Bias Correction Factor Research Articles

Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ

Abstract. United States (US) background ozone (O3) is the counterfactual O3 that would exist with zero US anthropogenic emissions. Estimates of US background O3 typically come from chemical transport models (CTMs), but different models vary in their estimates of both background and total O3. Here, a measurement–model data fusion approach is used to estimate CTM biases in US anthropogenic O3 and multiple US background O3 sources, including natural emissions, long-range international emissions, short-range international emissions from Canada and Mexico, and stratospheric O3. Spatially and temporally varying bias correction factors adjust each simulated O3 component so that the sum of the adjusted components evaluates better against observations compared to unadjusted estimates. The estimated correction factors suggest a seasonally consistent positive bias in US anthropogenic O3 in the eastern US, with the bias becoming higher with coarser model resolution and with higher simulated total O3, though the bias does not increase much with higher observed O3. Summer average US anthropogenic O3 in the eastern US was estimated to be biased high by 2, 7, and 11 ppb (11 %, 32 %, and 49 %) for one set of simulations at 12, 36, and 108 km resolutions and 1 and 6 ppb (10 % and 37 %) for another set of simulations at 12 and 108 km resolutions. Correlation among different US background O3 components can increase the uncertainty in the estimation of the source-specific adjustment factors. Despite this, results indicate a negative bias in modeled estimates of the impact of stratospheric O3 at the surface, with a western US spring average bias of −3.5 ppb (−25 %) estimated based on a stratospheric O3 tracer. This type of data fusion approach can be extended to include data from multiple models to leverage the strengths of different data sources while reducing uncertainty in the US background ozone estimates.

Concordance of Freehand 3D Ultrasound Muscle Measurements With Sarcopenia Parameters in a Geriatric Rehabilitation Ward.

Sarcopenia is a devastating disease for older adults, but it lacks accessible and reliable tools for measuring total appendicular skeletal muscle mass (ASMM). Two-dimensional muscle ultrasound (US) has been developed for its bedside clinical advantages and feasibility but lacks standardization and prediction performance. We previously validated a new 3D-US technique to measure muscle volume (MV) at bedside and applied it in a geriatric rehabilitation setting. Objectives were to analyse the concordance between 3D-US MV and ASMM and compare concordance between 3D-US MV and 2D-US parameters with ASMM. Participants were recruited in a Geriatric rehabilitation ward in Nantes, France, from May to October 2022. Exclusion criteria were as follows: oedema in the lower limbs or recent history of unilateral lower limb damage or stroke. ASMM was measured with bioelectrical impedance analysis; 3D-US and 2D-US acquisitions were performed on three muscles of the right lower limb. Measures of strength (hand grip, knee extension and ankle dorsiflexion) were also recorded. Reliability of 3D-US MV measurements on 10 participants was high (ICC = 0.99). We used Lin's concordance correlation coefficients (CCC) and bias correction factor for agreement between variables and linear regression models for prediction equations. Fifty-eight participants had an interpretable ASMM of whom 17 (29%) had a diagnosis of sarcopenia. Volumes of TA, RF and VL were all significantly concordant with ASMM measured by BIA (all p values < 0.001), with CCCs respectively of 0.72, 0.61 and 0.60. MV were all significantly concordant with isometric strength (p values < 0.001). Concordance and correlation with ASMM were higher with 3D-US than 2D-US measurements regardless of the muscle. Prediction of ASMM reached an adjusted R2 of 0.8 with tibialis anterior volume, biometrics and 2D measurements. This study was the first to use 3D-US in a geriatric setting and develop a model to predict ASMM in very old hospitalized patients. MV measurements with 3D-US proved to be reliable and more concordant with appendicular muscle mass and strength than 2D parameters.

Improving wind power modelling through granular spatial and temporal bias correction of reanalysis data

There is a need for efficient methods to simulate wind power output to assist the expected rapid uptake of new wind farms. Reanalysis products provide our best estimate for the previous state of the atmosphere and are popular due to their global coverage and convenience. However, these models are known to misestimate wind power output by up to ±50% due to significant spatial biases. Previous work applied bias correction methods to improve power simulations. However, there has been no assessment of the spatial and temporal resolution that these bias correction factors should be derived at for the best accuracy. In this paper, we investigate the impact of the spatial and temporal resolution by grouping turbines into a varying number of clusters and varying the frequency at which correction factors are calculated across a year. The correction factors are used to simulate the power output of 4,834 turbines across Denmark resulting in monthly capacity factors. The best correction scenario decreased the error of simulated outputs by 43%. Increasing the spatial resolution of bias correction reduced the error by up to 11%. Correction factors with a bimonthly (every two months) frequency decreased the error by 3% from the time-independent correction factors.

