Number Of Available Observations Research Articles

Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches

Top-down constraints of CO2 emissions from coal-fired power plants are critical to improving the accuracy of CO2 emission inventory and designing carbon reduction strategies. Different top-down models based on satellite observation have been proposed in previous studies, but discrepancies between these models and the underlying drivers are rarely explored, limiting the confidence of their application for monitoring point-source CO2 emissions from satellite. Here, we apply three top-down models to estimate CO2 emissions from individual coal-fired power plants in the United States (US) and China in 2014–2021 from Orbiting Carbon Observatory 2 (OCO-2) satellite observations. The first one applies the Gaussian plume model to optimize emissions by fitting modeled CO2 enhancement to the observation. The second and third methods apply the same inversion framework using the maximum likelihood estimation, but with WRF-Chem and WRF-FLEXPART as forward models, respectively. We evaluate consistency between the three methods in estimating emissions of 10 power plants in the US, using daily reported values from the US Environmental Protection Agency (EPA) as a benchmark, and then apply the three methods to quantify emissions of 13 power plants in China. Results show that the WRF-Chem and WRF-FLEXPART based inversion results are consistent with the EPA reported emission rates, with correlation coefficients (r) of 0.76 and 0.85 and mean biases (MB) of 4.06 and 3.97 ktCO2/d relative to EPA reports at all 10 power plants, respectively. The Gaussian plume model driven by wind fields from WRF-Chem model without the wind rotation shows comparable ability in reproducing the EPA reported emission rates at 7 power plants (r = 0.82, MB = 6.17), but is not applicable for the other three cases. We find that application of the high-resolution three-dimensional wind fields can better capture the shape of observed plumes, especially under complex wind conditions, compared to the Gaussian plume model which relies on wind field at a single point, and thus the Gaussian plume model has difficulty to optimize emissions under inhomogeneous wind fields or when observations are far away from the power plant. In general, using the WRF-FLEXPART model as the forward model in the inverse analysis shows advanced capability to simulate narrow-shape plumes in the absence of numerical diffusion which is inherent in Eulerian model such as WRF-Chem. Emissions estimated by the three top-town methods show a moderate consistency at 13 coal-fired power plant cases in China, with 8 of 13 cases showing differences of <30% between at least two methods. However, large differences emerge when wind fields are inhomogeneous and number of available observations is limited. Using different meteorological wind fields and OCO-2 data versions can also bring substantial differences to the posterior emissions for all three approaches. We find that the posterior CO2 emissions, though only reflecting instantaneous emission rates at satellite overpass time, are not proportional to the reported capacities of these power plants, indicating that attributing CO2 emissions simply based on the capacity of power plants in some bottom-up approaches may have significant discrepancies. Our study exposes the capability and limitation of different top-down approaches in quantifying point-source CO2 emissions, advancing their application for better serving increasing constellations of point-source imagers in the future.

Dependent conditional tail expectation for extreme levels

We consider the estimation of the dependent conditional tail expectation, defined for a random vector (X,Y) with X≥0 as E(X|X>QX(1−p),Y>QY(1−p)), when E(X)<∞, and where QX and QY denote the quantile functions of X and Y, respectively. The distribution of X is assumed to be of Pareto-type while the distribution of Y is kept general. Using extreme-value arguments we introduce an estimator for this risk measure for the situation p≤1/n, where n is the number of available observations, i.e., focus is on estimation with extrapolation. The convergence in distribution of our estimator is established and its finite sample performance is illustrated on a simulation study. The method is then applied on wind gusts data set.

The Simons Observatory: Beam Characterization for the Small Aperture Telescopes

We use time-domain simulations of Jupiter observations to test and develop a beam reconstruction pipeline for the Simons Observatory Small Aperture Telescopes. The method relies on a mapmaker that estimates and subtracts correlated atmospheric noise and a beam fitting code designed to compensate for the bias caused by the mapmaker. We test our reconstruction performance for four different frequency bands against various algorithmic parameters, atmospheric conditions, and input beams. We additionally show the reconstruction quality as a function of the number of available observations and investigate how different calibration strategies affect the beam uncertainty. For all of the cases considered, we find good agreement between the fitted results and the input beam model within an ∼1.5% error for a multipole range ℓ = 30–700 and an ∼0.5% error for a multipole range ℓ = 50–200. We conclude by using a harmonic-domain component separation algorithm to verify that the beam reconstruction errors and biases observed in our analysis do not significantly bias the Simons Observatory r-measurement

