Bias Correction Research Articles

The impact of self-report inaccuracy in the UK Biobank and its interplay with selective participation.

Although the use of short self-report measures is common practice in biobank initiatives, such a phenotyping strategy is inherently prone to reporting errors. To explore challenges related to self-report errors, we first derived a reporting error score in the UK Biobank (UKBB; n = 73,127), capturing inconsistent self-reporting in time-invariant phenotypes across multiple measurement occasions. We then performed genome-wide scans on the reporting error score, applied downstream analyses (linkage disequilibrium score regression and Mendelian randomization) and compared its properties to the UKBB participation propensity. Finally, we improved phenotype resolution for 24 measures and inspected the changes in genomic findings. We found that reporting error was present across all 33 assessed self-report measures, with repeatability levels as low as 47% (childhood body size). Reporting error was not independent from UKBB participation, evidenced by the negative genetic correlation between the two outcomes (rg = -0.77), their shared causes (for example, education) and the loss in self-report accuracy following participation bias correction. Across all analyses, the impact of reporting error ranged from reduced power (for example, for gene discovery) to biased estimates (for example, if present in the exposure variable) and attenuation of genome-wide quantities (for example, 21% relative attenuation in SNP heritability for childhood height). Our findings highlight that both self-report accuracy and selective participation are competing biases and sources of poor reproducibility for biobank-scale research.

Do Sustainable Agricultural Practices Boost Production and Technical Efficiency? A Case Study of Coffee Smallholders in Peru

ABSTRACTSmallholder agriculture remains a cornerstone for economic development and poverty reduction in Latin America. Their transition to sustainable agricultural production is critical in the face of the environmental challenges posed by conventional farming practices and is a key pathway to achieving the United Nations' Sustainable Development Goals. This study examines the effects of Sustainable Agricultural Practices (SAP) adoption on coffee production and technical efficiency (TE) among smallholders in Peru, focusing on both observable and unobservable factors. Using a multistage framework, we employ propensity score matching to control for observable factors, estimate a Stochastic Production Frontier model with sample selection bias correction to control for unobservable factors, and assess technological differences using a metafrontier approach. Our analysis of pooled cross‐sectional data from 2017 to 2019 indicates that age, farm accessibility, and having Quechua as a first language are factors that make farmers more likely to adopt SAP. Although SAP adopters initially show higher production levels, the difference is not statistically significant after accounting for unobserved factors. However, SAP adopters utilize better technology and exhibit higher TE compared to non‐adopters, highlighting the potential benefits of promoting sustainable practices in smallholder agriculture.

Climate change impacts on the Chiffa basin (northern Algeria) using bias-corrected RCM data

IntroductionThis study aims to assess the efficacy of Quantile mapping (QM) and Delta change (DC) bias correction methods to improve hydrological simulations of the Chiffa basin in northern Algeria. The main issue addressed is the need for corrected climate data to provide reliable hydrological projections in semi-arid climates.MethodsHydrological simulations were conducted using the GR2M conceptual rainfall-runoff model, recognized for its robustness in Mediterranean climates. This model was coupled with precipitation simulations from the Rossby Centre regional atmospheric model RCA4 of the Coordinated Regional Climate Downscaling Experiment (Cordex-Africa) forced by two global circulation models (MPI-ESM-LR and CRNM-CM5). Hydrological projections were produced for the future period 20702099 under RCP 4.5 and RCP 8.5 scenarios, comparing raw and bias-corrected data.Results and discussionThe findings indicate that raw precipitation data are inadequate for reflecting future rainfall trends and simulating future flows. Bias correction methods significantly improved the models performance, with the coefficient of determination (R2) increasing from 0.440.53 to 0.830.97. Additionally, regional climate models project a 5 to 8% decrease in annual flows by the end of the 21st century under RCP 4.5 and RCP 8.5 scenarios. These results highlight the importance of bias correction methods for hydrological impact studies, and we recommend implementing specific adaptation measures, such as improved irrigation efficiency, development of water storage infrastructure, and adoption of drought-resistant agricultural practices. Future research should focus on employing multivariate bias correction methods, utilizing higher-resolution climate data (≤10 km), and implementing ensemble modeling approaches to better characterize uncertainties.