A three-quantile bias correction with spatial transfer for the correction of simulated European river runoff to force ocean models

Abstract. In ocean or Earth system model applications, the riverine freshwater inflow is an important flux affecting salinity and marine stratification in coastal areas. However, in climate change studies, the river runoff based on climate model output often has large biases on local, regional, or even basin-wide scales. If these biases are too large, the ocean model forced by the runoff will drift into a different climate state compared to the observed state, which is particularly relevant for semi-enclosed seas such as the Baltic Sea. To achieve low biases in riverine freshwater inflow in large-scale climate applications, a bias correction is required that can be applied in periods where runoff observations are not available and that allows spatial transferability of its correction factors. In order to meet these requirements, we have developed a three-quantile bias correction that includes different correction factors for low-, medium-, and high-percentile ranges of river runoff over Europe. Here, we present an experimental setup using the Hydrological Discharge (HD) model and its high-resolution (1/12°) grid. First, bias correction factors are derived at the locations of the downstream stations with available daily discharge observations for many European rivers. These factors are then transferred to the respective river mouths and mapped to neighbouring grid boxes belonging to ungauged catchments. The results show that the bias correction generally leads to an improved representation of river runoff. Especially over northern Europe, where many rivers are regulated, the three-quantile bias correction provides an advantage compared to a bias correction that only corrects the mean bias of the river runoff. Evaluating two NEMO (Nucleus for European Modelling of the Ocean) model simulations in the German Bight indicated that the use of the bias-corrected discharges as forcing leads to an improved simulation of sea surface salinity in coastal areas. Although the bias correction is tailored to the high-resolution HD model grid over Europe in the present study, the methodology is suitable for any high-resolution model region with a sufficiently high coverage of river runoff observations. It is also noted that the methodology is applicable to river runoff based on climate hindcasts, as well as on historical climate simulations where the sequence of weather events does not match the actual observed history. Therefore, it may also be applied in climate change simulations.

Radiocarbon ageing of Indigenous culturally‐significant trees for bushfire management on Minjerribah (North Stradbroke Island), southeast Queensland

SummaryPartnerships between Indigenous People and governments for joint management of ancestral lands, designated as protected conservation areas for biodiversity and cultural heritage, provide an opportunity to leverage accumulated traditional land management knowledge with emerging science. In Australia, veteran or large old trees in natural landscapes are of significant ecological importance, and among the Indigenous communities, they are living monuments to historical cultural practices, but their survival, endurance and long lives are rarely acknowledged to afford them protection. In sub‐tropical Australia, the lack of consistent annual growth rings in stem wood makes it difficult to estimate the age of large trees using methods such as the periodic diameter increment. Ngugi et al., 2020 published radiocarbon (14C) dates using wood core (‘pith‐wood’) samples from 12 Indigenous culturally‐significant trees covering five species on Minjerribah (North Stradbroke Island). Due to the imprecise calendar age results of the single samples used in the original study (Ngugi et al., 2020), subsequent radiocarbon dating of an additional three wood core samples from each tree was undertaken to age the trees more precisely. The revised tree ages ranged from 63 to 531 years and suggest an important role of past Indigenous land management practices into protecting Bugari (Cypress Pine, Callitris columellaris F. Muell.) from deadly crown scorching fires. These results underscore the importance of incorporating Indigenous practices in current fire management strategies and plans. Estimating tree ages based on the periodic diameter increment method overestimated the age relative to that derived from radiocarbon dating. New bias correction factors were developed for adjusting the recorded periodic tree diameter increments.

Validity and Reliability of a New Wearable Chest Strap to Estimate Respiratory Frequency in Elite Soccer Athletes.