A far-field ground motion prediction model for interface earthquakes at the hill zone of Mexico City

This study presents an updated attenuation model to predict the peak ground acceleration (PGA), peak ground velocity (PGV), 5% damped pseudo-spectral acceleration (SA), and the average spectral acceleration (AvgSA) at the hill zone of Mexico City for interface earthquakes. The strong-motion dataset comprises 33 earthquakes recorded at CU station, covering a moment magnitude (Mw) range from 6.0 to 8.1 and a source-to-site distance (Rrup) range from 240 to 490 km. Given the small number of available observations, a Bayesian regression scheme is used to obtain the coefficients of the ground-motion prediction model (GMPM). In addition, the epistemic uncertainty in the estimation of the regression coefficients is evaluated, showing its impact on the framework of a probabilistic seismic hazard analysis (PSHA). The results are compared with models previously developed for the CU station, discussing the differences observed between the median predictions and their standard deviations. Likewise, seismic hazard curves are computed and compared with empirical curves obtained by counting the number of times per year that a given value of ground-motion intensity is exceeded. The results show that the dispersion of the GMPM proposed is lower than the previous models for PGA and SA, which means better predictability and more reliable estimates of the seismic hazard at the site.

Classification of Salt Marsh Vegetation in the Yangtze River Delta of China Using the Pixel-Level Time-Series and XGBoost Algorithm

Salt marshes are one of the world's most valuable and vulnerable ecosystems. The accurate and timely monitoring of the distribution and composition of salt marsh vegetation is crucial. With the increasing number of archived multi-source images, the time-series remote sensing approach could play an important role in monitoring coastal environments. However, effective construction and application of the time series over coastal areas remains challenging because satellite observations are severely affected by cloud weather. Here, we constructed a pixel-level time series by intercalibrating the Landsat images from different sensors. Based on the time series, the XGBoost algorithm was introduced for salt marsh vegetation classification. The feasibility and stability for the classification using the pixel-level time-series and XGBoost algorithm (PTSXGB) were evaluated. Five types of salt marsh vegetation from the 3 sites in the Yangtze River Delta, China, were classified. The results demonstrated that (a) the intercalibration for the Landsat images from different sensors is necessary for increasing the number of available observations and reducing the differences among spectral reflectances. (b) The salt marsh vegetation classification using PTSXGB achieved a favorable performance, with an overall accuracy of 81.37 ± 2.66%. The classification was especially excellent for the widespread Spartina alterniflora and Scirpus mariqueter . (c) Compared with the classifications using single images, the classifications using PTSXGB were more stable for different periods, with the mean absolute difference in the overall accuracy less than 3.90%. Therefore, PTSXGB is expected to monitor salt marsh vegetation's long-term dynamics, facilitating effective ecological conservation for the coastal areas.

Monitoring vegetation condition using microwave remote sensing: the standardized vegetation optical depth index (SVODI)

Abstract. Vegetation conditions can be monitored on a global scale using remote sensing observations in various wavelength domains. In the microwave domain, data from various spaceborne microwave missions are available from the late 1970s onwards. From these observations, vegetation optical depth (VOD) can be estimated, which is an indicator of the total canopy water content and hence of above-ground biomass and its moisture state. Observations of VOD anomalies would thus complement indicators based on visible and near-infrared observations, which are primarily an indicator of an ecosystem's photosynthetic activity. Reliable long-term vegetation state monitoring needs to account for the varying number of available observations over time caused by changes in the satellite constellation. To overcome this, we introduce the standardized vegetation optical depth index (SVODI), which is created by combining VOD estimates from multiple passive microwave sensors and frequencies. Different frequencies are sensitive to different parts of the vegetation canopy. Thus, combining them into a single index makes this index sensitive to deviations in any of the vegetation parts represented. SSM/I-, TMI-, AMSR-E-, WindSat- and AMSR2-derived C-, X- and Ku-band VODs are merged in a probabilistic manner resulting in a vegetation condition index spanning from 1987 to the present. SVODI shows similar temporal patterns to the well-established optical vegetation health index (VHI) derived from optical and thermal data. In regions where water availability is the main control on vegetation growth, SVODI also shows similar temporal patterns to the meteorological drought index scPDSI (self-calibrating Palmer drought severity index) and soil moisture anomalies from ERA5-Land. Temporal SVODI patterns relate to the climate oscillation indices SOI (Southern Oscillation index) and DMI (dipole mode index) in the relevant regions. It is further shown that anomalies occur in VHI and soil moisture anomalies before they occur in SVODI. The results demonstrate the potential of VOD to monitor the vegetation condition, supplementing existing optical indices. It comes with the advantages and disadvantages inherent to passive microwave remote sensing, such as being less susceptible to cloud coverage and solar illumination but at the cost of a lower spatial resolution. The index generation is not specific to VOD and could therefore find applications in other fields. The SVODI products (Moesinger et al., 2022) are open-access under Attribution 4.0 International and available at Zenodo, https://doi.org/10.5281/zenodo.7114654.

