RESUMO
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 analyze 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 prioritize 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 judgment on the choice of dataset, for the benefit of policy decision making.
RESUMO
High resolution simulations at 4.4 km and 1.5 km resolution have been performed for 12 historical tropical cyclones impacting Bangladesh. We use the European Centre for Medium-Range Weather Forecasting 5th generation Re-Analysis (ERA5) to provide a 9-member ensemble of initial and boundary conditions for the regional configuration of the Met Office Unified Model. The simulations are compared to the original ERA5 data and the International Best Track Archive for Climate Stewardship (IBTrACS) tropical cyclone database for wind speed, gust speed and mean sea-level pressure. The 4.4 km simulations show a typical increase in peak gust speed of 41 to 118 knots relative to ERA5, and a deepening of minimum mean sea-level pressure of up to -27 hPa, relative to ERA5 and IBTrACS data. The downscaled simulations compare more favourably with IBTrACS data than the ERA5 data suggesting tropical cyclone hazards in the ERA5 deterministic output may be underestimated. The dataset is freely available from https://doi.org/10.5281/zenodo.3600201 .