Assessing respiratory frequency (fR) is practical in monitoring training progress in competitive athletes, especially during exercise. This study aimed to validate a new wearable chest strap (wCS) to estimate fR against ergospirometry as a criterion device in soccer players. A total of 26 elite professional soccer players (mean [standard deviation]: 23.6 [4.8] years; 180.6 [5.7] cm; 77.2 [5.4] kg) from three Italian Serie A League teams participated in this cross-sectional study. The sample included attackers, midfielders, and defenders. fR was assessed during a maximal cardiopulmonary exercise test (CPET) on a treadmill using (i) a breath-by-breath gas exchange analyzer (Vyntus® CPX, Vyaire Medical) and (ii) a novel wCS with sensors designed to assess breath frequency following chest expansions. Pearson's correlation coefficient (r), adjusted coefficient of determination (aR2), Bland-Altman plot analysis, and Lin's concordance correlation coefficient (ρc) were used for comparative analysis (correlation and concordance) among the methods. The repeated measures correlation coefficient (rrm) was used to assess the strength of the linear association between the methods. The intraclass correlation coefficient (ICC) and the Finn coefficient (rF) were used for inter-rater reliability. All statistical analyses were performed within the R statistical computing environment, with 95% confidence intervals (95% CIs) reported and statistical significance set at p < 0.05. A total of 16529 comparisons were performed after collecting the CPET data. The robust time series analysis with Hodges-Lehmann estimation showed no significant differences between both methods (p > 0.05). Correlation among devices was statistically significant and very large (r [95% CI]: 0.970 [0.970, 0.971], p < 0.01; aR2 [95% CI]: 0.942 [0.942, 0.943], p < 0.01) with strong evidence supporting consistency of the new wCS (BF10 > 100). In addition, a high concordance was found (ρc [95% CI]: 0.970 [0.969, 0.971], bias correction factor: 0.999). VyntusTM CPX, as a standard criterion, showed moderate agreement with wCS after Bland-Altman analysis (bias [95% lower to the upper limit of agreement]; % agree: 0.170 [-4.582 to 4.923] breaths·min-1; 69.9%). A strong association between measurements (rrm [95% CI]: 0.960 [0.959, 0.961]), a high absolute agreement between methods (ICC [95% CI]: 0.970 [0.970, 0.971]), and high inter-rater reliability (rF: 0.947) were found. With an RMSE = 2.42 breaths·min-1, the new wCS seems to be an valid and reliable in-field method to evaluate fR compared to a breath-by-breath gas exchange analyzer. Notwithstanding, caution is advised if methods are used interchangeably while further external validation occurs.

Temporal and spatial variability in availability bias has consequences for marine bird abundance estimates during the non‐breeding season

Abstract To effectively monitor how marine ecosystems are being reshaped by anthropogenic pressures, we require understanding of species abundances and distributions. Due to their socio‐economic and ecological value, predatory species are often at the forefront of survey efforts. However, survey data are only valuable if they can reliably be converted into estimates of underlying distributions. We consider at‐sea surveys of marine predators that often inform ecological impact assessments of offshore windfarms. These surveys are subject to a form of detection bias called ‘availability bias’ whereby individuals which are submerged below the surface are consequently ‘unavailable’ for detection. Although correction factors are commonly used in these surveys, they are currently based on limited data that may not be species‐, time‐, or area‐specific. Here, we use time‐depth‐recorder data to investigate variation in marine bird availability bias. We found that the proportion of diving marine birds submerged below the sea surface during daylight hours, and therefore unavailable to be counted during surveys, varied by species, month, and area. For three of our focal species wintering around northwest Europe (Atlantic puffin, common guillemot, razorbill), our results were different to comparable values previously used to correct for the availability bias, whereas no correction factors are regularly used for the fourth species (red‐throated diver). We now present availability bias correction factors that are species‐ and month‐specific to the areas the study populations use during their non‐breeding seasons: the North Sea, the north and west coasts of the UK, the Baltic Sea, and Icelandic coastal waters. Practical implication: Variation in the proportion of daylight hours that marine birds spent submerged lead to differences in availability bias correction factors, thereby impacting estimations of their abundances. We encourage use of correction factors that use data from the species, marine area, and month during which surveys are conducted to provide more accurate abundance estimates. Using more relevant correction factors will result in increasingly accurate abundance and distribution estimates of diving marine birds, with relevance for a range of applications including planning for offshore windfarm developments, the designation and monitoring of protected areas, and understanding environmental change.

