Large Bias Research Articles

Importance of Considering Near-Surface Attenuation in Earthquake Source Parameter Estimation: Insights from Kappa at a Dense Array in Oklahoma

ABSTRACT Separating earthquake source spectra from propagation effects is challenging. The propagation effect contains a site-dependent term related to the high attenuation of shallow sediments. Neglecting the site-dependent attenuation can cause large biases and scattering in the corner-frequency (fc) estimates, resulting in significant stress-drop deviations. In this study, we investigate shallow attenuation at the LArge-n Seismic Survey in Oklahoma (LASSO) and site-related biases and scattering in source parameter measurements due to simplified attenuation models. We measure the high-frequency spectral decay parameter kappa on the vertical acceleration spectra of regional earthquakes (125 km away). The site-dependent kappa (κ0,acc) suggests that attenuation increases rapidly at shallow depth and is highly site-dependent. 10%–75% of the attenuation is site-dependent for S waves and even larger for P waves. The quality factor for S waves (QS) ranges from 10 to 100 in the upper 400 m. QP for P waves is mostly below 10 within the same depth. The Quaternary sediments tend to be more attenuating (QS<30), but the Permian rocks also can have high attenuation. We demonstrate that using a non-site-dependent attenuation model in single-spectra fitting leads to large scattering in fc estimates among stations with apparent good fits. The apparent fc can significantly deviate when the range of site-dependent kappa is large or with a higher assumed source spectral fall-off rate n. The biases in apparent fc depend on site condition and distance; however, the correlation between fc and these factors might not be obvious, depending on model assumptions. An apparent increase of stress drop with magnitude in a previous study for local microearthquakes (1.3<Mw<3.0) can be largely negated by including a site term, restricting to sites on the higher Q formations, and fixing n. This study highlights the importance of considering near-surface attenuation when modeling source parameters.

Rectification effects of regional air–sea interactions over western boundary current on large-scale sea surface temperature and extra-tropical storm tracks

The warm Western Boundary Currents (WBCs) and their zonal extensions are persistent, deep, strong and narrow oceanic currents. They are known to anchor and energize the Extra-Tropical storm tracks by frontal thermal air–sea interactions. However, even in the latest generation of climate models, WBCs are characterized by large biases, and both the present storm-track activity and its recent intensification are poorly estimated. Mesoscale air–sea interactions, and in particular the Current Feedback to the Atmosphere (CFB) have been shown to be important in ocean and in particular WBC dynamics as they modify the energy budget of the ocean. CFB causes eddy-killing by drag friction between currents and the atmosphere. It damps the oceanic eddy activity, and, thus, weakens the eddy-mean flow interaction, stabilizing WBCs. Based on cutting-edge high-resolution coupled global simulations, we show that the stabilization of WBCs by CFB modulates the mean Sea Surface Temperature and its meridional gradients as well and the turbulent heat fluxes between the ocean and the atmosphere. This alters the baroclinicity of the lower atmosphere, which in turn modulates the extra tropical storm-tracks intensity by up to 15%.

The effect of item pool size and stopping rule on bias and error parameters in computerized adaptive testing

This study aims to examine the effects of item pool size and stopping rules on bias and error parameters in computerized adaptive testing (CAT). Simulated data were generated and analyzed for item pools of 200 and 2000 items, fixed-length and error stopping rules, and eight different combinations of Maximum Likelihood (ML) and Expected a Posteriori (EAP) methods for ability estimation. For each sub-problem, the findings are listed in a general table and a collection of graphs. In the light of the findings, the error and bias parameters obtained with the ML skill estimation method with fixed-length stopping rule in both small and large item pools were found to be lower than the EAP method. Similarly, in both small and large item pools, the error and bias parameters obtained with the ML ability estimation method in which the error stopping rule was applied were found to be lower than the EAP method. While the number of items used in the analyses where the error stopping rule was applied was approximately six in both ability estimation methods for the small item pool, approximately five items were used with the EAP method and approximately 21 items were used with the ML method in the large item pool. The application with the lowest RMSE and bias values and the highest correlation between actual and predicted θ values was the application related to the sixth sub-problem in which a large item pool was used, a fixed-length stopping rule was used, and ability estimation was performed with the ML method. The results of the study provide clues for the development of testing strategies in CAT applications and provide guidance for more effective assessment processes in the field of education.

