Fmi Open Data on S3

Editor's Notes

• #5: - Large players are large enough to operate without NMSs
• #6: Using Open Source Software prevents from vendor locks Collaboration is open and easy No long and burden negotiations – just evaluate, use, develop If you need any changes or modifications, you can do them yourself or order them from 3rd party Even just finding users to the software boost development
• #7: - Even just finding users to the software boost development
• #9: Maximal coverage requires several different channels and services One organization can’t handle them all
• #10: Open data helps, but.. Weather and climate data is complicated to handle Huge volumes, complex formats, complicated domain Providing tools to handle and analyze the data empowers 3rd party users to correctly utilize it Proper tools ensures consistency between information regardless of channel and service providers
• #15: - Registration is not very convenient for users and is against open data principles but it provides us a useful information about the usage and makes it easier for us to prevent misusage of the portal
• #16: - Registration is not very convenient for users and is against open data principles but it provides us a useful information about the usage and makes it easier for us to prevent misusage of the portal
• #18: We have done well but this is not enough
• #20: weather forecast model Covers europe Updates 4 times a day Two days ahead Archive is provided
• #23: The pilot have been going on for 1 and half months now How well has it succeed? Before showing actual numbers I want to show some context This chart show how requests are divided between different types FMI provides the same data in different formats and data models Left column stands for different types of point requests and right (invisible) column stands for grid requests (in binary format) So: most of the requests are point data requests But there are still 40 thousand grid data requests from hundreds of different ip addresses during September
• #24: The first plot was for all open data but in AWS we have only Hirlam available So, here’s the same data for Hirlam The pattern is the same
• #25: This chart shows how Hirlam weather forecast data is divided between FMI data portal and S3 for roughly last year The blue line shows number of requests at FMI and yellow line is for requests at AWS Not that scale (and thus axis) is different But data in S3 is divided in a way that every parameter (like temperature or precipitation) is in separate files and from FMI data portal one can download everything with one request (17 for surface data and 8 for pressure level data) If we assume that every S3 user downloads everything, we can compare a popularity of these channels /shown in red column)
• #26: This chart shows how Hirlam weather forecast data is divided between FMI data portal and S3 for roughly last year The blue line shows number of requests at FMI and yellow line is for requests at AWS Not that scale (and thus axis) is different But data in S3 is divided in a way that every parameter (like temperature or precipitation) is in separate files and from FMI data portal one can download everything with one request (17 for surface data and 8 for pressure level data) If we assume that every S3 user downloads everything, we can compare a popularity of these channels / shown in with green line
• #27: And if we look trend (shown with dashed lines), we can see that AWS gets more popular and our users are actually moving from FMI portal to AWS
• #28: Pressure levels 9900 req/day Would mean 309 operative users. Surface data 6000-46000 req/day Mean 15 000 req/day Would mean 220 operative users
• #30: Leanheat is a software solution for optimized heating in centrally heated buildings. Traditionally, central heating is based purely on outdoor temperature. Leanheat replaces the traditional heating control method by generating a building specific heat demand forecast a few days into the future, and optimally adjusts heating power at all times based on the generated forecast model. As a part of a Leanheat installation, the apartments are equipped with wireless temperature sensors. The heat demand forecast is based on indoor temperature, and it automatically takes into account the thermodynamic properties of the building, temperature- and radiation forecasts as well as resident behavior. The goal of the heating power optimization is to maintain stable indoor conditions with the least amount of heating energy possible. Meanwhile, also peak power demand can be reduced, or the heating can be controlled based on e.g. a price signal from an energy provider.
• #31: “The Amazon AWS S3 service is a convenient way to share large data sets. It allows Leanheat to get weather forecasts to customized areas and thus enables Leanheat to provide it's world class heating optimization solution anywhere in Europe. Leanheat also uses Amazon AWS servers in its optimization solution which further benefits the overall solution.”
• #32: “The Amazon AWS S3 service is a convenient way to share large data sets. It allows Leanheat to get weather forecasts to customized areas and thus enables Leanheat to provide it's world class heating optimization solution anywhere in Europe. Leanheat also uses Amazon AWS servers in its optimization solution which further benefits the overall solution.”