Quantifying the Contamination from nearby Stellar Companions in Gaia DR3 Photometry

Abstract Identifying and removing binary stars from stellar samples is a crucial but complicated task. Regardless of how carefully a sample is selected, some binaries will remain and complicate interpretation of results, especially via flux contamination of survey photometry. One such sample is the data from the Gaia spacecraft, which is collecting photometry and astrometry of more than 109 stars. To quantify the impact of binaries on Gaia photometry, we assembled a sample of known binary stars observed with adaptive optics and with accurately measured parameters, which we used to predict Gaia photometry for each stellar component. We compared the predicted photometry to the actual Gaia photometry for each system, and found that the contamination of Gaia photometry because of multiplicity decreases nonlinearly from near-complete contamination (ρ ≤ 0 . ″ 15 ) to no contamination (binary projected separation, or ρ > 0 . ″ 3 ). We provide an analytic relation to analytically correct photometric bias in a sample of Gaia stars using the binary separation. This correction is necessary because the Gaia PSF photometry extraction does not fully remove the secondary star flux for binaries with separations with ρ ≲ 0 . ″ 3 . We also evaluated the utility of various Gaia quality-of-fit metrics for identifying binary stars and found that Renormalized Unit Weight Error (RUWE) remains the best indicator for unresolved binaries, but multipeak image fraction probes a separation regime not currently accessible to RUWE.

Concept and theoretical performance analysis for decentralised digital synchronisation in distributed radar sensor networks

AbstractThis paper presents a decentralised technique for achieving frequency, time, and phase synchronisation of platforms cooperating in a distributed radar sensor network. The proposed method is advantageous for existing digital radar systems as it can be implemented entirely in the software without the use of additional RF hardware required by other techniques. The synchronisation signal model for signals transmitted and received in various clock domains is presented and an estimation model is subsequently derived for estimating and correcting the clock drifts. A modified version of a previously developed phase and clock bias correction procedure is outlined for correcting time and phase after frequency synchronisation. A comprehensive theoretical performance analysis of the technique is performed in which the expected maximum achievable performance is derived in terms of the Cramér–Rao lower bound for frequency, time, and phase measurements. Multiple Monte Carlo simulations show that the proposed technique approaches this performance limit. Finally, a simulated distributed transmit beamforming scenario is provided to show the application of the proposed technique in a practical system architecture. The results of this show that as the signal‐to‐noise ratio approaches moderate levels, the proposed synchronisation technique enables the beamforming network to achieve nearly optimal coherent energy gain at the beamforming destination.

Testing the endogeneity of a spatial weight matrix in the weak-tied spatial dynamic panel data model

Weak ties in large-scale networks facilitate the diffusion of information and resources, generating spillover effects. While geographic proximity may generate straightforward networks, a non predetermined spillover framework better accommodates socioeconomic interactions. In such cases, it is important to test the endogeneity of a spatial weight matrix before full estimation to ensure valid and efficient model selection. I propose a robust score test to assess this endogeneity in the weak-tied spatial dynamic panel data model, emphasizing its asymptotic optimality and computational efficiency. First, I correct score function biases to address the incidental parameters problem. Second, I adjust the score functions to enhance robustness against local parametric misspecifications. Monte Carlo simulations demonstrate favorable finite sample properties. In my application, I illustrate how this test helps model selection for the Solow-Swan growth model, considering spillovers from geographic and knowledge proximities. The estimation results reveal endogenous spillover effects with weakly positive impacts on real income per worker, while savings show positive internal effects but negative spillover effects, possibly due to competition for investment. Notably, the labor growth rate positively influences knowledge accumulation and innovation, leading to beneficial effects on real income per worker through both internal and spillover channels.

