Bias Correction Algorithm Research Articles

Fine-grained Courier Delivery Behavior Recovery with a Digital Twin Based Iterative Calibration Framework

Recovering the fine-grained working process of couriers is becoming one of the essential problems for improving the express delivery systems because knowing the detailed process of how couriers accomplish their daily work facilitates the analyzing, understanding, and optimizing of the working procedure. Although coarse-grained courier trajectories and waybill delivery time data can be collected, this problem is still challenging due to noisy data with spatio-temporal biases, lacking ground truth of couriers’ fine-grained behaviors, and complex correlations between behaviors. Existing works typically focus on a single dimension of the process such as inferring the delivery time and can only yield results of low spatio-temporal resolution, which cannot address the problem well. To bridge the gap, we propose a digital-twin-based iterative calibration system (DTRec) for fine-grained courier working process recovery. We first propose a spatio-temporal bias correction algorithm, which systematically improves existing methods in correcting waybill addresses and trajectory stay points. Second, to model the complex correlations among behaviors and inherent physical constraints, we propose an agent-based model to build the digital twin of couriers. Third, to further improve recovery performance, we design a digital-twin-based iterative calibration framework, which leverages the inconsistency between the deduction results of the digital twin and the recovery results from real-world data to improve both the agent-based model and the recovery results. Experiments show that DTRec outperforms state-of-the-art baselines by 10.8% in terms of fine-grained accuracy on real-world datasets. The system is deployed in the industrial practices in JD Logistics with promising applications. The code is available at https://github.com/tsinghua-fib-lab/Courier-DTRec .

Comparison of CAMS and CMAQ analyses of surface-level PM2.5 and O3 over the conterminous United States (CONUS)

To reduce economic and health impacts from poor air quality (AQ) in the U.S., the National Air Quality Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) produces forecasts of surface-level ozone (O3), fine particulate matter (PM2.5), and other pollutants so that advance notice and warning can be issued to help individuals and communities limit their exposure. The NAQFC uses the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model for operational forecasts. This study is a first step in proposing a potential upgrade to the current operational NAQFC bias-correction system, by examining potential candidates for a gridded analysis (“truth”) dataset.In this paper, we compare the performance of the “analysis” time series over the period of August 2020–December 2021 at EPA AirNow stations for both PM2.5 and O3 from raw Copernicus Atmosphere Monitoring Service (CAMS) reanalyses, raw CAMS near real-time forecasts, raw near real-time CMAQ forecasts, bias-corrected CAMS forecasts, and bias-corrected CMAQ forecasts (CMAQ FC BC). This 17-month period spans two wildfire seasons, to assess model “analysis” performance in high-end AQ events. In addition to determining the best-performing gridded product, this process allows us to benchmark the performance of CMAQ forecasts against other global datasets (CAMS reanalysis and forecasts). For both PM2.5 and O3, the bias correction algorithm employed here greatly improved upon the raw model time series, and CMAQ FC BC was the best-performing model “analysis” time series, having the lowest RMSE, smallest bias error, and largest critical success index at multiple thresholds.

An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles

Upper air temperature measurements are critical for understanding weather patterns, boundary-layer processes, climate change, and the validation of space-based observations. However, there have been growing concerns over data discrepancies, the lack of homogeneity, biases, and discontinuities associated with historical climate data records obtained using these technologies. Consequently, this article reviews the progress of utilizing radiosondes and space-based instruments for obtaining upper air temperature records. A systematic review process was performed and focused on papers published between 2000 and 2023. A total of 74,899 publications were retrieved from the Google Scholar, Scopus, and Web of Science databases using a title/abstract/keyword search query. After rigorous screening processes using relevant keywords and the elimination of duplicates, only 599 papers were considered. The papers were subjected to thematic and bibliometric analysis to comprehensively outline the progress, gaps, challenges, and opportunities related to the utilization of radiosonde and space-based instruments for monitoring upper air temperature. The results show that in situ radiosonde measurements and satellite sensors have improved significantly over the past few decades. Recent advances in the bias, uncertainty, and homogeneity correction algorithms (e.g., machine learning approaches) for enhancing upper air temperature observations present great potential in improving numerical weather forecasting, atmospheric boundary studies, satellite data validation, and climate change research.

