Bias Correction Methods Research Articles

Identification of thresholds and key drivers on Water Use Efficiency in different maize ecoregions in Yellow River Basin of China

Identifying the constrains on water use efficiency (WUE) of crops along a wet-to-dry gradient is important due to irrigation water scarcity, as well as the increasing drought risk under climate change in China. This study coupled five high-resolution climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) with the Decision Support System for Agrotechnology Transfer (DSSAT)-CERES-Maize model to quantify drought risk and the drivers affecting WUE in five major maize ecoregions of the Yellow River Basin (YRB) under three future scenarios (SSP126, SSP370, and SSP585) for both the historical baseline (1985-2014) and three future periods: 2021-2040 (2030s), 2041-2070 (2050s), and 2071-2100 (2080s). And a bias correction method was implemented for the crop model to analyze optimal WUE thresholds for maize across varying dry-wet gradients. The results indicated that future drought risk will likely persist in the YRB under all scenarios, but with regional differences in drought severity and frequency. The southwestern region (V) experienced the highest frequency of drought (62.50%-SSP126), while the northwestern region (III) exhibited the lowest frequency (33.00%-SSP585) in 2030s, and 83.30% of areas in the southwestern (V) showed significant wetting in the 2080s under SSP126. The bias-corrected CERES-Maize model effectively simulated crop yield and evapotranspiration (ET), resulting in an average reduction of 4.00% and 9.73% in normalized root mean square error (nRMSE) respectively. Distinct WUE thresholds ranging from 1.96 to 8.41 kg ha−1 mm-1 were observed across various scenarios-periods in the maize regions, mostly under slight and moderate dry/wet conditions. Notably, all SSP585 scenarios demonstrated a decrease in WUE thresholds compared to the baseline. Across all scenarios and periods, WUE was mainly driven by yield in the eastern regions (I and II) but by ET in the western regions (III, IV, and V). These findings suggest that regions experiencing varying degrees of drought severity should undergo differentiated management and optimization of agricultural practices to improve WUE under future climate scenarios.

A comprehensive comparison of bias correction methods in climate model simulations: application on ERA5-Land across different temporal resolutions

A comprehensive comparison of bias correction methods in climate model simulations: application on ERA5-Land across different temporal resolutions

Bias correction of CORDEX-Africa regional climate model simulations for climate change projections in northeastern Lake Chad: Comparative analysis of three bias correction methods

Bias correction of CORDEX-Africa regional climate model simulations for climate change projections in northeastern Lake Chad: Comparative analysis of three bias correction methods

Relationship between physical exercise and college students' social adaptation: the chain mediating role of self-esteem and peer attachment.

Physical exercise is an important way for college students to keep healthy, and social adaptation is an important part of college students' mental health. Therefore, this study explores strategies to enhance college students' social adaptation from the perspective of physical exercise, examining the correlation between physical exercise and college students' social adaptation, and delving into the roles of self-esteem and peer attachment in this relationship. A stratified cluster sampling method was used to collect data from 809 college students at Zhaoqing University (average age 19.88 ± 1.22, of whom 399 were male and 410 were female) using the physical exercise scale, college students' social adaptation scale, self-esteem scale, and peer attachment scale. For data analysis, Pearson correlation analysis, structural equation modeling, and bias-corrected percentile bootstrap methods were sequentially performed. (1) Physical exercise was positively correlated with college students' social adaptation (r = 0.58, p < 0.01), and the direct path between physical exercise and college students' social adaptation was significant (β = 0.28, p < 0.01, CI[0.22, 0.33]); (2) Physical exercise was positively correlated with self-esteem (β = 0.56, p < 0.01, CI[0.50, 0.62]) and peer attachment (β = 0.18, p < 0.01, CI[0.11, 0.26]); self-esteem was positively correlated with peer attachment (β = 0.36, p < 0.01, CI[0.28, 0.43]) and college students' social adaptation (β = 0.43, p < 0.01, CI[0.37, 0.49]); peer attachment was positively correlated with college students' social adaptation (β = 0.18, p < 0.01, CI[0.12, 0.23]); (3) The relationship between physical exercise and social adaptation was not only mediated independently by self-esteem and peer attachment, but also indirectly by the same two factors in a chain reaction. Physical exercise can not only directly predict college students' social adaptation, but also indirectly predict college students' social adaptation through the independent mediation and chain mediation of self-esteem and peer attachment. It reveals that we should combine more important physical exercise with mental health education for students.

