EXPLORING TRENDS THROUGH “RAINSPHERE”: Research Data Transformed into Public Knowledge

With automatically generated time series, spatial plots, and basic trend analysis, users can swiftly explore historical precipitation estimates and future projections tailored to their specific interests. PERSIANN-CDR is derived from the parent PERSIANN algorithm (Hsu et al. 1997), which utilizes an artificial neural network to assign a surface rain rate based on brightness temperature retrievals of infrared information from geostationary Earth-orbiting satellites and passive microwave information from low-Earth-orbiting satellites. Complementing the past precipitation estimates from PERSIANN-CDR, CHRS RainSphere features global precipitation projections from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) based on three carbon emission scenarios (RCP2.6, RCP4.5, and RCP8.5 for low, stabilized, and high emissions scenarios, respectively) from the Intergovernmental Panel on Climate Change (IPCC). By organizing a dataset of large historical precipitation and future precipitation projections into a system that includes intuitive search capabilities as well as the ability to automatically generate reports with basic statistics and summaries, users can quickly and easily gain meaningful information without the inconvenience of data processing and plotting. ACKNOWLEDGMENTS This research was partially supported by the Cooperative Institute for Climate and Satellites (CICS) program (NOAA prime award #NA14NES4320003, subaward #2014-2913-03) for OHD-NWS student fellowship, the Army Research Office (award#W911NF-11-1-0422), and the National Science Foundation (NSF award #1331915).