Enabling quantitative comparison of wastewater surveillance data across methods through data standardization without method standardization

Wastewater surveillance for COVID-19 and other pathogens has expanded globally. Rapid development and availability of various assays has facilitated swift adoption of wastewater surveillance in localities with diverse requirements. However, it presents challenges in comparing data due to methodological variations. Using surrogates for recovery control to address quantification biases has limitations as the recovery of surrogates and target pathogens often diverges significantly. Using non-spiked field-obtained wastewater samples as reference samples in an inter-lab study, this article proposes a straightforward, inexpensive, and most representative way of measuring relative quantification biases that occurs in analyzing field wastewater samples. Five labs participated in the study, testing five types of assays, resulting in a total of seven methods of lab-assay combinations. Each method quantified the concentration of SARS-CoV-2 and pepper mild mottle virus (PMMoV) RNAs in two types of reference samples. The results showed significant variations in quantification among methods, but the relative quantification biases were consistent across reference samples. This suggests that relative quantification biases measured with the reference samples are contingent on methods rather than wastewater samples, and that the once-determined method-specific factors can be used to correct for quantification biases in routine wastewater surveillance results. Subsequent data standardization was performed on year-long observational data from seven cities, serving as a preliminary validation of the proposed approach. This process demonstrated the potential for quantitative data comparison through the bias correction factors obtained in this inter-lab study.

Estimating beluga whale abundance from space: using drones to ground‐validate VHR satellite imagery

AbstractRoutine monitoring of cetaceans is imperative for understanding their population trends and making informed management decisions. However, the inherent nature of cetaceans and the marine ecosystems they inhabit make annual population surveys logistically and economically challenging with current survey methods. One emerging solution is utilizing very high‐resolution (VHR) satellite imagery, which is a logistically efficient method for providing an instantaneous view of areas spanning hundreds of square kilometers. The objective of this study was to determine two factors required to reliably conduct beluga whale population abundance estimates with VHR satellite imagery: (1) depths that beluga whales are visible in VHR satellite images, which are used to define availability bias correction factors, and (2) a comparison of abundance estimates in VHR satellite imagery to current aerial methods. We submerged beluga whale models to different depths in two different water clarities and determined that beluga whales are distinguished only at the surface in turbid water (Secchi depth: 2.56 m) and at depths of 0–2 m in clear water (Secchi depth: 4.04 m). Based on the proportion of time beluga whales spend at these depths, an availability bias correction factor for Western Hudson Bay beluga whales was defined as 2.40 ± 0.16 for turbid water and 1.89 ± 0.05 for clear water. Synchronous ground‐validation surveys determined availability corrected beluga whale abundance estimates in 0.31 m VHR satellite imagery (n = 173 beluga whales) and imagery that was HD sharpened using a proprietary algorithm to approximate 0.15 m resolution (n = 170) to be comparable to drone imagery (n = 164). VHR satellite imagery has the potential to increase the frequency of beluga whale population surveys, which has become increasingly important as beluga whales face rapid ecosystem changes and increased anthropogenic disturbances.

Assessment of Vcheck® analyzer for rapid progesterone concentration measurement including recommendations for achieving the optimal breeding time in bitches

Background and Aim: Serum progesterone concentration plays critical role in determining the optimal breeding time in bitches and diagnosing reproductive-related issues. This study aimed to conduct a comparative analysis of serum progesterone results obtained from commercial point-of-care immunological analyzers, namely, Vcheck®, with those obtained using chemiluminescent microparticle immunoassay (CMIA). Our overarching goal was to evaluate these analyzers’ accuracy and establish standardized guidelines for optimal breeding timing. Materials and Methods: Ninety-four serum samples from bitches were analyzed using the Vcheck® analyzer and compared with CMIA. Thorough documentation included the mean, standard deviation, 95% confidence interval (CI), and minimum and maximum values of serum progesterone concentrations. Furthermore, Pearson’s correlation coefficient, Lin’s concordance correlation coefficient, and the bias correction factor were meticulously recorded. Results: The mean progesterone concentration measured using the Vcheck® analyzer was significantly lower than that measured using CMIA, with a mean difference of 1.26 ng/mL of serum. The Bias correction factor was 0.935, which was nearly 1.00, indicating that the line of best-fit was on the perfect line of agreement, providing insight into the measurement accuracy. Pearson’s correlation coefficient, a measure of precision, was also close to 1 (0.939), confirming the reliability of the data. Furthermore, Lin’s concordance correlation coefficient was 0.877, indicating a fair overall agreement between the Vcheck® and CMIA methods. These results support the validity of the Vcheck® analyzer’s results. The present study was developed by aligning with established CMIA guidelines and adapting them using the range and 95% CI derived from each set of results, ensuring a standardized and rigorous approach. Conclusion: The Vcheck® analyzer provides a rapid assessment of serum progesterone concentration in bitches, with results comparable to those measured using the CMIA technique. However, when considering the use of the Vcheck® analyzer, it is recommended that the results should be interpreted carefully and the interpretation guidelines should be followed. In conclusion, Vcheck® provides a reliable and convenient method for veterinarian practitioners to measure canine progesterone levels in a clinical/hospital setting. Keywords: chemiluminescent microparticle immunoassay, optimal breeding time, progesterone, Vcheck®.

