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This article discusses nonparametric estimation of a survival function in the presence of measurement errors on the observation of the failure time of interest. One situation where such issues arise would be clinical studies of chronic diseases where the observation on the time to the failure event of interest such as the onset of the disease relies on patient recall or chart review of electronic medical records. It is easy to see that both situations can be subject to measurement errors. To resolve this problem, we propose a simulation extrapolation approach to correct the bias induced by the measurement error. To overcome potential computational difficulties, we use spline regression to approximate the unspecified extrapolated coefficient function of time, and establish the asymptotic properties of our proposed estimator. The proposed method is applied to nonparametric estimation based on interval‐censored data. Extensive numerical experiments involving both simulated and actual study datasets demonstrate the feasibility of this proposed estimation procedure. [ABSTRACT FROM AUTHOR]

For high-dimensional classification, interpolation of training data manifests as the data piling phenomenon, in which linear projections of data vectors from each class collapse to a single value. Recent research has revealed an additional phenomenon known as the 'second data piling' for independent test data in binary classification, providing a theoretical understanding of asymptotically perfect classification. This paper extends these findings to multi-category classification and provides a comprehensive characterization of the double data piling phenomenon. We define the maximal data piling subspace, which maximizes the sum of pairwise distances between piles of training data in multi-category classification. Furthermore, we show that a second data piling subspace that induces data piling for independent data exists and can be consistently estimated by projecting the negatively-ridged discriminant subspace onto an estimated 'signal' subspace. By leveraging this second data piling phenomenon, we propose a bias-correction strategy for class assignments, which asymptotically achieves perfect classification. The present research sheds light on benign overfitting and enhances the understanding of perfect multi-category classification of high-dimensional discrimination with a help of high-dimensional asymptotics. [ABSTRACT FROM AUTHOR]

High-resolution temperature forecasting plays a crucial role in various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. The existing low-resolution forecast data may not accurately capture the fine-grained temperature patterns required for localized predictions. These forecasts may contain biases that need to be corrected for accurate results. Therefore, there is a need for an effective framework that can downscale low-resolution forecast data and correct biases to generate high-resolution temperature forecasts. The paper proposes a machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting. The framework utilizes low-resolution forecast data from the European Centre for Medium-Range Weather Forecasts and real-time 1km analysis product data from the National Meteorological Administration, to generate high-resolution 1km forecast temperature data. The framework consists of four modules: data acquisition module, data preprocessing module, downscaling and correction model module, and post-processing and visualization module. Through experiments, it demonstrated that the framework has superior performance and potential in meteorological data downscaling and correction and can be used to achieve real-time high-resolution temperature forecasting, which has important significance for various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. [ABSTRACT FROM AUTHOR]

Allele-specific expression plays a crucial role in unraveling various biological mechanisms, including genomic imprinting and gene expression controlled by cis -regulatory variants. However, existing methods for quantification from RNA-sequencing (RNA-seq) reads do not adequately and efficiently remove various allele-specific read mapping biases, such as reference bias arising from reads containing the alternative allele that do not map to the reference transcriptome or ambiguous mapping bias caused by reads containing the reference allele that map differently from reads containing the alternative allele. We present Ornaments, a computational tool for rapid and accurate estimation of allele-specific transcript expression at unphased heterozygous loci from RNA-seq reads while correcting for allele-specific read mapping biases. Ornaments removes reference bias by mapping reads to a personalized transcriptome and ambiguous mapping bias by probabilistically assigning reads to multiple transcripts and variant loci they map to. Ornaments is a lightweight extension of kallisto, a popular tool for fast RNA-seq quantification, that improves the efficiency and accuracy of WASP, a popular tool for bias correction in allele-specific read mapping. In experiments with simulated and human lymphoblastoid cell-line RNA-seq reads with the genomes of the 1000 Genomes Project, we demonstrate that Ornaments improves the accuracy of WASP and kallisto, is nearly as efficient as kallisto, and is an order of magnitude faster than WASP per sample, with the additional cost of constructing a personalized index for multiple samples. Additionally, we show that Ornaments finds imprinted transcripts with higher sensitivity than WASP, which detects imprinted signals only at gene level. [Display omitted] Allele-specific expression is essential for understanding gene regulation in diploid organisms. However, its estimation from RNA-seq is challenging due to allele-specific read mapping bias and computational cost. Here, we introduce Ornaments, a tool that corrects for the bias with high efficiency to estimate expression at heterozygous SNP and indel loci. [ABSTRACT FROM AUTHOR]

The link between the escalation of global warming and the increase in extreme precipitation events necessitates a deeper understanding of future trends. This study focused on the dynamics of extreme rainfall in Hubei Province throughout the 21st century, a region already sensitive to climatic shifts and extreme weather occurrences. Using the high-resolution global climate model RegCM4 driven by another high-resolution model, HadGEM2-ES, and based on the representative concentration pathway (RCP8.5) emissions scenario, this research predicted the changes in rainfall patterns in Hubei Province during the summer of the 21st century. The accuracy of the adjusted model was confirmed through the use of five extreme rainfall indices (EPIs), namely maximum 5-day amount of precipitation (RX5day), number of heavy rain days (R10), the simple daily intensity index (SDII), consecutive dry days (CDD), and consecutive wet days (CWD), that measured the intensity and frequency of such events. In particular, excluding the index for continuous dry days (CDD), there was an anticipated increase in extreme rainfall during the summer in the mid-21st century. The number of heavy rain days (R10mm) increased significantly (p < 0.05) in the southeastern parts, especially for Wuhan, Xiantao, Qianjiang, Jinzhou, and Ezhou. The EPI values were higher in southeastern Hubei. Consequently, areas such as Wuhan, Xiantao, and Qianjiang in Hubei Province are projected to face more frequent and severe extreme rainfall episodes as the century progresses. [ABSTRACT FROM AUTHOR]

• Double bias fields are introduced into fidelity term to approximate image intensity inhomogeneity. • The proposed energy function is strictly convex, and allows flexible initialization. • A TV (total variation) regularization term is introduced to keep convex level-set function smooth. • The proposed method is robust against to noise and intensity inhomogeneity. The existing active contour models (ACMs) based on bias field (BF) correction mostly rely on a single BF assumption and lack in-depth discussion on the convexity of the energy functional, often leading to the problem of local minima. To address this issue, this paper introduces a dual BF and proposes a convex level-set (LS) method based on multiplicative-additive (MA) model to achieve global minima. Firstly, a MA model is adopted as the fidelity term, and a kernel function is introduced to adjust the size of the intensity inhomogeneous neighborhood, enhancing the adaptability to intensity inhomogeneity. Then, the convex LS function is embedded in the variational framework to ensure convexity of each variable in the energy functional. This transformation turns the segmentation problem into a convex optimization problem. By introducing the total variation regularization term to smooth the LS function, the model's resistance to noise is effectively enhanced. Finally, by minimizing the proposed energy functional, image segmentation and BF correction are successfully achieved. Experimental results validate the global minima property of our model, while also demonstrating good flexibility in the initial contour. The proposed model achieves superior segmentation results compared to other classical ACMs on various types of images. [ABSTRACT FROM AUTHOR]

