Proliferation of atmospheric datasets can hinder policy making: a data blending technique offers a solution - Steptoe, Hamish; Economou, Theo (Frontiers in Big Data, 2023)
The proliferation of atmospheric datasets is a key outcome from the continued development and advancement of our collective scientific understanding. Yet often datasets describing ostensibly identical processes or atmospheric variables provide widely varying results. As an example, we analyse several datasets representing rainfall over Nepal. We show that estimates of extreme rainfall are highly variable depending on which dataset you choose to look at. This leads to confusion and inaction from policy-focused decision makers. Scientifically, we should use datasets that sample a range of creation methodologies and prioritise the use of data science techniques that have the flexibility to incorporate these multiple sources of data. We demonstrate the use of a statistically interpretable data blending technique to help discern and communicate a consensus result, rather than imposing a priori judgement on the choice of dataset, for the benefit of policy decision making.