A Precision Assessment of a Point-of-Care Immunological Analyzer for Swift Progesterone Measurement and Guidance for Determining the Optimal Breeding Time in Bitches

The measurement of serum progesterone often varies due to different laboratory methodologies and individual canine characteristics. In this investigation, serum progesterone outcomes obtained from a commercial point-of-care immunological analyzer, designed for efficient serum progesterone assessment in bitches, were compared with results derived from chemiluminescent microparticle immunoassay from reference laboratories in Thailand. Our thorough documentation encompassed various parameters: mean, standard deviation, 95% confidence interval, and minimum and maximum serum progesterone concentration values. Additionally, we meticulously recorded the Pearson’s correlation coefficient, Lin’s concordance correlation coefficient, and the bias correction factor. Interestingly, there was no significant difference (p &gt; 0.05) in the means obtained by the point-of-care immunological analyzer and chemiluminescent microparticle immunoassay. The Pearson’s correlation coefficient between the point-of-care immunological analyzer and chemiluminescent microparticle immunoassay stood at 0.957, with Lin’s concordance correlation coefficient for point-of-care immunological analyzer recorded as 0.949. Furthermore, the bias correction factor was established at 0.991. This investigation followed established chemiluminescent microparticle immunoassay guidelines, modified to incorporate the mean and 95% confidence interval as criteria for optimal breeding time using the point-of-care immunological analyzer. In conclusion, the commercial point-of-care immunological analyzer emerges as a valuable tool, aiding in precisely determining the optimal timing for natural mating or artificial insemination in bitches.

Millet yield estimations in Senegal: Unveiling the power of regional water stress analysis and advanced predictive modeling

Crop yield is usually affected by impending weather, climate conditions, and human interventions like irrigation. Hence, the prompt detection of regions experiencing water scarcity can aid in implementing effective mitigation strategies. Our study utilized a data-driven approach to compute a Water Demand Index (WDI), which incorporates crucial first-order geophysical variables like ambient temperature, vegetation status, and soil moisture, to identify water-stressed fields in Senegal’s agricultural regions during the millet planting, growing, and harvesting periods. We have also explored various scenarios for enhancing the accuracy of millet yield prediction by incorporating other drought indices, soil characteristics, and a bias correction factor. The novel aspects of this research include the development of regional crop yield predictive models based on sub-seasonal and unaggregated crop information to reflect local variances. To optimize the hyperparameters of machine learning (ML) models, various techniques were utilized. Meanwhile, the performance of these ML models was evaluated using a nested cross-validation approach. The outcomes of the analysis demonstrate that the Random Forest Regressor model exhibits superior predictive performance. The results imply that a holistic approach, encompassing diverse environmental factors and crop growth stages, could result in more precise and dependable millet yield predictions. These results mark a leap forward in crop yield prediction for data-poor regions, paving the way for more accurate and efficient agricultural management. By emphasizing the critical role of detailed, spatio-temporal data, this work substantially enhances model precision, informing targeted strategies to boost yields. This advancement stands to serve producers and consumers alike, fostering sustainable, high-yield agriculture.

