Bias Correction Methods Research Articles

A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data

BackgroundCRISPR-Cas9 dropout screens are formidable tools for investigating biology with unprecedented precision and scale. However, biases in data lead to potential confounding effects on interpretation and compromise overall quality. The activity of Cas9 is influenced by structural features of the target site, including copy number amplifications (CN bias). More worryingly, proximal targeted loci tend to generate similar gene-independent responses to CRISPR-Cas9 targeting (proximity bias), possibly due to Cas9-induced whole chromosome-arm truncations or other genomic structural features and different chromatin accessibility levels.ResultsWe benchmarked eight computational methods, rigorously evaluating their ability to reduce both CN and proximity bias in the two largest publicly available cell-line-based CRISPR-Cas9 screens to date. We also evaluated the capability of each method to preserve data quality and heterogeneity by assessing the extent to which the processed data allows accurate detection of true positive essential genes, established oncogenetic addictions, and known/novel biomarkers of cancer dependency.Our analysis sheds light on the ability of each method to correct biases under different scenarios. AC-Chronos outperforms other methods in correcting both CN and proximity biases when jointly processing multiple screens of models with available CN information, whereas CRISPRcleanR is the top performing method for individual screens or when CN information is not available. In addition, Chronos and AC-Chronos yield a final dataset better able to recapitulate known sets of essential and non-essential genes.ConclusionsOverall, our investigation provides guidance for the selection of the most appropriate bias-correction method, based on its strengths, weaknesses and experimental settings.

Projection of Changes in Rainfall and Drought Based on CMIP6 Scenarios on the Ca River Basin, Vietnam

In this study, future precipitation and drought in the Ca river basin, Vietnam, were projected based on an ensemble of 27 CMIP6 models for four climate change scenarios. The impact of climate change on precipitation and drought was investigated. Monthly precipitation observation data were adjusted using the bias correction method. To detect drought events, the Standard Precipitation Index (SPI) was employed. Changes in drought were assessed using SPI3, SPI6, and SPI12. Although the amount of annual total precipitation slightly increased, the drought events may become more severe. There is a high likelihood of increased drought intensity and severity in Vietnam due to climate change. The frequency of droughts is likely to change depending on the location and climate change scenario. We found that the frequency and severity of droughts may be altered depending on the window size of SPI. The short-term drought events will be more frequent and severe, and long-term drought events will become more severe in the Ca river basin.

A Probabilistic Data Recovery Framework Against Load Redistribution Attacks Based on Bayesian Network and Bias Correction Method

A Probabilistic Data Recovery Framework Against Load Redistribution Attacks Based on Bayesian Network and Bias Correction Method

Estimation of effective number of breeders and effective population size in an abundant and heavily exploited marine teleost.

Obtaining reliable estimates of the effective number of breeders (N b) and generational effective population size (N e) for fishery-important species is challenging because they are often iteroparous and highly abundant, which can lead to bias and imprecision. However, recent advances in understanding of these parameters, as well as the development of bias correction methods, have improved the capacity to generate reliable estimates. We utilized samples of both single-cohort young of the year and mixed-age adults from two geographically and genetically isolated stocks of the Australasian snapper (Chrysophrys auratus) to investigate the feasibility of generating reliable N b and N e estimates for a fishery species. Snapper is an abundant, iteroparous broadcast spawning teleost that is heavily exploited by recreational and commercial fisheries. Employing neutral genome-wide SNPs and the linkage-disequilibrium method, we determined that the most reliable N b and N e estimates could be derived by genotyping at least 200 individuals from a single cohort. Although our estimates made from the mixed-age adult samples were generally lower and less precise than those based on a single cohort, they still proved useful for understanding relative differences in genetic effective size between stocks. The correction formulas applied to adjust for biases due to physical linkage of loci and age structure resulted in substantial upward modifications of our estimates, demonstrating the importance of applying these bias corrections. Our findings provide important guidelines for estimating N b and N e for iteroparous species with large populations. This work also highlights the utility of samples originally collected for stock structure and stock assessment work for investigating genetic effective size in fishery-important species.

