J3.8 Providing Impact-based Decision Support Services through the Applied Climate Information System (ACIS)

Tuesday, 12 January 2016: 5:15 PM
Room 255/257 ( New Orleans Ernest N. Morial Convention Center)
Arthur T. DeGaetano, Northeast Regional Climate Center, Cornell University, Ithaca, NY; and W. Noon

Despite the proliferation of in situ, modeled and remotely sensed climate data over the last decade, users of these data sets outside of the research community cite numerous hurdles to the effective use of these data in decision making. Based on decades of experience, the Regional Climate Center (RCC) Program has found that end users are often frustrated by the onerous tasks of downloading and parsing large volumes of irrelevant data, reformatting files, and recreating commonly used analyses. This is problematic, as according to the World Bank, “Users tend to seek data they can easily access and understand—which may not be the most current, comprehensive or robust data available”.

In this presentation we describe the latest version of the Applied Climate Information System (ACIS) using several operational examples. These examples are chosen to demonstrate the functionality, system architecture and range of users relying on ACIS. A tool designed to support real-time decision-making at the local level by a federal agency highlights a user-friendly interface to ACIS that relies on station-based data. Using this tool we will first illustrate the processes by which ACIS data are continually updated and synchronized between multiple servers. Then, we will show applications for the various ACIS temporal reduction methods.

We will also demonstrate a range of products developed as part of the New York State Climate Change Science Clearinghouse (NYCCSC). Through this example we will illustrate the web service capabilities of ACIS. Using the web service interface, ACIS data and products can be accessed by non-RCC developers to build customized tools. The NYCCSC also highlights ACIS' ability to accommodate both station-based and gridded data sets. This example will allow us to show the use of ACIS to generate products that require spatial aggregation of gridded data.

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