Regularized Functional Connectivity in Schizophrenia.

Regularization may be used as an alternative to dimensionality reduction when the number of variables in a model is much larger than the number of available observations. In a recent study from our group regularized regression was employed to quantify brain functional connectivity in a sample of healthy controls using a brain parcellation and resting state fMRI images. Here regularization is applied to evaluate resting state connectivity abnormalities at the voxel level in a sample of patients with schizophrenia. Specifically, ridge regression is implemented with different degrees of regularization. Results are compared to those delivered by the weighted global brain connectivity method (GBC), which is based on averaged bivariate correlations and from the non-redundant connectivity method (NRC), a dimensionality reduction approach that applies supervised principal component regressions. Ridge regression is able to detect a larger set of abnormally connected regions than both GBC and NRC methods, including schizophrenia related connectivity reductions in fronto-medial, somatosensory and occipital structures. Due to its multivariate nature, the proposed method is much more sensitive to group abnormalities than the GBC, but it also outperforms the NRC, which is multivariate too. Voxel based regularized regression is a simple and sensitive alternative for quantifying brain functional connectivity.

Is Precipitation Responsible for the Most Hydrological Model Uncertainty?

Rainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological processes are quite complex, and their simplifications in the models lead to inaccuracies. Model parameters themselves are uncertain—physical parameters because of their observations and conceptual parameters due to their limited identifiability. Furthermore, the main model input—precipitation is uncertain due to the limited number of available observations and the high spatio-temporal variability. The quantification of model output uncertainty is essential for their use. Most approaches used for the quantification of uncertainty in rainfall-runoff modeling assign the uncertainty to the model parameters. In this contribution, the role of precipitation uncertainty is investigated. Instead of a standard sensitivity analysis of the model output with respect to the input variations, it is investigated to what extent realistic precipitation fields could improve model performance. Realistic precipitation fields are defined as gridded realizations of precipitation which reproduce the observed values at the observation locations, with values which reproduce the distribution of the observed values and with spatial variability the same as the spatial variability of the observations. The above conditions apply to each observation time step. Through an inverse modeling approach based on Random Mixing precipitation fields fulfilling the above conditions and reproducing the discharge output better than using traditional interpolated observations can be obtained. These realizations show how much rainfall runoff models may profit from better precipitation input and how much remains for the parameter and model concept uncertainty. The methodology is applied using two hydrological models with a contrasting basis, SHETRAN and HBV, for three different mesoscale sub-catchments of the Neckar basin in Germany. Results show that up to 50% of the model error can be attributed to precipitation uncertainty. Further, inverting precipitation using hydrological models can improve model performance even in neighboring catchments which are not considered explicitly.

Sibship Size, Height and Cohort Selection: A Methodological Approach.

This article deals with the historical relationship between the number of siblings in a family or household and height, a proxy for biological living standards. Ideally, this relationship is better assessed when we have evidence on the exact number of siblings in a family from its constitution onwards. However, this generally requires applying family reconstitution techniques, which, unfortunately, is not always possible. In this latter case, scholars must generally settle for considering only particular benchmark years using population censuses, from which family and household structures are derived. These data are then linked to the height data for the young males of the family or household. Height data are generally obtained from military records. In this matching process, several decisions have to be taken, which, in turn, are determined by source availability and the number of available observations. Using data from late 19th-century Catalonia, we explore whether the methodology used in matching population censuses and military records as described above might affect the relationship between sibship size and biological living standards and, if so, to what extent. We conclude that, while contextual factors cannot be neglected, the methodological decisions made in the initial steps of research also play a role in assessing this relationship.

Construction of mixed-level supersaturated split-plot designs

This paper considers the construction of mixed-level supersaturated split-plot designs (SSSPDs) which are very useful in screening situations where the number of factors is larger than the number of available observations and several of these factors have levels that they are hard to change. As a benchmark of obtaining optimal SSSPDs, lower bounds to our proposed designs are established. Illustrative examples are presented supporting our constructed designs.