Projections of rainfall erosivity in climate change scenarios for mainland China

This study focuses on improving the accuracy of projections for future rainfall erosivity by selecting five CMIP6-based and bias-adjusted Global Climate Models (GCMs) in China. These models provide more precise estimations of rainfall erosivity under two future periods (2041–2065 and 2076–2100) and two emission scenarios (SSP1-RCP2.6 and SSP5-RCP8.5). The evaluation of the models' performance involved comparing their estimations of two erosivity indices, namely the annual average rainfall erosivity (R-factor) and the extreme storm EI30 with a 10-year return period (10-year storm EI), with reference erosivity maps of China interpolated from hourly observations at 2381 stations. The results indicate that three models, GFDL-ESM4, IPSL-CM6A-LR, and UKESM1-0-LL, outperform the others in terms of higher Nash-Sutcliffe efficiencies and better spatial correlation, especially in regions prone to water erosion. However, it was observed that the R-factor and 10-year storm EI estimated using Multi-Model Ensembles (MMEs) were significantly underestimated for the historical period due to scaling differences. To address this issue, bias-correction factors were determined for each grid cell to improve future projections. The medians of these correction factors were found to be 0.80 for the R-factor and 0.66 for the 10-year storm EI. Overall, the study projects an increase in rainfall erosivity for most regions in China. Under the SSP1-RCP2.6 and SSP5-RCP8.5 scenarios, the R-factor is expected to rise by 18.9% and 19.8% for the near-term and 26.0% and 46.5% for the long-term, respectively. Similarly, the 10-year storm EI is projected to increase by 14.2% and 17.4% for the near-term and 14.9% and 45.0% for the long-term, respectively. These projected increases are primarily attributed to the elevated probability of extreme precipitation events, highlighting the need for enhanced soil and water conservation measures in China to mitigate the challenges posed by global warming.

Modeling reference evapotranspiration using machine learning and remote sensing techniques for semi-arid subtropical climate of Indian Punjab

Abstract A study was carried out to develop and evaluate the performance of different machine learning (ML) models for predicting reference evapotranspiration (ET0). The models included multiple linear regression (MLR), least square-support vector machine (LS-SVM), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). The daily meteorological data for 50 years (1970–2019) were used to estimate ET0 using FAO-ET calculator. The FAO-ET calculator was compared with ML models to investigate the best-fit ML model for predicting ET. Thereafter, ET predicted by the best-fit ML model was compared with satellite (Moderate Resolution Imaging Spectroradiometer – MODIS) ET, which was finally mapped to a larger landscape (over entire Punjab and Haryana). Modeling of ET0 was best performed through LS-SVM followed by ANN2, ANN1, ANFIS10, ANFIS2, MLR and ANFIS9 models. Among developed models, coefficient of determination (R2) value varied from 0.800 to 0.998, being highest (0.998) under LS-SVM model. MODIS overestimated ET when compared with LS-SVM having R2 and root mean square error (RMSE) values of 0.73 and 3.95 mm, respectively. After applying the bias correction factor, R2 and RMSE were 0.74 and 1.19 mm, respectively. The ML and satellite-based ET estimation would be useful for timely water budgeting to manage the water scarcity problems from local to regional levels.

Climate projections of precipitation and temperature in cities from ABC Paulista, in the Metropolitan Region of São Paulo—Brazil

Cities are increasingly vulnerable to climate change's impacts and poorly adapted to extreme variability. This study aimed to evaluate climate projections of air temperature and precipitation in seven cities in the Metropolitan Region of São Paulo that correspond to the Greater ABC Paulista. We used high-resolution models Eta-HADGEM2_ES (CMIP5), CNRM-CM6-1-HR (CMIP6), and the TerraClimate database to analyze future projections and the specific warming levels (SWLs), respectively. Model data were validated with observed data and bias was removed. A bias correction factor was generated and used in the climate projections for the different emission scenarios. The results show a consensus between the models and the SWLs (2 and 4°C) for the increase in maximum (Tmax) and minimum (Tmin) temperatures for all municipalities in ABC Paulista in different emission scenarios until the end of the 21st century. For the nearest future (2020–2040), the city of São Caetano do Sul (SCS) shows the highest positive annual anomalies of Tmax concerning the climatological period (1985–2015), for the scenario RCP4.5 (2.8°C) and the RCP8.5 (7.4°C), according to projections from the Eta-HADGEM2_ES, highlighting summer and autumn as the hottest. For precipitation, there was a consensus between the Eta-HADGEM2_ES and the CNRM-CM6-1-HR for a reduction in all scenarios and time-slices 2020–2040 and 2041–2070. The municipalities of Diadema (−78.4%) and SCS (−78%) showed the most significant reductions in December for the RCP8.5, and for SSP5-8.5, SCS shows −30.9% in December for the 2020–2040 time-slice. On the other hand, TerraClimate presents excess rain for Ribeirão Pires (+24.8%) and Santo André (+23.7%) in winter for SWL4°C. These results suggest that an increase in Tmax and Tmin, as projected, should influence the intensity of extreme heat events. Furthermore, a reduction in annual and seasonal rainfall does not mean a decrease in the region's extreme daily events that cause floods and landslides. However, it leaves an alert of water scarcity for the supply and demand of the population. ABC Paulista does not have adaptation plans to face extreme climate change. The results can contribute to the first phase of creating an adaptation plan, giving a first view of the climate threat that should intensify until the end of the twentieth century affecting the most vulnerable municipalities.

