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

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

[2,3:5,6]Dibenzo[2.2.2]octa-2,5,7-triene (C2/c) and [2,3:5,6]dibenzo[2.2.2]octa-2,5-diene

Two barrelene homologs are reported. Strain in the bicyclic framework of [2,3:5,6]dibenzo[2.2.2]octa-2,5,7- triene, (I) (C16H12), which is manifest in the deviations from ideality of the bond angles in the central bicyclic ringoSyStem and compression of the double bond [1.312 (3)A], is reduced in the more saturated derivative, [2,3:5,6]dibenzo[2.2.2]octa-2,5-diene, (II) (CI6H14), with the corresponding single bond being 1.5380 (19)A. The formation of isomorphs of (I) in both chiral (C2) and achiral (C2/c) space groups has implications for asymmetric syntheses involving solid (I) which rely on a non-centrosymmetric space group

[2,3:5,6]Dibenzo[2.2.2]octa-2,5,7-triene (C2/c) and [2,3:5,6]dibenzo[2.2.2]octa-2,5-diene

Two barrelene homologs are reported. Strain in the bicyclic framework of [2,3:5,6]dibenzo[2.2.2]octa-2,5,7- triene, (I) (C16H12), which is manifest in the deviations from ideality of the bond angles in the central bicyclic ringoSyStem and compression of the double bond [1.312 (3)A], is reduced in the more saturated derivative, [2,3:5,6]dibenzo[2.2.2]octa-2,5-diene, (II) (CI6H14), with the corresponding single bond being 1.5380 (19)A. The formation of isomorphs of (I) in both chiral (C2) and achiral (C2/c) space groups has implications for asymmetric syntheses involving solid (I) which rely on a non-centrosymmetric space group

Remote Sensing of CDOM, CDOM Spectral Slope, and Dissolved Organic Carbon in the Global Ocean

A Global Ocean Carbon Algorithm Database (GOCAD) has been developed from over 500 oceanographic field campaigns conducted worldwide over the past 30 years including in situ reflectances and coincident satellite imagery, multi- and hyperspectral Chromophoric Dissolved Organic Matter (CDOM) absorption coefficients from 245715 nm, CDOM spectral slopes in eight visible and ultraviolet wavebands, dissolved and particulate organic carbon (DOC and POC, respectively), and inherent optical, physical, and biogeochemical properties. From field optical and radiometric data and satellite measurements, several semi-analytical, empirical, and machine learning algorithms for retrieving global DOC, CDOM, and CDOM slope were developed, optimized for global retrieval, and validated. Global climatologies of satellite-retrieved CDOM absorption coefficient and spectral slope based on the most robust of these algorithms lag seasonal patterns of phytoplankton biomass belying Case 1 assumptions, and track terrestrial runoff on ocean basin scales. Variability in satellite retrievals of CDOM absorption and spectral slope anomalies are tightly coupled to changes in atmospheric and oceanographic conditions associated with El Nio Southern Oscillation (ENSO), strongly covary with the multivariate ENSO index in a large region of the tropical Pacific, and provide insights into the potential evolution and feedbacks related to sea surface dissolved carbon in a warming climate. Further validation of the DOC algorithm developed here is warranted to better characterize its limitations, particularly in mid-ocean gyres and the southern oceans

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

Neural Networks as a Tool for Constructing Continuous NDVI Time Series from AVHRR and MODIS

The long term Advanced Very High Resolution Radiometer-Normalized Difference Vegetation Index (AVHRR-NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non-stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor-specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at one degree is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product