Non-Stationary Flood Frequency Analysis Using Cubic B-Spline-Based GAMLSS Model

Under changing environments, the most widely used non-stationary flood frequency analysis (NFFA) method is the generalized additive models for location, scale and shape (GAMLSS) model. However, the model structure of the GAMLSS model is relatively complex due to the large number of statistical parameters, and the relationship between statistical parameters and covariates is assumed to be unchanged in future, which may be unreasonable. In recent years, nonparametric methods have received increasing attention in the field of NFFA. Among them, the linear quantile regression (QR-L) model and the non-linear quantile regression model of cubic B-spline (QR-CB) have been introduced into NFFA studies because they do not need to determine statistical parameters and consider the relationship between statistical parameters and covariates. However, these two quantile regression models have difficulties in estimating non-stationary design flood, since the trend of the established model must be extrapolated infinitely to estimate design flood. Besides, the number of available observations becomes scarcer when estimating design values corresponding to higher return periods, leading to unreasonable and inaccurate design values. In this study, we attempt to propose a cubic B-spline-based GAMLSS model (GAMLSS-CB) for NFFA. In the GAMLSS-CB model, the relationship between statistical parameters and covariates is fitted by the cubic B-spline under the GAMLSS model framework. We also compare the performance of different non-stationary models, namely the QR-L, QR-CB, and GAMLSS-CB models. Finally, based on the optimal non-stationary model, the non-stationary design flood values are estimated using the average design life level method (ADLL). The annual maximum flood series of four stations in the Weihe River basin and the Pearl River basin are taken as examples. The results show that the GAMLSS-CB model displays the best model performance compared with the QR-L and QR-CB models. Moreover, it is feasible to estimate design flood values based on the GAMLSS-CB model using the ADLL method, while the estimation of design flood based on the quantile regression model requires further studies.

Analysis of credit-rating migrations with genetic algorithms

Modelling dependent credit-rating migrations of assets classified into M credit classes and S industries, M × S + 2M×S parameters have to be estimated. For a realistic choice of M and S, this number is huge and it greatly exceeds the number of available observations. To avoid brute-force calculations, we suggest sequential and parallel genetic algorithms. Considering a practically important combination of M = 7 and S = 6, the approach is tested on Standard and Poor's data.

Optimizing digitalization effort in morphometrics.

Quantifying phenotypes is a common practice for addressing questions regarding morphological variation. The time dedicated to data acquisition can vary greatly depending on methods and on the required quantity of information. Optimizing digitization effort can be done either by pooling datasets among users, by automatizing data collection, or by reducing the number of measurements. Pooling datasets among users is not without risk since potential errors arising from multiple operators in data acquisition prevent combining morphometric datasets. We present an analytical workflow to estimate within and among operator biases and to assess whether morphometric datasets can be pooled. We show that pooling and sharing data requires careful examination of the errors occurring during data acquisition, that the choice of morphometric approach influences amount of error, and that in some cases pooling data should be avoided. The demonstration is based on a worked example (Sus scrofa teeth) using a combinations of 18 morphometric approaches and datasets for which we identified and quantified several potential sources of errors in the workflow. We show that it is possible to estimate the analytical power of a study using a small subset of data to select the best morphometric protocol and to optimize the number of variables necessary for analysis. In particular, we focus on semi-landmarks, which often produce an inflation of variables in contrast to the number of available observations use in statistical testing. We show how the workflow can be used for optimizing digitization efforts and provide recommendations for best practices in error management.

Application of records theory on the COVID-19 pandemic in Lebanon: prediction and prevention.

Given the fast spread of the novel coronavirus (COVID-19) worldwide and its classification by the World Health Organization (WHO) as being one of the worst pandemics in history, several scientific studies are carried out using various statistical and mathematical models to predict and study the likely evolution of this pandemic in the world. In the present research paper, we present a brief study aiming to predict the probability of reaching a new record number of COVID-19 cases in Lebanon, based on the record theory, giving more insights about the rate of its quick spread in Lebanon. The main advantage of the records theory resides in avoiding several statistical constraints concerning the choice of the underlying distribution and the quality of the residuals. In addition, this theory could be used, in cases where the number of available observations is somehow small. Moreover, this theory offers an alternative solution in case where machine learning techniques and long-term memory models are inapplicable because they need a considerable amount of data to be performant. The originality of this paper lies in presenting a new statistical approach allowing the early detection of unexpected phenomena such as the new pandemic COVID-19. For this purpose, we used epidemiological data from Johns Hopkins University to predict the trend of COVID-2019 in Lebanon. Our method is useful in calculating the probability of reaching a new record as well as studying the propagation of the disease. It also computes the probabilities of the waiting time to observe the future COVID-19 record. Our results obviously confirm the quick spread of the disease in Lebanon over a short time.

