Regional climate modeling over Ontario using the WRF model

Global climate models (GCMs) are widely used to study climate change. Due to their coarse resolutions, GCMs cannot resolve some microscale and mesoscale processes such as topographical effects. Dynamic downscaling simulations using Regional Climate Models (RCMs) are often required to provide higher spatial- and temporal-resolution climate variabilities in specific regions. Uncertainties in dynamic downscaling simulations due to errors in the atmospheric state and models need to be understood first in the present climate simulations. Then the reliability for future projections can be inferred. This research contains three parts. The first part gives an assessment of temperature and precipitation over Ontario based on the North American Regional Climate Change Assessment Program (NARCCAP) RCM simulation data. In part two, five 8-year downscaling simulations using the Weather Research and Forecasting (WRF) model driven by five NARCCAP model data over Ontario are studied. Each of these simulation results and their mean are analyzed to address the dynamic downscaling effect on temperature and precipitation and their variabilities. Lastly in the third part, a 14-member perturbed ensemble simulation using the WRF model was conducted. The ensemble means of temperature and precipitation are evaluated and the uncertainties in regional climate modeling are discussed. The temperature and precipitation in seven NARCCAP RCM simulations from 1979 to 2004 are compared to the observations over Ontario. The observed annual area mean temperature has a remarkable rising trend in the late 1990s after decades of fluctuation. It is mainly due to a significant rise of winter area mean temperature during that period. This rising trend has been revealed in all seven models. For the annual area mean precipitation, the observed values fluctuate during this period, and the NARCCAP RCM model simulations show larger discrepancies. One focus of this thesis is to assess the impact of increased model resolution on regional climate simulations. Five NARCCAP RCM (MM5I, RCM3, HRM3, CRCM and WRFG) simulation data with 50-km horizontal resolution are downscaled to 10-km horizontal grid over Ontario to provide initial and boundary conditions for the WRF downscaling simulations in the period from 1991 to 1998. The model results show that the high resolution has great impact on regional climate simulations. Three sets of ensembles, the seven-member NARCCAP RCM simulations, the five-member WRF downscaling simulations, and a 14-member perturbed ensemble simulations using WRF model with the stochastic kinetic energy backscatter scheme are analyzed to assess the performance of the ensemble approach in regional climate simulations. The ensemble mean temperature and precipitation are compared to reanalysis data and the observations. The root mean square errors (RMSE) and the correlations are calculated. The results show that the ensemble method improves the accuracy of simulations, for both temperature and precipitation

Numerical Air Quality Forecast over Eastern China: Development, Uncertainty and Future

Air pollution is severely focused due to its distinct effect on climate change and adverse effect on human health, ecological system, etc. Eastern China is one of the most polluted areas in the world and many actions were taken to reduce air pollution. Numerical forecast of air quality was proved to be one of the effective ways to help to deal with air pollution. This chapter will present the development, uncertainty and thinking about the future of the numerical air quality forecast emphasized in eastern China region. Brief history of numerical air quality modeling including that of Shanghai Meteorological Service (SMS) was reviewed. The operational regional atmospheric environmental modeling system for eastern China (RAEMS) and its performance on forecasting the major air pollutants over eastern China region was introduced. Uncertainty was analyzed meanwhile challenges and actions to be done in the future were suggested to provide better service of numerical air quality forecast

Uniaxial Tension Simulation Using Real Microstructure-based Representative Volume Elements Model of Dual Phase Steel Plate

AbstractDual-phase steels have become a favored material for car bodies. In this study, the deformation behavior of dual-phase steels under uniaxial tension is investigated by means of 2D Representative Volume Elements (RVE) model. The real metallographic graphs including particle geometry, distribution and morphology are considered in this RVE model. Stress and strain distributions between martensite and ferrite are analyzed. The results show that martensite undertakes most stress without significant strain while ferrite shares the most strain. The tensile failure is the result of the deforming inhomogeneity between martensite phase and ferrite phase, which is the key factor triggering the plastic strain localization on specimen section during the tensile test

Insights and approaches using deep learning to classify wildlife.

