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From its early beginnings over 150 years ago, weather forecasting at the Met Office has been driven by data. Simple observations recorded and used to hand-plot synoptic charts have been exchanged for the billions of observations received and handled every day, mainly from satellites but also from weather stations, radar , ocean buoys, planes, shipping and the public.

The key stages of the weather data value chain are as follows: Ÿ Monitoring and observation of the weather and environment, e.g. by NMSs. Ÿ Numerical weather prediction (NWP) and climate modelling carried out by NMSs to create global, regional and limited area weather forecasts. Private companies are growing their presence in the market and challenging the traditional role of NMSs to provide forecasts to the public, by statistically blending data from NMS models to create their own forecast models, for example. Other companies providing data via online channels and/or apps include The Weather Company, Accuweather or the Climate Corporation. Ÿ Communication and dissemination of forecasts by news, NMS and media organisations like the BBC, Yahoo and Google, or within consumer-targeted mobile and web applications. Ÿ Decision making by individuals and businesses across a variety of sectors, which draws on weather data and reporting.

The core data asset of our global weather data infrastructure is observation data that captures a continuous record of weather and climate data around the world. This includes temperature, rainfall, wind speed and details of a host of other atmospheric, surface and marine conditions.

it enables people to contribute data about them to it and, on a case-by-case basis, people can choose to permit third parties to access that data. This is the pattern that many personal data stores and personal data management systems adopt in holding data and enabling users to unlock new apps and services that can plug into it. Health Bank enables people to upload their medical records and other information like wearable readings and scans to share with doctors or ‘loved ones’ to help manage their care; Japan’s accredited information banks might undertake a similar role. Other examples — such as Savvy and Datacoup — seem to be focused on sharing data with market research companies willing to offer a form of payment. Some digital identity services may also conform to this pattern.

it enables people to contribute data about them to it and, on a case-by-case basis, people can choose whether that data is shared with third parties as part of aggregate datasets. OpenHumans is an example that enables communities of people to share data for group studies and other activities. Owners of a MIDATA account can “actively contribute to medical research and clinical studies by granting selective access to their personal data”. The approach put forward by the European DECODE project would seem to support this type of individual buy-in to collective data sharing, in that case with a civic purpose. The concept of data unions advocated by Streamr seeks to create financial value for individuals by creating aggregate collections of data in this way. Although Salus Coop asks its users to “share and govern [their] data together.. to put it at the service of collective return”, it looks as though individuals can choose which uses to put it to.

it enables people to contribute data about them to it and decisions about what third parties can access aggregate datasets are taken collectively. As an example, The Good Data seeks to sell browsing data generated by its users “entirely on their members’ terms… [where] any member can participate in deciding these rules”. The members of the Holland Health Data Cooperative would similarly appear to “determine what happens to their data” collectively, as would drivers and other workers who contribute data about them to Workers Info Exchange.