ACRUISE: deep-learning inferred shiptrack clouds from AQUA MODIS daylight satellite data for 2002-2021

Large dataset of emission induced "shiptrack" clouds, detected using deep-learning, from satellite based remote sensing data with global coverage, from 2002 to 2021 for the Atmospheric Composition and Radiative forcing changes due to UN International Ship Emissions regulations (ACRUISE) project. Shiptracks were inferred from every daylight granule captured by the MODerate Imaging Spectroradiometer (MODIS) instrument, onboard the NOAA-AQUA satellite from 2002-2021 inclusive and stored in a compressed netcdf file. In addition, polygons corresponding to contours of level 0.5 and 0.8 from the inference images are provided as a light-weight alternative. These are stored in annual geopackages in the geographic projection. The model is a standard neural-network based segmentation model with a UNet architecture, a resnet-152 backbone and sigmoid activation on the final layer that was pre-trained on the 2012 ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) ImageNet dataset. This model was trained to segment clouds formed by ship exhausts, known as shiptracks, from MODIS level 1b, day microphysics composite granules enhanced through histogram stretching. The purpose of these data is to measure the effect that shipping fuel regulation has on climate change and to reduce the uncertainty in the relationship between aerosols and cloud formation and properties. This allows the determination of where tracks are more likely to form and the sensitivity of clouds to such perturbations.The data indicate a sharp reduction in tracks due to the more stringent ship emission regulations since 2020. A small minority of granules (<0.5%) are missing due to a combination of missing or corrupt files and/or unexpected computational processing failures. These remained unresolved as they were judged insignificant compared to model uncertainties and and of negligible additional benefit to warrant the overheads to resolve each missing granule.