Probing z ≳ six massive black holes with gravitational waves

ABSTRACT We investigate the coalescence of massive black hole ($M_{\rm BH}\gtrsim 10^{6}~\rm {\rm M}_{\odot }$) binaries (MBHBs) at 6 &amp;lt; z &amp;lt; 10 by adopting a suite of cosmological hydrodynamical simulations of galaxy formation, zoomed-in on biased (&amp;gt;3σ) overdense regions (Mh ∼ 1012 M⊙ dark matter haloes at z = 6) of the Universe. We first analyse the impact of different resolutions and AGN feedback prescriptions on the merger rate, assuming instantaneous mergers. Then, we compute the halo bias correction factor due to the overdense simulated region. Our simulations predict merger rates that range between 3 and 15 $\rm yr^{-1}$ at z ∼6, depending on the run considered, and after correcting for a bias factor of ∼20−30. For our fiducial model, we further consider the effect of delay in the MBHB coalescence due to dynamical friction. We find that 83 per cent of MBHBs will merge within the Hubble time, and 21 per cent within 1 Gyr, namely the age of the Universe at z &amp;gt; 6. We finally compute the expected properties of the gravitational wave (GW) signals and find the fraction of LISA detectable events with high signal-to-noise ratio (SNR &amp;gt; 5) to range between 66 per cent and 69 per cent. However, identifying the electro-magnetic counterpart of these events remains challenging due to the poor LISA sky localization that, for the loudest signals ($\mathcal {M}_c\sim 10^6~{{\rm M}_{\odot }}$ at z = 6), is around 10 $\rm deg^2$.

Development and validation of a smart system for medullation and diameter assessment of alpaca, llama and mohair fibres

Medullated fibres, due to their higher resistance to bending and pressure, constitute a problem for the textile industry. Thus, having practical instruments to identify them is essential. Therefore, the aim of this research was to develop and validate a novel, swift, automatic system (referred to as S-FiberMed) for medullation and diameter assessment of animal fibres based on artificial intelligence. The medullation of 88 samples of alpaca, llama and mohair fibres (41, 43 and 4, respectively) was evaluated. Additionally, 269 samples of alpacas were considered for average fibre diameter (AFD) and the results were compared with the Portable Fiber Tester (PFT) and Optical Fibre Diameter Analyser (OFDA) methods (72 and 197 samples, respectively). The preparation of each sample to be analysed followed the procedure described in IWTO-8-2011. Version 5 of “You Only Look Once” and DenseNet models were used to recognise the type of medullation and diameter of the fibres, respectively. Within each image (n = 661 for alpaca), all fibres were labelled (as Non-Medullated, Fragmented Medulla, Uncontinuous Medulla, Continuous Medulla and Strongly Medullated) using the LabelImg tool. Data augmentation technique was applied to obtain 3 966 images. Such data set was divided into 3 576 and 390 images for training and test data, respectively. For mohair samples (n = 321), a similar process was carried out. The data to train the model used to infer the diameter contained 16 446 fibres labelled with his respective AFD. A complementary hardware composed of three subsystems (mechanical, electronic, and optical) was developed for evaluation purposes. T-test, Pearson and Concordance correlation, Bland-Altman plot and linear regression analyses were used to validate and compare the S-Fiber Med with other methods. Results indicate that there was no significant difference between medullation percentage obtained with the projection microscope and the S-Fiber Med. The Pearson and Concordance correlation analysis shows a strong, high and significant relationship (P-value < 0.001). The AFDs of alpaca and llama fibre samples obtained with the two methods are very similar, because no significant difference was found at the t-test (P-value > 0.172), and they have a strong, high and significant relationship between them, given the high Pearson correlation value (r ≥ 0.96 with P-value < 0.001), high Concordance coefficient and bias correction factor. Similar results were found when PFT and OFDA100 were compared with S-Fiber Med. As a conclusion, this new system provides precise, accurate measurements of medullation and AFD in an expeditious fashion (40 seconds/sample).

