We introduce a 3 part course module on SciSpark, our AIST14 funded project for Highly Interactive and Scalable Climate Model Metrics and Analytics. The three part course session includes 101, 201, and 301 classes for learning how to use Spark for science.
SciSpark 301 is a 1.5 hr course in which we will provide lessons learned from our experience in SciSpark as well as a selection of notebooks for attendees to explore, learn from, expand on, and venture out on their own. This session is intended for individuals who have a desire to play with SciSpark and investigate its possible uses in their own work. We plan to have notebooks prepared that show use of a K-means clustering algorithm for identification of Probability Density Functions for climate extremes, the Open Climate WorkBench, and the Climate Model Diagnostic Analyzer. This session will include ample time for more in-depth discussion and problem-solving of attendees’ interests.
P. C. Loikith, J. Kim, H. Lee, B. Linter, C. Mattmann, J. J. D. Neelin, D. E. Waliser, L. Mearns, S. McGinnis. Evaluation of Surface Temperature Probability Distribution Functions in the NARCCAP Hindcast Experiment. Journal of Climate, Vol. 28, No. 3, pp. 978-997, February 2015. doi:10.1175/JCLI-D-13-00457.1.
Lee, Seungwon, et al. "Climate model diagnostic analyzer." Big Data (Big Data), 2015 IEEE International Conference on. IEEE, 2015.