The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the methods can be abstruse and the results mystifying. Here, in the context of applying cutting edge methods to classify wildlife species from camera-trap data, we shed light on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications. The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-learning algorithms. We outline these methods and present results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We demonstrate the application of a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that in some cases are similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species. We then interpret the features in the image where the strongest responses occur, and present dataset biases that were revealed by these extracted features. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated the relative unfamiliarity of images that were not part of the training set when these images were one of the 20 species "known" to our CNN in contrast to images of the species that were "unknown" to our CNN

A Bilevel Optimization Model Based on Edge Computing for Microgrid

With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control module and a lower-level optimal control module. The purpose of the two-layer optimization modules is to optimize the cost of the power distribution of microgrids. The function of the upper-level optimal control module is to set decision variables for the lower-level module, while the function of the lower-level module is to find the optimal solution by mathematical methods on the basis of the upper-level and then feed back the optimal solution to the upper-layer. The upper-level and lower-level modules affect system decisions together. Finally, the feasibility of the bilevel optimization model is demonstrated by experiments

Optimization and scale-up of cell culture and purification processes for production of an adenovirus-vectored tuberculosis vaccine candidate

Tuberculosis (TB) is the second leading cause of death by infectious disease worldwide. The only available TB vaccine is the Bacille Calmette-Guerin (BCG). However, parenterally administered Mycobacterium bovis BCG vaccine confers only limited immune protection from pulmonary tuberculosis in humans. There is a need for developing effective boosting vaccination strategies. AdAg85A, an adenoviral vector expressing the mycobacterial protein Ag85A, is a new tuberculosis vaccine candidate, and has shown promising results in pre-clinical studies and phase I trial. This adenovirus vectored vaccine is produced using HEK 293 cell culture. Here we report on the optimization of cell culture conditions, scale-up of production and purification of the AdAg85A at different scales. Four commercial serum-free media were evaluated under various conditions for supporting the growth of HEK293 cell and production of AdAg85A. A culturing strategy was employed to take advantages of two culture media with respective strengths in supporting the cell growth and virus production, which enabled to maintain virus productivity at higher cell densities and resulted in more than two folds of increases in culture titer. The production of AdAg85A was successfully scaled up and validated at 60L bioreactor under the optimal conditions. The AdAg85A generated from the 3L and 60L bioreactor runs was purified through several purification steps. More than 98% of total cellular proteins was removed, over 60% of viral particles was recovered after the purification process, and purity of AdAg85A was similar to that of the ATCC VR-1516 Ad5 standard. Vaccination of mice with the purified AdAg85A demonstrated a very good level of Ag85A-specific antibody responses. The optimized production and purification conditions were transferred to a GMP facility for manufacturing of AdAg85A for generation of clinical grade material to support clinical trials

Exogenous brassinosteroids alleviate calcium deficiency-induced tip-burn by maintaining cell wall structural stability and higher photosynthesis in mini Chinese Cabbage

Tip-burn has seriously affected the yield, quality and commodity value of mini Chinese cabbage. Calcium (Ca2+) deficiency is the main cause of tip-burn. In order to investigate whether exogenous brassinosteroids (BRs) can alleviate tip-burn induced by calcium (Ca2+) deficiency and its mechanism, in this study, Ca2+ deficiency in nutrient solution was used to induced tip-burn, and then distilled water and BRs were sprayed on leaves to observe the tip-burn incidence of mini Chinese cabbage. The tip-burn incidence and disease index, leaf area, fluorescence parameters (Fv/Fm, NPQ, qP andφPSII) and gas exchange parameters (Tr, Pn, Gs and Ci), pigment contents, cell wall components, mesophyll cell ultrastructure and the expression of genes related to chlorophyll degradation were measured. The results showed that exogenous BRs reduced the tip-burn incidence rate and disease index of mini Chinese cabbage, and the tip-burn incidence rate reached the highest on the ninth day after treatment. Exogenous BRs increased the contents of cellulose, hemifiber, water-soluble pectin in Ca2+ deficiency treated leaves, maintaining the stability of cell wall structure. In addition, BRs increased photosynthetic rate by increasing the activities of Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and fructose 1,6-bisphosphatase (FBPase) related to Calvin cycle, maintaining relatively complete chloroplast structure and higher chlorophyll content via down-regulating the expression of BrPPH1 and BrPAO1 genes related to chlorophyll degradation. In conclusion, exogenous BRs alleviated calcium deficiency-induced tip-burn by maintaining cell wall structural stability and higher photosynthesis