Improving Daily Precipitation Estimates by Merging Satellite and Reanalysis Data in Northeast China

Precipitation plays a key control in the water, energy, and carbon cycles, and it is also an important driving force for land surface modeling. This study provides an optimal least squares merging approach to merge precipitation data sets from multiple sources for an accurate daily precipitation estimate in Northeast China (NEC). Precipitation estimates from satellite-based IMERG and SM2RAIN-ASCAT, as well as reanalysis data from MERRA-2, were used in this study. The triple collocation (TC) approach was used to quantify the error uncertainties in each input data set, which are associated with the weights assigned to each data set in the merging procedure. The results revealed that IMERG provides a better consistency with the other two input data sets and thus was more relied on during the merging process. The accuracy of both SM2RAIN-ASCAT and MERRA-2 showed obvious spatio-temporal patterns due to their retrieval algorithms and resolution limits. The merged TC-based daily precipitation provides the highest correlation coefficient with ground-based measurements (R = 0.52), suggesting its capability to represent the temporal variation in daily precipitation. However, it largely overestimated the precipitation intensity in the summer, leading to a large positive bias.

CCN estimations at a high-altitude remote site: role of organic aerosol variability and hygroscopicity

Abstract. High-altitude remote sites are unique places to study aerosol–cloud interactions, since they are located at the altitude where clouds may form. At these remote sites, organic aerosols (OAs) are the main constituents of the overall aerosol population, playing a crucial role in defining aerosol hygroscopicity (κ). To estimate the cloud condensation nuclei (CCN) budget at OA-dominated sites, it is crucial to accurately characterize OA hygroscopicity (κOA) and how its temporal variability affects the CCN activity of the aerosol population, since κOA is not well established due to the complex nature of ambient OA. In this study, we performed CCN closures at a high-altitude remote site during summer to investigate the role of κOA in predicting CCN concentrations under different atmospheric conditions. In addition, we performed an OA source apportionment using positive matrix factorization (PMF). Three OA factors were identified from the PMF analysis: hydrocarbon-like OA (HOA), less-oxidized oxygenated OA (LO-OOA), and more-oxidized oxygenated OA (MO-OOA), with average contributions of 5 %, 36 %, and 59 % of the total OA, respectively. This result highlights the predominance of secondary organic aerosol (SOA) with a high degree of oxidation at this high-altitude site. To understand the impact of each OA factor on the overall OA hygroscopicity, we defined three κOA schemes that assume different hygroscopicity values for each OA factor. Our results show that the different κOA schemes lead to similar CCN closure results between observations and predictions (slope and correlation ranging between 1.08–1.40 and 0.89–0.94, respectively). However, the predictions were not equally accurate across the day. During the night, CCN predictions underestimated observations by 6 %–16 %, while, during morning and midday hours, when the aerosol was influenced by vertical transport of particles and/or new particle formation events, CCN concentrations were overestimated by 0 %–20 %. To further evaluate the role of κOA in CCN predictions, we established a new OA scheme that uses the OA oxidation level (parameterized by the f44 factor) to calculate κOA and predict CCN. This method also shows a large bias, especially during midday hours (up to 40 %), indicating that diurnal information about the oxygenation degree does not improve CCN predictions. Finally, we used a neural network model with four inputs to predict CCN: N80 (number concentration of particles with diameter > 80 nm), OA fraction, f44, and solar global irradiance. This model matched the observations better than the previous approaches, with a bias within ± 10 % and with no daily variation, reproducing the CCN variability throughout the day. Therefore, neural network models seem to be an appropriate tool to estimate CCN concentrations using ancillary parameters accordingly.

Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies

Causal conclusions from observational studies may be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the minimum amount of hidden biases or the minimum strength of unmeasured confounding needed in order to explain away the observed association between treatment and outcome. If the needed bias is large, then the treatment is likely to have significant effects. The Rosenbaum sensitivity analysis is a modern approach for conducting sensitivity analysis in matched observational studies. It investigates what magnitude the maximum of hidden biases from all matched sets needs to be in order to explain away the observed association. However, such a sensitivity analysis can be overly conservative and pessimistic, especially when investigators suspect that some matched sets may have exceptionally large hidden biases. In this paper, we generalize Rosenbaum’s framework to conduct sensitivity analysis on quantiles of hidden biases from all matched sets, which are more robust than the maximum. Moreover, the proposed sensitivity analysis is simultaneously valid across all quantiles of hidden biases and is thus a free lunch added to the conventional sensitivity analysis. The proposed approach works for general outcomes, general matched studies and general test statistics. In addition, we demonstrate that the proposed sensitivity analysis also works for bounded null hypotheses when the test statistic satisfies certain properties. An R package implementing the proposed approach is available online.

