Halogens and the chemistry of the free troposphere

The role of halogens in both the marine boundary layer and the stratosphere has long been recognized, while their role in the free troposphere is often not considered in global chemical models. However, a careful examination of free-tropospheric chemistry constrained by observations using a full chemical data assimilation system shows that halogens do play a significant role in the free troposphere. In particular, the chlorine initiation of methane oxidation in the free troposphere can contribute more than 10%, and in some regions up to 50%, of the total rate of initiation. The initiation of methane oxidation by chlorine is particularly important below the polar vortex and in northern mid-latitudes. Likewise, the hydrolysis of alone can contribute more than 35% of the production rate in the free-troposphere

Using neural networks to describe tracer correlations

Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH₄-N₂O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and methane volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH₄-N₂O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH_4  (but not N₂O) from 1991 till the present. The neural network Fortran code used is available for download

Halogens and the chemistry of the free troposphere

International audienceThe role of halogens in both the marine boundary layer and the stratosphere has long been recognized, while their role in the free troposphere is often not considered in global chemical models. However, a careful examination of free-tropospheric chemistry constrained by observations using a full chemical data assimilation system shows that halogens do play a significant role in the free troposphere. In particular, the chlorine initiation of methane oxidation in the free troposphere can contribute more than 10%, and in some regions up to 50%, of the total rate of initiation. The initiation of methane oxidation by chlorine is particularly important below the polar vortex and in northern mid-latitudes. Likewise, the hydrolysis of alone can contribute more than 35% of the production rate in the free-troposphere

Implications of Teacher Motivation and Renewal Indicators in Arkansas Toward Professional Growth and Achievement

This qualitative study was designed to determine if the teaching population in the state of Arkansas had a more favorable attitude toward specific motivational theories and practices; and to determine if that attitude significantly affected the teacher retention rate and the quality of work produced. The literature reviewed included the role of the school leader, motivational theory, and other relevant studies on teacher motivation. Eight National Board Certified teachers in Arkansas were interviewed about motivation as it related to professional improvement and development. The interviews focused on characteristics that serve as intrinsic motivators toward professional improvement and development and their impact on the teachers\u27 attitudes. The interviews also attempted to identify differences between experienced and non-experienced teachers in relation to intrinsic motivational factors. The data revealed achievement and acceptance as the intrinsic motivational factors with the most significant impact on the teachers\u27 attitudes toward professional improvement and development. The data did not reveal a difference between experienced and non-experienced teachers in relation to motivation and professional growth. This study contributed to the field of education by providing an extension of the established research on intrinsic motivation. Intrinsic motivators vary in the effect they have on teachers and their desire to improve professionally. This study outlines the significance of these intrinsic motivators and the impact they have on accomplished teachers

Towards Identification of Relevant Variables in the observed Aerosol Optical Depth Bias between MODIS and AERONET observations

Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this paper, we present a general framework towards identifying relevant set of variables responsible for the observed bias. We present a general framework to identify the possible factors influencing the bias, which might be associated with the measurement conditions such as the solar and sensor zenith angles, the solar and sensor azimuth, scattering angles, and surface reflectivity at the various measured wavelengths, etc. Specifically, we performed analysis for remote sensing Aqua-Land data set, and used machine learning technique, neural network in this case, to perform multivariate regression between the ground-truth and the training data sets. Finally, we used mutual information between the observed and the predicted values as the measure of similarity to identify the most relevant set of variables. The search is brute force method as we have to consider all possible combinations. The computations involves a huge number crunching exercise, and we implemented it by writing a job-parallel program

Using an extended Kalman filter learning algorithm for feed-forward neural networks to describe tracer correlations

International audienceIn this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download

Maximum Joint Entropy and Information-Based Collaboration of Automated Learning Machines

We are working to develop automated intelligent agents, which can act and react as learning machines with minimal human intervention. To accomplish this, an intelligent agent is viewed as a question-asking machine, which is designed by coupling the processes of inference and inquiry to form a model-based learning unit. In order to select maximally-informative queries, the intelligent agent needs to be able to compute the relevance of a question. This is accomplished by employing the inquiry calculus, which is dual to the probability calculus, and extends information theory by explicitly requiring context. Here, we consider the interaction between two question-asking intelligent agents, and note that there is a potential information redundancy with respect to the two questions that the agents may choose to pose. We show that the information redundancy is minimized by maximizing the joint entropy of the questions, which simultaneously maximizes the relevance of each question while minimizing the mutual information between them. Maximum joint entropy is therefore an important principle of information-based collaboration, which enables intelligent agents to efficiently learn together.Comment: 8 pages, 1 figure, to appear in the proceedings of MaxEnt 2011 held in Waterloo, Canad

Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies

Update on a Continuing Saga: Eelgrass and Green Crabs in Casco Bay, Maine (Poster)

https://digitalcommons.usm.maine.edu/cbep-graphics-maps-posters/1035/thumbnail.jp

Strategies for assessing the implications of malformed frogs for environmental health.

The recent increase in the incidence of deformities among natural frog populations has raised concern about the state of the environment and the possible impact of unidentified causative agents on the health of wildlife and human populations. An open workshop on Strategies for Assessing the Implications of Malformed Frogs for Environmental Health was convened on 4-5 December 1997 at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina. The purpose of the workshop was to share information among a multidisciplinary group with scientific interest and responsibility for human and environmental health at the federal and state level. Discussions highlighted possible causes and recent findings directly related to frog deformities and provided insight into problems and strategies applicable to continuing investigation in several areas. Possible causes of the deformities were evaluated in terms of diagnostics performed on field amphibians, biologic mechanisms that can lead to the types of malformations observed, and parallel laboratory and field studies. Hydrogeochemistry must be more integrated into environmental toxicology because of the pivotal role of the aquatic environment and the importance of fates and transport relative to any potential exposure. There is no indication of whether there may be a human health factor associated with the deformities. However, the possibility that causal agents may be waterborne indicates a need to identify the relevant factors and establish the relationship between environmental and human health in terms of hazard assessment