Large‐scale dynamical and thermodynamical processes are common environmental drivers of high‐impact weather systems causing extreme weather events. However, such large‐scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high‐impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large‐scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ∼1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high‐impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large‐scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias‐corrected large‐scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large‐scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large‐scale storm environments simulated by low‐resolution climate models. Plain Language Summary: A machine learning model is employed to bias correct the large‐scale dynamical and thermodynamical fields simulated by a low‐resolution global climate model. The impact of the machine learning model on the large‐scale storm environment associated with tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs) was evaluated. It is found that the ML bias correction can effectively reduce the mean climate biases in large‐scale wind, temperature, and humidity fields associated with the three types of high‐impact weather systems. For storms such as ETCs and ARs that can be partly resolved by the low‐resolution climate models, the machine learning bias correction shows skills in improving the long‐term statistics of these weather events. For storms such as TCs that can not be well resolved in the low‐resolution climate models, the machine learning approach produces more realistic statistics of the tropical cyclone tracks by providing more realistic large‐scale steering winds for downscaling approaches. By reducing model biases without affecting the climate change signals in large‐scale storm environments derived from the low‐resolution climate model simulations, machine learning bias correction has the potential to provide more reliable projections for assessing future changes in extreme weather events. Key Points: A machine learning approach with a long short‐term memory network is used to bias correct climate simulations from the E3SM atmosphere modelThe approach effectively reduces biases in the simulated large‐scale model states while preserving their responses to climate changeImproving large‐scale storm environment with bias correction facilitates modeling of high‐impact weather systems and their future changes [ABSTRACT FROM AUTHOR]

Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias‐corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub‐periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade‐offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques. Plain Language Summary: Global Climate Models (GCMs) are useful tools to characterize the historical and future evolution of the Earth's climate and its impacts on water resources. Because these models contain errors and their horizontal resolution is too coarse for local impact assessments, spatial downscaling, and bias correction are required steps. In particular, bias correction methods can be trained and applied using all the available historical data or by splitting the time series (e.g., by season or months). Since there are no guidelines for selecting a temporal stratification (TS), we analyze bias‐corrected GCM outputs obtained using three types of strategies (entire period, seasons, and months) and seven bias‐correction techniques over continental Chile. We show that the choice of BCM and the TS applied can modify the projected precipitation signal and seasonality. We also propose using a simple statistical technique to identify if the TS may be a relevant decision for climate impact assessments for a given climate model. Key Points: The choice of temporal stratification (TS) for GCM bias correction is crucial for removing biases, even for GCMs with good raw seasonalityDifferent temporal stratifications used for GCM bias correction may yield different future seasonalities and signals in projected changesThe linear scaling approach can be used to easily identify GCMs whose projections are sensitive to the choice of TS [ABSTRACT FROM AUTHOR]

Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation. Plain Language Summary: Natural hazards, such as flooding and extreme heat, can damage our infrastructure and local communities. These hazards often stem from extreme weather conditions such as heavy precipitation and hot temperatures. Global climate change is expected to affect precipitation and air temperature, leading to more extreme weather events and hazards in the future. Predicting frequency and severity of these future events is key to protect our infrastructure and communities. Climate models have been widely used to project statistics of future extremes. However, they come with uncertainties due to numerical approximations, low spatial resolutions etc. Consequently, predictions generated by these models are inherently uncertain. This paper used a new approach to improve future projections of extreme climatic events, focusing on South Florida. Our findings revealed substantial improvements in projecting future events, particularly for extreme precipitation. We also showed that nationally and globally derived simulations may not be suitable for accurately projecting heavy precipitation, but can suffice for air temperature and low/medium precipitation rates. This paper offers promising avenues for refining projections of future weather events. By enhancing our ability to anticipate upcoming weather events more accurately, we can better protect our infrastructure and communities against the challenges posed by climate change. Key Points: A hybrid statistical approach to improve bias correction of GCM precipitation and air temperature simulationsMajor improvements in both climatic variables, especially extreme precipitation, using our hybrid statistical approach in South FloridaCoarse resolution downscaled data may be insufficient for deriving high quantile precipitation at the regional scale [ABSTRACT FROM AUTHOR]

This paper addresses the problem of fitting concentric ellipses under general assumptions. We study two methods of obtaining an estimator of the concentric ellipse parameters under this model, namely the least squares and the gradient weighted algebraic fits. We address some practical issues in obtaining these estimators. In this paper, we study the statistical properties of those estimators and we developed a refinement with the highest accuracy for each estimator. We also address a practical issue in concentric ellipse fitting, namely, that each observation in the data set should be recognized as belonging to only one of the concentric ellipses. Most well-known clustering methods, such as spectral clustering, fail for this problem. We propose a clustering approach that can effectively be used for the implementation of our model. Our theory has been validated through intensive numerical experiments on synthetic and real data. [ABSTRACT FROM AUTHOR]

This paper proposes an inference procedure for the interactive fixed effects model that is valid in the presence of cross-sectional dependence. When the error terms are cross-sectionally dependent, the least square (LS) estimator of this model is asymptotically biased and therefore the associated confidence interval tends to have a large coverage error. To address this, we propose a bias correction of the LS estimator and a cross-sectional dependence robust variance estimator to construct associated test statistics. The paper also discusses practical issues in implementing the proposed method, including the construction of distance that reflects the decaying pattern of cross-sectional dependence and the selection of the bandwidth parameters. Monte Carlo simulations show our procedure works well in finite samples. As empirical illustrations, we apply our procedure to study the effect of divorce law reforms on divorce rates and the impact of clean water and sewerage interventions on child mortality. [ABSTRACT FROM AUTHOR]

The expectile is a prime candidate for being a standard risk measure in actuarial and financial contexts, for its ability to recover information about probabilities and typical behavior of extreme values, as well as its excellent axiomatic properties. A series of recent papers has focused on expectile estimation at extreme levels, with a view on gathering essential information about low-probability, high-impact events that are of most interest to risk managers. The obtention of accurate confidence intervals for extreme expectiles is paramount in any decision process in which they are involved, but actual inference on these tail risk measures is still a difficult question due to their least squares nature and sensitivity to tail heaviness. This article focuses on asymptotic Gaussian inference about tail expectiles in the challenging context of heavy-tailed observations. We use an in-depth analysis of the proofs of asymptotic normality results for two classes of extreme expectile estimators to derive bias-reduced and variance-corrected Gaussian confidence intervals. These, unlike previous attempts in the literature, are well-rooted in statistical theory and can accommodate underlying distributions that display a wide range of tail behaviors. A large-scale simulation study and three real data analyses confirm the versatility of the proposed technique. [ABSTRACT FROM AUTHOR]

