Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics

We applied the parametric variance Kalman filter (PvKF) data assimilation designed in Part I of this two-part paper to GOSAT methane observations with the hemispheric version of CMAQ to obtain the methane field (i.e., optimized analysis) with its error variance. Although the Kalman filter computes error covariances, the optimality depends on how these covariances reflect the true error statistics. To achieve more accurate representation, we optimize the global variance parameters, including correlation length scales and observation errors, based on a cross-validation cost function. The model and the initial error are then estimated according to the normalized variance matching diagnostic, also to maintain a stable analysis error variance over time. The assimilation results in April 2010 are validated against independent surface and aircraft observations. The statistics of the comparison of the model and analysis show a meaningful improvement against all four types of available observations. Having the advantage of continuous assimilation, we showed that the analysis also aims at pursuing the temporal variation of independent measurements, as opposed to the model. Finally, the performance of the PvKF assimilation in capturing the spatial structure of bias and uncertainty reduction across the Northern Hemisphere is examined, indicating the capability of analysis in addressing those biases originated, whether from inaccurate emissions or modelling error.

Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System

We present a parametric Kalman filter data assimilation system using GOSAT methane observations within the hemispheric CMAQ model. The assimilation system produces forecasts and analyses of concentrations and explicitly computes its evolving error variance while remaining computationally competitive with other data assimilation schemes such as 4-dimensional variational (4D-Var) and ensemble Kalman filter (EnKF). The error variance in this system is advected using the native advection scheme of the CMAQ model and updated at each analysis while the error correlations are kept fixed. We discuss extensions to the CMAQ model to include methane transport and emissions (both anthropogenic and natural) and perform a bias correction for the GOSAT observations. The results using synthetic observations show that the analysis error and analysis increments follow the advective flow while conserving the information content (i.e., total variance). We also demonstrate that the vertical error correlation contributes to the inference of variables down to the surface. In a companion paper, we use this assimilation system to obtain optimal assimilation of GOSAT observations.

A Comparison of Various Correction and Blending Techniques for Creating an Improved Satellite-Gauge Rainfall Dataset over Australia

Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, Australian station data along with the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and the Bureau of Meteorology’s (BOM) Australian Gridded Climate Dataset (AGCD) rainfall analysis are combined to develop an improved satellite-gauge rainfall analysis over Australia that uses the strengths of the respective data sources. We investigated a variety of correction and blending methods with the aim of identifying the optimal blended dataset. The correction methods investigated were linear corrections to totals and anomalies, in addition to quantile-to-quantile matching. The blending methods tested used weights based on the error variance to MSWEP (Multi-Source Weighted Ensemble Product), distance to the closest gauge, and the error from a triple collocation analysis to ERA5 and Soil Moisture to Rain. A trade-off between away-from- and at-station performances was found, meaning there was a complementary nature between specific correction and blending methods. The most high-performance dataset was one corrected linearly to totals and subsequently blended to AGCD using an inverse error variance technique. This dataset demonstrated improved accuracy over its previous version, largely rectifying erroneous patches of excessive rainfall. Its modular use of individual datasets leads to potential applicability in other regions of the world.

Measuring language ability of students with compensatory multidimensional CAT: A post-hoc simulation study

The computerized adaptive tests (CAT) apply an adaptive process in which the items are tailored to individuals' ability scores. The multidimensional CAT (MCAT) designs differ in terms of different item selection, ability estimation, and termination methods being used. This study aims at investigating the performance of the MCAT designs used to measure the language ability of students and to compare the results of MCAT designs with the outcomes of corresponding paper–pencil tests. For this purpose, items in the English Proficiency Tests (EPT) were used to create a multi-dimensional item pool that consists of 599 items. The performance of the MCAT designs was evaluated and compared based on the reliability coefficients, root means square error (RMSE), test-length, and root means squared difference (RMSD) statistics, respectively. Therefore, 36 different conditions were investigated in total. The results of the post-hoc simulation designs indicate that the MCAT designs with the A-optimality item selection method outperformed MCAT designs with other item selection methods by decreasing the test length and RMSD values without any sacrifice in test reliability. Additionally, the best error variance stopping rule for each MCAT algorithm with A-optimality item selection could be considered as 0.25 with 27.9 average test length and 30 items for the fixed test-length stopping rule for the Bayesian MAP method. Overall, MCAT designs tend to decrease the test length by 60 to 65 percent and provide ability estimations with higher precision compared to the traditional paper–pencil tests with 65 to 75 items. Therefore, it is suggested to use the A-optimality method for item selection and the Bayesian MAP method for ability estimation for the MCAT designs since the MCAT algorithm with these specifications shows better performance than others.

Exchange Rate Volatility and Monetary Policy Shocks

The study investigated the influence of innovations in monetary policy on the rate of exchange volatility in Nigeria. The research adopted vector error correction model as well as impulse response function and forecast error variance decomposition function in the estimation using two models derived in the study. Monthly data between the periods 2009 and 2019 were adopted for the research. Our findings show that in the long run; all the monetary policy variables have a significant long run correlation with volatility in the exchange rate; but that money supply and the rate of exchange seem to have significant short run impact on volatility in the exchange rate, the other variables such as liquidity ratio or monetary policy rate did not show a significant short run relationship with the volatility in the exchange rate. Further findings on the volatility impulse response and the forecast error variance decomposition suggest a significant link between volatility in the exchange rate and money supply though the link was much more pronounced. The use of monthly data shows that the managed exchange rate regime by the CBN seems to have the desired effect in exchange rate volatility and thus having a critical impact on inflationary spikes.