Time-division multiplexing (TDM) sequence removes bias in T2 estimation and relaxation-diffusion measurements.

To compare the performance of multi-echo (ME) and time-division multiplexing (TDM) sequences for accelerated relaxation-diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures. The ME, TDM, and the reference single-echo (SE) sequences with six TEs were implemented using Pulseq with single-band (SB) and multi-band 2 (MB2) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized RMS error (NRMSE). Shinnar-Le Roux (SLR) pulses were implemented for the SB-ME and SB-SE sequences to investigate the impact of slice profiles on ME sequences. For the in-vivo brain scan, besides the image intensity comparison and T2-estimates, different methods were used to assess sequence-related effects on microstructure estimation, including the relaxation diffusion imaging moment (REDIM) and the maximum-entropy relaxation diffusion distribution (MaxEnt-RDD). TDM performance was similar to the gold standard SE acquisition, whereas ME showed greater biases (3-4× larger NRMSEs for phantom, 2× for in-vivo). T2 values obtained from TDM closely matched SE, whereas ME sequences underestimated the T2 relaxation time. TDM provided similar diffusion and relaxation parameters as SE using REDIM, whereas SB-ME exhibited a 60% larger bias in the <R2> map and on average 3.5× larger bias in the covariance between relaxation-diffusion coefficients. Our analysis demonstrates that TDM provides a more accurate estimation of relaxation-diffusion measurements while accelerating the acquisitions by a factor of 2 to 3.

Anomalous noise spikes beating from a fine-splitting mode of surface-emitting laser with imperfect true-roundness aspect

High-frequency discrete and distinct mode-beating noise (MBN) spectra are an anomalous phenomenon observed in the related intensity noise spectrum from the vertical-cavity surface-emitting laser (VCSEL), which reveals strong correlation with fine-splitting transverse modes caused by non-circular emission aspect of the VCSEL with imperfect true-roundness. Such a phenomenon is demonstrated for the first time, which seriously degrades the transmission performance of high-speed and broad-band encoding data carried by the multi-mode (MM) VCSEL biased under a large DC bias. In this work, the 4-ary quadrature-amplitude modulation (4-QAM) orthogonal frequency-division multiplexing (OFDM) and the non-return-to-zero on–off keying (NRZ-OOK) transmissions with the direct modulation of different 850-nm MM VCSELs, as suffered from the MBN-induced notches of the signal-to-noise-ratio (SNR) spectra, are characterized under their high-level lasing conditions with multi-mode numbers. The significant SNR degradation on the receiving and decoding performances caused by the cross-correlation between MBN and data spectra is also examined by tuning the lensed fiber along the transverse (X/Y)-axis of the VCSEL (perpendicular to the surface normal) when collimating the output beam, which finds the disorderly shifted MBN spectra, as attributed to the collection of light emitted from the overlapped gain region of different transverse modes. In contrast, the MBN reveals invariant spectral features while the lensed fiber is either back-and-forth tuning along the vertical (Z)-axis (parallel with the surface normal) or tilting its angles from the surface normal under the same horizontal position and distance concerning the MM VCSEL. Such MM VCSEL exhibits several MBN spikes to decay the SNRs of NRZ-OOK and QAM OFDM data encoded around 21 and 34 GHz. For other MM VCSELs with reducing numbers of their transverse lasing modes and deforming roundness of their circular emitting aspects, the corresponding MBN spectral peaks can also be respectively observed at 14/17/24 GHz and 4.5/10.5/13 GHz to degrade the SNRs of the encoded data at such frequencies. Even though pre-emphasized encoding technology is employed, such an MBN effect is strongly correlated with the asymmetric circular aspect of the VCSEL with a degraded roundness ratio, which still affects the data modulation performance of the MM VCSEL for data center communication.

