Principle 4: WIS2.0 requires provision of Web service(s) to access or interact with digital resources (e.g. data, information, products) published using WIS
BENEFIT:
Web services support 'machine-actionability' (i.e. the capacity of software systems to access, interoperate, and reuse data with little or no human intervention) because humans increasingly rely on computational support to deal with data as a result of an increase in volume, complexity, and velocity (i.e. creation speed) of data.
BENEFIT:
NMHSs develop their capacity to build and operate Web services, allowing them to extract more value from their data holdings through the delivery of higher value services to their users.
Note that the Manual on WIS (WMO-No. 1060) will be amended to specify the basic principles to which all Web services registered in WIS 2.0 shall conform. Based on the standards and conventions commonly used in their target user community (or communities), WMO programmes may identify particular technical specifications to which participating centres should conform in addition to the basic principles.
When designing their Web service offerings, NCs and DCPCs publishing 'big data' into WIS should consider the capability of their users to work with those data. Cg-17 identified that most Members were ill-prepared for the predicted explosion in data volumes. Many Members are already unable to effectively use the data published and made available today. Data volumes are rapidly increasing to sizes that require significant investment in technical infrastructure to manage and use those data. Perhaps more challenging is that such large volumes are impractical to move between collaborating organizations fast enough to meet operational requirements.
Web services may be used to provide a network API to process or simplify complex or high-volume data to better match the needs of the user or create a bespoke product. These services may range in complexity from simple query APIs that allow a user to extract only a geographic subset of data corresponding to the user's area of interest, through to remote execution a local area weather prediction model according to the user's specification and visualization of the model output. What both of these examples have in common is that the data is processed on the data provider's infrastructure to create a result or product that is small enough to be conveniently downloaded and used. Where the data processing is complex, intensive or requires a lot of user-specific configuration, NCs and DCPCs should consider use of cloud technologies to underpin their data processing services.