Announcement: a training session to familiarize our contestants with the data will be hosted by our expert panels at NASA JPL, IBM, LMU. Keep posted for more updates about the competition and training!
Contestants are asked to create a statistical model to predict tropical cyclone (TC) intensity change in 24 hours using advanced machine learning techniques. There are 2 sets of TC data from 1998 to 2010; one is the predictors operationally used in a statistical TC intensity forecast model (SHIPS1998-2010.nc). The other is satellite observations with spatially and temporally varying features of TC structures (Prcpxxxx.nc). Combined use of two datasets are expected to yield better results than using one dataset only. All the data are in NetCDF format.
- Time range: 1998 – 2014
- Multiple 1-Dimensional parameters. Each “DATE” represents 1 sample.
- Time range: one year for each file
- “prcp”: Satellite observations with spatially and temporally varying features of TC structures in 4-dimensional. The “RLAT” and “RLON” coordinates represent the spatially varying features of TC structures. The “RHOUR” coordinate represents the temporally varying features of TC structures. -12 in RHOUR means 12 hours before current time.
In the competition, we will focus on predicting TC rapid intensification occurrence in 24 hours (RI: dvmax>=25 knots in the data). – RI : 1 (DVMAXT-24)>=25 or 0 (DVMAXT-24)<25. RI here is a categorical variable that will need to be derived from the data using the difference in VMAX from the past 24 hours.
||Oct 23, 2019
||Dec 11, 2019
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