An Improved CZT Algorithm for High-Precision Frequency Estimation

Estimating the frequencies of multiple superimposed exponentials in noise is an important problem due to its various applications in engineering. In order to obtain good inhibition of spectral leakage and improve the estimation accuracy, an improved Chirp-Z transform (CZT) algorithm is proposed for high-precision frequency estimation. Firstly, the proposed algorithm analyzes the characteristics of the CZT spectrum and utilizes the CZT spectrum to construct a bias correction factor for frequency bias estimation. Then, an expression between the bias correction factor and the frequency estimation error is derived to obtain a more accurate estimate of the frequency bias. Finally, the frequency estimate of the CZT is corrected by the estimated frequency bias so as to obtain a higher frequency estimation accuracy. Compared with the conventional CZT algorithm, the proposed improved CZT algorithm achieves a higher frequency estimation accuracy by correcting the frequency estimate of the CZT method using the estimated frequency bias. The proposed improved CZT algorithm is verified using simulation studies and experimental results, and the results show that it has a higher accuracy and better robustness than the existing methods.

Enhancing drought resilience and energy security through complementing hydro by offshore wind power—The case of Brazil

During atypical droughts, power systems with a heavy reliance on hydropower risk increased greenhouse gas emissions if they are balanced with fossil-fired generation. This work investigates the role of offshore wind energy in reducing the vulnerability of power systems dependent on such hydrological patterns, thereby eliminating this emission increase, using Brazil as a case study. Offshore wind potential and its complementarity with hydro resources are addressed by considering bias-corrected reanalysis data. Then, a cost-minimizing model is built to analyze the effect of integrating wind farms (considering bottom-fixed and floating structures and distance to shore) into the existing power system in Brazil. Applying a lower, median, and upper bias correction factor, potentials are reduced by 8%–44% compared to uncorrected data. Irrespective of systematic bias, the findings indicate a high complementarity between Northeastern wind regimes and most hydropower basins. The share of offshore wind energy grows in scenarios with reduced costs, but wind farms are part of the optimal system even with the current costs. With increasing wind power capacity, dynamic dispatch changes, and natural gas no longer plays a role in the dry season as it currently does, but only in the rainy season on a significantly reduced scale. Existing reservoirs support the integration of offshore wind farms into highly renewable scenarios, but they are insufficient in a complete fossil fuel phaseout, where other electricity storage must be deployed to help balance the system. Yet, power systems in the scenarios with large wind capacity have less stored hydropower in the dry season than in the current system, while they store more in the rainy season, implying a reduced risk of empty reservoirs. The Brazilian power system with offshore wind farms can eliminate 54.4 Mton CO2eq/year (97% of current power sector emissions) without additional electricity storage.

A Hydrograph-Based Approach to Improve Satellite-Derived Snow Water Equivalent at the Watershed Scale

For the past few decades, remote sensing has been a valuable tool for deriving global information on snow water equivalent (SWE), where products derived from space-borne passive microwave radiometers are favoured as they respond to snow depth, an important component of SWE. GlobSnow, a novel SWE product, has increased the accuracy of global-scale SWE estimates by combining remotely sensed radiometric data with other physiographic characteristics, such as snow depth, as quantified by climatic stations. However, research has demonstrated that passive microwaves algorithms tend to underestimate SWE for deep snowpack. Approaches were proposed to correct for such underestimation; however, they are computer intensive and complex to implement at the watershed scale. In this study, SWEmax information from the near real time 5-km GlobSnow product, provided by Copernicus and the European Space Agency (ESA) and GlobSnow product at 25 km resolution were corrected using a simple bias correction approach for watershed scale applications. This method, referred to as the Watershed Scale Correction (WSC) approach, estimates the bias based on the direct runoff that occurs during the spring melt season. Direct runoff is estimated on the one hand from SWEmax information as main input. Infiltration is also considered in computing direct runoff. An independent estimation of direct runoff from gauged stations is also performed. Discrepancy between these estimates allows for estimating the bias correction factor. This approach is advantageous as it exploits data that commonly exists i.e., flow at gauged stations and remotely sensed/reanalysis data such as snow cover and precipitation. The WSC approach was applied to watersheds located in Eastern Canada. It was found that the average bias moved from 33.5% with existing GlobSnow product to 18% with the corrected product, using the recommended recursive filter coefficient β of 0.925 for baseflow separation. Results show the usefulness of integrating direct runoff for bias correction of existing GlobSnow product at the watershed scale. In addition, potential benefits are offered using the recursive filter approach for baseflow separation of watersheds with limited in situ SWE measurements, to further reduce overall uncertainties and bias. The WSC approach should be appealing for poorly monitored watersheds where SWE measurements are critical for hydropower production and where snowmelt can pose serious flood-related damages.