Model selection aims to identify a sufficiently well performing model that is possibly simpler than the most complex model among a pool of candidates. However, the decision-making process itself can inadvertently introduce non-negligible bias when the cross-validation estimates of predictive performance are marred by excessive noise. In finite data regimes, cross-validated estimates can encourage the statistician to select one model over another when it is not actually better for future data. While this bias remains negligible in the case of few models, when the pool of candidates grows, and model selection decisions are compounded (as in step-wise selection), the expected magnitude of selection-induced bias is likely to grow too. This paper introduces an efficient approach to estimate and correct selection-induced bias based on order statistics. Numerical experiments demonstrate the reliability of our approach in estimating both selection-induced bias and over-fitting along compounded model selection decisions, with specific application to forward search. This work represents a light-weight alternative to more computationally expensive approaches to correcting selection-induced bias, such as nested cross-validation and the bootstrap. Our approach rests on several theoretic assumptions, and we provide a diagnostic to help understand when these may not be valid and when to fall back on safer, albeit more computationally expensive approaches. The accompanying code facilitates its practical implementation and fosters further exploration in this area. [ABSTRACT FROM AUTHOR]

Precipitation is a fundamental component of the Earth's hydrological cycle. Therefore, monitoring precipitation is paramount, as accurate information is needed to prevent natural hydrological disasters, such as floods and droughts. However, measuring precipitation using rain gauges is complicated due to their sparse spatial distribution. Satellite precipitation products (SPPs) are an alternative source of rainfall data. This study aimed to evaluate the performance of PERSIANN-CCS and PDIR-Now SPPs over the Tulijá River Basin (Chiapas, Mexico) using scatter plots, categorical statistics, descriptive statistics, and decomposing total bias. Additionally, bias correction was performed using the quantile mapping (QM) method. QM is a technique used to improve the fit of SPPs with respect to rainfall observations through a transfer function, aiming to reduce systematic errors in SPPs. The results indicate that the PDIR-Now product tends to overestimate rainfall to a large extent, thus showing better performance in detecting rain events. Meanwhile, PERSIANN-CCS underestimates precipitation to a lesser extent. The findings of this study demonstrate that correcting the bias of SPPs improves estimations of rainfall records, thereby reducing the percentage bias and root mean square error. [ABSTRACT FROM AUTHOR]

Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) and has elevated incidence among individuals with HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted a genome-wide association study (GWAS) of diabetes-related HF with correction for collider bias. We first performed a GWAS of HF to identify genetic instrumental variables (GIVs) for HF and to enable bidirectional Mendelian randomization (MR) analysis between T2D and HF. We identified 61 genomic loci, significantly associated with all-cause HF in 114,275 individuals with HF and over 1.5 million controls of European ancestry. Using a two-sample bidirectional MR approach with 59 and 82 GIVs for HF and T2D, respectively, we estimated that T2D increased HF risk (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04–1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36–1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to the study design of index cases. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B−AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program and replicated the associations in the UK Biobank. Our MR findings provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci. This study demonstrated the bidirectional relationship between type 2 diabetes and heart failure, addressed the impact of collider bias on the genome-wide association study of diabetes-related heart failure, and identified and replicated two genetic loci. These discoveries offer pivotal insights into the intricate mechanisms and comorbidities associated with heart failure. [ABSTRACT FROM AUTHOR]

The general circulation models (GCMs) are state-of-art tools available to investigate the response of climate system to external and internal forcing. They are used to predict/project climate in seasonal to decadal time scales. The general circulation models (GCMs) have more or less biases, and bias correction methods are the techniques used to correct their biases. The Coupled model intercomparison project phase 6 (CMIP6) has been widely used to simulate the historical period and project the future climate. However, due to the uncertainty of the models and their coarse resolution, GCMs are not directly used to assess the impacts of climate change. Therefore, to reduce the uncertainty in CMIP6 models, bias correction is necessary in the first step. This study evaluates three methods of bias correction including, Linear Scaling, Variance Scaling of Temperature, Empirical Quantile Mapping, Quantile mapping using a smoothing spline and Empirical Robust Quantile Mapping for two variables of minimum and maximum temperature against 46 synoptic stations in Iran during 1980-2014 using the EC-Earth3-CC. To evaluate direct model output (DMO) and bias correction methods, we used three metrics including root-meansquare error (RMSE), percent bias (PBIAS), index of agreement (d), and interannual variability skill score (IVS). The results showed that the direct model output of the EC-Earth3-CC model has a cold bias (underestimation) for both minimum and maximum temperature in all climate zones of Iran, as well as the area-averaged values of the country. The bias correction methods examined in this research have significantly reduced the bias in Iran. It is found that the bias decreased to 51.8 percent for the minimum temperature in the highlands of Azerbaijan, northeastern Iran, as well as parts of the Alborz and Zagros mountains. In general, the results of the three methods are close and they are not much different from each other. Based on the analysis of RMSE values, bias correction methods significantly reduced RMSE in comparison with DMO. So that the value of this metric in DMO has been more than 2 oC in most of Iran's climate zones, while the use of bias correction methods has reduced the error value to less than 1 oC. Also, bias correction methods have increased the index of agreement (d) by more than two times in average climate zones. Since the EC-Earth3- CC DMO has a good performance in depicting interannual climate variability (IVS) and is close to the observations, this has caused the DMO not to differ greatly from the results of using bias correction methods such as linear scaling. Finally, the bias correction methods used in this research estimate the maximum temperature with higher accuracy than that for the minimum temperature. There is no single bias correction method that provides the best performance in all regions. Therefore, each of these methods has its own advantages and limitations, which are caused by spatiotemporal differences and local geographical features . [ABSTRACT FROM AUTHOR]

High‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally. Plain Language Summary: The STAR‐ESDM tool is able to quickly and accurately generate future climate projections for weather stations and high‐resolution grids anywhere in the world. It does this by breaking down global or regional climate model output into different components, from the long‐term trend to the day‐to‐day variability, then merging projected changes with observations. When tested against projections generated by a complex and computationally expensive dynamical global model, STAR‐ESDM produced almost the same output, even for extreme temperature and precipitation values, at a fraction of the computational cost. Moreover, unlike most statistical downscaling models, this method isn't tied to any specific geographic area or predictand and/or predictor data set. It can be applied to any regional or global data set, whether generated by a climate or reanalysis model, derived from satellite observations, recorded at weather stations, and more. As climate impacts escalate, STAR‐ESDM offers a flexible and effective way to generate the high‐resolution climate projections needed to better gauge climate risk and enhance resilience anywhere in the world where reliable observational or quasi‐observational data, including reanalysis or satellites, are available. This is particularly relevant to under‐resourced regions, which are often most vulnerable to climate impacts as well as most lacking in future projections. Key Points: STAR‐ESDM is a rapid, flexible and generalizable approach to bias‐correct and downscale climate model output to any finer‐resolution data setPredictors/predictands can be derived from global or regional models, satellites, reanalysis, gridded observations and weather stationsProjected changes in temperature and precipitation mirror those of a high‐resolution global model at a fraction of the computational cost [ABSTRACT FROM AUTHOR]

Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias correction method has no effect on the higher‐order variance of semiparametrically efficient parametric estimators, so long as the estimate of the bias is asymptotically linear. It is also shown that bootstrap, jackknife, and analytical bias estimates are asymptotically linear for estimators with higher‐order expansions of a standard form. In particular, we find that for a variety of estimators the straightforward bootstrap bias correction gives the same higher‐order variance as more complicated analytical or jackknife bias corrections. In contrast, bias corrections that do not estimate the bias at the parametric rate, such as the split‐sample jackknife, result in larger higher‐order variances in the i.i.d. setting we focus on. For both a cross‐sectional MLE and a panel model with individual fixed effects, we show that the split‐sample jackknife has a higher‐order variance term that is twice as large as that of the "leave‐one‐out" jackknife. [ABSTRACT FROM AUTHOR]

The widespread utilization of reanalysis-based precipitation (RP) data has significantly facilitated hydrometeorological studies in areas where observations are scarce. However, assessing the applicability of the RP data before being applied in any basin is necessary considering that the inherited errors of the RP data vary with surface conditions, seasonal cycles, and different climatic zones. In this study, the Ili River Basin (IRB) was selected as the study area to evaluate the accuracy and impact of ERA5 reanalysis precipitation data, a type of RP data, on the hydrological cycling in the IRB. Four methods, linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM), were tested to correct the bias of the ERA5 reanalysis precipitation. We found the following results. (1) The ERA5 precipitation exhibits a significant overestimation of gauge-observed precipitation within the IRB, indicating poor accuracy. Compared to the gauge-observed precipitation, the comprehensive performance of ERA5 precipitation in the dry season is better than that in the entire year, while the one in the entire year is better than that in the rainy season. The main error of the ERA5 precipitation lies in its insufficient ability to distinguish between no rain (0–0.1 mm) and light rain (0.1–4 mm) events, (2) All bias correction methods effectively address the biases in the ERA5 precipitation, although the extent of correction varies. The LS and DM methods outperformed the others regarding the time-series-based indices, while the DM method outperformed the others regarding the exceedance probability, and (3) In evaluating runoff simulation accuracy indices, hydrologic simulations driven by the corrected ERA5 precipitation using the DM method markedly outperform those driven by the ERA5 precipitation alone and even those corrected by the PT and LS methods. Specifically, the DM method exhibits the best correction of the flow duration curve and peak flow, with Nash-Sutcliffe efficiency coefficient (NSE) at 0.83 and relative deviation (RD) at 9.23% in the calibration period, and NSE at 0.85 and RD at 1.29% in the verification period. Corrected ERA5 precipitation data can fill in the gaps left by meteorological stations, effectively addressing the issue of inaccurate peak values in runoff simulations caused by using uncorrected ERA5 precipitation data. Practical Applications: Due to the constraints of complex terrain and natural conditions, the hydrometeorological monitoring stations are sparse and unevenly distributed in western China's Ili River Basin (IRB). The precipitation data obtained from the monitoring sites can hardly meet the needs of hydrological simulations. Furthermore, it challenges us to manage the IRB better when facing extreme weather conditions (e.g., drought and flooding). The ERA5 reanalysis precipitation data with comprehensive coverage and high resolution is a promising alternative to observing precipitation at the basin level. However, verifying the feasibility of using ERA5 precipitation data in IRB is necessary. In this study, we first noticed that the ERA5 precipitation data has a significant deviation and low accuracy in IRB by comparing it with available observed precipitation. Thus, we corrected ERA5 reanalysis precipitation using four different methods: linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). We found that the DM method can better capture extreme precipitation events, and the corresponding simulated runoff has the highest agreement with the observed runoff. [ABSTRACT FROM AUTHOR]

Significant shifts in hydro-climatic regimes are expected in many parts of the world during the 21st century, affecting the water cycle. Vulnerability, impact, and adaptation studies often use tailored modeling chains to assess the expected effects of climate change, but the robustness of these chains is rarely investigated. This highlights the need for more rigorous evaluation of modeling chains to ensure that they are reliable for informed decision-making processes. To address this gap, we propose a framework for evaluating the sensitivity of hydrological scenario production to the bias correction step. We apply the framework to the Senegal River Basin, using three bias correction methods (linear scale, empirical quantile mapping, and nested bias correction) and three procedures (climate-correction, hydrological-correction, and climate-hydrological-correction). Our results show that the choice of modeling chain has a significant impact on future hydro-climatic trajectories. In particular, the combination of climate-and-hydrological-correction procedures may be optimal when both climate biases and hydrological model errors are significant. Moreover, using multiple bias correction methods can strengthen the ensemble of future hydro-climatic conditions. These findings have implications for vulnerability–impact–adaptation studies and underscore the importance of rigorous modeling chain design and sensitivity analysis. HIGHLIGHTS The choice of bias correction method can significantly impact the resulting hydrological scenarios.; Removing the biases with a climate-correction procedure, hydrological-correction procedure, or a combination of both also notably influences the hydrological-scenario outcomes.; Resorting to several bias correction methods can be seen as a factor to strengthen the ensemble of future hydro-climatic conditions.;

Firms often engage in manipulating online reviews as a promotional activity to influence consumers' evaluation on their products. With the prevalence of the promotional activities, consumers may notice and discount the reviews generated by the promotional activities. Discounting the firm-generating reviews may cause systematic measurement errors in the valence variable and lead to a negative bias when estimating the effect of consumers' organic reviews on demand. To correct the bias, this study proposes including product-specific bias-correction terms representing the proportion of extreme reviews in analysis. For illustration, the proposed method is applied to a demand model for data of movies released in South Korea. The results confirm a negative bias in the estimate of the valence sensitivity of demand. The negative bias potentially leads to an underestimation of the magnitude of the contagion effect through social interactions, a key component of evaluating the value of a satisfied consumer.

