Strong surface wind gusts and heavy rain are meteorological hazards that are predominantly produced by storms such as east coast lows, tropical cyclones or thunderstorms. Interest in these hazards from a response agency point of view lies in their impact on the natural and built environment. At present, weather forecast models still predict mostly 'raw' meteorological output such as surface wind speeds at certain times, or rain accumulations over a specified period. This model output needs to be combined with exposure and vulnerability information to translate the forecast hazard into predicted impact. The Bushfire and Natural Hazards CRC project Impact-based forecasting for the coastal zone: East-Coast Lows attempts to demonstrate a pilot capability to deliver impact forecasts for residential housing from an ensemble of weather prediction models runs. The project is a collaborative effort between the Australian Bureau of Meteorology and Geoscience Australia. The project is initially focusing on the wind and rainfall impact from the 20-22 April 2015 east coast low event in NSW. The wind and rainfall hazard data are provided by a 24-member ensemble of the ACCESS model on a 1.3 km grid, with damage data acquired from NSW State Emergency Services (SES) and the Emergency Information Coordination Unit (EICU) for the 2015 event. We will show that the multi-hazard nature of an east coast low event makes attributing the observed building damage to a single hazard difficult. Wind damage to residential housing in this case is largely due to tree fall. This 'damage-by-intermediary' mechanism requires not just the knowledge of building properties in an exposed area, but also additional knowledge of the surrounding vegetation and its response to strong winds. We will discuss enhancements to the SES/EICU damage survey templates that would lead to improvements in the development of the hazard-damage relationships.

<div>Forecasting large earthquakes along active faults is of critical importance for seismic hazard assessment. Statistical models of recurrence intervals based on compilations of paleoseismic data provide a forecasting tool. Here we compare five models and use Bayesian model-averaging to produce time-dependent, probabilistic forecasts of large earthquakes along 93 fault segments worldwide. This approach allows better use of the measurement errors associated with paleoseismic records and accounts for the uncertainty around model choice. Our results indicate that although the majority of fault segments (65/93) in the catalogue favour a single best model, 28 benefit from a model-averaging approach. We provide earthquake rupture probabilities for the next 50 years and forecast the occurrence times of the next rupture for all the fault segments. Our findings suggest that there is no universal model for large earthquake recurrence, and an ensemble forecasting approach is desirable when dealing with paleoseismic records with few data points and large measurement errors. <b>Citation:</b> Wang, T., Griffin, J.D., Brenna, M. et al. Earthquake forecasting from paleoseismic records. <i>Nat Commun</i><b> 15</b>, 1944 (2024). https://doi.org/10.1038/s41467-024-46258-z

National meteorological and hydrological services (NMHSs) provide severe weather warning information to inform decision-making by emergency management (EM) services and to allow communities to take defensive and mitigation action prior to and during severe weather events. Globally, warning information issued by NMHSs varies widely from solely hazard-based to impact-based forecasting encompassing the exposure and vulnerability of communities to severe weather. The most advanced of these systems explicitly and quantitatively model the impacts of hazards on sectors of interest. Incorporating impact information into severe weather warnings contextualises and personalises the warning information, increasing the likelihood that individuals and communities will take preparatory action. This paper reviews a selection of current efforts towards severe weather warnings and impact forecasting capabilities globally and highlights uncertainties that currently limit forecasts and modelling of multi-hazard events.