Evaluation of bias correction methods for multi-satellite rainfall estimation products

Rainfall data from satellites provides hydrological studies with special temporal and spatial advantages. However, recognising the biases in satellite data is critical, so robust validation and correction methods using ground-based observational data are necessary. This research seeks to correct and validate multi-satellite rainfall data (TRMM, GPM-IMERG, and GSMaP) in order to enable hydrological applications. The corrective methods include linear scaling (LS), empirical quantile mapping (EQM), and local intensity scaling (LOCI). In validation, three statistical metrics are employed: Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Relative Bias (RB). Assessing ten years of monthly data from the Kuranji watershed, LS and EQM emerged as optimal bias correction algorithms for all satellites, with LOCI outperforming TRMM and GSMaP. Corrected monthly rainfall patterns using LS and EQM closely correlate with observed data, substantially reducing discrepancies between field records and satellite-derived rainfall data. This enhances the usability of satellite data for in-depth hydrological studies.

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases.

A hybrid bias correction algorithm for estimating straight line parameters

In the common robust weighted total least squares estimator of the errors-in-variables model for straight line fitting, a bias exists in the estimated variance component used to reweight the weight matrix of observations when outliers are present. Additionally, when the second-order Taylor expansion of the functional model is not considered, the estimator is often computed with bias. A hybrid bias correction algorithm is proposed to reduce the two biases simultaneously and achieve unbiased results, which is demonstrated by three examples. The results show that the proposed algorithm can realize the bias correction of parameters and variance component estimated by robust weighted total least squares (RWTLS) ,and obtain the minimum variance component estimates. The new method is more suitable for correlation and/or outliers case and can detect outliers that cannot be detected by RWTLS.

Deep neural network with empirical mode decomposition and Bayesian optimisation for residential load forecasting

In the context of a resilient energy system, accurate residential load forecasting has become a non-trivial requirement for ensuring effective management and planning strategy/policy development. Due to the highly stochastic nature of energy load profiles, it is difficult to predict accurately, and usually, predictions are error-prone. This paper explores the potential of Empirical Mode Decomposition (EMD) in simplifying the dynamics of complex demand profiles. The simplified components are then embedded within a deep learning model, specifically Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM), to forecast short-term residential loads. The novel modelling framework integrates Bayesian optimisation strategy, feature decomposition technique, feature engineering phase, and percentile-based bias correction algorithm to enhance model accuracy. The model is developed using a case-study residential dwelling located in Fintry (Scotland), and the model performance is assessed over four forecast horizons. The overall efficiency of framework is also investigated for three algorithms: random forest, gradient boosting decision trees (GBDT), and an LSTM network. While EMD and feature engineering were found to greatly improve prediction accuracy, the number of IMFs used was shown to significantly impact the model’s performance and computational complexity. The model was tested on two further case studies from Fintry.

Forage calibration transfer from laboratory to portable near infrared spectrometers

