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 highimpact 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 to Atmospheric Infrared Sounder (AIRS) temperature and moisture profiles. For a given retention rate, background, and observation error, the optimal 1D analyses (i.e., lowest MSE) tend to have observations that are near regions of large curvature and gradients. Observation error leads to the selection of spurious data in homogeneous regions of the intelligent algorithms. In the 2D experiments, simple thinning tends to perform better within the homogeneous data regions. Analyses produced using AIRS data demonstrate that observations selected via a combination of the simple and intelligent approaches reduce clustering, provide a more even distribution along the satellite swath edges, and, in general, have lower error and comparable computational requirements compared to standard operational thinning methodologies.
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.
S 1 Informal surveys and anecdotal evidence indicate time spent on such data issues to be 50% or more.
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 airglow 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.
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