Improving the Seasonal Forecast of Summer Precipitation in Southeastern China Using a CycleGAN-based Deep Learning Bias Correction Method

Improving the Seasonal Forecast of Summer Precipitation in Southeastern China Using a CycleGAN-based Deep Learning Bias Correction Method

Light-to-Spike Encoding Using Indium-Gallium-Zinc Oxide Phototransistor for all-Color Image Recognition with Dynamic Range and Precision Tunability.

To enhance the efficiency of machine vision system, physical hardware capable of sensing and encoding is essential. However, sensing and encoding color information has been overlooked. Therefore, this work utilizes an indium-gallium-zinc oxide (IGZO) phototransistor to detect varying densities of red, green, and blue (RGB) light, converting them into corresponding drain current (ID) states. By applying stochastic gate voltage (VG) pulses to the IGZO phototransistor, the fluctuations are generated in these ID states. When the ID exceeds the threshold current (ITC), a spike signal is generated. This approach enables the conversion of light densities into spike signals, achieving spike-rate encoding. Moreover, adjusting the standard deviation (σ) of the VG pulses controls the range of light densities converted into spike rates, while altering the mean (μ) of the VG pulses changes the baseline level of spike rates. Remarkably, separate RGB channels offer a tunable encoding process, which can emphasize individual colors and correct color bias. The encoded spike rates are also fed into a spiking neural network (SNN) for CIFAR-10 pattern recognition, achieving an accuracy of 86%. The method allows the operation of SNN and shows the tunability in the process of light-to-spike encoding, opening possibilities for color image processing.

An improved empirical quantile mapping approach for bias correction of extreme values in climate model simulations

Abstract Quantifying and correcting biases in modeling simulations is crucial for deriving meaningful findings across various scientific disciplines. Climate model simulations, in particular, often exhibit systemic biases when compared to observations. These biases may persist in future climate simulations, affecting the results of many climate change impact assessment studies. Empirical Quantile Mapping (QM) is a widely used method to correct such biases, mapping quantiles between observed and simulated Cumulative Distribution Functions (CDFs). However, empirical QM faces a fundamental challenge when the CDF of future simulations differs from historical simulations, potentially leading to extreme values falling outside the historical CDF range. To address this issue, our study introduces a novel approach to extrapolate future extreme values for bias correction, preserving the rank order of simulated future extremes. By construction, our approach ensures that bias corrected values are not exaggerated and retain the rank structure of the original simulated data, while preserving climate change signals in the bias corrected outputs. Additionally, our approach includes a technique to adjust the wet-day frequency for precipitation by preserving the ratio of wet-day frequency between observations and historical model simulations.

Testing the Ce Limit of Mass Bias Correction Using 145Nd/142Nd as Normalizing Ratio in Radiogenic Neodymium Isotope Analysis by MC-ICP-MS.

Neodymium isotopes are a powerful geochemical tool that has widely been used in terrestrial and extraterrestrial studies. Modern multicollector inductively coupled plasma mass spectrometers (MC-ICP-MS) allow fast, accurate, and precise analysis of the radiogenic Nd isotope ratio 143Nd/144Nd. These analyses comprise relatively high instrumental mass bias that is typically corrected for using the stable 146Nd/144Nd of 0.7219 and an exponential law. The instrument is usually tuned to optimize the operating conditions for isotope analysis, but this tuning is a trade-off primarily between signal intensity, stability, and accuracy. Alternative, more effective approaches for mass bias correction have been proposed that use 145Nd/142Nd as normalizing ratio. However, one drawback of using this ratio is that the efficient removal of Ce from Nd is required to minimize the effect of isobaric interference of 142Ce on 142Nd. Here, we analyzed international Nd and rock reference materials using a Thermo Scientific Neptune Plus MC-ICP-MS to evaluate the sensitivity of 145Nd/142Nd-based mass bias correction to varying Ce/Nd and in comparison with the commonly used 146Nd/144Nd-based correction. Our results show that the corrected 143Nd/144Nd of Ce-doped JNdi-1 and Ce-containing USGS BCR-2, NOD-A-1, and NOD-P-1 reference materials are insensitive to Ce/Nd of up ~1. The correction of instrumental mass bias with 145Nd/142Nd as a normalizing ratio yields, as previously suggested, improved trueness and precision of 143Nd/144Nd data in comparison with 146Nd/144Nd-based corrections, even under high Ce/Nd of to ~1. This allows improved optimization of signal intensity during instrument tuning, which is particularly useful for natural samples with low Nd content.

