(PhD09) In-Situ Simulation Data Compression for Climate Domain
TimeMonday, June 17th1pm - 6pm
DescriptionThe need for storing of climate simulation data has been underlined by exploring of climate change. A climate model is a very complex multi-component system, which is ordinarily represented as multidimensional arrays of numbers with type of ﬂoating-point and contain a speciﬁc climate elements with such atmospheric parameters as temperature, humidity, precipitation, wind speed and power, and other. Usually dimensions of datasets are longitude, latitude, height and time. The amount of the data is growing exponentially, mostly at 40% to 60% year. Compression costs time for decoding and encoding data, but it reduces resource usage, data storage space and transmission capacity (throughput).
This poster represents plan and methodology for the PhD work on data compression for climate domain. It consists of such sections: motivation of the work on data compression for this field, goals, methodology, related works, an overview of the most popular algorithms, that will be useful for the evaluation, and its suitability for this purpose. Maximal compression rate can be achieved with lossy compression. Thats why there is need to define accuracy. The Scientific Compression Library has been used for evaluation of different compression techniques.