Portable near infrared (NIR) spectrometers are now readily available on the market and with their smaller size, weight and cost have provided the opportunity to analyze forages both on farms and directly in the field. As new technologies and new portable NIR instruments become available on the market, calibrations for these instruments become a major constraint due to the costs and time necessary to collect reference data. This study evaluated techniques to transfer calibrations for alfalfa and grass forage samples that were developed for a scanning benchtop monochromator (FOSS 6500, 400–2498 nm, LAB) to a diode array instrument (AuroraNir, 950–1650 nm, DA), a digital light processing instrument (NIR-S-G1, 950–1650 nm, DLP) and a short wavelength instrument (SCiO, 740–1070 nm, SCIO). Alfalfa (N = 612) and grass (N = 516) samples from eight agronomic studies were analyzed by wet chemistry for crude protein, neutral detergent fiber (NDF), acid detergent fiber (ADF), in-vitro digestibility (IVTD) and NDF digestibility (NDFD) and divided into calibration, test-set, standardization and inoculation/prediction datasets. Different calibration transfer strategies were evaluated: Spectral Bias Correction (SBC), Shenk and Westerhaus algorithm (SW), Piecewise Direct Standardization (PDS), Dynamic Orthogonal Projection (DOP) or creating a new calibration using LAB predictions of the inoculation/prediction dataset as reference values. All computations for trimming, calibration, validation and standardization were developed using R. SBC with inoculation was an effective method to transfer calibrations for DA. Validation errors for DA transferred calibrations were about 15% lower than LAB for alfalfa data but 6% greater for grass data. For SCIO after DOP spectral adjustment, predicting errors were slightly greater than LAB for both data sets, while prediction errors with DLP were two to three times greater than LAB even after inoculation. PDS created spectral artifacts in the spectra of all three portables, which then resulted in large validation errors. Using LAB predictions as reference values was suitable only for DA, while DLP and DA had large prediction errors. This study showed that calibration sharing between a benchtop and portable instruments is challenging, but possible depending on the portable technologies and the transfer method. Spectral bias correction plus inoculation was the best method to transfer multivariate models for the forage components’ prediction from LAB to handhelds, particularly for DA. Application of DOP was beneficial for SCIO to successfully maintain performance of the original calibration, while for DLP the prediction models were not accurate. Additional studies are necessary to verify these transferring techniques can also be applied to fresh forages, allowing an easier and extended implementation of NIR analysis directly in fields.

Performance Evaluation of an ANN based Bias Correction algorithm in Monthly and Daily Precipitation Time Series of La Farge Station, USA

Understanding the change of future precipitation over long run is highly necessary in climate change impact studies. Mostly, simulated future precipitation series are found to be biased more with the historically observed precipitation series which need to be corrected before use for any impact studies. Many conventional and data-driven methods are available to correct this bias. In this study, to bias correct the monthly and daily precipitation series, Artificial Neural Network based method is applied and compared with the conventional methods. The normalized root mean squared errors obtained for monthly and daily series are 0.786 and 2.55 respectively. It is found that the performance of ANN-based method is poor in daily series and good only in monthly series. The reason for poor performance in daily series is analysed. In addition, the superiority of ANN based method over conventional method is established in monthly precipitation time series.

Future Projection of Extreme Precipitation Indices over the Qilian Mountains under Global Warming.

The Qilian Mountains are a climate-sensitive area in northwest China, and extreme precipitation events have an important impact on its ecological environment. Therefore, considering the global warming scenario, it is highly important to project the extreme precipitation indices over the Qilian Mountains in the future. This study is based on three CMIP6 models (CESM2, EC-Earth3, and KACE-1-0-G). A bias correction algorithm (QDM) was used to correct the precipitation outputs of the models. The eight extreme precipitation indices over the Qilian Mountains during the historical period and in the future were calculated using meteorological software (ClimPACT2), and the performance of the CMIP6 models to simulate the extreme precipitation indices of the Qilian Mountains in the historical period was evaluated. Results revealed that: (1) The corrected CMIP6 models could simulate the changes in extreme precipitation indices over the Qilian Mountains in the historical period relatively well, and the corrected CESM2 displayed better simulation as compared to the other two CMIP6 models. The CMIP6 models performed well while simulating R10mm (CC is higher than 0.71) and PRCPTOT (CC is higher than 0.84). (2) The changes in the eight extreme precipitation indices were greater with the enhancement of the SSP scenario. The growth rate of precipitation in the Qilian Mountains during the 21st century under SSP585 is significantly higher than the other two SSP scenarios. The increment of precipitation in the Qilian Mountains mainly comes from the increase in heavy precipitation. (3) The Qilian Mountains will become wetter in the 21st century, especially in the central and eastern regions. The largest increase in precipitation intensity will be observed in the western Qilian Mountains. Additionally, total precipitation will also increase in the middle and end of the 21st century under SSP585. Furthermore, the precipitation increment of the Qilian Mountains will increase with the altitude in the middle and end of the 21st century. This study aims to provide a reference for the changes in extreme precipitation events, glacier mass balance, and water resources in the Qilian Mountains during the 21st century.

