Using forecasts run on a NASA supercomputer, scientists from Oak Ridge National Laboratory (ORNL); NASA Goddard Space Flight Center; the University of California, Berkeley; and the Korea National University of Transportation assessed the ability of NASA’s Goddard Earth Observing System Subseasonal to Seasonal Version 2 (GEOS-S2S-2) forecasting system to predict atmospheric and land surface conditions throughout the High Mountain Asia region. “Across the High Mountain Asia region, seasonal variability of the hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management,” said Elias Massoud, a Computational Ecohydrologist in ORNL’s Computational Sciences and Engineering Division. “This study provides a benchmark of the seasonal forecasting skill for many variables relevant to water resources management and hazard mitigation.” The study team ran GEOS-S2S-2 on the NASA Center for Climate Simulation (NCCS) Discover supercomputer. The retrospective forecast period (1981–2016) consisted of monthly 4-member ensemble forecasts with a 9-month duration; the production period (2017–present) had 10-member ensemble forecasts of the same frequency and duration. Each 9-month forecast harnessed 384 processor cores and required 54 hours of compute time. Model outputs are stored on Discover online disk and the NCCS DataPortal. The researchers evaluated skill for 1-, 2-, and 3-month forecasts against many information sources including reanalysis data (for example, GMAO’s MERRA-2), satellite data (for example, NASA’s MODIS and GRACE), and other blended data products. “Utilizing different sources of information is critical for thoroughly addressing the uncertainty of the evaluation results,” Massoud said.
