Peter Manshausen(He/Him)University of Oxford
Peter is a PhD student in Atmospheric Physics and ML at the University of Oxford, working with Philip Stier. His research interests are in applying ML methods to climate problems, such as in his work on aerosol-cloud interactions and ship tracks. Other projects include computer vision for heavy industry detection, climate model emulation, and data assimilation with diffusion models. |
 |
TalksPredicting visible ship tracks23-Apr-24Aerosol-cloud interactions continue to resist reliable quantification, partly owing to their strong dependence on cloud and weather regimes. For a long time, opportunistic experiments such as ship tracks have been used to overcome issues of confounding. Recent advances leverage (i) Machine Learning (ML) to drastically enlarge ship track data bases, and (ii) 'invisible ship tracks', found by advecting ship emissions, to overcome selection biases in ship track studies. Here, we combine both approaches, to advance our understanding of how meteorology, and emissions amounts, control cloud responses to aerosol. We identify meteorological regimes favourable to the visibility of tracks, using ML methods such as Random Forests. The regime favourable to visible tracks is defined by a stable lower troposphere and little vertical movement, low sea surface temperatures, high cloud cover, and low boundary layer heights. Building on this relationship, a predictive model like our Random Forest has applications in deliberate Marine Cloud Brightening by predicting the days that are most susceptible to aerosol perturbations. |