Role of the quasi-biennial oscillation in alleviating biases in the semi-annual oscillation

Abstract. Model representations of the stratospheric semi-annual oscillation (SAO) show a common easterly bias, with a weaker westerly phase and stronger easterly phase compared to observations. Previous studies have shown that both resolved and parameterized tropical waves in the upper stratosphere are too weak. These waves propagate vertically through the underlying region dominated by the stratospheric quasi-biennial oscillation (QBO) before reaching the SAO altitudes. The influence of biases in the modelled QBO on the representation of the SAO is therefore explored. Correcting the QBO biases helps to reduce the SAO easterly bias through improved filtering of resolved and parameterized waves that contribute to improving both the westerly and the easterly phases of the SAO. The time-averaged zonal-mean zonal winds at SAO altitudes change by up to 25 % in response to the QBO bias corrections. The annual cycle in the equatorial upper stratosphere is improved as well. Most of the improvements in the SAO occur during the QBO easterly phase, coinciding with the period when the model's QBO exhibits the largest bias. Nevertheless, despite correcting for the QBO bias, there remains a substantial easterly bias in the SAO, suggesting that westerly wave forcing in the upper stratosphere and lower mesosphere is still severely under-represented.

Reprocessing of eXpendable BathyThermograph (XBT) profiles from the Ligurian and Tyrrhenian seas over the time period 1999–2019 with a full metadata upgrade

Abstract. The advent of open science and the United Nations Decade of Ocean Science for Sustainable Development are revolutionizing the ocean-data-sharing landscape for an efficient and transparent ocean information and knowledge generation. This blue revolution raised awareness on the importance of metadata and community standards to activate interoperability of the digital assets (data and services) and guarantee that data-driven science preserves provenance, lineage and quality information for its replicability. Historical data are frequently not compliant with these criteria, lacking metadata information that was not retained, crucial at the time of data generation and further ingestion into marine data infrastructures. The present data review is an example attempt to fill this gap through a thorough data reprocessing starting from the original raw data and operational log sheets. The data gathered using XBT (eXpendable BathyThermograph) probes during several monitoring activities in the Tyrrhenian and Ligurian seas between 1999 and 2019 have first been formatted and standardized according to the latest community best practices and all available metadata have been inserted, including calibration information never applied, uncertainty specification and bias correction from Cheng et al. (2014). Secondly, a new automatic quality control (QC) procedure has been developed and a new interpolation scheme applied. The reprocessed (REP) dataset has been compared to the data version, presently available from the SeaDataNet (SDN) data access portal, processed according to the pioneering work of Manzella et al. (2003) conducted in the framework of the European Union Mediterranean Forecasting System Pilot Project (Pinardi et al., 2003). The comparison between REP and SDN datasets has the objective to highlight the main differences derived from the new data processing process. The maximum discrepancy among the REP and SDN data versions always resides within the surface layer (REP profiles are warmer than SDN ones) until 150 m depth generally when the thermocline settles (from June to November). The overall bias and root mean square difference are equal to 0.002 and 0.041 °C, respectively. Such differences are mainly due to the new interpolation technique (Barker and McDougall, 2020) and the application of the calibration correction in the REP dataset. The REP dataset (Reseghetti et al., 2024; https://doi.org/10.13127/rep_xbt_1999_2019.2) is available and accessible through the INGV (Istituto Nazionale di Geofisica e Vulcanologia, Bologna) ERDDAP (Environmental Research Division's Data Access Program) server, which allows for machine-to-machine data access in compliance with the FAIR (findable, accessible, interoperable and reusable) principles (Wilkinson et al., 2016).