A complete workflow was presented for estimating the concentration of microorganisms in biological samples by automatically counting spots that represent viral plaque forming units (PFU) bacterial colony forming units (CFU), or spot forming units (SFU) in images, and modeling the counts. The workflow was designed for processing images from dilution series but can also be applied to stand-alone images. The accuracy of the methods was greatly improved by adding a newly developed bias correction method. When the spots in images are densely populated, the probability of spot overlapping increases, leading to systematic undercounting. In this paper, this undercount issue was addressed in an empirical way. The proposed empirical bias correction method utilized synthetic images with known spot sizes and counts as a training set, enabling the development of an effective bias correction function using a thin-plate spline model. Its application focused on the bias correction for the automated spot counting algorithm LoST proposed by Lin et al. Simulation results demonstrated that the empirical bias correction significantly improved spot counts, reducing bias for both fixed and random spot sizes and counts. [ABSTRACT FROM AUTHOR]

This study aimed to improve the daytime fog detection algorithm GK2A_HR_FDA using the GEO-KOMPSAT-2A (GK2A) satellite by increasing the resolution (2 km to 500 m), improving predicted surface temperature by the numerical model, and optimizing some threshold values. GK2A_HR_FDA uses numerical model prediction temperature to distinguish between fog and low clouds and evaluates the fog detection level using ground observation visibility data. To correct the errors of the numerical model prediction temperature, a dynamic bias correction (DBC) technique was developed that reflects the geographic location, time, and altitude in real time. As the numerical model prediction temperature was significantly improved after DBC application, the fog detection level improved (FAR: −0.02–−0.06; bias: −0.07–−0.23) regardless of the training and validation cases and validation method. In most cases, the fog detection level was improved due to DBC and threshold adjustment. Still, the detection level was abnormally low in some cases due to background reflectance problems caused by cloud shadow effects and navigation errors. As a result of removing navigation errors and cloud shadow effects, the fog detection level was greatly improved. Therefore, it is necessary to improve navigation accuracy and develop removal techniques for cloud shadows to improve fog detection levels. [ABSTRACT FROM AUTHOR]

The stable tail dependence function provides a full characterisation of the extremal dependence structures. Unfortunately, the estimation of the stable tail dependence function often suffers from significant bias, whose scale relates to the Peaks-Over-Threshold (POT) second-order parameter. For this second-order parameter, this paper introduces a penalised estimator that discourages it from being too close to zero. This paper then establishes this estimator's asymptotic consistency, uses it to correct the bias in the estimation of the stable tail dependence function, demonstrates its desirable empirical properties in the estimation of the extremal dependence structures, and illustrates it with an application to liability claim data. [ABSTRACT FROM AUTHOR]

Background Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB. Methods Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias. Results Volunteer bias in all associations, as naively estimated in UKB, was substantial—in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size. Conclusions Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available. [ABSTRACT FROM AUTHOR]

Boundary conditions are important constraints when solving partial differential equations to obtain solutions for the interior. Global ocean prediction models have supplied such boundary conditions for regional ocean models. However, the biases present in these global ocean models can significantly affect the accuracy of predictions within nested regional ocean models. In the present study, open boundary bias correction and data assimilation were applied using the observed temperature and salinity data. The bias correction was conducted at the upstream open boundary, the Korea Strait, while the data assimilation was performed in the interior, including Ulleung Basin in the southwestern East Sea, to reduce the prediction errors in the nested regional model. The effects of each correction method were quantified through four numerical experiments. In the Korea Strait and Ulleung Basin, root mean square errors (RMSEs) in temperature decreased by 44% in February using data assimilation compared to free run, while RMSEs in salinity decreased by 31% in August using bias correction. Bias correction of open boundary data enhanced the simulation performance in salinity near the open boundary; however, it did not lead to improvements in temperature distribution within the interior. Conversely, data assimilation improved temperature distribution within the interior; however, salinity bias persisted near the open boundary. Notably, the combined use of the two correction methods resulted in a lower RMSE in temperature in the Ulleung Basin during winter, and a lower RMSE in salinity in the Korea Strait and Ulleung Basin during the summer. Hence, the simultaneous use of open boundary bias correction and data assimilation is expected to improve the interior solution of salinity and temperature in various other regional ocean models. [ABSTRACT FROM AUTHOR]

Regression trees and forests are widely used due to their flexibility and predictive accuracy. Whereas typical tree induction assumes independently identically distributed (i.i.d.) data, in many applications the training sample follows a complex sampling structure. This includes unequal probability sampling, which is often found in survey data. Then, a 'naive estimation' that simply ignores the sampling weights may be substantially biased. This article analyzes the bias arising from a naive estimation of regression trees or forests under complex sample designs and proposes ways of de-biasing. This is achieved by bridging tree learning to survey statistics, due to the correspondence of the mean-squared-error criterion in regression trees and variance estimation. Transferring population variance estimation approaches from survey statistics to tree induction, indeed considerably reduces the bias in the resulting trees, both in predictions and the tree structure. The latter is particularly crucial if the trees are to be interpreted. Our methodology is extended to random forests, where we show on simulated data and a housing dataset that correcting for complex sample designs leads to overall much better predictive accuracy and more trustworthy interpretation. Interestingly, corrected forests can surpass forests learned on i.i.d. samples in terms of accuracy, which also has important implications for adaptive data collection approaches. [ABSTRACT FROM AUTHOR]

Polarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean‐state Global Climate Model (GCM) biases: (a) overly strong lower‐tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid‐tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers. Plain Language Summary: Global Climate Models (GCMs) are known to have biases that, when dynamically downscaled, damage the credibility of the e. A longstanding way around this problem is bias correction (BC) after downscaling, but this practice rarely involves physics and can mislead climate data users into overvaluing the quality of the downscaled data. Further, post‐downscaling BC techniques can over correct the higher‐order statistics, calling into question the faithful preservation of the original simulated signals. For the first time, we apply a minimally invasive BC procedure to a group of 9 GCMs in order to define physical relationships between mean GCM biases and their dynamically downscaled hydroclimate variables across the western United States. We find that native GCMs tend to exhibit surprisingly common mean biases that, when downscaled, effectuate an overly wet, cold, and snowy climate across the region. Key Points: Bias correction of Global Climate Models (GCMs) reduces biases in downscaled mean precipitation, snow, and temperature across the western United StatesCascading cold, thermodynamically unstable, and cyclonic vorticity biases from GCMs to regional climate models drive wet biases in dynamical downscalingCMIP6‐wide GCM biases are similar suggesting that biases in dynamically downscaled precipitation and temperature can be anticipated [ABSTRACT FROM AUTHOR]

