Automated Quality Control Scheme for GPM Satellite Precipitation Products

Abstract The constellation approach underpinning precipitation products such as the Integrated Multi‐satellitE Retrievals for GPM (IMERG) is key to achieving high resolution, but the use of data from multiple sources can unintentionally incorporate instrumental artifacts. Here, we introduce a machine learning–based anomaly detection scheme called SPEEDe, which processes a two‐dimensional precipitation field into a re‐estimated precipitation field that can be compared with the input. Large differences identify IMERG fields with bad orbit data, separating most of the bad cases from the good cases. When modified to process the passive microwave inputs, SPEEDe can pick out orbits with bad data, enabling quality control on these IMERG inputs. SPEEDe works by producing a locally realistic‐looking precipitation field when given unphysical data, which results in a larger‐than‐normal difference between the input and the output. SPEEDe is implemented as an automated quality control for GPM precipitation products.