ESTİMATİON OF GENETİC AND ENVİRONMENTAL PARAMETERS AFFECTİNG PRODUCTİVİTY İN MORKARAMAN SHEEP AND ECONOMİC EVALUATİON OF PARAMETERS

This study was carried out to protect the continuity of productivity in morkaraman sheep raised in Turkey and determine their economic importance. Morkaraman sheep are concentrated in the Eastern Regions of the country. The province of Bingöl, where the study was conducted, is located in this region and has an important morkaraman population. The study was carried out between 2008-2018. Sixty-eight morkaraman sheep were used during the study period out of 317 lambing lambs. In the study, the total number of lambs born per sheep (TNLBS), the number of weaned lambs (NWL), the weights of the lambs weaned per sheep (WLWS) and the total weight of the lambs weaned in the first period (TWLWFP) were determined. In addition, Additive genetic variance, Error variance, Phenotypic variance, Heritability and Ratio of error variation were determined for these variables. As a result, the correlation between the examined variables was significant and positive, except for the relationship between TNLBS and TWLWFP. The relationship between these two variables was significant but negative. Significant changes were also observed in terms of genetic parameters. It was concluded that the economic aspects of the examined variables should not be ignored in terms of sustainability. Keywords: Sheep, morkaraman, sustainability, genotypic and phenotypic variance.

Influence of Labor Management Relationship on Employee’s Performance

This article theoretically examined the effect of employment relations on employees in multinational corporations with a special focus on Dangote Cement. The article observed that labour-management relations also known as industrial relations play an imperative role in improving and sustaining employees' performance. It serves as the nerves of industrial harmony. The target population of this study, which isfinite (being defined), isthe total number ofDangote Cement Plantin Ibese, Ogun State, Nigeria. The population comprises staff whose population is two hundred and sixty (260). The use of a questionnaire was adopted to elicit information from the cross-section of the selected population. The correlation coefficient value ranges from 0 – 1 and has an acceptable value of 0.88, which indicates higher reliability of the measurement instrument and low error variance, implying that the instrument is reliable. While the analysis was done using regression analysis. The findings revealed that there is a significant influence of employee’s participation, trade union, and industrial harmony on employee performance. Organizations should encourage mutual relationships among employees, as well as provide conducive working conditions/ environment for employees, like organizational learning, effective communication among employees, which will enhance their productivity and employee's performance.

Impact of tourism development on inclusive growth: A panel vector autoregression analysis for African economies

The persistent debate among policy makers and academics around combating the high rates of poverty and income inequality can be further illuminated by understanding how tourism contributes to inclusive growth, especially in developing economies. Tourism sector can be regarded as one of the key contributors to inclusive growth and where it has the capacity to generate prospects for productive employment. The goal of this article is thus to investigate the link between inclusive growth and tourism in the African context. To do this, we utilized a recent panel vector autoregression (pVAR) and data for 45 African countries spanning the period 1995 to 2019. Thus, by the error variance decomposition and impulse response functions, our results showed a weak positive effect of international tourism arrivals and the composite tourism indicator on inclusive growth, while tourism receipts and tourism expenditure insignificantly decreases inclusive growth in the sampled African economies. Our result is further supported by the panel system generalized method of moments (GMM). We provide some policy implications from our findings.

The dynamic interaction between investor attention and green security market: an empirical study based on Baidu index

PurposeThis paper aims to explore the interaction between investor attention and green security markets, including green bonds and stocks.Design/methodology/approachThis study takes the Baidu index of “green finance” as the proxy for investor attention and constructs several generalized prediction error variance decomposition models to investigate the interdependence. It further analyzes the dynamic interaction between investor attention and the return and volatility of green security markets using the rolling time window.FindingsThe empirical analysis and robustness test results reveal that the spillovers between investor attention and the return and volatility of the green bond market are relatively stable. In contrast, the spillover level between investor attention and the green stock market displays significant time-varying and asymmetric effects. Moreover, the volatility spillover between investor attention and green securities is vulnerable to major financial events, while the return spillover is extremely sensitive to market performance.Originality/valueThe conclusion further expands the practical application and theoretical framework of behavioral finance in green finance and provides a new reference for investors and regulators. Besides, this study also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management in green securities.

The Integration of Tactile and Visual Cues Increases Golf Putting Error in a Mixed-Reality Paradigm.

The integration of visual and tactile cues can enhance perception. However, the nature of this integration, and the subsequent benefits on perception and action execution, are context-dependent. Here, we examined how visual-tactile integration can influence performance on a complex motor task using virtual reality. We asked participants to wear a VR head-mounted display while using a tracked physical putter to make golf putts on a VR golf course in two conditions. In the ‘tactile’ condition, putter contact with the virtual golf ball coincided with physical contact with a physical ball. In a second ‘no tactile’ condition, no physical ball was present, such that only the virtual ball contacted the putter. In contrast to our pre-registered prediction that performance would benefit from the integration of visual and tactile cues, we found golf putting accuracy was higher in the no tactile condition compared to the tactile condition. Participants exhibited higher lateral error variance and over/undershooting when the physical ball was present. These differences in performance between the conditions suggest that tactile cues, when available, were integrated with visual cues. Second, this integration is not necessarily beneficial to performance. We suggest that the decreased performance caused by the addition of a physical ball may have been due to minor incongruencies between the virtual visual cues and the physical tactile cues. We discuss the implications of these results on the use of VR sports training and highlight that the absence of matched tactile cues in VR can result in sub-optimal learning and performance.