1458 metScanR: An R Package for Gathering Meteorological Data from Various Networks

Wednesday, 25 January 2017
4E (Washington State Convention Center )
Joshua A. Roberti, National Ecological Observatory Network, Boulder, CO; and D. E. Smith, L. Stanish, C. Flagg, and S. Weintraub

The need for environmental data is growing.  Scientists, such as meteorologists, climatologists, and ecologists, as well as policy makers are just some of the groups that need access to these data on a regular basis. Although there is an abundance of freely available meteorological data throughout the United States, gathering data from environmental monitoring networks can be arduous.  This is especially true if a user is interested in compiling numerous datatypes from a handful of networks. For instance, a scientist might need soil moisture, radiation, and wind vector components, to properly investigate and reconstruct local climate for a certain temporal period and spatial area.  Unfortunately, there may be gaps in how, where, and when data are collected. For example, variables of interest may not be collected at specific sites, or sites that do report these variables may not have been active during the time period the scientist is interested in investigating. This in turn generally leads to a large number of hours spent only to find that the data of interest do not exist.

Here, we introduce the metScanR package for the R Programming language.  This package will enable individuals to search for data across numerous meteorological networks based on key parameters such as data type and observation dates. It checks the availability of specific meteorological variables for a specified spatial area of interest and during the requested time period.  Additionally, it allows the user to compile and export various datatypes from multiple networks into usable R formats, e.g., lists and data frames, so analysis can be easily conducted.  This R package will increase the efficiency and ease for individuals to obtain environmental data from various sensor networks.

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