1. Open Weather Weather on the Web
Rich Carne
March 2015

2. The Information Economy
Generator
Provider
Curator
Aggregator
Possessive
Federated
NMS

3. Threats, Risks and Issues
Obs
Modelling
Heterogeneous Data Aggregation
Impact based Multi hazard
IoT
Private networks
Multi source
Physical Model computational science
MO/DWD/NWS
Analytical model
Machine learning
IBM/Pana/Google
Aggregation of disparate data for greater insight
High value offerings
Warnings and forecasts that cover multiple hazards derived through rich obs, Physical and statistical modelling coupled with aggregations of heterogeneous data

4. Current Landscape
Apple use The Weather Channel in bundled IOS weather App.
Google also receive data from TWC for integration with their ecosystem

5. Current Landscape
British Airways IOS App – uses data from AccuWeather to deliver forecasts for arrival destination.

6. Inconsistency ? Weather Forecast Services Authoritative Warnings
Public
Authoritative Warnings
Google
Yahoo
Amazon BA
Met Office
DWD
MeteoF
NWS

7. Contextual Diagram Common Interfaces Global Aggregators
Commercial partners
Public facing organisations
Google
Yahoo
Amazon
Xn...
Common Interfaces
Common service layer
Standard Data Provision
Met Office
DWD
MeteoF
NWS
Other NMSs
The Met Office is a world leading Scientific organisation in the area of weather and climate research. we have some of the worlds best weather scientists.
Through the Scientific process, hypothesise are tested and establish a body of accepted knowledge
This knowledge is then turned into computer code inside something that we call the Unified Model. This is the suite of weather and climate models that the Met Office operates unique in that they cover all geospatial dimensions in what we call a seamless prediction system.
the process results in a massive set of data that is the low level model outputs.
Much of this data is then converted into useful information such as visualisations, graphs and plots
These create knowledge for our users
Upon which they can make informed decisions

8. Sharing data across boundaries
GTS closed system
Unable to handle growing data volumes
Push not pull service based B A C
GTS ?

9. International Standard Grid
By standardising model output at level 1 NMSs will benefit by providing common data improving our interface with private sector and potentially allowing for greater business continuity between centres.
Standard, model independent Diagnostics
Standard Vertical levels independent of model level
Standard Horizontal grids, and coordinate reference system
Standard Temporal range and interval
Standard format (CF-netCDF, GRIB2)

10. Data Gravity Model data volume is outpacing network bandwidth
Solutions are required that allow for the sub setting and selection of specific parts of a dataset.

11. Services for the Information Economy - INSPIRE
Discovery
Catalogue
View
WMS (data in context/use guidance)
Download
FTP (batch / cut-outs and re-projections)
Re-use
WCS (IDS/pull/on-demand/domain)
Invoke
WPS / ? (problems (algorithms) to data)

12. Sharing data across boundaries
Participating NMS’s would
commit to surfacing
highest resolution data possible. Achieving
a global coverage
Capability for the Met
community B A
Common Interfaces C
Met Office
DWD
MeteoF
NWS
Other NMSs

13. Sharing data across boundaries
A shared library of Met and
Climate algorithms as a
Service (MCaaS) would
allow participating NMSs
to benefit from wider
capability, reducing cost
and risk for all.
Built on common
Re-useable building
blocks such as discovery, access control,
cataloguing and API
management. B A
Common Interfaces C
Met Office
DWD
MeteoF
NWS
Other NMSs

14. Sharing data across boundaries
Define standards to allow
Access to hi-res data
From disparate datasets.
Create interfaces for
provision of trajectories,
points, Areas etc.. Across
international boundaries.
Develop Standards and
Capability for distributed
processing of data
through common
interface. B A
Common Interfaces C
Met Office
DWD
MeteoF
NWS
Other NMSs

15. High Level Benefits
Providing a single access point for global aggregators to access hi-res global weather information supports the sustainability of NMSs mitigating threat of marginalisation
Sharing of capability between participating centres allowing for business continuity and running of centre specific processing
Shared cost and risk of developing standards and implementation (ESSP experience)
Aviation and Maritime efficiencies and reduction of risk to life through provision best quality data from multiple centres

16. Links to OpenWIS
The OpenWIS consortium could be a good vehicle to collaboration and development of reference implementation to support real world services.
Already set up as independent entity
Has all the tools and processes
Motivated participants
Help to drive forward Next Generation WIS

17. Collaboration Activity
Definition of standards
Accessing distributed datasets
Processing across multiple data centres
Discovery of services and data
Cataloguing and Metadata
Development of discovery and access layer
Federated access
API management
Develop Services catalogue for specific use cases
Platform for Met Climate Algorithms as a Service
Services for Aviation, Marine, Public

18. Collaboration Alignment
There are currently specific relevant activities underway such as:
SESAR and NextGen (currently not interoperable WCS)
Open Geospatial Met Ocean domain working group
MeteoAlarm
WIS

19. Future Vision of Attribution
Berlin

20. Thank You