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from Climate Models

Abstract Precipitation values produced by climate models are biased due to the parameterization of physical processes and limited spatial resolution. Current bias-correction approaches usually focus on correcting lower-order statistics (mean and standard deviation), which make it difficult to capture precipitation extremes. However, accurate modeling of extremes is critical for policymaking to mitigate and adapt to the effects of climate change. We develop a deep learning framework, leveraging information from key dynamical variables impacting precipitation to also match higher-order statistics (skewness and kurtosis) for the entire precipitation distribution, including extremes. The deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Using climate model simulation data and observations that are climatologically similar but not strictly paired, the UFNet identifies and corrects the climate model biases, significantly improving the estimation of daily precipitation as measured by a broad range of spatiotemporal statistics. In particular, UFNet significantly improves the underestimation of extreme precipitation values seen with current bias-correction methods. Our approach constitutes a generalized framework for correcting other climate model variables which improves the accuracy of the climate model predictions, while utilizing a simpler and more stable training process.

A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware river basin, USA

Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called ‘noise’. Traditional point-scale metrics (e.g., R2) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of depth to bedrock (DTB) in the Delaware River Basin. Geostatistical analysis shows that approximately one third of the DTB variance is at spatial scale smaller than 2 km. Hence, we interpret our point-scale R2 of 0.3 (testing data) to be sufficient for regional-scale modelling. Bias-correction methods improve performance at two of the three MPE scales: point-scale change is negligible, while distributional and geostatistical performance improves. In contrast, bias correction applied to a global DTB model does not improve MPE performance. This work encourages scale-appropriate performance evaluations to enable effective model intercomparison.

Assessment of CMIP6 GCMs for selecting a suitable climate model for precipitation projections in Southern Thailand

The selection of General Circulation Models (GCMs) is critical due to computational limitations and underlying uncertainties. This study provides a comprehensive assessment of three bias correction (BC) methods, namely the delta change method (DT), quantile mapping (QM), and empirical quantile mapping (EQM). Utilizing precipitation data from 30 observation stations across Southern Thailand, the evaluation encompasses five CMIP6 GCM models (CAMS-CSM1-0, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, IPSL-CM6A-LR). Evaluation metrics, root mean square error (RMSE), mean absolute error (MAE), Pearson's correlation (r), index of agreement (d), and mean bias error (MBE) are employed to assess BC methods. Evaluation measures suggest that the DT method outperforms EQM and QM, with higher accuracy and lower errors (DT: RMSE = 3.61, MAE = 2.32; EQM: RMSE = 3.82, MAE = 3.70). Taylor diagrams show that CNRM-ESM2-1 has the highest correlation across sites (r = 0.36), albeit with a wider dispersion, while CanESM5 has a more balanced performance. Significant annual precipitation increases are projected for different spans 2021-30, 2061-70, and 2091–2100, particularly under SSP585, which will influence flood risk, water management, and climate adaptation. Future projections with SSP585 continuously projecting a larger probability of increased precipitation. The DT method is recommended for the downscaling of CMIP6 GCMs for precipitation projections in Southern Thailand, recognizing its superior performance. The study's findings provide a foundation for informed decision-making and adaptation planning in southern Thailand, urging policymakers to prioritize climate resilience and adaptation strategies while using a multi-model ensemble approach for robust climate forecasts.

Bias-Correction Methods for the Unit Exponential Distribution and Applications

The bias of the maximum likelihood estimator can cause a considerable estimation error if the sample size is small. To reduce the bias of the maximum likelihood estimator under the small sample situation, the maximum likelihood and parametric bootstrap bias-correction methods are proposed in this study to obtain more reliable maximum likelihood estimators of the unit exponential distribution parameters. The procedure to implement the bias-corrected maximum likelihood estimation method is derived analytically, and the steps to obtain the bias-corrected bootstrap estimators are presented. The simulation results show that the proposed maximum likelihood bootstrap bias-correction method can significantly reduce the bias and mean squared error of the maximum likelihood estimators for most of the parameter combinations in the simulation study. A soil moisture data set and a numerical example are used for illustration.

Machine learning and CORDEX-Africa regional model for assessing the impact of climate change on the Gilgel Gibe Watershed, Ethiopia