Assessment of extreme climate stress across China’s maize harvest region in CMIP6 simulations

Climate change is expected to increase the frequency and severity of climate extremes, which will negatively impact crop production. As one of the main food and feed crops, maize is also vulnerable to extreme climate events. In order to accurately and comprehensively assess the future climate risk to maize, it is urgent to project and evaluate the stress of extreme climate related maize production under future climate scenarios. In this study, we comprehensively evaluated the spatio-temporal changes in the frequency and intensity of six extreme climate indices (ECIs) across China’s maize harvest region by using a multi-model ensemble method, and examined the capability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to capture these variations. We found that the Independence Weight Mean (IWM) ensemble results calculated by multiple Global Climate Models (GCMs) with bias correction could better reproduce each ECI. The results indicated that heat stress for maize showed consistent increase trends under four future climate scenarios in the 21st century. The intensity and frequency of the three extreme temperature indices in 2080s were significantly higher than these in 2040s, and in the high emission scenario were significantly higher than these in the low emission scenario. The three extreme precipitation indices changed slightly in the future, but the spatial changes were more significant. Therefore, with the uncertainty of climate change and the differences of climate characteristics in different regions, the optimization of specific management measures should be considered in combination with the specific conditions of future local climate change.

All-Sky Assimilation of GOES-16 Water Vapor Channels In Consideration of Cloud-Dependent Interchannel Observation-Error Correlations

Abstract The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-16 includes three water vapor channels (8, 9, 10) that are specifically designed to monitor tropospheric water vapor at high, middle, and low levels. This study investigates an effective approach for assimilating the all-sky brightness temperatures (BTs) derived from these three water vapor channels while considering the characteristics of interchannel observation-error correlations (IOECs). The NASA Unified Weather Research and Forecasting (NU-WRF) model, alongside the NCEP Gridpoint Statistical Interpolation (GSI)-based three-dimensional ensemble-variational hybrid data assimilation system, is utilized. First, an optimal bias correction (BC) scheme and data assimilation (DA) configuration, previously developed for all-sky assimilation of GOES-16 channel 8, are confirmed to be effective for channels 9 and 10. Then, the IOECs with respect to the symmetric cloud proxy variable among these three channels are analyzed. It is found that the IOECs display sigmoid function characteristics, being lower and roughly invariant with respect to under clear sky conditions and higher in cloudy conditions. Given the unique properties of IOECs, sensitivity experiments with various configurations for assimilating the all-sky BTs from the three water vapor channels are conducted with Hurricanes Laura (2020) and Ida (2021). The results indicate that a strategy combining the assimilation of all-sky BTs from one of the GOES-16 channels, especially channel 10, with clear-sky BTs from the other water vapor channels yields superior analysis and forecasts in most scenarios, thereby highlighting the importance of appropriately estimating and accounting for cloud-dependent IOECs when assimilating all-sky BTs from the infrared channels in operational DA systems.

The widening gender wage gap in the gig economy in China: the impact of digitalisation

The gig economy, fundamentally relying on the digital economy, is often celebrated for its potential to expand employment opportunities and close gender wage gaps. However, research on its gender impacts in China, the largest developing country with a rapidly expanding gig sector, is notably lacking. Employing discrimination theory, the findings challenge the prevailing optimistic view on the role of the gig economy. Utilising the China Labour Dynamics Survey data from 2014, 2016, and 2018, and applying the Bourguignon, Fournier, and Gurgand model to correct selection bias, this study reveals that, digitalisation has exacerbated gender wage gaps, with the wage growth of female gig workers significantly lagging behind their male counterparts, especially among married and older cohorts. Adopting the Neumark decomposition, the analysis confirms that discrimination largely accounts for this gap. This study uses Generalised Propensity Score Matching as robustness test and concludes with policy recommendations.

Predicting particulate matter ( ) levels in Morocco: a 5-day forecast using the analog ensemble method.