Consistent Initial Error Modes Causing the Largest Prediction Errors and the Strongest Predictability Barrier for Two Types of El Niño Events in CMIP6

AbstractBased on the coupled conditional nonlinear optimal perturbation (C‐CNOP) method, this study explores the season‐dependent predictability barrier (PB) affecting the forecasts of two types of El Niño (central Pacific, CP; eastern Pacific, EP) events by using CMIP6 models. It is found that CP (EP) El Niño forecasts often occurs summer (spring) PB, and only powerful season‐dependent PB can lead to large prediction errors. Further investigating the initial causes of the largest prediction errors and strongest PB, we find that the spatial pattern of initial errors consistently exhibits the sea temperature anomaly dipole of east positive–west negative in the equatorial Pacific, and errors over upper layers of North (South) Pacific are similar to the negative Victoria mode (South Pacific Meridional Mode). Physically, the mode evolution of initial errors in the equatorial Pacific, North and South Pacific are all positive feedback processes, which together ultimately lead to large cold biases over the central‐eastern (CP) or eastern (EP) equatorial Pacific in December. Analysis shows that the initial error mode of North Pacific mainly affects the cold bias of the central Pacific, whereas the mode of South Pacific mostly controls the bias in the eastern Pacific. These initial error modes found in this study can have more serious impacts on forecasts of two types of El Niño events than that in previous studies. The results of this study offer valuable scientific guidance for the adaptive observation of ENSO, which will likely be able to maximize the prediction skills for two types of El Niño events.

Exploring discrepancies in muscle analysis with ImageJ: understanding the impact of tool selection on echo intensity and muscle area measurements.

The aim was to compare the use of different tools within the ImageJ program (polygon vs. segmented line) and their impact on the calculation of muscle area and echo intensity (EI) values in ultrasound imaging of the vastus lateralis muscle. Thirteen volunteers participated in this study. Ultrasound images of the vastus lateralis muscle were acquired using 2D B-mode ultrasonography and analyzed using both the polygon and segmented line tools by the same evaluator. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) assessed the tools' reliability. Bland-Altman plots were employed to verify the agreement between measurements, and linear regression analysis determined proportional bias. A paired t-test was conducted to analyze differences between the tools. The reliability between tools for muscle area calculation was weak (r = 0.000; CV = 138.03 ± 0.34%), while it was excellent for EI (r = 0.871; CV = 15.19 ± 2.96%). The Bland-Altman plots indicated a large bias for muscle area (d = 195.2%) with a proportional bias (p < 0.001). For EI, the bias was (d = 15.2) with proportional bias (p = 0.028). The paired t-test revealed significant differences between the tools for area (p < 0.001) but not for EI (p = 0.060). The study found significant differences in measurements obtained with the polygon and segmented line tools in ImageJ, with the polygon tool showing higher values for muscle area and lower values for EI.

Prioritizing future evidence needs for marine and freshwater mammal conservation action

AbstractMarine and freshwater mammals are increasingly threatened due to human activity. To improve conservation practice, decisions should be informed by the available evidence on the effectiveness of conservation actions. Using a systematically collated database of studies that test the effectiveness of actions to conserve marine and freshwater mammals, we investigated the gaps and biases in the available scientific evidence base. While there is a growing evidence base covering actions to address key threats (e.g. fisheries and bycatch) to marine and freshwater mammal populations, we identified large geographic and taxonomic biases. There was no relationship between the number of studies and marine mammal species per ecoregion and we found biases towards coastal areas of the Global North, with many regions and species having little or no evidence available. The number of studies per species did not correlate with (1) the threat level, (2) evolutionary distinctiveness or (3) the public ‘popularity’ of the study species. We also found a mismatch between actions tested and the actions suggested as needed in the International Union for the Conservation of Nature (IUCN) Red List. Several of these gaps and biases likely reflect the feasibility of researching marine mammal populations; many species can be difficult to access, with limited baseline information on populations and threats, and testing actions can require costly long‐term monitoring. Prioritizing the most cost‐effective conservation strategies for marine and freshwater mammal species will require a comprehensive evidence base on the effects of actions. Continuing to build the necessary baseline data, and focusing future research and funding towards the priority gaps identified in this study will be important to deliver this target.

Criterion Validity and Reliability of a New Medicine Ball Rotational Power Test.

