THE DETERMINATION OF CONSISTENT AND ACCURATE VALUES OF rHo(CaHbOd, a ≤ 16) OF ATOMIZATION OF THE AROMATIC COMPOUNDS BY THE SIMULTANEOUS USE OF THE EMPIRICALLY CORRECTED RESULTS AND UNCERTAINTIES OF SEVERAL QUANTUM MECHANICAL APPROACHES.

The empirical linear scaling dependencies between the literature (rHo(Xn,TAB)) and the calculated (rHo((Xn)i,CALC)) values of atomization of 31 reference aromatic and related compounds (T=298.15K), as well as their standard errors ((SE4)i(4)i, Stand.Dev.), are determined for the different quantum mechanical (QMi) approaches. These dependencies are compared and used for the determination of the values of rHo((Xn)i,CORRE)±3(SE4)i of atomization reactions of considered not reference aromatic compounds, as well as for the determination of their values of fHo((Xn)i,CORRE)±3(SE4)i. The values of fHo((Xn)i,CORRE)MEAN±3SEYE (≥99.4% confidence interval), determined using the intersections of the 3(SE4)i intervals, are consistent with all QMi approaches and their literature values. The M06-2X/6-311++G(d,p), M08HX/6-311++G(d,p) and wB97XD/6-311++G(d,p) approaches are recommended for the achievement of accuracy (SEYE)≤3.8 kJ/mol of the calculated values of fHo((Xn)i,CORRE)MEAN. The effects of the number of O-H groups, size and multiplicity of compounds on values of error, also studied in this work, demonstrate the limitations of using of several scaled dependencies.

Statistical bias correction on the climate model for el nino index prediction

El Nino can harm many sectors in Indonesia by reducing precipitation levels in some areas. The occurrence of El Nino can be estimated by observing the sea surface temperature in Nino 3.4 region. Therefore, an accurate model on sea surface temperature prediction in Nino 3.4 region is needed to optimize the estimation of the occurrence of El Nino, such as ECMWF. However, the prediction model released by ECMWF still consists of some systematic errors or biases. This research aims to correct these biases using statistical bias correction techniques and evaluate the prediction model before and after correction. The statistical bias correction uses linear scaling, variance scaling, and distribution mapping techniques. The results show that statistical bias correction can reduce the prediction model bias. Also, the distribution mapping and variance scaling are more accurate than the linear scaling technique. Distribution mapping has better RMSE in December-March, and variance scaling has better RMSE in April-June also in October and November. However, in July-September, prediction from ECMWF has better RMSE. The application of statistical bias correction techniques has the highest refinement in January-March at the first lead time and in April at the fifth until the seventh lead time.

Moving Mountains: Reshaping the Activity Volcano of Electrocatalysis with Fluxional Subnano Clusters

The activity volcano derived from Sabatier analysis provides intuitive guide for catalyst design, but it also imposes fundamental limitations on the maximal activity and the pool of high-performance elements. Here we show that the activity volcano for oxygen reduction reaction (ORR) can be shifted and reshaped in the subnano regime. The fluxional behavior of subnano clusters, in both isolated and graphite-supported forms, not only breaks the linear scaling relationships but also causes an overall strengthening in adsorbate binding. The metals with optimal adsorbate binding in the bulk form (Pt/Pd) thus suffer over-binding issues, while the metals that under-bind in the bulk form (Ag/Au) gain optimal reaction energetics. In addition, the potential-dependence of isomer energies differ, causing non-linear reaction free energy-potential relations and enabling population-tuning of specific isomers, thereby surpassing the apex of the activity volcano. The shift of the volcano that puts under-binding elements closer to the top is likely general in fluxional cluster catalysis, and can be used for cluster catalyst design.

Simulation of Rainfall over Bangladesh Using Regional Climate Model (RegCM4.7)

Regional climate model is a scientific tool to monitor present climate change and to provide reliable estimation of future climate projection. In this study, the Regional Climate Model version 4.7 (RegCM4.7) developed by International Centre for Theoretical Physics (ICTP) has been adopted to simulate rainfall scenario of Bangladesh. The study examines model performance of rainfall simulation through the period of 1991-2018 with ERA-Interim75 data of 75 km horizontal resolution as lateral boundaries, downscaled at 25km resolution using the mixed convective precipitation scheme; MIT-Emanuel scheme over land and Grell scheme with Fritsch-Chappell closure over ocean. The simulated rainfall has been compared both at spatial and temporal scales (monthly, seasonal and annual) with observed data collected from Bangladesh Meteorological Department (BMD) and Climate Research Unit (CRU). Simulated annual rainfall showed that the model overestimated in most of the years. Overestimation has been observed in the monsoon and underestimation in pre-monsoon and post-monsoon seasons. Spatial distribution of simulated rainfall depicts overestimation in the southeast coastal region and underestimation in the northwest and northeast border regions of Bangladesh. Better estimation of rainfall has been found in the central and eastern parts of the country. The simulated annual rainfall has been validated through the Linear Scaling bias correction method for the years of 2016, 2017, and 2018 considering the rainfall of 1991-2015 as reference. The bias correction with linear scaling method gives fairly satisfactory results and it can be considered in the future projection of rainfall over Bangladesh.

Performance assessment of six bias correction methods using observed and RCM data at upper Awash basin, Oromia, Ethiopia

Selecting a suitable bias correction method is important to provide reliable inputs for evaluation of climate change impact. Their influence was studied by comparing three discharge outputs from the SWAT model. The result after calibration with original RCM indicate that the raw RCM are heavily biased, and lead to streamflow simulation with large biases (NSE = 0.1, R2 = 0.53, MAE = 5.91 mm/°C, and PBIAS = 0.51). Power transformation and linear scaling methods performed best in correcting the frequency-based indices, while the LS method performed best in terms of the time series-based indices (NSE = 0.87, R2 = 0.78, MAE = 3.14 mm/°C, PBIAS = 0.24) during calibration. Meanwhile, daily translation was underestimating simulated streamflow compared with observed and considered as the least performing method. Precipitation correction method has higher visual influence than temperature, and its performance in streamflow simulations was consistent and significantly considerable. Power transformation and variance scaling showed highly qualified performance compared to others with indicated time series value (NSE = 0.92, R2 = 0.88, MAE = 1.58 mm/°C and PBIAS = 0.12) during calibration and validation of streamflow. Hence, PT and VARI methods were the dominant methods which remove biasness from RCM models at Akaki River basin.