Enhancing Data Interoperability with Web Services

Wednesday, 17 December 2014: 8:15 AM
Sudhir R Shrestha1, Daniel Adam Zimble2, Wanqiu Wang1, David Herring3 and Mike Halpert1, (1)NOAA College Park, Climate Prediction Center, College Park, MD, United States, (2)ESRI, Redlands, CA, United States, (3)NOAA Washington DC, Washington, DC, United States
In an effort to improve data access and interoperability of climate and weather data, the National Oceanic and Atmospheric Administration’s (NOAA) and Climate Prediction Center (CPC) are exploring various platform solutions to enhance a user’s ability to locate, preview, and acquire the data.

The and CPC data team faces multiple challenges including the various kinds of data and formats, inconsistency of metadata records, variety of data service implementations, very large volumes of data and geographically distributed locations. We have created the Data Access and Interoperability project to design a web-based platform, where interoperability between systems can be leveraged to allow greater data discovery, access, visualization and delivery. In the interoperable data platform, systems can integrate with each other to support the synthesis of climate and weather data. Interoperability is the ability for users to discover the available climate and weather data, preview and interact with the data, and acquire the data in common digital formats through a simple web-based interface.

The goal of the interoperable data platform is to leverage existing web services, implement the established standards and integrate with existing solutions across the earth sciences domain instead of creating new technologies.

Towards this effort to improve the interoperability of the platform, we are collaborating with ESRI Inc. to provide climate and weather data via web services. In this presentation, we will discuss and demonstrate how to use ArcGIS to author RESTful based scientific web services using open standards. These web services are able to encapsulate the logic required to handle and describe scientific data through a variety of service types including, image, map, feature, geoprocessing, and their respective service methods. Combining these types of services and leveraging well-documented APIs, including the ArcGIS JavaScript API, we can afford to focus our attention on the design and development of user-friendly maps and apps. As a use case scenario, we will demonstrate the maps and apps that we developed as prototypes for CPC Climate Forecast System (CFSv2) products including Total Monthly Sea Ice Concentration that were built to improve the user’s overall experience.