The accurate prediction of particulate matter ( ) levels, an indicator of natural pollutants such as those resulting from dust storms, is crucial for public health and environmental planning. This study aims to provide accurate forecasts of over Morocco for 5days. The analog ensemble (AnEn) and the bias correction (AnEnBc) techniques were employed to post-process forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition forecasts, using CAMS reanalysis data as a reference. The results show substantial prediction improvements: the root mean squared error (RMSE) decreased from 63.83 in the original forecasts to 44.73 with AnEn and AnEnBc, while the mean absolute error (MAE) reduced from 36.70 to 24.30 . Additionally, the coefficient of determination ( ) increased more than twofold from 29.11 to 65.18%, and the Pearson correlation coefficient increased from 0.61 to 0.82. The integrating reanalysis data and the utilization of the AnEn substantially improved the accuracy of 5-day forecasting in Morocco. This is the first application of this approach in Morocco and the Middle East and North Africa (MENA) and has the potential for translation into early and more accurate warnings of pollution events. The application of such approaches in environmental policies and public health decision-making can minimize the health impacts of air pollution.

Assessing the Effect of Bias Correction Methods on the Development of Intensity–Duration–Frequency Curves Based on Projections from the CORDEX Central America GCM-RCM Multimodel-Ensemble

This work aims to examine the effect of bias correction (BC) methods on the development of Intensity–Duration–Frequency (IDF) curves under climate change at multiple temporal scales. Daily outputs from a 9-member CORDEX-CA GCM-RCM multi-model ensemble (MME) under RCP 8.5 were used to represent future precipitation. Two stationary BC methods, empirical quantile mapping (EQM) and gamma-pareto quantile mapping (GPM), along with three non-stationary BC methods, detrended quantile mapping (DQM), quantile delta mapping (QDM), and robust quantile mapping (RQM), were selected to adjust daily biases between MME members and observations from the SJO weather station located in Costa Rica. The equidistant quantile-matching (EDQM) temporal disaggregation method was applied to obtain future sub-daily annual maximum precipitation series (AMPs) based on daily projections from the bias-corrected ensemble members. Both historical and future IDF curves were developed based on 5 min temporal resolution AMP series using the Generalized Extreme Value (GEV) distribution. The results indicate that projected future precipitation intensities (2020–2100) vary significantly from historical IDF curves (1970–2020), depending on individual GCM-RCMs, BC methods, durations, and return periods. Regardless of stationarity, the ensemble spread increases steadily with the return period, as uncertainties are further amplified with increasing return periods. Stationary BC methods show a wide variety of trends depending on individual GCM-RCM models, many of which are unrealistic and physically improbable. In contrast, non-stationary BC methods generally show a tendency towards higher precipitation intensities as the return period increases for individual GCM-RCMs, despite differences in the magnitude of changes. Precipitation intensities based on ensemble means are found to increase with the change factor (CF), ranging between 2 and 25% depending on the temporal scale, return period, and non-stationary BC method, with moderately smaller increases for short-durations and long-durations, and slightly higher for mid-durations. In summary, it can be concluded that stationary BC methods underperform compared to non-stationary BC methods. DQM and RQM are the most suitable BC methods for generating future IDF curves, recommending the use of ensemble means over ensemble medians or individual GCM-RCM outcomes.

Statistical biases correction in channelized Hotelling model observers

Channelized Hotelling model observers are efficient at simulating the human observer visual performance in medical imaging detection tasks. However, channelized Hotelling observers (CHO) are subject to statistical biases from zero-signal and finite-sample effects. The point estimate of the d' value is also not always symmetric with exact confidence interval (CI) bounds determined for the infinitely trained CHO. A method for correcting these statistical biases and CI asymmetry is studied. CHO d' values and CI bounds with hold-out and resubstitution methods were computed for a range of 200x200 pixels images from 20 to 10 000 images for 10, 40 and 96 channels from a set of 20 000 images with gaussian coloured simulated noise and simulated signal. The median of the non-central F cumulative distribution (F'), which is the CHO underlying statistical behaviour for the resubstitution method, was computed, and compared to d' values and CI bounds. A set of experimental data was used to evaluate F' median values. The F' median allows to get accurate corrected simulated d' values down to zero-signals. For small d' values, the variation of d' values with the inverse of number of images is not linear while the F' median allows a good correction in such conditions. The F' median is also inherently symmetric with regards to the confidence interval. With experimental data, F' median values in a range of about 1 to 10 d' values were within -0.8% to 4.7% of linearly extrapolated values at an infinite number of images. The F' median correction is an effective simultaneous correction of the zero-signal statistical bias and finite-sample statistical bias, and of confidence interval asymmetry of channelized Hotelling observers.&#xD.

