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Ponencia presentada en: XIX Congreso de la Asociación Española de Teledetección celebrado en Pamplona del 29 de junio al 1 de julio de 2022. [ES]Los datos de los satélites meteorológicos tienen gran aplicabilidad en la vigilancia y predicción meteorológicas inmediatas. Estos datos forman parte de una amplia gama de productos relacionados con la vigilancia. Aunque los datos satelitales proporcionan información insustituible, también tienen limitaciones inherentes. Esto recomienda la explotación sinérgica de los datos satelitales junto con otras redes de observación, tanto remotas como in situ. El documento presenta la estrategia seguida por AEMET y el NWC SAF (EUMETSAT Satellite Aplication Facility for Nowcasting) para proporcionar una alta disponibilidad al catálogo de datos en un entorno de producción. La solución es un desarrollo que traduce los datos a estándares (netCDF CF, geoJSON, WMS, WFS, WCS) que los hacen explotables simultáneamente, tanto para alimentar algoritmos numéricos como para explotación visual. Se presenta el resultado del funcionamiento completo del sistema y las referencias para implementar el sistema. [EN]Data from meteorological satellites have high applicability in nowcasting. These data are part of a wide range of relevant products for nowcasting. Even though satellite data provide irreplaceable information, they have also inherent limitations. This recommends the synergistic exploitation of satellite data alongside other observation networks, of both kinds: remote and in situ. The paper presents the strategy followed by AEMET and the NWCSAF to provide high availability to the data catalog in a production environment. The solution is a development that translates the data into standards (netCDF CF, geoJSON, WMS, WFS, WCS) that make them exploitable simultaneously, as well as for feeding numerical algorithms as for visual exploitation. The result of the complete operation of the system and the steps to implement the system are presented.

Historically the atmospheric and meteorological communities are separate worlds with their own data formats and tools for data handling making sharing of data difficult and cumbersome. On the other hand, these information sources are becoming increasingly of interest outside these communities because of the continuously improving spatial and temporal resolution of e.g. model and satellite data and the interest in historical datasets. New user communities that use geographically based datasets in a cross-domain manner are emerging. This development is supported by the progress made in Geographical Information System (GIS) software. The current GIS software is not yet ready for the wealth of atmospheric data, although the faint outlines of new generation software are already visible: support of HDF, NetCDF and an increasing understanding of temporal issues are only a few of the hints.

The remote sensing and GIS communities are still separate worlds with their own tools and data formats. It is extremely difficult to easily share data among scientists representing these communities without performing some cumbersome conversions. This paper shows in a case study how these two worlds can benefit from each other by implementing online satellite derived soil moisture in a GIS based operational flood early warning system. We obtained near real time satellite data from the currently active satellite microwave sensor AQUA AMSR-E from the National Snow and Ice Data Center data pool and converted the data to soil moisture maps with the Land Parameter Retrieval Model. The soil moisture maps, with a spatial resolution of 0.1 degree and temporal resolution of approximately 1 day, were converted in a gridded format and directly added to an operational Flood Early Warning System. The developed opportunity to directly visualize soil moisture in such a system appears to be a powerful tool, because it creates the ability to study both the spatial and temporal evolution of soil moisture within the river basin. Furthermore, near real time qualitative information on soil moisture conditions prior to rainfall events, such as generated by our system, can even lead to more accurate estimations for flood hazard conditions. Finally, the current and future role and value of remote sensing products in flood forecasting systems are discussed.