Evaluating Satellite-Based Aerosol Retrievals Over Mountainous Regions Of The U.S.

Satellite-based aerosol retrievals from NASA’s Moderate Resolution Imaging Spectrometer (MODIS) and NASA’s Multi-angle Imaging Spectrometer (MISR) are evaluated above four mountainous U.S. sites. We (1) examine the influence of spatial and temporal variability in aerosol and surface properties on satellite / sunphotometer agreement, (2) apply and assess an automated method for optimizing collocation window and radius in the context of variability in surface properties and aerosol optical depth (AOD), and (3) compare the performance of satellite AOD products above the four sites, and examine factors influencing their performance. Maps of the Normalized Differential Vegetation Index (NDVI), topography, and land cover are used to characterize the surface properties within a 50 km radius of each site. At the eastern sites, satellite-sunphotometer mean bias is primarily influenced by topography, urban regions, and water bodies. Collocations at the western sites are complicated by heterogeneous surface types and NDVI. The collocation window optimization algorithm is insensitive to temporal window size and spatial radius for the eastern sites but is less successful at optimization for the western sites. Averages performed at the selected collocation window size indicate little seasonal influence at the eastern sites and reduced collocation frequency during winter at the western sites

On the Benefits of Transparent Compression for Cost-Effective Cloud Data Storage

International audienceInfrastructure-as-a-Service (IaaS) cloud computing has revolutionized the way we think of acquiring computational resources: it allows users to deploy virtual machines (VMs) at large scale and pay only for the resources that were actually used throughout the runtime of the VMs. This new model raises new challenges in the design and development of IaaS middleware: excessive storage costs associated with both user data and VM images might make the cloud less attractive, especially for users that need to manipulate huge data sets and a large number of VM images. Storage costs result not only from storage space utilization, but also from bandwidth consumption: in typical deployments, a large number of data transfers between the VMs and the persistent storage are performed, all under high performance requirements. This paper evaluates the trade-off resulting from transparently applying data compression to conserve storage space and bandwidth at the cost of slight computational overhead. We aim at reducing the storage space and bandwidth needs with minimal impact on data access performance. Our solution builds on BlobSeer, a distributed data management service specifically designed to sustain a high throughput for concurrent accesses to huge data sequences that are distributed at large scale. Extensive experiments demonstrate that our approach achieves large reductions (at least 40%) of bandwidth and storage space utilization, while still attaining high performance levels that even surpass the original (no compression) performance levels in several data-intensive scenarios