacts of varying mesh sizes, sampling and analysis methods, and evaluate consistency in multiple microplastic observation datasets. Twenty-seven datasets on surface marine microplastics with particle size &amp;gt;100 µm in the Baltic Sea are compiled. Results show that the trawl datasets have a spatiotemporal sampling error of 25% for microlitter concentration, 36% for microplastic fiber concentrations and 40-56% for microplastic particle concentration. By taking surface currents and wave-induced Stokes drift into account, the sample volume of the trawl measurements is corrected, leading to a mean microplastic concentration correction of 12%. The differences of microplastic concentration between datasets with varying mesh sizes from 100 – 500 µm are not statistically significant. Analysis methods, however, can lead to significant differences in microplastic datasets. The dataset consistency is further examined among the three dataset categories using trawl, pump and bulk sampling techniques. It is found that an individual dataset is often self-consistent. Most of the datasets within one monitoring category are more consistent than those from different categories. More than 70% of the datasets within individual categories are consistent, which have mean microplastic concentration significantly smaller than the rest of the datasets. Significant inconsistencies are identified between different data categories. Six out of eight highest relative standard deviations are found in the pump and bulk datasets. The median value of the mean microplastic concentration from the 10 pump datasets is about 4.5 times as much as that of the 14 trawl datasets, both for fiber and non-fiber particles. Significant differences are also identified on microplastic fiber fraction in different dataset categories. Two thirds of the 13 bulk and pump datasets have a microplastic fiber fraction &amp;gt;85% while the 14 trawl datasets show much lower microplastic fiber fractions between 45-70%. In addition, the particle collection efficiency, potential leakage of particles with irregular shapes, clogging, the false zero samples and related lower limit of the detectable microplastic concentration for given sampling methods and water environment, are also discussed.

A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.

Estimating Sampling Errors in Consumer Price Indices

SummarySeveral different approaches have been proposed for the calculation of sampling errors in consumer price indices (CPIs), but only one is in regular use providing published estimates. The history of the development of sampling error estimation for CPIs is reviewed, and the development in the USA (where most research has taken place) is summarised. The proposed approaches for sampling error estimation are set out, with their properties and their different conceptualisations of the sampling error. The frameworks for the measurement of errors more widely in the CPI are also reviewed, and the evidence for the importance of the sampling error among the other error sources is presented. Some opportunities and directions for further work are provided.

Using American Community Survey Data to Improve Estimates from Smaller U.S. Surveys Through Bivariate Small Area Estimation Models

Abstract We demonstrate the potential for borrowing strength from estimates from the American Community Survey (ACS), the largest US household survey, to improve estimates from smaller US household surveys. We do this using simple bivariate area-level models to exploit strong relationships between population characteristics estimated by the smaller surveys and ACS estimates of the same, or closely related, quantities. We illustrate this idea with two applications. The first shows impressive variance reductions for state estimates of health insurance coverage from the National Health Interview Survey when modeling these jointly with corresponding ACS estimates. The second application shows impressive variance reductions in ACS one-year county estimates of poverty of school-aged children from modeling these jointly with previous ACS five-year county estimates of school-age poverty. Simple theoretical calculations show how the amount of variance reduction depends on characteristics of the underlying data. In our applications, we examine three alternative bivariate models, starting with a simple bivariate Gaussian model. Since our applications involve modeling proportions, we also examine a bivariate binomial logit normal model, and an unmatched model that combines the Gaussian sampling model with the bivariate logit normal model for the population proportions. Given the strong relationships between the population characteristics estimated by the smaller surveys and the corresponding ACS estimates, and the low levels of sampling error in the ACS estimates, the models achieve large variance reductions even without using regression covariates drawn from auxiliary data sources.