Climate change is one of the most pressing challenges of our time, profoundly impacting global water resources and sustainability. This study aimed to predict the long-term effects of climate change on the Gilgel Gibe watershed by integrating machine learning (ML) methods and climate model scenarios. Utilizing an ensemble mean of four regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa project, we forecast future climatic conditions. Although global and regional climate simulations offer valuable insights, their limitations necessitate alternative approaches, such as ML, for improved accuracy. Employing an ensemble ML model with Random Forest (RF), Extra Tree (ET), and CatBoost (CB) algorithms, we assessed various bias-correction methods using historical data from 1993 to 2009. Our results highlight the effectiveness of distribution mapping (DM) in capturing temperature variability and precipitation patterns, using the power transpiration (PT) method to represent precipitation variability. Projections indicate a decline in future precipitation under the RCP 8.5 (−32.2%) and SSP 4.5 (−88.8%) for 2024–2049, with further decreases expected for 2050–2099. Conversely, temperatures will rise under RCP 4.5 (TMAX 0.67 °C) and RCP 8.5 (TMAX 0.25 °C and TMIN 1.11 °C) in the near term, exacerbated by higher emissions under SSP 4.5 and 8.5. By leveraging an ensemble mean of four observed RCMs in an ML framework, our study successfully reproduced future Coupled Model Intercomparison Project (CMIP5) and (CMIP6) climatic datasets, with the CB model demonstrating superior performance in predicting future precipitation and temperature trends. These findings offer valuable insights for shaping future climate scenarios and informing policy decisions for the Gilgel Gibe Watershed, thereby enhancing water resource management in the basin and its environs.

Improved automated spot counting and modeling with bias correction

ABSTRACT 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.

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

ABSTRACT The objective of this study was to evaluate the best performed bias correction methods to simulate the regional climate models for future climate change projections in Muger Subbasin. Delta change methods perform very well with a coefficient of correlation of 0.99 and a percent of bias –3. When we compare its corrected simulation result with observed data, the delta change method seems to have with no biases for maximum temperature, but increases by 1.67 °C from the mean for minimum temperature of 0.39 and 38.41 mm for monthly and annual precipitation, respectively. Delta change methods underestimate the model result for both temperature and precipitation. Linear scaling and variance scaling methods overestimate the maximum temperature of the simulation by 0.002 and 0.004 °C from the mean of the observed data, but it underestimates 1.59 and 1.56 °C the minimum temperature, respectively. The long-term temperature projection values (2060–2090) are higher than the near-term projections (2030–2060) for both RCP2.6 and RCP8.5 scenarios. Similarly, the change in annual precipitation for the long-term is higher than the near-term projections. As a conclusion, the results draw attention to the fact that bias-adjusted regional climate models data are crucial for the provision of local climate change impact studies in the Muger Subbasin.

"Characterization of a New Iron Isotopic Reference Material Using Two Independent Methods for Instrumental Mass Bias Correction "

"Characterization of a New Iron Isotopic Reference Material Using Two Independent Methods for Instrumental Mass Bias Correction "

How virtuous are the bias corrected CMIP6 models in the simulation of heatwave over different meteorological subdivisions of India?

With rising global temperatures, extreme weather events have become more frequent, intense and of longer duration. CMIP6 GCMs provide improved climate simulations that need robust evaluation for historical period for reliable future projections. The present study assesses the ability of bias corrected CMIP6 14 Global Climate Models (GCMs) in simulating heat wave over India for March–June during the historical period (1951–2014). Heat waves were identified using IITM criteria. Model biases were removed using variance scaling bias correction method that showed higher correlation (0.93) and lower root mean square error (2.15) and improvement in approximating the inter-annual variability as well as spatial patterns as observed maximum temperature after bias correction. Evaluation of model performance for 95th and 99th percentile maximum temperature and heatwaves showed that most of the models simulate these extremes similar to observation. Northwestern, Central and South-central regions recorded highest number of heatwaves with a frequency of 50 heatwave days/decade, which were captured by the most of the GCMs varying in decadal frequency over the region. Among the GCM, although all models were found competent, ACCESS-ESM1–5, MPI-ESM1–2HR and MRI-ESM2–0 models were found to be the best performing models for extreme indices and heat wave simulation over India. The study will aid to the current understanding of CMIP6-GCMs performances over the different meteorological subdivisions of India and pave way for future projection of heat waves as well as reduction in uncertainty among the models.

No optimal spatial filtering distance for mitigating sampling bias in ecological niche models