HiCPC: A new 10-km CMIP6 downscaled daily climate projections over China

Accurate climate projections are critical for various applications and impact assessments in environmental science and management. This study presents HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China), a novel dataset tailored to China’s specific needs. HiCPC leverages outputs from 22 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). To address inherent biases in daily GCM simulations, an advanced Bias Correction and Spatial Disaggregation (BCSD) method is employed, using the China Meteorological Forcing Dataset (CMFD) as a reference. HiCPC offers detailed daily precipitation and temperature data across China at an enhanced spatial resolution of 0.1° × 0.1°. It covers both the historical period (1979–2014) and future projections (2015–2100) based on four CMIP6 Shared Socioeconomic Pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Upon validation, HiCPC demonstrates good performance, surpassing CMIP6 GCMs across the historical period. This reinforces its significance for essential research in climate change evaluation and its associated implications within China.

Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran

In this study, the performances of 40 Coupled Model Intercomparison Project Phase 6 are evaluated against observational data at synoptic stations in Iran using various evaluation criteria. The results reveal diverse model accuracy across different climate conditions and criteria, emphasizing particularly notable disparities in the nonstationarity R criterion compared to others. Although according to the ranking of the raw and bias-corrected outputs of CMIP6 GCMs for Iran, the NorESM2-MM, AWI-ESM-1-1-LR, and MPI-ESM1-2-LR models are consistently among the top six ranked models for precipitation in both raw and corrected outputs. For temperature, MPI-ESM1-2-LR, TaiESM1, INM-CM4-8, and IITM-ESM are consistently among the top six models for both the raw and bias-corrected outputs of CMIP6 GCMs. The Bias correction methods, including quantile mapping and linear scaling, integrated with Bayesian model averaging, were applied. While quantile mapping demonstrates superior performance and less disparity than linear scaling, it proves ineffective for correcting biases at stations with bias nonstationarity over time. The RMSE for monthly precipitation ranges from almost 0 to 200 mm, with a large RMSE value related to the high precipitation stations, and the monthly temperature exhibits a range of 0 to 4 °C. The use of a multi-model ensemble improves accuracy compared to individual models, resulting in a reduction in the differences between the minimum and maximum RMSE values from 178.6 to 91.0. Additionally, the range for mean absolute error decreases from 126.9 to 93.3, and the difference in the correlation coefficient narrows from 0.9 to 0.42. Averaging models after bias correction prevents significant fluctuations while maintaining higher accuracy, in contrast to the second method, which involves bias-correcting models after averaging.

Bias and multiscale correction methods for variational state estimation

Data assimilation performance can be significantly impacted by biased noise in observations, altering the signal magnitude and introducing fast oscillations or discontinuities when the system lacks smoothness. To mitigate these issues, this paper employs variational state estimation using the so-called parametrized-background data-weak method. This approach relies on a background manifold parametrized by a set of constraints, enabling the state estimation by solving a minimization problem on a reduced-order background model, subject to constraints imposed by the input measurements. The proposed formulation incorporates a novel bias correction mechanism and a manifold decomposition that handles rapid oscillations by treating them as slow-decaying modes based on a two-scale splitting of the classical reconstruction algorithm. The method is validated in different examples, including the assimilation of biased synthetic data, discontinuous signals, and Doppler ultrasound data obtained from experimental measurements.

Inference of $$S^{\prime }_{pmk}$$ based on bias-corrected methods of estimation for generalized exponential distribution

Inference of $$S^{\prime }_{pmk}$$ based on bias-corrected methods of estimation for generalized exponential distribution

Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes

Climate change is increasingly impacting water availability. National-scale hydrologic models simulate streamflow resulting from many important processes, but often without processes such as human water use and management activities. This work explores and tests methods to account for such omitted processes using one national-scale hydrologic model. Two bias correction methods, Flow Duration Curve (FDC) and Auto-Regressive Integrated Moving Average (ARIMA), are tested on streamflow simulated by the US Geological Survey National Hydrologic Model (NHM-PRMS), which omits irrigation pumping. A semi-arid agricultural case study is used. FDC and ARIMA perform better for correcting low and high flows, respectively. A hybrid method performs well at both low and high flows; typical Nash-Sutcliffe values increased from <-1.00 to about 0.75. Results suggest methods with which national-scale hydrologic models can be bias-corrected for omitted processes to improve regional streamflow estimates. Utility of these correction methods in simulation of future projections is discussed.

Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective

General circulation models are vital tools in climate change research, profoundly influencing numerous hydroclimatological studies, but often exhibit systematic biases due to limitations in parameterization. To correct these biases, bias correction (BC) methods, which are heavily dependent on the quality of historical reference data, are employed. Ideally, accurate data comes from a dense network of gauges, but due to their limited availability, reanalysis data is frequently used instead. Although reanalysis data provides consistent global and temporal coverage, its assimilated nature can introduce additional biases, potentially increasing error propagation particularly in the multivariate perspective. Thus, this study investigates the reliability of reanalysis data as a reference for multivariate bias correction (MBC) and utilizes a two-stage bias correction approach that integrates both gauge and reanalysis data to leverage their respective strengths. Conducting a case study in South Korea, we utilized gauge data, ERA5 reanalysis data, and outputs from 10 CMIP6 General Circulation Models. We implemented three-nested experiments to test the robustness of this two-stage approach across various dimensions. Results show that using reanalysis data as a reference can lead to bias propagation, especially misrepresenting precipitation and wind dependencies in high elevation areas. However, the two-stage approach has shown an enhancement in mitigating these limitations, even when gauge data is temporally sparse, and across different combinations of MBC methods. Finally, we highlight the current limitations and potential for future research to focus on effectively combining gauge and reanalysis data to compensate for each other’s weaknesses, thereby enhancing the accuracy and reliability in future climate realizations.

Assessing Climate Extremes in Dynamical Downscaling Simulations Driven by a Novel Bias‐Corrected CMIP6 Data

AbstractDynamical downscaling is a widely‐used approach for generating regional projections of climate extremes at a finer scale. However, the systematic bias of the global climate model (GCM) generally degrades the reliability of projections. Recently, novel bias‐corrected CMIP6 data was generated using a mean‐variance‐trend (MVT) bias correction method for dynamical downscaling simulation. To validate the effectiveness of this data in the dynamical downscaling simulation of climate extremes, we carry out three Weather Research and Forecasting (WRF) simulations over Asia‐western North Pacific with a 25 km grid spacing from 1980 to 2014. The dynamical downscaling simulations driven by the raw GCM data set (hereafter WRF_GCM) and the bias‐corrected GCM (hereafter WRF_GCMbc) were assessed against the simulation driven by the European Center for Medium‐Range Weather Forecasts Reanalysis 5 data set. The results indicate that the MVT bias correction significantly improves the climatological mean and inter‐annual variability of downscaled climate extreme indices. In terms of the climatological mean, the WRF_GCMbc shows a 25%–82% decrease in root mean square errors (RMSEs) against the WRF_GCM. As for the inter‐annual variability, the MVT bias correction can improve the downscaling simulation of almost all precipitation extreme indices and 70% of the temperature extreme indices, characterized by the RMSEs' reduction of 1%–58%. The improvements of the climate extremes in terms of the climatological mean in the WRF_GCMbc primarily stem from the improved large‐scale circulation and ocean evaporation in the WRF, which in turn improves the downscaled precipitation and 2 m temperature through the advection, radiation, and surface energy exchange process.

Does Applying Subsampling in Quantile Mapping Affect the Climate Change Signal?

Bias in regional climate model (RCM) data makes bias correction (BC) a necessary pre-processing step in climate change impact studies. Among a variety of different BC methods, quantile mapping (QM) is a popular and powerful BC method. Studies have shown that QM may be vulnerable to reductions in calibration sample size. The question is whether this also affects the climate change signal (CCS) of the RCM data. We applied four different QM methods without subsampling and with three different subsampling timescales to an ensemble of seven climate projections. BC generally improved the RCM data relative to observations. However, the CCS was significantly modified by the BC for certain combinations of QM method and subsampling timescale. In conclusion, QM improves the RCM data that are fundamental for climate change impact studies, but the optimal subsampling timescale strongly depends on the chosen QM method.

Bias correction and ensemble techniques in statistical downscaling model for rainfall prediction using Tweedie-LASSO in West Java, Indonesia

Rainfall is a climate element with high variations in space and time scales, so it is not easy to predict. One way to predict rainfall is statistical downscaling (SD). SD can predict local rainfall based on Global Circulation Model (GCM) data. The Decadal Climate Prediction Project (DCPP), one of the GCMs, originates from adjacent grids and experiences multicollinearity problems. Rainfall as a response variable is Tweedie Compound Poisson Gamma (TCPG) distribution data because it has a discrete component (rainfall events) and a continuous component (rainfall intensity), so SD modelling will be carried out using Tweedie-LASSO. This research aims to compare the performance of bias correction and ensemble methods in SD in predicting rainfall in West Java, Indonesia. Bias correction uses Empirical Quantile Mapping (EQM) with CHIRPS data, and the ensemble method uses a stacking technique with Random Forest (Stacking-RF) due to the varied characteristics of DCPP model sources. Evaluation results using Root Mean Square Error Prediction (RMSEP) and correlation coefficient show that bias correction improves single-model performance but not ensemble models. Besides that, ensemble models outperform single models both before and after bias correction. The combination of bias correction and ensemble modelling can be recommended when conducting SD to enhance the prediction capability of rainfall at stations and other areas.

Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model

AbstractMonthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.

A Finite-sample bias correction method for general linear model in the presence of differential measurement errors

A Finite-sample bias correction method for general linear model in the presence of differential measurement errors

Bootstrap Methods for Bias-Correcting Probability Distribution Parameters Characterizing Extreme Snow Accumulations

Accurately quantifying the threat of collapse due to the weight of settled snow on the roof of a structure is crucial for ensuring structural safety. This quantification relies upon direct measurements of the snow water equivalent (SWE) of settled snow, though most weather stations in the United States only measure snow depth. The absence of direct load measurements necessitates the use of modeled estimates of SWE, which often results in the underestimation of the scale/variance parameter of the distribution of annual maximum SWE. This paper introduces a novel bias correction method that employs a bootstrap technique with regression-based models to calibrate the variance parameter of the distribution. The efficacy of this approach is demonstrated on real and simulated datasets. The findings reveal varied levels of success, with the efficacy of the proposed approach being inherently dependent on the quality of the selected regression-based model. These findings demonstrate that integrating our approach with a suitable regression-based model can produce unbiased or nearly unbiased annual maximum SWE distribution parameters in the absence of direct SWE measurements.

SIMEX-Based and Analytical Bias Corrections in Stocking–Lord Linking

Stocking–Lord (SL) linking is a popular linking method for group comparisons based on dichotomous item responses. This article proposes a bias correction technique based on the simulation extrapolation (SIMEX) method for SL linking in the 2PL model in the presence of uniform differential item functioning (DIF). The SIMEX-based method is compared to the analytical bias correction methods of SL linking. It turned out in a simulation study that SIMEX-based SL linking performed best, is easy to implement, and can be adapted to other linking methods straightforwardly.

The Key Role of Temporal Stratification for GCM Bias Correction in Climate Impact Assessments

AbstractCharacterizing 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.

Evaluation and correction of solar irradiance in Somaliland using ground measurements and global reanalysis products

Properly understanding solar irradiance can help accurately quantify the solar energy resource and guide sustainable development projects, particularly where measured solar data are scarce or suffer from detrimental data quality issues. This study aims to assess and improve solar global horizontal irradiance (GHI) data from a diverse range of global reanalysis datasets by utilizing measured data from two ground weather stations located in Somaliland. A comprehensive evaluation framework is employed, combining various statistical and regression error metrics, whereas bias correction methods are implemented. The comparative study covers several analytical facets such as the hourly, daily and monthly data analysis before and after correction along with analyzing the seasonal variations, clear-sky and all-sky conditions. The analysis revealed pattern of an overall underestimation of GHI with varying degrees of accuracy in the estimated GHI datasets before and after correction. The annual ranges for rMBE, rMAE, rRMSE and R2 extend from 3–31%, 12–33%, 19–53% and 0.797–0.979, respectively, across all datasets for six-hourly data in the two observed stations. Following bias correction, the ranges for rMBE, rMAE, rRMSE reduce to is 0%, 8–31%, 11–34% and R2 increase to 0.897–0.984 respectively. While certain datasets such as MERRA-2 and SARAH-2 demonstrate close alignment with ground data before correction, other especially ERA5-Land exhibit exceptional improvement after the bias correction.

A novel method addressing NGS-based mappability bias for sensitive detection of DNA alterations.

A turning point in cancer research is the introduction of massively parallel sequencing technology which greatly reduced the cost and time for genome sequencing. This enhanced the scope for detecting and analyzing the role of structural alterations in cancer. However, certain bias exists in NGS-based approaches, which badly affects the CNV identification process. Moreover, DNA repeats existing in CNV regions need special attention as they will degrade the performance of majority of the existing CNV detection tools, even after applying generalized bias correction method. This motivated this work, where a novel method has been designed to address the issue of DNA repeats and thereby mappability bias existing in regions of CNV.The method consists of three phases, where the first phase computes the alignment information of uniquely mapped DNA reads, considering the base quality and base mismatch parameters at nucleotide level precision. The second and the third phase use a novel approach to allocate the non-uniquely mapped reads to an optimal region of the DNA repeats based on a probabilistic membership model. The proposed method is capable of identifying CNVs present in coding, as well as non-coding region of the DNA, and is also capable of detecting CNVs existing in DNA repeat regions. The methodology achieves a sensitivity greater than [Formula: see text] during the performed simulations, and on real data, the detected variants are validated with the database of genomic variants, where the percentage overlap is also greater than 95%, and has achieved much better breakpoint prediction, as compared with other popular bias correction CNV detection methods.