Validation of the improved GOES-16 aerosol optical depth product over North America

The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) every 5 min. However, the current version of the official GOES-16 ABI AOD product is affected by diurnally varying biases. An improved ABI AOD product based on an empirical bias correction algorithm has become available recently. This study aims to comprehensively validate this improved ABI AOD product over land and ocean. Three years of Aerosol Robotic Network (AERONET) ground-based measurements at 95 stations over North America are used for the validation. Results suggest the product performs moderately well with an overall R2 of 0.46, an MBE of 0.04, a slope of 0.97, and ∼70.0% retrievals falling within the expected error (EE) envelope. A slight overestimation is found at a majority of ground sites: ≈87% of those sites have MBE values between 0 and 0.1. The analysis shows that the retrievals perform better over water and forests, but perform poorly over urban areas, grasslands and shrublands, and is worst over mountains. Besides, the present results indicate that the combination of the retrieval and bias correction algorithms yields a systematic underestimation of AOD for coarse particles over croplands, as well as large uncertainties for smaller-size particles over shrublands. At seasonal scale, the AOD retrievals perform better in summer and autumn than in spring and winter. Despite the empirical bias correction algorithm, a systematic overestimation of 0.07 at 1700 UTC and 0.08 during 1000–1300 UTC still exist. The sampling rate of the top-2 quality AOD retrievals are 14%, 12%, 10% and 11% for the four seasons, respectively, which constitutes a significant reduction of valid data in comparison with the sampling rate of all four quality retrievals. The limited availability and accuracy of the top-2 bias-corrected ABI AOD product raises concerns about its application in air quality and energy-related applications, in particular. This study also suggests a few potential improvements to the aerosol retrieval algorithm.

Errors of five satellite precipitation products for different rainfall intensities

Revealing the errors of the satellite precipitation products (SPPs) for various rainfall intensity groups is crucial for their applications in the hydro-meteorological field. However, the spatial maps of errors of SPPs for different rainfall intensity groups and the impact of elevation on them are still unclear. In this study, the spatial maps of errors of the five satellite precipitation products (i.e., IMERG-Late, IMERG-Final, GSMaP-MVK, GSMaP-Gauge, and PERSIANN-CCS) for the four rainfall intensity groups (i.e., light precipitation, moderate precipitation, heavy precipitation, and storm) are depicted. A novel score, namely the relative rainfall occurrence bias (ROB), is proposed as a means to evaluate the reproducibility of the spatial patterns of SPPs for different surface rainfall intensity occurrences. All five SPPs cannot reproduce the spatial patterns of the four surface rainfall intensity occurrences. Despite that, all SPPs except for PERSIANN-CCS can capture almost all rainfall intensity groups (except light precipitation) because their probability of detection (POD) values exceeds 0.8. Moreover, we found that the detection capability of SPPs for light precipitation decreases with the increasing standard deviation of elevation (SDE) but is the opposite for bias, while the impacts of elevation on the errors of SPPs for other rainfall intensity groups are unapparent. The overestimations of the number of surface heavy precipitation and storm occurrences for IMERG-Late and GSMaP-MVK are mainly due to overestimating the rainfall amount of the lower rain-rate surface rainfall events, while their total biases for heavy precipitation and storms originate from hit bias. The potential limitations of the bias correction algorithms used in the IMERG and GSMaP retrieval systems in correcting different rainfall intensity groups are identified by comparing the performance between gauge-adjusted products and satellite-only products. Finally, the optimal SPP precipitation product for each rainfall intensity group is discussed. The findings reported in this study can provide valuable information for data users to select a product that best meets the requirements of the applications.

Rumen Microbial Predictors for Short-Chain Fatty Acid Levels and the Grass-Fed Regimen in Angus Cattle.

