SAHARA
We have developed a software tool, the SOM-Assisted Hazard Area Risk Analysis (SAHARA), to reduce large climate datasets to more manageable sizes - yet statistically similar - which are then used to produce ensembles of potential hazard outcomes.The Self-Organizing Map (SOM) is a machine learning / data clustering algorithm which is well-suited for data that have strong topological properties. By employing the SOM algorithm to analyze topological patterns of climatological fields over a regional domain for a 30 year span, we can find a close statistical equivalent with fewer, non-contiguous input days. When using SOMs to cluster monthly climate data in this way, we find that by sampling only 150 days, it reduces computational time by greater than a factor of 6 compared to using the entire climate dataset. (See Figure 1)