Understanding climate change and land use impacts is crucial for mitigating environmental degradation. This study assesses the environmental vulnerability of the Doce River Basin for 2050, considering future climate change and land use and land cover (LULC) scenarios. Factors including slope, elevation, relief dissection, precipitation, temperature, pedology, geology, urban distance, road distance, and LULC were evaluated using multicriteria analysis. Regional climate models Eta-HadGEM2-ES and Eta-MIROC5 under RCP 4.5 and RCP 8.5 emission scenarios were employed. The Land Change Modeler tool simulated 2050 LULC changes and hypothetical reforestation of legal reserve (RL) areas. Combining two climate and two LULC scenarios resulted in four future vulnerability scenarios. Projections indicate an over 300 mm reduction in average annual precipitation and an up to 2 °C temperature increase from 2020 to 2050. Scenario 4 (RCP 8.5 and LULC for 2050 with reforested RLs) showed the greatest basin area in the lowest vulnerability classes, while scenario 3 (RCP 4.5 and LULC for 2050) exhibited more high-vulnerability areas. Despite the projected relative improvement in environmental vulnerability by 2050 due to reduced rainfall, the complexity of associated relationships must be considered. These results contribute to mitigating environmental damage and adapting to future climatic conditions in the Doce River Basin. [ABSTRACT FROM AUTHOR]

When designing confirmatory Phase 3 studies, one usually evaluates one or more efficacious and safe treatment option(s) based on data from previous studies. However, several retrospective research articles reported the phenomenon of "diminished treatment effect in Phase 3" based on many case studies. Even under basic assumptions, it was shown that the commonly used estimator could substantially overestimate the efficacy of selected group(s). As alternatives, we propose a class of computational methods to reduce estimation bias and mean squared error with a broader scope of multiple treatment groups and flexibility to accommodate summary results by group as input. Based on simulation studies and a real data example, we provide practical implementation guidance for this class of methods under different scenarios. For more complicated problems, our framework can serve as a starting point with additional layers built in. Proposed methods can also be widely applied to other selection problems. [ABSTRACT FROM AUTHOR]

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility. [ABSTRACT FROM AUTHOR]

In this study, we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model (GCM) data to drive a regional climate model (RCM) over the Asia-western North Pacific region. Three simulations were conducted with a 25-km grid spacing for the period 1980–2014. The first simulation (WRF_ERA5) was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset and served as the validation dataset. The original GCM dataset (MPI-ESM1-2-HR model) was used to drive the second simulation (WRF_GCM), while the third simulation (WRF_GCMbc) was driven by the bias-corrected GCM dataset. The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models. Results demonstrate that the WRFGCMbc significantly reduced the root-mean-square errors (RMSEs) of the climatological mean of downscaled variables, including temperature, precipitation, snow, wind, relative humidity, and planetary boundary layer height by 50%–90% compared to the WRF_GCM. Similarly, the RMSEs of interannual-to-interdecadal variances of downscaled variables were reduced by 30%–60%. Furthermore, the WRFGCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities. The leading empirical orthogonal function (EOF) shows a monopole precipitation mode in the WRFGCM. In contrast, the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China. This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction. [ABSTRACT FROM AUTHOR]

This work proposes a hybrid modeling framework based on recurrent neural networks (RNNs) and the finite element (FE) method to approximate model discrepancies in time-dependent, multi-fidelity problems, and use the trained hybrid models to perform bias correction of the low-fidelity models. The hybrid model uses FE basis functions as a spatial basis and RNNs for the approximation of the time dependencies of the FE basis' degrees of freedom. The training data sets consist of sparse, non-uniformly sampled snapshots of the discrepancy function, pre-computed from trajectory data of low- and high-fidelity dynamic FE models. To account for data sparsity and prevent overfitting, data upsampling and local weighting factors are employed, to instigate a trade-off between physically conforming model behavior and neural network regression. The proposed hybrid modeling methodology is showcased in three highly non-trivial engineering test-cases, all featuring transient FE models, namely, heat diffusion out of a heat sink, eddy-currents in a quadrupole magnet, and sound wave propagation in a cavity. The results show that the proposed hybrid model is capable of approximating model discrepancies to a high degree of accuracy and accordingly correct low-fidelity models. • A RNN/FE hybrid model is developed to learn model discrepancies of multi-fidelity FE simulations based on trajectory data. • The RNN captures the dynamics of the discrepancy function while spatial discrepancies are resolved by a FE basis. • Upsampling and Gaussian priors are employed to account for sparsity in the discrepancy data and prevent overfitting. • The model is employed as a correction operator for low-fidelity models in three non-trivial test cases. • The bias corrected models are significantly more accurate compared to the original low-fidelity models. [ABSTRACT FROM AUTHOR]

We assess the impact of three bias correction approaches on present day means and extremes, and climate change signal, for six climate variables (precipitation, minimum and maximum temperature, radiation, vapour pressure and mean sea level pressure) from dynamically downscaled climate simulations over Queensland, Australia. Results show that all bias‐correction methods are effective at removing systematic model biases, however the results are variable and season‐dependent. Importantly, our results are based on fully independent cross‐validation, an advantage over similar studies. Linear scaling preserves the climate change signals for temperature, while quantile mapping and the distribution‐based transfer function modify the climate change signal and patterns of change. The Perkins score for all the values above the 95th percentile and below the 5th percentile was used to evaluate how well the climate model matches the observational data. Bias correction improved Perkins score for extremes for some variables and seasons. We rank the bias‐correction methods based on the Kling–Gupta efficiency (KGE) score calculated during the validation period. We find that linear scaling and empirical quantile mapping are the best approaches for Queensland for mean climatology. On average, bias correction led to an improvement in the KGE score of 23% annually. However, in terms of extreme values, quantile mapping and statistical distribution‐based transfer function approaches perform best, and linear scaling tends to perform worst. Our results show that, except linear scaling, all approaches impact the climate change signal. [ABSTRACT FROM AUTHOR]

For assessing the impact of climate change on socio-eco-hydrological systems at the catchment scale, reasonable and reliable meteorological input data are essential. The modelled, gridded meteorological datasets of several fields, e.g., precipitation, temperature, surface moisture, etc. from Regional Climate Models (RCMs) are widely used as inputs to numerically ecological or hydrological model and estimate the climate change impact on several natural systems inside a catchment. However, in the case of mountainous regions, like the Himalayas due to the scarcity of station observations or human inaccessibility, most of the RCMs contain inherent and systemic biases. Hence, before forcing these RCMs on ecological or hydrological models, the biases present in RCMs must be corrected. This study aims to provide a set of reasonable, bias-corrected precipitation field from 12 simulated CORDEX-CORE model experiments, to provide inputs for the estimation of hydrological changes over the mountainous Upper Teesta River Basin (UTRB) situated at the eastern Himalayas. The model precipitation field from 12 CORDEX-CORE model experiments and the corresponding observed precipitation field from CHELSA V2.1 climatic reanalysis were considered for the reference period of 1979–2005. Their performances were inter-compared and linear scaling, distribution mapping, and power transformation bias correction methods were applied on each grid of the precipitation fields from 12 CORDEX-CORE model experiments. After the application of bias correction methods, all the CORDEX-CORE model experiments show a reduction in bias. Among the bias correction methods, the distribution mapping method had altered the model precipitation fields while preserving the statistical characteristics of the observed data and was found to be more efficient than the other two, while the linear scaling method was found to be worst performing. Although the modified precipitation fields weren't forced with a hydrological model in the present study, the evaluation of performances of bias-corrected model outputs shows that the precipitation field from the ERAINT-COSMO model experiment corrected with the distribution mapping method could be the best fit RCM for studying the hydrological impacts due to climatic changes over the data-scarce basins like the UTRB. [ABSTRACT FROM AUTHOR]