Hardy, SGJ, Stelzer-Hiller, OW, Edwards, KM, and Freeston, J. Criterion validity and reliability of a new medicine ball rotational power test. J Strength Cond Res XX(X): 000-000, 2024-This study assessed the validity and reliability of 2 medicine ball rotational power assessments, the novel push for maximum velocity by radar (MBvel), and the commonly used push for maximum distance by tape measure (MBdis), against the criterion reference 3-dimensional motion capture (MoCap) to identify the best-practice field-based assessment. Fifteen professional female cricketers volunteered for 2 testing sessions each comprising of a specific warm-up and 24 (12 MBvel, 12 MBdis) maximal throws of a 2-kilogram medicine ball. Radar velocity and tape measure distance were compared with MoCap velocity and projectile motion calculated distance overall, and by dominant and nondominant sides. Statistical analysis included intraclass correlations (ICCs) for accuracy (1, 1) and reliability (3,1), Bland-Altman plots for bias precision and limits of agreement, linear regression (R2) for variance, and Pearson's (r) for correlation. Significance was set α = 0.05. MBvel demonstrated excellent accuracy (ICC = 0.97 [0.97-0.98]), and nearly perfect agreement for bias (-0.09%) and precision (1.49%). Side-to-side analysis showed the same profile for MBvel dominant (ICC = 0.96 [0.95-0.97], bias -0.15%, precision = 1.55%) and nondominant sides (ICC = 0.97 [0.96-0.98], bias -0.05%, precision = 1.53%). MBvel demonstrated excellent reliability overall (ICC = 0.94 [0.82-0.98]) for dominant (ICC = 0.88 [0.69-0.97]) and nondominant sides (ICC = 0.93 [0.80-0.98]). MBdis showed poor accuracy (ICC = 0.38 [0.28-0.47]), large bias (12.43%), lower precision (4.55%), and moderate reliability (ICC = 0.72 [0.32-0.90]). The MBvel assessment validly and reliably measures rotational power performance, enabling practitioners to profile, benchmark, and assess the quality in the field.

Rerandomization and covariate adjustment in split-plot designs

Split-plot designs are widely used in agricultural, industrial, social, and biomedical experiments to accommodate hard-to-change factors. Given multiple factors of interest and experimental units that are nested within groups, the design assigns a subset of the factors by a cluster randomization at the group level and assigns the remaining factors by a stratified randomization at the unit level. The randomization mechanism ensures covariate balance on average at both the unit and group levels, allowing for consistent inference of average treatment effects. However, chance covariate imbalance is common in specific allocations and subjects subsequent inference to possibly large variability and conditional bias. Building on the literature on using rerandomization to improve covariate balance and inference efficiency in other design types, we propose two strategies for conducting rerandomization in split-plot designs and establish their guaranteed efficiency gains for inferring average treatment effects by the Horvitz–Thompson and Hajek estimators. We then propose two covariate adjustment methods that further improve inference efficiency when combined with rerandomization. All theoretical guarantees are design-based and robust to model misspecification.

How Skilful Are Cloud Cover Products in Representing Observed Cloudiness in Québec?

ABSTRACT In this study, we performed, for the first time, a detailed analysis of cloudiness in Québec using station based observations and evaluated the trustworthiness of various satellite and reanalysis based cloud cover products. We found only 12 stations with observational time series long enough for providing robust analysis due to various issues related to observing methods and recording of information during the 1990s. Our results showed that Québec can be fairly cloudy throughout the year with annual mean cloud cover fraction ranging between 0.5 and 0.7 with autumn being the cloudiest season at most station locations. We also found a significantly increasing trend in cloudiness in the winter season over Québec during 1982–2012 (∼2–6% per decade depending on the location). Among the cloudiness products evaluated in this study, a satellite based cloudiness product (i.e. based on AVHRR) performed best in representing the observed cloudiness over Québec including the wintertime increasing trend. Two reanalysis based cloudiness products (i.e. ERA5 and NARR) also performed well in representing the observed cloudiness indicated by high correlations, relatively lower mean biases and smaller root mean square errors. However, the reanalysis products did not capture well the observed seasonal trends. All products showed relatively larger biases in the winter season except JRA-25 reanalysis product. During the warmer months, however, JRA-25 showed largest biases followed by MERRA-2 reanalysis product. JRA-25 and MERRA-2 also had lower correlation for annual and winter means and highest root mean square errors for all seasons. Furthermore, based on two skill scores, we found that the ERA5, AVHRR and NARR performed relatively better than JRA-25 and MERRA-2. Based on these results we conclude that the satellite product and the two reanalysis products can be useful resources (as observational proxies) along with station based observations for understanding long term changes in cloudiness of this region and also potentially evaluating regional climate model simulations.