The health benefits of grass-fed beef are well documented. However, the rumen microbiome features in beef steers raised in a grass-fed regimen have yet to be identified. This study examined the rumen microbiome profile in the feeding regimes. Our findings show that the rumen microbiome of the grass-fed cattle demonstrated greater species diversity and harbored significantly higher microbial alpha diversity, including multiple species richness and evenness indices, than the grain-fed cattle. Global network analysis unveiled that grass-fed cattle's rumen microbial interaction networks had higher modularity, suggesting a more resilient and stable microbial community under this feeding regimen. Using the analysis of compositions of microbiomes with a bias correction (ANCOM-BC) algorithm, the abundance of multiple unclassified genera, such as those belonging to Planctomycetes, LD1-PB3, SR1, Lachnospira, and Sutterella, were significantly enriched in the rumen of grass-fed steers. Sutterella was also the critical genus able to distinguish the two feeding regimens by Random Forest. A rumen microbial predictor consisting of an unclassified genus in the candidate division SR1 (numerator) and an unclassified genus in the order Bacteroidales (denominator) accurately distinguished the two feeding schemes. Multiple microbial signatures or balances strongly correlated with various levels of SCFA in the rumen. For example, a balance represented by the log abundance ratio of Sutterella to Desulfovibrio was strongly associated with acetate-to-propionate proportions in the rumen (R2 = 0.87), which could be developed as a valuable biomarker for optimizing milk fat yield and cattle growth. Therefore, our findings provided novel insights into microbial interactions in the rumen under different feed schemes and their ecophysiological implications. These findings will help to develop rumen manipulation strategies to improve feed conversion ratios and average daily weight gains for grass- or pasture-fed cattle production.

TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile

Determining transcriptional factor binding sites (TFBSs) is critical for understanding the molecular mechanisms regulating gene expression in different biological conditions. Biological assays designed to directly mapping TFBSs require large sample size and intensive resources. As an alternative, ATAC-seq assay is simple to conduct and provides genomic cleavage profiles that contain rich information for imputing TFBSs indirectly. Previous footprint-based tools are inheritably limited by the accuracy of their bias correction algorithms and the efficiency of their feature extraction models. Here we introduce TAMC (Transcriptional factor binding prediction from ATAC-seq profile at Motif-predicted binding sites using Convolutional neural networks), a deep-learning approach for predicting motif-centric TF binding activity from paired-end ATAC-seq data. TAMC does not require bias correction during signal processing. By leveraging a one-dimensional convolutional neural network (1D-CNN) model, TAMC make predictions based on both footprint and non-footprint features at binding sites for each TF and outperforms existing footprinting tools in TFBS prediction particularly for ATAC-seq data with limited sequencing depth.

Validation and Bias Correction of Monthly δ18O Precipitation Time Series from ECHAM5-Wiso Model in Central Europe

Simulated stable oxygen isotopic composition (δ18O) of precipitation from isotope-enabled GCMs (iGCMs) have gained significant visibility nowadays. This study evaluates bias correction techniques to reduce the systematic and dispersion biases of the modelled δ18O by the ECHAM5-wiso model compared to the Global Network of Isotopes in Precipitation (GNIP) observations over Central Europe. mean bias error (MBE) and Root Mean Square Error (RMSE) are substantially reduced by more than 70% and 10%, respectively, depending on the bias correction scheme, with better results for Generalized Additive Model (GAM) and linear scaling approach (SCL) methods. The bias-corrected δ18OECHAM5-wiso values successfully describe the long-term isotopic composition of precipitation and the isotopic amplitude with the best performances for the EQM method. The necessity of applying bias correction algorithms is verified by the excellent agreement between the corrected δ18OECHAM5-wiso with GNIP in high-altitude areas where ECHAM5-wiso fails to reproduce the observed isotopic variability. The results are expected to bring valuable insights into the utilization of iGCMs’ relationships in climate studies for understanding the present and past water cycle under the isotopic perspective.

