E ach year across the US, mesoscale weather events-flash floods, tornadoes, hail, strong winds, lightning, and localized winter storms-cause hundreds of deaths, routinely disrupt transportation and commerce, and lead to economic losses averaging more than US$13 billion.1 Although mitigating the impacts of such events would yield enormous economic and societal benefits, research leading to that goal is hindered by rigid IT frameworks that can't accommodate the real-time, on-demand, dynamically adaptive needs of mesoscale weather research; its disparate, high-volume data sets and streams; or the tremendous computational demands of its numerical models and data-assimilation systems.In response to the increasingly urgent need for a comprehensive national cyberinfrastructure in mesoscale meteorology-particularly one that can interoperate with those being developed in other relevant disciplines-the US National Science Foundation (NSF) funded a large information technology research (ITR) grant in 2003, known as Linked Environments for Atmospheric Discovery (LEAD). A multidisciplinary effort involving nine institutions and more than 100 scientists, students, and technical staff in meteorology, computer science, social science, and education, LEAD addresses the fundamental research challenges needed to create an integrated, scalable framework for adaptively analyzing and predicting the atmosphere.LEAD's foundation is dynamic workflow orchestration and data management in a Web services framework. These capabilities provide for the use of analysis tools, forecast models, and data repositories,
Boundary layer cumulus clouds are hard to detect in satellite imagery, especially for GOES imagery due to the coarse resolution of the infrared channels. Two different approaches for the detection of cumulus clouds in GOES satellite imagery are discussed and intercompared. The first type, structural thresholding, uses the morphology of cumulus cloud fields for detection. The second type, uses 1)classifiers based on texture and spectral, 2)edge detection and spectral, and 3)purely spectral features. For five selected scenes, cumulus cloud masks are created using these various methods and are compared against the expert-labeled masks. The structural thresholding method has the highest percentage of correct classification (76%), followed by classifier based on Laplacian edge detection features (74%). The classification time is lowest for the structural thesholding method, followed by classifiers based on spectral, edge detection, textural features. The structural thresholding method also is capable of detecting individual cumulus clouds within cloud fields. For the five scenes investigated, the average percentage of correct labeling of cumulus clouds by the structural thresholding method is 86%.
We describe a deep learning convolutional neural network (CNN) for enhancing low resolution multispectral satellite imagery without the use of a panchromatic image. For training, low resolution images are used as input and corresponding high resolution images are used as the target output (label). The CNN learns to automatically extract hierarchical features that can be used to enhance low resolution imagery. The trained network can then be effectively used for super-resolution enhancement of low resolution multispectral images where no corresponding high resolution image is available. The CNN enhances all four spectral bands of the low resolution image simultaneously and adjusts pixel values of the low resolution to match the dynamic range of the high resolution image. The CNN yields higher quality images than standard image resampling methods. CCS CONCEPTS • Computing methodologies → Machine learning approaches • Computing methodologies → Neural networks • Applied computing → Earth and atmospheric sciences
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