Accelerating Earth diagnostics and metrics with Python

In order to develop metrics and diagnostics to assess the reliability of climate models, the output variables of said models need to be post-processed. This usually involves simple math operations, but the large amounts of data that need to be handled slow down the computations. At the Computational Earth Sciences group, within BSC-CNS Earth Sciences department, we are exploring the use of Python’s compiler Numba to improve the performance of the metrics and diagnostics targeting both the CPUs and GPUs available at the CTE-POWER cluster.