The Influence of Aerosols on Satellite Infrared Radiance Simulations and Jacobians: Numerical Experiments of CRTM and GSI

For a variational data assimilation (DA) system that assimilates radiance observations, the simulated brightness temperature (BT) at the top of the atmosphere and the corresponding Jacobians carried out by the radiance observation operator are needed information. Previous studies reported that the incorporation of aerosol information into the radiance observation operator leads to cooler simulated infrared (IR) BTs and warmer temperature analyses at low levels of the atmosphere. However, the role of the aerosol-affected Jacobians in the DA system, which not only affect the determination of analysis increments but also influence the quality control and the bias correction algorithm, is yet to be investigated. This study examines the aerosol impacts on the sensitivity of IR radiance simulations, Jacobians, and the analysis increments by conducting two experiments: (i) sensitivity tests to assess how the different aspects of the aerosol profiles (i.e., mass loading, peak aerosol level, aerosol thickness layer, and bin partition) affect the simulated BT and the Jacobians from the Community Radiative Transfer Model (CRTM), which is the radiance observation operator in the Gridpoint Statistical Interpolation (GSI) analysis system; (ii) single IR observation experiments using GSI to investigate how the aerosol-affected atmospheric Jacobians influence the analysis increment. The results show that dust aerosols produce the strongest cooling to simulated BTs under similar aerosol optical depths; simulated BTs and Jacobians are most sensitive to the loading and peak altitude of the aerosol layer; simulated BTs become more sensitive to the temperature of the aerosol layer; aerosol-induced differences in atmospheric Jacobians lead to considerable changes to temperature and moisture increments. These results provide a better understanding of the aerosol impacts on each component involved in radiance DA, which can provide guidance for assimilating aerosol-affected IR observations.

Evaluating consistency between total column CO<sub>2</sub> retrievals from OCO-2 and the in situ network over North America: implications for carbon flux estimation

Abstract. Feedbacks between the climate system and the carbon cycle represent a key source of uncertainty in model projections of Earth's climate, in part due to our inability to directly measure large-scale biosphere–atmosphere carbon fluxes. In situ measurements of the CO2 mole fraction from surface flasks, towers, and aircraft are used in inverse models to infer fluxes, but measurement networks remain sparse, with limited or no coverage over large parts of the planet. Satellite retrievals of total column CO2 (XCO2), such as those from NASA's Orbiting Carbon Observatory-2 (OCO-2), can potentially provide unprecedented global information about CO2 spatiotemporal variability. However, for use in inverse modeling, data need to be extremely stable, highly precise, and unbiased to distinguish abundance changes emanating from surface fluxes from those associated with variability in weather. Systematic errors in XCO2 have been identified and, while bias correction algorithms are applied globally, inconsistencies persist at regional and smaller scales that may complicate or confound flux estimation. To evaluate XCO2 retrievals and assess potential biases, we compare OCO-2 v10 retrievals with in situ data-constrained XCO2 simulations over North America estimated using surface fluxes and boundary conditions optimized with observations that are rigorously calibrated relative to the World Meteorological Organization X2007 CO2 scale. Systematic errors in simulated atmospheric transport are independently evaluated using unassimilated aircraft and AirCore profiles. We find that the global OCO-2 v10 bias correction shifts the distribution of retrievals closer to the simulated XCO2, as intended. Comparisons between bias-corrected and simulated XCO2 reveal differences that vary seasonally. Importantly, the difference between simulations and retrievals is of the same magnitude as the imprint of recent surface flux in the total column. This work demonstrates that systematic errors in OCO-2 v10 retrievals of XCO2 over land can be large enough to confound reliable surface flux estimation and that further improvements in retrieval and bias correction techniques are essential. Finally, we show that independent observations, especially vertical profile data, such as those from the National Oceanic and Atmospheric Administration aircraft and AirCore programs are critical for evaluating errors in both satellite retrievals and carbon cycle models.

