Web services for multiplatform exploratory analysis of level 2 and 3 NEWS merged A-Train data

Abstract

To simplify access to large and complex satellite data sets for climate analysis and model verification, a service-oriented architecture-based tool was developed to help study long-term and global-scale trends in climate, water and energy cycle, and weather variability. NASA's A-Train satellite constellation set of Level 2 data can be used to enable creation of climatologies that include correlation between observed temperature, water vapor and cloud properties from the A-Train sensors. However, the volume and inhomogeneity of Level 2 data have typically been difficult or time consuming to search and acquire. This tends to result in small-scale or short-term analysis. Instead of imposing on the user an often rigid and limiting web-based analysis environment, we recognize the need for well-designed distributed services so that users can perform analysis in their own familiar computing environments. Voluminous merged Level 2 data containing the various instrument data from the A-Train have recently been generated. Scientists next want to efficiently access selected sets of this merged data and perform their analysis. Server-side capabilities were developed to off-load processing and reduce the amount of data to be transferred to the client. Correspondingly, client-side processing APIs were developed to enable scientists to perform analysis of voluminous server-side data from within their own familiar computing environment (Java, Python, Matlab, IDL, C/C++, and Fortran90).