LASSO-type instrumental variable selection methods with an application to Mendelian randomization.

Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments. In this paper, we derive a LASSO procedure for the k-class IV estimation methods in the linear IV model. In addition, we propose the jackknife IV method by using LASSO to address the problem of many weak invalid instruments in the case of heteroscedastic data. The proposed methods are robust for estimating causal effects in the presence of many invalid and valid instruments, with theoretical assurances of their execution. In addition, two-step numerical algorithms are developed for the estimation of causal effects. The performance of the proposed estimators is demonstrated via Monte Carlo simulations as well as an empirical application. We use Mendelian randomization as an application, wherein we estimate the causal effect of body mass index on the health-related quality of life index using single nucleotide polymorphisms as instruments for body mass index.

Informing Near-Airport Satellite NO2 Retrievals Using Pandora Sky-Scanning Observations.

Airports are a large and growing source of NO x . Tracking airport-related emissions is especially difficult, as a portion of emissions are elevated above the surface. While satellite-based NO2 observations show hot-spots near airports, near-source retrievals often have large biases related to uncertainties in the NO2 vertical distribution and resultant air mass factors (AMF). Here we use observations from UV-vis spectrometers (Pandora 1S, SciGlob) deployed near the Atlanta Hartsfield-Jackson International airport from April 2020-May 2021 to assess the impact of aviation on NO2 vertical profiles. We show the first near-airport sky-scanning Pandora observations, which are used to distinguish the airport plume from the urban background. We find that increasing aviation leads to higher NO2 over the airport, and the enhancement is distributed across the mixed layer near-equally. We compare observed profiles with those modeled by the Goddard Earth Observing System composition forecast (GEOS-CF) system. We find that modeled profiles attribute a larger portion of the column closer to the surface and underestimate the NO2 mixing height. Observed profiles typically exhibited greater NO2 concentrations up to 2.5 km above ground level. Air mass factors (AMF) calculated using observations (AMFFused) are similar over Hartsfield-Jackson to those calculated using GEOS-CF (AMFGEOS-CF). The unexpected similarity in alternative AMFs is attributed to the altitude-dependent sensitivity of AMFFused to changes in NO2 concentration. Using either AMFFused or AMFGEOS-CF to evaluate TROPOMI NO2 against independent direct-sun observations produces consistent normalized mean differences of -22% and -29%, respectively. Overall, these results demonstrate the benefits of a combined ground and satellite-based approach for probing a complex distribution of NO x emissions in an urban area.

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.

Recent progress in atmospheric modeling over the Andes – part I: review of atmospheric processes

The Andes is the longest mountain range in the world, stretching from tropical South America to austral Patagonia (12°N-55°S). Along with the climate differences associated with latitude, the Andean region also features contrasting slopes and elevations, reaching altitudes of more than 4,000 m. a.s.l., in a relatively narrow crosswise section, and hosts diverse ecosystems and human settlements. This complex landscape poses a great challenge to weather and climate simulations. The interaction of the topography with the large-scale atmospheric motions controls meteorological phenomena at scales of a few kilometers, often inadequately represented in global (grid spacing ∼200–50 km) and regional (∼50–25 km) climate simulations previously studied for the Andes. These simulations typically exhibit large biases in precipitation, wind and near-surface temperature over the Andes, and they are not suited to represent strong gradients associated with the regional processes. In recent years (∼2010–2024), a number of modeling studies, including convection permitting simulations, have contributed to our understanding of the characteristics and distribution of a variety of systems and processes along the Andes, including orographic precipitation, precipitation hotspots, mountain circulations, gravity waves, among others. This is Part I of a two-part review about atmospheric modeling over the Andes. In Part I we review the current strengths and limitations of numerical modeling in simulating key atmospheric-orographic processes for the weather and climate of the Andean region, including low-level jets, downslope winds, gravity waves, and orographic precipitation, among others. In Part II, we review how climate models simulate surface-atmosphere interactions and hydroclimate processes in the Andes Cordillera to offer information on projections for land-cover/land-use change or climate change. With a focus on the hydroclimate, we also address some of the main challenges in numerical modeling for the region.

