Detecting atmospheric rivers in large climate datasets

Extreme precipitation events on the western coast of North America are often traced to an unusual weather phenomenon known as atmospheric rivers. Although these storms may provide a significant fraction of the total water to the highly managed western US hydrological system, the resulting intense weather poses severe risks to the human and natural infrastructure through severe flooding and wind damage. To aid the understanding of this phenomenon, we have developed an efficient detection algorithm suitable for analyzing large amounts of data. In addition to detecting actual events in the recent observed historical record, this detection algorithm can be applied to global climate model output providing a new model validation methodology. Comparing the statistical behavior of simulated atmospheric river events in models to observations will enhance confidence in projections of future extreme storms. Our detection algorithm is based on a thresholding condition on the total column integrated water vapor established by Ralph et al. (2004) followed by a connected component labeling procedure to group the mesh points into connected regions in space. We develop an efficient parallel implementation of the algorithm and demonstrate good weak and strong scaling. We process a 30-year simulation output on 10,000 cores in under 3 seconds