Epidemiologic screening programs often make use of tests with small, but nonzero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect test with known sensitivity and specificity to a sample of individuals from the population. In this setting, we propose an enhanced inferential approach for use in conjunction with sampling-based bias-corrected prevalence estimation. While ignoring the finite nature of the population can yield markedly conservative estimates, direct application of a standard finite population correction (FPC) conversely leads to underestimation of variance. We uncover a way to leverage the typical FPC indirectly toward valid statistical inference. In particular, we derive a readily estimable extra variance component induced by misclassification in this specific but arguably common diagnostic testing scenario. Our approach yields a standard error estimate that properly captures the sampling variability of the usual bias-corrected maximum likelihood estimator of disease prevalence. Finally, we develop an adapted Bayesian credible interval for the true prevalence that offers improved frequentist properties (i.e., coverage and width) relative to a Wald-type confidence interval. We report the simulation results to demonstrate the enhanced performance of the proposed inferential methods. [ABSTRACT FROM AUTHOR]

A bias-correction approach that can recover the total average treatment effect of a ranking algorithm based on past A/B tests even if those tests suffer from interference issues. Online marketplaces use ranking algorithms to determine the rank-ordering of items sold on their websites. The standard practice is to determine the optimal algorithm using A/B tests. We present a theoretical framework to characterize the total average treatment effect (TATE) of a ranking algorithm in an A/B test and show that naive TATE estimates can be biased because of interference. We propose a bias-correction approach that can recover the TATE of a ranking algorithm based on past A/B tests even if those tests suffer from a combination of interference issues. Our solution leverages data across multiple experiments and identifies observations in partial equilibrium in each experiment, that is, items close to their positions under the true counterfactual equilibrium of interest. We apply our framework to data from a travel website and present comprehensive evidence for interference bias in this setting. Next, we use our solution concept to build a customized deep learning model to predict the true TATE of the main algorithm of interest in our data. Counterfactual estimates from our model show that naive TATE estimates of click and booking rates can be biased by as much as 15% and 29%, respectively. History: Olivier Toubia served as the senior editor. Funding: This work was supported by the Marketing Science Institute [Grant 4001313]. Supplemental Material: The web appendix and data are available at https://doi.org/10.1287/mksc.2022.0046. [ABSTRACT FROM AUTHOR]

The applicability of the ERA5 reanalysis for estimating wind and solar energy generation over the contiguous United States is evaluated using wind speed and irradiance variables from multiple observational data sets. After converting ERA5 and observed meteorological variables into wind power and solar power, comparisons demonstrate that significant errors in the ERA5 reanalysis exist that limit its direct applicability for a wind and solar energy analysis. Overall, ERA5-derived solar power is biased high, while ERA5-derived wind power is biased low. During winter, the ERA5-derived solar power is biased high by 23% on average, while on an annual basis, the ERA5-derived wind power is biased low by 20%. ERA5-derived solar power errors are found to have consistent characteristics across the contiguous United States. Errors for the shortest duration and most extreme solar negative anomaly events are relatively small in the ERA5 when completely overcast conditions occur in both the ERA5 and observations. However, longer-duration anomaly events on weekly to monthly timescales, which include partially cloudy days or a mix of cloudy and sunny days, have significant ERA5 errors. At 10 days duration, the ERA5-derived average solar power produced during the largest negative anomaly events is 62% greater than observed. The ERA5 wind speed and derived wind power negative biases are largely consistent across the central and northwestern U.S., and offshore, while the northeastern U.S. has an overall small net bias. For the ERA5-derived most extreme negative anomaly wind power events, at some sites at 10 days duration, the ERA5-derived wind power produced can be less than half of that observed. Corrections to ERA5 are derived using a quantile–quantile method for solar power and linear regression of wind speed for wind power. These methods are shown to avoid potential over-inflation of the reanalysis variability resulting from differences between point measurements and the temporally and spatially smoother reanalysis values. The corrections greatly reduce the ERA5 errors, including those for extreme events associated with wind and solar energy droughts, which will be most challenging for electric grid operation. [ABSTRACT FROM AUTHOR]

Satellite rainfall estimates (SRE) offer spatial-temporal rainfall representations in regions with limited ground-based gauge rainfall measurements. However, differences exist between SRE and gauge measured rainfall, which needs assessment and reduction. This study presents a method to correct errors in SRE to make their use in agro-hydrological applications and models meaningful. The main scientific objective is the determination of effective window sizes for SRE bias correction. To conclude on effective window sizes, the crop water requirement satisfaction index (WRSI) for gauged rainfall, uncorrected SRE and bias corrected SRE were estimated and propagation effects of SRE errors on respective WRSI estimates were assessed. WRSI indicates how much of the crop water needs are satisfied by rainfall. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) SRE was bias corrected using gauged rainfall data from 20 stations in the Lake Victoria basin of Kenya from 2012 to 2018. The results show that the error in WRSI can serve to determine effective window sizes for SRE bias correction rather than using SRE bias error itself. This proposed correction method resulted in improved estimates of WRSI. [ABSTRACT FROM AUTHOR]

Climate extremes pose significant risks to human health, agriculture, and water resources. These extremes are defined as long-term, unusual events that fall into the 10th or 90th percentile of a probability density function derived from observations at a certain location (e.g., drought and wildfire). The quantification of future climate risks is based on climate model predictions. Here, we present a review of literature focusing on extreme climate projections in the latest generation of climate models, namely, Coupled Model Intercomparison Project Phase 6 (CMIP6) from 2020 to the present. We highlight the extreme events that could cause potential societal risks, including precipitation (the 90th percentile of the cumulative frequency distribution of daily precipitation), temperature (the 10th or 90th percentile of daily temperature within a reference period), droughts (meteorological, hydrological, and agricultural), floods, heat waves, and compound/concurrent extremes. Regionally, the precipitation extremes are projected to increase in North Africa (Ethiopia, Uganda, and Kenya), followed by drying in South Africa. Heatwaves will increase in a warming scenario (SSP3-7.0) in Asia (Indo-Gangetic Plain) and Afghanistan. The rise in heat stress intensity in Asia will augment the climate risks to agriculture under the SSP2-4.5 and SSP5-8.5 scenarios. On a global scale, land areas are projected to face severe drought specifically in higher biomass regions, under the SSP5-8.5 scenario. Future droughts bring hazards to Europe and the Amazon River basin with severe aridification over Australia, the Middle East, South and North Africa, and Central Asia. The CMIP6 model projections on a regional and global scale over the US Southwest predict intense drought and hot dry summers. The study supplements the discussion section by providing insights on sources of uncertainty in extreme event projections, the role of emergent constraints in uncertainty reduction, and the impact of extremes on water resources, agriculture, and human health. [ABSTRACT FROM AUTHOR]

