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MeteoSwiss Tames Climatology Data with FME Server

See how INSER SA used FME Server to collect, store, prepare, and distribute both the gridded climatology data and its metadata for MeteoSwiss.

As the national provider for weather and climate services in Switzerland, MeteoSwiss has a lot of data. Monitoring a climate that ranges from near-Mediterranean to alpine glacial, with unique local phenomena like the föhn winds, they collect information from an array of surface observation systems and remote sensors, including radar, satellites, and radio soundings. The data supports weather forecasting, climate analysis, and high-resolution modeling of developments in the alpine region, ultimately warning authorities and the public of dangerous weather conditions.

And the information can change as quickly as, well, the weather.

To tackle this mountain of data, MeteoSwiss brought in the experts at INSER SA of Le Mont-sur-Lausanne – and FME Server – to enhance their existing Data Warehouse System with an infrastructure to deal with gridded spatial data. The goal was to collect, store, prepare, and distribute both the gridded climatology data and its metadata. They designed a system with two primary components – one to handle the collection and preparation of the data, and the other to facilitate end-user access.

On the collection side, FME Server handles automated importing of NetCDF, GIF, Esri® ASCII Grids and assorted GIS formats to a central geodatabase raster catalog, along with the population of a metadata repository. Data aggregation and QA is performed along the way.

Data consumers have a variety of FME Server-enabled choices on the delivery side. Automated exports are available, and a flexible framework design lets users customize delivery or incorporate it into other systems. Format-wise, NetCDF, TIFF, ASCII, and GIFs annotated with metadata are output, plus a web streaming service providing RDATA rasters. “FME’s flexibility has let us develop or integrate custom interfaces to some exotic formats like NetCDF and R”, says Pierre Terrettaz of INSER.

FME’s Python support also helped realize the project – handling GIF metadata annotation, job automation, some file operations, unusual data interpretation, and external application communications.

Now that the system is in production mode, users are seeing the benefits daily, with ease and efficiency of access not possible until now. “We were very impressed with how open-minded, flexible and responsive Safe was to our sometimes quite unusual requirements,” says Estelle Grueter of MeteoSwiss. “We’ve had a very good experience so far and look forward to further collaboration with INSER and Safe. Thanks very much!”

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