Sampling nesting sea turtles: optimizing survey design to minimize error

Many sea turtle studies globally use counts of nesting activities as a proxy for population abundance estimates and as an indicator of trends within the population. Often these populations are sampled temporally and spatially, but few previous studies have examined the impact of different sampling techniques on the accuracy of these estimates. We investigated temporal sampling errors using a multi-species approach, examining 10 populations comprising green, loggerhead and leatherback sea turtles. Sampling errors were investigated from random, regular and continuous sampling regimes spanning 5-80% coverage. A count approach was used rather than an individual-based capture-mark-recapture approach to broaden the scope and application of the research. Modelling showed that even low survey coverage of 5% gave reasonably accurate estimates of annual nesting activity, with estimated errors of ca. 20% (mean + 1 SD equalling 84.1% of surveys). Survey error is low relative to changes in abundance from the inter-annual variations in nesting activity that occur in sea turtle populations. Thus, annual studies are important to estimate sea turtle abundance, even if these studies have low survey coverage. An increase in survey effort may be more cost effective if spent combining estimates of total nesting activity with sampling turtles as part of a capture-mark-recapture study. This approach will provide a second estimate of annual abundance as well as an estimate of demographic parameters including clutch frequencies, remigration intervals, survivorship, immigration and emigration. This estimation of sampling errors may be useful in the design of monitoring programmes and can be used to guide management and policy decisions.

Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture-recapture data.

Population dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density dependence. This is typically addressed using state‐space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture–recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state‐space models. We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state‐space models for density‐dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model for a closed population, using a conditional multinomial likelihood, followed by a Horvitz–Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R‐package VGAM, for different parameter specifications. The methods were then applied to a real data set of gray‐sided voles Myodes rufocanus from Northern Norway. We found that density‐dependence estimation was improved when explicitly modeling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.

Using Pooled Heteroskedastic Ordered Probit Models to Improve Small-Sample Estimates of Latent Test Score Distributions

This article describes an extension to the use of heteroskedastic ordered probit (HETOP) models to estimate latent distributional parameters from grouped, ordered-categorical data by pooling across multiple waves of data. We illustrate the method with aggregate proficiency data reporting the number of students in schools or districts scoring in each of a small number of ordered “proficiency” levels. HETOP models can be used to estimate means and standard deviations of the underlying (latent) test score distributions but may yield biased or very imprecise estimates when group sample sizes are small. A simulation study demonstrates that the pooled HETOP models described here can reduce the bias and sampling error of standard deviation estimates when group sample sizes are small. Analyses of real test score data demonstrate the use of the models and suggest the pooled models are likely to improve estimates in applied contexts.

Estimation of extremes of non-Gaussian wind pressure on building roof: Sampling error in moment-based translation process model with no monotonic limit

Estimation of extremes of non-Gaussian wind pressure on building roof is necessary for cladding design. When limited length of non-Gaussian wind pressure is used for calculation, the estimated extreme involves sampling error. The moment-based Hermite polynomial model is extensively applied for estimation of extreme wind pressure due to the straightforwardness and accuracy, however, Hermite polynomial model has a monotonic limit resulting in a restricted application region of skewness and kurtosis combination. However, another two moment-based translation process models with no monotonic limit including Johnson transformation model and piecewise Hermite polynomial model have attracted some attention as these two models can be applied to a broader region of skewness and kurtosis combination. The sampling error in estimation of extremes of non-Gaussian wind pressure on building roof by Hermite polynomial model is proposed in the literature recently. Nevertheless, the sampling errors in Johnson transformation model and piecewise Hermite polynomial model have not been addressed. In this study, sampling errors in estimation of extremes of non-Gaussian wind pressures by Johnson transformation model are investigated. Formulations for estimating sampling errors of newly defined statistical moments and subsequent extremes in piecewise Hermite polynomial model are presented. The performance of sampling errors in Hermite polynomial model, Johnson transformation model, and piecewise Hermite polynomial model are finally compared with each other. Based on very long wind pressures from wind tunnel tests, it is shown that the sampling error of minimum wind pressure (suction) in Hermite polynomial model is generally the smallest compared to Johnson transformation model and piecewise Hermite polynomial model, while that of maximum wind pressure in piecewise Hermite polynomial model seems to be the smallest.

Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications

In this paper, the potential benefits of quasi-Monte Carlo (QMC) methods for uncertainty propagation are assessed via two applications: a numerical case study and a realistic and complex building physical case study. The sampling efficiency of four quasi-Monte Carlo sampling strategies — Optimized Latin hypercube, Sobol’ sequence, Niederreiter–Xing sequence and lattice sequence — are quantified and the errors of these quasi-Monte Carlo methods are estimated. In addition, for getting a better understanding of the potential factors that may influence the performance of quasi-Monte Carlo methods, the effect of the different parameters and the smoothness of the target function for the building physical case study are investigated for two quantities of interest. A sensitivity analysis is performed in which first and total order Sobol’ indices are calculated and a kernel smoother is used to show the effect on the quantity of interest for certain input parameters. The outcomes show that quasi-Monte Carlo methods perform at least as well as the Monte Carlo method and are capable of outperforming the standard Monte Carlo method if the target function is sufficiently smooth and mainly depends on a limited number of parameters.

Approximation of multivariate periodic functions based on sampling along multiple rank-1 lattices

In this work, we consider the approximate reconstruction of high-dimensional periodic functions based on sampling values. As sampling schemes, we utilize so-called reconstructing multiple rank-1 lattices, which combine several preferable properties such as easy constructability, the existence of high-dimensional fast Fourier transform algorithms, high reliability, and low oversampling factors. Especially, we show error estimates for functions from Sobolev Hilbert spaces of generalized mixed smoothness. For instance, when measuring the sampling error in the L2-norm, we show sampling error estimates where the exponent of the main part reaches those of the optimal sampling rate except for an offset of 1∕2+ε, i.e., the exponent is almost a factor of two better up to the mentioned offset compared to single rank-1 lattice sampling. Various numerical tests in medium and high dimensions demonstrate the high performance and confirm the obtained theoretical results of multiple rank-1 lattice sampling.

Evaluation of GPM sampling error over major basins in Indian subcontinent using bootstrap technique

The study investigates the evaluation and comparison of sampling error for the Global Precipitation Measurement (GPM) mission orbital data products by implementing a bootstrap technique over the two major basins in the Indian subcontinent i.e the Ganga and the Mahanadi basin. The relative sampling error evaluated over both the Ganges and Mahanadi basins showed commendable results thus giving the confidence to adopt the bootstrap technique to evaluate the sampling error. The region over India with large seasonal rainfall seems to have less sampling uncertainty and vice versa with some regions showing exceptions which might be due to the difference in precipitation variability and space-time correlation length. The scale dependence was verified for four grid sizes along with seasonal time scale. Results indicate that the relative sampling error estimates are inversely proportional to the scale of the grid size. The comparative study of evaluation of sampling uncertainty to different precipitation types resulted to have maximum sampling error in GPM Microwave Imager (GMI) in comparison to Dual Precipitation Radar (DPR) convective and DPR total precipitation. Thus, the comparable results of sampling uncertainty between the major basins in the Indian sub-continent provides the user a decision making criteria before utilizing the GPM orbital products in any applications.

Bias and sampling errors in estimation of extremes of non-Gaussian wind pressures by moment-based translation process models

Accurate estimation of extremes of non-Gaussian wind pressures is important for cladding design. The translation process model approach is often used to calculate extreme value distribution of a non-Gaussian process. The translation model can be given as moment-based Hermite polynomial function or derived from three-parameter Gamma distribution with mapping of cumulative distribution functions. This study investigates bias and sampling errors of these two approaches. The prediction error or bias is examined using wind pressure data of a saddle type roof. A refined three-parameter Gamma distribution is proposed and its improved performance is demonstrated. This study also presents formulations for estimating sampling errors of the first four statistical moments. By using non-Gaussian moment closure technique, the sampling errors of statistical moments are estimated from the first four moments. The sampling errors in the estimated extremes of wind pressures are further predicted analytically based on First Order Second Moment (FOSM) method. The proposed framework is different from those presented in literature, where the sampling errors of skewness and kurtosis are calculated by multiple time history samples which are often not available. The accuracy and effectiveness of the proposed framework are examined using the wind pressure data with a very long duration.