The effects of creator credibility and backer endorsement in donation crowdfunding campaigns success

PurposeThe purpose of this paper is to investigate the effects of creator credibility and backer endorsement on the levels of donation to crowdfunding campaigns in a focal muslim community and to determine the success factors of projects in developing and developed countries.Design/methodology/approachThis research adopts a quantitative approach, utilizing 223 crowdfunding projects from LaunchGood to examine the expected connections. The crowdfunding projects in the distinctive data set (Ramadan) are similar, permitting exploration of the role of creator credibility and backer endorsement in a unique context of religious-based crowdfunding activity.FindingsCreators’ credibility and backers’ endorsements are decisive factors in crowdfunding success. Results from an additional test related to location (developing and developed countries) suggest that creators’ credibility is more important than backers’ endorsements, especially in developing countries.Research limitations/implicationsThe projects used in the data set are homogenous and take place in a specific context. The limited number of available observations influences the testing function and capacity of the models and variables. Further, the investigation is related to a specific community, so might not be applicable to all communities.Social implicationsPotential supporters from the community are vital for the success of crowdfunding projects. This study informs campaign efforts in a religious community by stressing the importance of creators’ credibility and backers’ endorsements. The study also sheds light on factors that affect crowdfunding success in developing and developed countries.Originality/valueThis study helps to explain successful crowdfunding practices in donation-based crowdfunding projects within a religious community context. Results improve scholars’ ability to comprehend individuals’ supporting behavior, providing empirical evidence of creators’ credibility and backers’ endorsement on project success. The study also addresses the determinants of crowdfunding success in developing and developed countries.

Geometric Matrix Completion With Deep Conditional Random Fields.

The problem of completing high-dimensional matrices from a limited set of observations arises in many big data applications, especially recommender systems. The existing matrix completion models generally follow either a memory- or a model-based approach, whereas geometric matrix completion (GMC) models combine the best from both approaches. Existing deep-learning-based geometric models yield good performance, but, in order to operate, they require a fixed structure graph capturing the relationships among the users and items. This graph is typically constructed by evaluating a pre-defined similarity metric on the available observations or by using side information, e.g., user profiles. In contrast, Markov-random-fields-based models do not require a fixed structure graph but rely on handcrafted features to make predictions. When no side information is available and the number of available observations becomes very low, existing solutions are pushed to their limits. In this article, we propose a GMC approach that addresses these challenges. We consider matrix completion as a structured prediction problem in a conditional random field (CRF), which is characterized by a maximum a posteriori (MAP) inference, and we propose a deep model that predicts the missing entries by solving the MAP inference problem. The proposed model simultaneously learns the similarities among matrix entries, computes the CRF potentials, and solves the inference problem. Its training is performed in an end-to-end manner, with a method to supervise the learning of entry similarities. Comprehensive experiments demonstrate the superior performance of the proposed model compared to various state-of-the-art models on popular benchmark data sets and underline its superior capacity to deal with highly incomplete matrices.

Assessment of the theoretical limit in instrumental detectability of northern high-latitude methane sources using &amp;lt;i&amp;gt;δ&amp;lt;/i&amp;gt;&amp;lt;sup&amp;gt;13&amp;lt;/sup&amp;gt;C&amp;lt;sub&amp;gt;CH4&amp;lt;/sub&amp;gt; atmospheric signals