AbstractAimThe continuous development of statistical tools applied to ecology has contributed to great advances for modelling species' niches and distributions from opportunistic observations. However, as these observations are subject to biases caused by spatial variation in sampling effort, ecological niche models (ENMs) are also frequently biased. Among several bias correction methods that have been proposed, spatial filtering—imposing a minimum distance between occurrences—is widely used, yet lacks clear guidelines for choosing the filtering distance. Here, we aimed to explore the impact of spatial filtering distances on the performance of ENMs.LocationEurope.TaxonVirtual species.MethodsWe applied ENMs to two virtual species with contrasting levels of specialisation, across a spectrum of modelling conditions, bias types and sample sizes.ResultsModels applied to the specialist species had on average a lower performance than those applied to the generalist species. Using a biased sample reduced model performance, especially when the bias was strong, and when the sample size was large. In many cases, spatial filtering failed to improve model performance or even reduced it. We did find an improvement for the generalist species modelled with large and strongly biased datasets. However, there was no optimal filtering distance, as this improvement was linearly and positively associated with filtering distance. Moreover, because the initial bias was strong and the filtered dataset became very small, the resulting models had only very low accuracy.Main ConclusionsOur results suggest that there is no optimal filtering distance for dealing with sampling bias in ENMs, and that spatial filtering never improves model performance enough to draw accurate predictions. We therefore recommend spatial filtering to be employed cautiously, only when enough data are available, and bearing in mind that its effectiveness remains highly uncertain.

Bias correction in species distribution models based on geographic and environmental characteristics

Correcting sampling bias in species distribution models (SDMs) is challenging. The difficulty lies in accurately identifying and quantifying bias and the scarcity of samples, which greatly impedes the implementation of bias correction. Current methods often adjust the distribution of presence or background points within geographic or environmental spaces to correct the sampling bias in probability estimation within SDMs. However, these methods may lead to information loss, rely on subjective assumptions, and often separate geography and environment when correcting for bias. This study proposes a novel and easily implementable method termed “aggregation background.” This method selects background data based on the aggregation degree of presence points in the geographic and environmental feature space, thereby approximating the representation and correction of sampling bias in the presence samples. We compared this new method with other prevalent sampling bias correction methods in the existing literature by analyzing ecological authenticity. Under varying biases and sample sizes, the aggregation background and geographic filtering methods achieved more accurate species distribution predictions compared to the target group background and other methods. Notably, when the sample size was small (≤70), the aggregation background was superior to that obtained using the geographic filtering method. These findings underscore the effectiveness of the aggregation background in improving bias correction using limited available presence sample data, without relying on assumptions about sampling bias. Our method provides a new approach for correcting complex unknown biases in SDMs.

Bias-corrected NESM3 global dataset for dynamical downscaling under 1.5 °C and 2 °C global warming scenarios

Dynamical downscaling is vital for generating finer-scale climate projections. Recently, a set of simulations under four types of 1.5/2 °C global warming scenarios are available with Nanjing University of Information Science and Technology Earth System Model (NESM). However, NESM3’s bias in large-scale driving variables would degrade downscaled simulations. We corrected NESM3 bias in terms of climate mean and inter-annual variance against ERA5 using a novel bias correction method and then produced a set of bias-corrected datasets for dynamical downscaling. The bias-corrected NESM3 spans the historical period for 1979–2014 and four future scenarios (i.e., 1.5 °C overshoot for 2070–2100, stabilized 1.5/2 °C for 2070–2100, and transient 2 °C for 2031–2061) with 1.25° × 1.25° horizontal resolution at six-hourly intervals. Our evaluation suggests that bias-corrected NESM3 outperforms the original NESM3 in the climatological mean of seasonal mean and variability, as well as climate extreme events during the historical period. This bias-corrected dataset is expected to generate more reliable projections for regional climate and environment under 1.5/2 °C global warming.

Impact of Future Climate Scenarios and Bias Correction Methods on the Achibueno River Basin

Impact of Future Climate Scenarios and Bias Correction Methods on the Achibueno River Basin

Assessing Climate Change Impacts on Streamflow and Baseflow in the Karnali River Basin, Nepal: A CMIP6 Multi-Model Ensemble Approach Using SWAT and Web-Based Hydrograph Analysis Tool