Postharvest ripeness assessment of ‘Hass’ avocado based on development of a new ripening index and Vis-NIR spectroscopy

A classification model using Vis-NIR spectroscopy (380–2000 nm) coupled with partial least square discriminant analysis (PLS-DA) was developed to segregate avocados in three classes, predefined by a warehouse using destructive FF tests performed on a small number of samples. This classification showed a satisfactory general accuracy of 62 %, with 100 % well-classified samples in Class 1, 20 % in Class 2 and 65 % in Class 3. To improve classification, a ripening index (RI) was developed, which combines FF and DMC. The discrimination ability in the three classes was tested using Wilk’s lambda, calculated as between-class variance to the total variance ratio. Results showed values of 0.648 for RI, 0.516 for FF and 0.038 for DMC. A regression model was subsequently developed using Partial Least Squares (PLS) regression to predict RI using Vis-NIR spectroscopy in an independent dataset. The PLS results were satisfactory with the whole spectrum wavelength range (380–2000 nm), with R2 of 0.62, SEP of 0.69 (), but presented a large bias value of 1.22 (). The same occurs in models developed in the wavelength range from 400 to 1100 nm, with R2 of 0.63, SEP of 0.68 () and bias of 1.03 (). This could be corrected using a bias and slope correction algorithm. Study of the correlation coefficients of the PLS regression models showed that the region 400–1100 nm has a huge influence in the model, which indicates the potential of using cost-effective short Vis-NIR spectrophotometers for RI prediction.

Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks

Climate model outputs are commonly corrected using statistical univariate bias correction methods. Most of the time, those 1d-corrections do not modify the ranks of the time series to be corrected. This implies that biases in the spatial or inter-variable dependences of the simulated variables are not adjusted. Hence, over the last few years, some multivariate bias correction (MBC) methods have been developed to account for inter-variable structures, inter-site ones, or both. As proof-of-concept, we propose to adapt a computer vision technique used for Image-to-Image translation tasks (CycleGAN) for the adjustment of spatial dependence structures of climate model projections. The proposed algorithm, named MBC-CycleGAN, aims to transfer simulated maps (seen as images) with inappropriate spatial dependence structure from climate model outputs to more realistic images with spatial properties similar to the observed ones. For evaluation purposes, the method is applied to adjust maps of temperature and precipitation from climate simulations through two cross-validation approaches. The first one is designed to assess two different post-processing schemes (Perfect Prognosis and Model Output Statistics). The second one assesses the influence of nonstationary properties of climate simulations on the performance of MBC-CycleGAN to adjust spatial dependences. Results are compared against a popular univariate bias correction method, a “quantile-mapping” method, which ignores inter-site dependencies in the correction procedure, and two state-of-the-art multivariate bias correction algorithms aiming to adjust spatial correlation structure. In comparison with these alternatives, the MBC-CycleGAN algorithm reasonably corrects spatial correlations of climate simulations for both temperature and precipitation, encouraging further research on the improvement of this approach for multivariate bias correction of climate model projections.

Optimized bias and signal inference in diffusion-weighted image analysis (OBSIDIAN).

Correction of Rician signal bias in magnitude MR images. A model-based, iterative fitting procedure is used to simultaneously estimate true signal and underlying Gaussian noise with standard deviation on a pixel-by-pixel basis in magnitude MR images. A precomputed function that relates absolute residuals between measured signals and model fit to is used to iteratively estimate . The feasibility of the method is evaluated and compared to maximum likelihood estimation (MLE) for diffusion signal decay simulations and diffusion-weighted images of the prostate considering 21 linearly spaced b-values from 0 to 3000s/mm2 . A multidirectional analysis was performed with publically available brain data. Model simulations show that the Rician bias correction algorithm is fast, with an accuracy and precision that is on par to model-based MLE and direct fitting in the case of pure Gaussian noise. Increased accuracy in parameter prediction in a low signal-to-noise ratio (SNR) scenario is ideally achieved by using a composite of multiple signal decays from neighboring voxels as input for the algorithm. For patient data, good agreement with high SNR reference data of diffusion in prostate is achieved. OBSIDIAN is a novel, alternative, simple to implement approach for rapid Rician bias correction applicable in any case where differences between true signal decay and underlying model function can be considered negligible in comparison to noise. The proposed composite fitting approach permits accurate parameter estimation even in typical clinical scenarios with low SNR, which significantly simplifies comparison of complex diffusion parameters among studies.