The strapdown shipborne gravimetry for ocean based on SINS/GNSS: comparison and optimization

Abstract The strapdown inertial scalar gravimetry (SISG) has become one of the main methods for measuring marine gravity. SIGS requires specific force, attitude, velocity, and position information for gravimetry. The attitude can be obtained by the strapdown inertial navigation system/global navigation satellite system (SINS/GNSS) integration, and the displacement information comes from GNSS or SINS/GNSS. Therefore, based on the different sources of displacement information, shipborne SISG can be divided into two modes. Namely, the gravimetry method of different parameter sources (DPGM) based on displacement information of GNSS and the attitude of SINS/GNSS, as well as the full parameter gravimetry method (FPGM) based on the attitude and displacement information of SINS/GNSS. Since the accuracy of GNSS can meet the requirements for gravimetry, the theoretical studies and applications of DPGM are more common. For FPGM, this idea is more often used in underwater applications (SINS/ doppler velocity log (SINS/DVL), etc.). However, it rarely appears in shipborne applications. Moreover, there is relatively little theoretical analysis on FPGM. In fact, FPGM also has certain application prospects due to the high accuracy. Based on this, this article first provides the measurement model and steps of FPGM, and conducts the theoretical analysis of FPGM. The comparison is made with DPGM from the perspective of error terms. And it is pointed out that FPGM has better potential for anti-interference improvement than DPGM. Then, from the perspective of application, we propose the investigated linear robust Kalman filter (LRKF) based on Huber-M estimation without traversing all dimensions and introduce the horizontal acceleration and bias corrections to improve FPGM. Finally, the optimized FPGM (OFPGM) is obtained. The results of lake and sea tests show that FPGM has certain research value. And OFPGM has higher performance in high dynamic condition and when GNSS occurs interference, which is more suitable for offshore measurement.

Elevation dependency of snowfall changes under climate change over the tibetan plateau: Evidence from CMIP6 GCMs

Snowfall plays a crucial role in the mountainous cryosphere cycle and is significantly influenced by climate change. This study utilizes the global climate models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) with multivariate bias correction (MBC) to explore potential future variations in snowfall and its elevation dependency across the Tibetan Plateau (TP). Findings indicate a consistent decline in annual snowfall across the majority of the TP by the end of the century, except for certain high-elevation regions in the northwest. The decreasing trend is projected to intensify with strengthen Shared Socioeconomic Pathway (SSP) scenarios and exhibits elevation dependency below 5000 m. Specifically, under the SSP5–8.5 scenario, snowfall over the TP is expected to decrease by 39.74 % in the far future (2071–2100), with the elevation zone below 2000 m experiencing the most intense decline of approximately 62 %. This trend is largely attributed to the significant warming, which reduces the snow fraction as more precipitation falls as rain rather than snow. This shift is evidenced by the identification of turning points in snow fraction in the mid-2040s to 2050s, coinciding with rapid temperature increases. Furthermore, substantial decreases in future (heavy) snowfall days contribute to the overall reduction in snowfall. However, complex interplay between increased precipitation and temperature effects results in a slight increase in snowfall over high elevation areas in the northern edge. Uncertainty analysis indicates model uncertainty as the dominant source in snowfall projections, accounting for over 50 % of total variance. The projected declines in snowfall and snow fraction, as well as shortened snowfall days could considerably impact the cryosphere, hydrological and ecological systems of the TP.