Sunil Movva

Comparing different thresholding algorithms for segmenting auroras

Extracting aurora oval boundary from spacecraft UV imagery is not a trivial problem. The distinction between aurora and background varies depending on the factors such as the date, time of the day, and satellite position. Thresholding technique is a well-known technique for detecting aurora boundary from satellite imagery. In this study, three distinct thresholding algorithms, mixture modeling, fuzzy sets and entropy thresholding were applied to a selected set of UV images measured on board Polar satellite to examine their effectiveness in aurora boundary detection. Two thresholding approaches were taken: global thresholding and adaptive thresholding. As expected, adaptive thresholding approach showed better results. In addition to these algorithms, another new algorithm (edge-based) was examined using adaptive approach. This thresholding algorithm detects aurora oval by identifying the boundary transition between aurora and background. The results from these different algorithms are presented.

Dayglow removal from FUV auroral images

Aurora study is a key area in understanding the connection and interaction of the solar-terrestrial system. Auroral events are monitored on the global scale at the Far Ultraviolet (FUV) spectrum by satellite-based sensors. However, the existence of dayglow emission significantly limits scientists is the ability to determine the location and the size of auroral ovals. A dynamic methodology to remove day airglaw emission from the LBHL band UVI images on the Polar satellite is presented in this paper. First, the methodology identifies the geomagnetic latitude bound of the night side auroral oval. Then, the maximum range of geomagnetic latitude bound of the auroral is inferred based on the domain knowledge. Using the non-auroral dayglow pixels, a multi-variable regression fit of the intensity as function of the cosine of the solar zenith angle and cosine of the satellite viewing zenith angle is obtained. The methodology then uses this fitting function to estimate the dayglow intensity and to remove its effect in the LBHL band FUV images. This methodology does not require an external source of input solar flux and the dayglow removal is solely based on individual images. Experiment results show that dayglow effect is removed significantly from the original LBHL FUV images. Using this methodology, the performance of auroral oval detection algorithms is significantly improved

Talkoot: software tool to create collaboratories for earth science

Abstract“Open science,” where researchers share and publish every element of their research process in addition to the final results, can foster novel ways of collaboration among researchers and has the potential to spontaneously create new virtual research collaborations. Based on scientific interest, these new virtual research collaborations can cut across traditional boundaries such as institutions and organizations. Advances in technology allow for software tools that can be used by different research groups and institutions to build and support virtual collaborations and infuse open science. This paper describes Talkoot, a software toolkit designed and developed by the authors to provide Earth Science researchers a ready-to-use knowledge management environment and an online platform for collaboration. Talkoot allows Earth Science researchers a means to systematically gather, tag and share their data, analysis workflows and research notes. These Talkoot features are designed to foster rapid knowledge sharing within a virtual community. Talkoot can be utilized by small to medium sized groups and research centers, as well as large enterprises such a national laboratories and federal agencies.

Syntactic and semantic metadata integration for science data use

This paper proposes a novel metadata solution to allow applications to intelligently use science data in an automated fashion. The solution provides rich syntactic and semantic metadata, where the semantic metadata is linked with an ontology to define the semantic terms. This solution allows applications to exploit the syntactic metadata to read the data and the semantic metadata to infer the content and the meaning of the data. The solution presented in this paper leverages the Earth Science Markup Language for providing the syntactic metadata and adds a semantic metadata component along with links to the appropriate ontology. This new semantic component is orthogonal to the syntactic metadata, so it does not perturb the existing design. An example application was designed and built that integrates this syntactic and semantic metadata via an ontology to perform a data processing operation.

Evaluation of Data Reduction Algorithms for Real-Time Analysis

Abstract Data reduction tools are developed and evaluated using a data analysis framework. Simple (nonadaptive) and intelligent (adaptive) thinning algorithms are applied to both synthetic and real data and the thinned datasets are ingested into an analysis system. The approach is motivated by the desire to better represent high-impact weather features (e.g., fronts, jets, cyclones, etc.) that are often poorly resolved in coarse-resolution forecast models and to efficiently generate a set of initial conditions that best describes the current state of the atmosphere. As a precursor to real-data applications, the algorithms are applied to one- and two-dimensional synthetic datasets. Information gleaned from the synthetic experiments is used to create a thinning algorithm that combines the best aspects of the intelligent methods (i.e., their ability to detect regions of interest) while reducing the impacts of spatial irregularities in the data. Both simple and intelligent thinning algorithms are then applied...

Agent framework for intelligent data processing

Data preparation is an important part of the mining process. This paper describes MIDAS, an agent framework for intelligent data processing. The objective framework is to provide end users automated data processing services such as subsetting and data format translation by coupling Earth science markup language (ESML) interchange technology and ontologies. These ontology driven agents guide the user through the process of data processing and are able to make decisions for them based on their output requirements. This paper describes the design approach used to extend the ESML schema to incorporate semantics using ontologies. It explains the overall architecture including the infrastructure layer, organization and the agent design used in this framework. The subset of performatives derived from the knowledge query and manipulation language (KQML) used by the agents to interact will also be described

Customizable Search Engine with Semantic and Resource Aggregation Capability

There is a need for specialized search engines focused on specific disciplines that can use domain knowledge to guide the user to find exactly the resources they are searching for. In addition, these engines should be able search multiple and heterogeneous resource catalogs simultaneously and aggregate results from these catalogs. Noesis, a customizable search engine with semantic and resource aggregation capabilities is presented in this paper. The Noesis architecture has been designed to be modular, allowing it to be adapted by projects in different disciplines. This paper describes the Noesis architecture, highlights its application in several projects, and presents a user scenario for one of the projects is presented in this paper.

Intelligent Data Thinning Algorithms for Satellite Imagery

This paper presents a study on intelligent data thinning for satellite data. In particular, the focus is on the thinning of the Atmospheric Infrared Sounder (AIRS) profiles. A direct thinning method is first applied to a synthetic data set in order to identify optimal data selection strategies. Experiments on synthetic data suggest that a thinned data set should combine homogeneous samples, and high gradient and variance of gradient samples for optimal performance. This result leads to the modification of our previously developed Density Adjustment Data Thinning algorithm (DADT). The modified DADT (mDADT) algorithm is used to thin the AIRS profiles. Experiments are conducted to compare the thinning performances of mDADT with two simple thinning algorithms. Experiment results show that mDADT algorithm performs better than the two simple thinning algorithms, especially over the regions of significant atmospheric features.

Automated detection of frontal systems from numerical model-generated data

Fronts are significant meteorological phenomena of interest. The extraction of frontal systems from observations and model data can greatly benefit many kinds of research and applications in atmospheric sciences. Due to the huge amount of observational and model data available nowadays, automated extraction of front systems is necessary. This paper presents an automated method to detect frontal systems from numerical model-generated data. In this method, a frontal system is characterized by a vector of features, comprised of parameters derived from the model wind field. K-means clustering is applied to the generated sample set of the feature vectors to partition the feature space and to identify clusters representing the fronts. The probability that a model grid belongs to a front is estimated based on its feature vector. The probability image is generated corresponding to the model grids. A hierarchical thresholding technique is applied to the probability image to identify the frontal systems and a Gaussian Bayes classifier is trained to determine the proper threshold value. This is followed by post processing to filter out false signatures. Experiment results from this method are in good agreement with the ones identified by the domain experts.