Assessing the Climatological Characteristics of Observed and Simulated Seasonal Onset of Precipitation Over Southern and Eastern Africa

ABSTRACTPrediction of seasonal onset is crucial to agriculture in southern and eastern Africa. Here, we applied two definitions of onset, namely meteorological and agricultural (crop‐germination), to evaluate CMIP6 models through the lens of rainfall onset over representative maize agricultural regions of South Africa, Tanzania, Malawi and Zambia. We use the ERA5 reanalysis as a proxy for observations, and robust regression to calculate a statistical comparison of the onset definitions for the period 1979–2021. Evaluation of ERA5 reanalysis shows similar magnitude and pattern as gauge based MSWEP. Our results show that, for meteorological onset, Johannesburg, with a subtropical highland climate, experienced earliest onset after 23 December; and an increasing trend (later onset) but not statistically significant (p = 0.2). Over Bethlehem, which has continental climate, the earliest onset date was after October 9 and an increasing interannual variability since 2000 is noted. The standard deviation of onset dates across the regions shows an East‐Central‐South gradient. We also found that the crop‐germination onset definition shows earlier onset of seasonal rains, it differs considerably across regions, and has higher interannual variability, in comparison with the meteorological definition. Over Lilongwe, Mbeya and Lusaka, late meteorological onset with a weak positive and insignificant trend is observed. The CMIP6 model's representation of onset trend differs from reanalysis data, with inter‐model differences. Late meteorological onset is underestimated by GFDL‐CM4 and MPI while INM5, MPI and NorESM overestimate the observed earliest onset. The largest bias is shown by INM and MPI which simulate earliest and latest onset as 190 (07 January) and 206 (23 January) respectively. In addition, models often fail to simulate sufficient precipitation to produce onset for seed germination and crop development. The ACCESS model showed an insignificant trend (p value = 2) and later onset over Lilongwe, an insignificant trend (p value = 0.9) over Lusaka, and an earlier onset over Mbeya. Using the agricultural onset definition, over Bethlehem, all the models and the ERA5 reanalysis did not produce enough precipitation to meet onset conditions. We suggest that rainfall onset studies use several definitions or metrics of onset and that the choice of metric be informed by the research question. Using such an ensemble of onset metrics contributes to a better understanding of variability and uncertainties in agricultural productivity.

Euclid preparation

LENSMC is a weak lensing shear measurement method developed for Euclid and Stage-IV surveys. It is based on forward modelling in order to deal with convolution by a point spread function (PSF) with comparable size to many galaxies, sampling the posterior distribution of galaxy parameters via Markov chain Monte Carlo, and marginalisation over nuisance parameters for each of the 1.5 billion galaxies observed by Euclid. We quantified the scientific performance through high-fidelity images based on the Euclid Flagship simulations and emulation of the Euclid VIS images, realistic clustering with a mean surface number density of 250 arcmin−2 (IE &lt; 29.5) for galaxies, and 6 arcmin−2 (IE &lt; 26) for stars, and a diffraction-limited chromatic PSF with a full width at half maximum of 0′.′2 and spatial variation across the field of view. LENSMC measured objects with a density of 90 arcmin−2 (IE &lt; 26.5) in 4500 deg2. The total shear bias was broken down into measurement (our main focus here) and selection effects (which will be addressed in future work). We found measurement multiplicative and additive biases of m1 = (−3.6 ± 0.2) × 10−3, m2 = (−4.3 ± 0.2) × 10−3, c1 = (−1.78 ± 0.03) × 10−4, and c2 = (0.09 ± 0.03) × 10−4; a large detection bias with a multiplicative component of 1.2 × 10−2 and an additive component of −3 × 10−4; and a measurement PSF leakage of α1 = (−9 ± 3) × 10−4 and α2 = (2 ± 3) × 10−4. When model bias is suppressed, the obtained measurement biases are close to Euclid requirement and largely dominated by undetected faint galaxies (−5 × 10−3). Although significant, model bias will be straightforward to calibrate given its weak sensitivity on galaxy morphology parameters. LENSMC is publicly available at gitlab.com/gcongedo/LensMC.