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra's mountainous region to improve performance. [ABSTRACT FROM AUTHOR]

The exponentiated Teissier distribution (ETD) offers an alternative for modeling survival data, taking into account flexibility in modeling data with increasing and decreasing hazard rate functions. The most popular method for parameter estimation of the ETD distribution is the maximum likelihood estimation (MLE). The MLE, on the other hand, is notoriously biased for its small sample sizes. We are therefore driven to generate virtually unbiased estimators for ETD parameters. More specifically, we focus on two methods of bias correction, bootstrapping and analytical approaches, to reduce MLE biases to the second order of bias. The performances of these approaches are compared through Monte Carlo simulations and two real-data applications. [ABSTRACT FROM AUTHOR]

The maximum range of long-term operational predictions was limited to one year until 2018, while with the implementation of the Decadal Climate Prediction Project (DCPP), their range was increased to a decade. These predictions are made by initializing global climate models using observational data. The current study aims to predict the precipitation and air temperature of Iran and neighboring countries for the next 5 years (2022 to 2026) in three time scales of seasonal, annual and five-year, using the output of DCPP models initialized in November 2021. For this purpose, the precipitation and temperature coarse data of the MPI-ESM1.2-LR (Max Plank Institute in Germany), MIROC6 (Japan Agency for Marine-Earth Science and Technology) and CNRMESM2-1 (Centre National de Recherches Meteorologiques, France) models were used. The horizontal resolution of the MPI-ESM1.2-LR, MIROC6 and CNRM-ESM2-1 models are 200×200, 250×250 and 250×250 km, respectively. The correction of the output of the climate models was done based on the standard methodology proposed by the WCRP (World Climate Research Program) working group of World Meteorological Organization (WMO). To correct the raw output of precipitation and temperature of the DCPP models, the GPCC and ERA5 datasets were used for precipitation and temperature, respectively. The results showed that Iran's precipitation is unlikely to be more than normal in any of the next five years. The highest decrease in precipitation will likely occur in 2022 and 2025, and the precipitation of the 2023 will most likely be normal. The high predictability of ENSO and the expectation of El Niño occurrence in 2023 confirm that the precipitation of Iran and neighboring countries is within the normal range for 2023. It is more likely that, in none of the next 5 years, the average temperature of the Iran will be below normal, and the temperature anomaly is at least in the range of 0.3-0.5 degree Celsius, and the largest increase is expected in the western half of Iran and the northeast region under study. The minimum and maximum temperature increase will most likely occur in 2022 and 2026 over Iran. In the studied period, the precipitation of West Asia, especially the areas adjacent to the Arabian Sea and the Red Sea, is most likely more than normal and other countries are estimated to be within the normal range. Also, the average air temperature of the next five years in West Asia will be between 0.3 and 1.2 degree of Celsius above normal, with the largest increase of 1 to 1.2 degrees occurring in eastern Turkmenistan, Tajikistan and Kyrgyzstan. It is expected that the air temperature anomaly in the Arabian Peninsula will be in the range of 0.3 to 0.5 degrees, which will be about 0.5 degrees lower than other countries in the region. [ABSTRACT FROM AUTHOR]

The regression of mutually independent time series, whether stationary or non-stationary, will result in autocorrelation in the random error term. This leads to the over-rejection of the null hypothesis in the conventional t-test, causing spurious regression. We propose a new method to reduce spurious regression by applying the Cochrane-Orutt feasible generalized least squares method based on a bias-corrected method for a first-order autoregressive model in finite samples. This method eliminates the requirements for a kernel function and bandwidth selection, making it simpler to implement than the traditional heteroskedasticity and autocorrelation consistent method. A series of Monte Carlo simulations indicate that our method can decrease the probability of spurious regression among stationary, non-stationary, or trend-stationary series within a sample size of 10-50. We applied this proposed method to the actual data studied by Yule in 1926, and found that it can significantly minimize spurious regression. Thus, we deduce that there is no significant regressive relationship between the proportion of marriages in the Church of England and the mortality rate in England and Wales. [ABSTRACT FROM AUTHOR]

Assessing the impact of climate change is essential for developing water resource management plans, especially in areas facing severe issues regarding ecosystem service degradation. This study assessed the effects of climate change on the hydrological balance using the SWAT (Soil and Water Assessment Tool) hydrological model in the Mixteco River Basin (MRB), Oaxaca, Mexico. Temperature and precipitation were predicted with the projections of global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6); the bias was corrected using CMhyd software, and then the best performing GCM was selected for use in the SWAT model. According to the GCM MPI-ESM1-2-LR, precipitation might decrease by between 83.71 mm and 225.83 mm, while temperature might increase by between 2.57 °C and 4.77 °C, causing a greater atmospheric evaporation demand that might modify the hydrological balance of the MRB. Water yield might decrease by 47.40% and 61.01% under the climate scenarios SP245 and SSP585, respectively. Therefore, adaptation and mitigation measures are needed to offset the adverse impact of climate change in the MRB. [ABSTRACT FROM AUTHOR]

Soil temperature plays a critical role in many soil functions, particularly in arid ecosystems, but is scarcely reported as potential indicator of climate change. Although studies have been conducted worldwide to investigate (both measurements and modeling) temperature changes in the soil profile in response to ambient temperature, no information on soil temperature is available in the state of Kuwait. Hydrological and many bio-geochemical processes are more sensitive to soil temperature than air temperature. In this study, we used observed soil temperature data (2007–2016) from three sites with three soil depth increments (5, 50, 100 cm) and compared to regional climate and regression models' output. The most salient finding of this study is the tight association between observed and simulated soil temperature at shallower soil depths from both the regional climate model (RegCM4) and linear regression model. The RegCM4 model poorly predicted (underestimated) soil temperature at deeper soil layers relative to shallower soil layers. Application of the linear scaling (LS) method has significantly improved the RegCM4 model performance with respect to measured soil temperature, which allows an accurate evaluation of the impact of climate change on the soil temperature under different soil management systems. These findings indicate that RegCM4 can be applied as a reliable predictor of soil temperature under arid ecosystems for which there is a gap in site data availability. [ABSTRACT FROM AUTHOR]