Our study aims to understand how the hydrological cycle is affected by climate change in river basins. This study focused on the Karnali River Basin (KRB) to examine the impact of extreme weather events like floods and heat waves on water security and sustainable environmental management. Our research incorporates precipitation and temperature projections from ten Global Circulation Models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6). We applied thirteen statistical bias correction methods for precipitation and nine for temperatures to make future precipitation and temperature trend projections. The research study also utilized the Soil and Water Assessment Tool (SWAT) model at multi-sites to estimate future streamflow under the Shared Socioeconomic Pathway (SSP) scenarios of SSP245 and SSP585. Additionally, the Web-based Hydrograph Analysis Tool (WHAT) was used to distinguish between baseflow and streamflow. Our findings, based on the Multi-Model Ensemble (MME), indicate that precipitation will increase by 7.79–16.25% under SSP245 (9.43–27.47% under SSP585) and maximum temperatures will rise at rates of 0.018, 0.048, and 0.064 °C/yr under SSP245 (0.022, 0.066, and 0.119 °C/yr under SSP585). We also anticipate that minimum temperatures will increase at rates of 0.049, 0.08, and 0.97 °C/yr under SSP245 (0.057, 0.115, and 0.187 °C/yr under SSP585) for near, mid, and far future periods, respectively. Our research predicts an increase in river discharge in the KRB by 27.12% to 54.88% under SSP245 and 45.4% to 93.3% under SSP585 in different future periods. Our finding also showed that the expected minimum monthly baseflow in future periods will occur earlier than in the historical period. Our study emphasizes the need for sustainable and adaptive management strategies to address the effects of climate change on water security in the KRB. By providing detailed insights into future hydrological conditions, this research serves as a critical resource for policymakers and stakeholders, facilitating informed decision-making for the sustainable management of water resources in the face of climate change.

Continental-scale bias-corrected climate and hydrological projections for Australia

Abstract. The Australian Bureau of Meteorology has developed a national hydrological projections (NHP) service for Australia. The NHP aimed to provide nationally consistent hydrological projections across jurisdictional boundaries to support planning of water-dependent industries. NHP is complementary to those previously produced by federal and state governments, universities, and other organisations for limited geographical domains. The projections comprise an ensemble of application-ready bias-corrected climate model data, derived hydrological projections at daily temporal and 0.05° × 0.05° spatial resolution for the period 1960–2099, and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5). The spatial resolution of the projections matches that of gridded historical reference data used to perform the bias correction and the Bureau of Meteorology's operational gridded hydrological model. Three bias correction techniques were applied to four CMIP5 global climate models (GCMs), and one method was applied to a regional climate model (RCM) forced by the same four GCMs, resulting in a 16-member ensemble of bias-corrected GCM data for each emission scenario. The bias correction was applied to fields of precipitation, minimum and maximum temperature, downwelling shortwave radiation, and surface winds. These variables are required inputs to the Bureau of Meteorology's landscape water balance hydrological model (AWRA-L), which was forced using the bias-corrected GCM and RCM data to produce a 16-member ensemble of hydrological output. The hydrological output variables include root zone soil moisture (moisture in the top 1 m soil layer), potential evapotranspiration, and runoff. Here we present an overview of the production of the hydrological projections, including GCM selection, bias correction methods and their evaluation, technical aspects of their implementation, and examples of analysis performed to construct the NHP service. The data are publicly available on the National Computing Infrastructure (https://doi.org/10.25914/6130680dc5a51, Bureau of Meteorology, 2021), and a user interface is accessible at https://awo.bom.gov.au/products/projection/ (last access: 24 November 2023).

Amplified potential for vegetation stress under climate-change-induced intensifying compound extreme events in the Greater Mediterranean Region

Abstract. The Mediterranean Basin is one of the regions most affected by climate change, which poses significant challenges to agricultural efficiency and food security. While rising temperatures and decreasing precipitation levels already impose great risks, the effects of compound extreme events (CEEs) can be significantly more severe and amplify the risk. It is therefore of high importance to assess these risks under climate change on a regional level to implement efficient adaption strategies. This study focuses on false-spring events (FSEs), which impose a high risk of crop losses during the beginning of the vegetation growing period, as well as heat–drought compound events (HDCEs) in summer, for a high-impact future scenario (Representative Concentration Pathway (RCP) 8.5). The results for 2070–2099 are compared to 1970–1999. In addition, deviations of the near-surface atmospheric state under FSEs and HDCEs are investigated to improve the predictability of these events. We apply a multivariate, trend-conserving bias correction method (MBCn) accounting for temporal coherency between the inspected variables derived from the European branch of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). This method proves to be a suitable choice for the assessment of percentile-threshold-based CEEs. The results show a potential increase in frequency of FSEs for large portions of the study domain, especially impacting later stages of the warming period, caused by disproportionate changes in the behavior of warm phases and frost events. Frost events causing FSEs predominantly occur under high-pressure conditions and northerly to easterly wind flow. HDCEs are projected to significantly increase in frequency, intensity, and duration, mostly driven by dry, continental air masses. This intensification is several times higher than that of the univariate components. This study improves our understanding of the unfolding of climate change in the Mediterranean and shows the need for further, locally refined investigations and adaptation strategies.