Wildfire is a constant risk around the Globe, due of its danger on the both human and natural resources. As a global awareness, wildfire is a topic of scientific research in all domains. One of these important domains, is the modeling and simulation of wildfire. An important aspect of any simulation model is to define a way to store, compare and exchange observation and model results. Without a clear and standardized data structure and format, models and observation lacks useability,

more »

... rability, and data rich observation that does not use a well documented format may become unusable because it cannot be read. In this article we propose such a well defined data format that is able to present observation and model outputs in the same fashion. This specification enables the creation of a basic experimental frame that is tailored to investigate forest fire simulation models, while not being linked to a specific simulation code. An open source Python API that implements this standard is also proposed. It provides a convenient way to transform a few program specific data format into the proposed format, and is able to export any simulated or observed fire into KML for easy result visualization and exchange. This API has also been used to reformat a database of more than 600 fires, enabling a potential large scale simulation reanalysis for any simulation code that can be adapted to the proposed experimental frame.