Abstract. Recent efforts have brought together bottom-up quantification approaches (inventories and process-based models) and top-down approaches using regional observations of methane atmospheric concentrations through inverse modelling to better estimate the northern high-latitude methane sources. Nevertheless, for both approaches, the relatively small number of available observations in northern high-latitude regions leaves gaps in our understanding of the drivers and distributions of the different types of regional methane sources. Observations of methane isotope ratios, performed with instruments that are becoming increasingly affordable and accurate, could bring new insights on the contributions of methane sources and sinks. Here, we present the source signal that could be observed from methane isotopic 13CH4 measurements if high-resolution observations were available and thus what requirements should be fulfilled in future instrument deployments in terms of accuracy in order to constrain different emission categories. This theoretical study uses the regional chemistry-transport model CHIMERE driven by different scenarios of isotopic signatures for each regional methane source mix. It is found that if the current network of methane monitoring sites were equipped with instruments measuring the isotopic signal continuously, only sites that are significantly influenced by emission sources could differentiate regional emissions with a reasonable level of confidence. For example, wetland emissions require daily accuracies lower than 0.2 ‰ for most of the sites. Detecting East Siberian Arctic Shelf (ESAS) emissions requires accuracies lower than 0.05 ‰ at coastal Russian sites (even lower for other sites). Freshwater emissions would be detectable with an uncertainty lower than 0.1 ‰ for most continental sites. Except Yakutsk, Siberian sites require stringent uncertainty (lower than 0.05 ‰) to detect anthropogenic emissions from oil and gas or coal production. Remote sites such as Zeppelin, Summit, or Alert require a daily uncertainty below 0.05 ‰ to detect any regional sources. These limits vary with the hypothesis on isotopic signatures assigned to the different sources.

Trend analysis of the airborne fraction and sink rate of anthropogenically released CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;

Abstract. Is the fraction of anthropogenically released CO2 that remains in the atmosphere (the airborne fraction) increasing? Is the rate at which the ocean and land sinks take up CO2 from the atmosphere decreasing? We analyse these questions by means of a statistical dynamic multivariate model from which we estimate the unobserved trend processes together with the parameters that govern them. We show how the concept of a global carbon budget can be used to obtain two separate data series measuring the same physical object of interest, such as the airborne fraction. Incorporating these additional data into the dynamic multivariate model increases the number of available observations, thus improving the reliability of trend and parameter estimates. We find no statistical evidence of an increasing airborne fraction, but we do find statistical evidence of a decreasing sink rate. We infer that the efficiency of the sinks in absorbing CO2 from the atmosphere is decreasing at approximately 0.54 % yr−1.

Development of a Job-Exposure Matrix for Assessment of Occupational Exposure to High-Frequency Electromagnetic Fields (3 kHz-300 GHz).

The aim of this work was to build a job-exposure matrix (JEM) using an international coding system and covering the non-thermal intermediate frequency (IF) (3-100 kHz, named IFELF), thermal IF (100 kHz-10 MHz, named IFRF), and radiofrequency (RF) (>10 MHz) bands. Detailed occupational data were collected in a large population-based case-control study, INTEROCC, with occupations coded into the International Standard Classification of Occupations system 1988 (ISCO88). The subjects' occupational source-based ancillary information was combined with an existing source-exposure matrix and the reference levels of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) for occupational exposure to calculate estimates of level (L) of exposure to electric (E) and magnetic (H) fields by ISCO88 code and frequency band as ICNIRP ratios (IFELF) or squared ratios (IFRF and RF). Estimates of exposure probability (P) were obtained by dividing the number of exposed subjects by the total number of subjects available per job title. With 36 011 job histories collected, 468 ISCO88 (four-digit) codes were included in the JEM, of which 62.4% are exposed to RF, IFRF, and/or IFELF. As a reference, P values for RF E-fields ranged from 0.3 to 65.0% with a median of 5.1%. L values for RF E-fields (ICNIRP squared ratio) ranged from 6.94 × 10-11 to 33.97 with a median of 0.61. The methodology used allowed the development of a JEM for high-frequency electromagnetic fields containing exposure estimates for the largest number of occupations to date. Although the validity of this JEM is limited by the small number of available observations for some codes, this JEM may be useful for epidemiological studies and occupational health management programs assessing high-frequency electromagnetic field exposure in occupational settings.

Global evolution of solar magnetic fields and prediction of activity cycles

AbstractPrediction of solar activity cycles is challenging because physical processes inside the Sun involve a broad range of multiscale dynamics that no model can reproduce and because the available observations are highly limited and cover mostly surface layers. Helioseismology makes it possible to probe solar dynamics in the convective zone, but variations in differential rotation and meridional circulation are currently available for only two solar activity cycles. It has been demonstrated that sunspot observations, which cover over 400 years, can be used to calibrate the Parker-Kleeorin-Ruzmaikin dynamo model, and that the Ensemble Kalman Filter (EnKF) method can be used to link the modeled magnetic fields to sunspot observations and make reliable predictions of a following activity cycle. However, for more accurate predictions, it is necessary to use actual observations of the solar magnetic fields, which are available only for the last four solar cycles. In this paper I briefly discuss the influence of the limited number of available observations on the accuracy of EnKF estimates of solar cycle parameters, the criteria to evaluate the predictions, and application of synoptic magnetograms to the prediction of solar activity.