Impact Forecasting to Support Emergency Management of Natural Hazards

Forecasting and early warning systems are important investments to protect lives, properties, and livelihood. While early warning systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services, or financial loss. Complementing early warning systems with impact forecasts has a twofold advantage: It would provide decision makers with richer information to take informed decisions about emergency measures and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multihazard early warning systems. This review discusses the state of the art in impact forecasting for a wide range of natural hazards. We outline the added value of impact-based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe

Deterministic prediction of stratospheric sudden warming events in the Global/Regional Integrated Model system (GRIMs)

The boreal-winter stratospheric sudden warming (SSW) events and their prediction skills by an operational numerical weather prediction model are examined by applying the Global/Regional Integrated Model system (GRIMs) for 18 SSW events from 1980–2012. Based on the mean squared skill score of the 10-hPa geopotential height field, which considers the SSW spatial structure, most SSW events are predicted with a maximum forecast lead time of approximately 15 days. The vortex-displacement SSW events are slightly better predicted than the vortex-split SSW events, and the predictions are improved during El Niño or easterly quasi-biennial oscillation winters. However, the skill difference in vortex morphology and background state is statistically insignificant. The decomposition of model errors into zonal-mean and eddy errors reveals that the model errors mostly result from eddy components. In particular, eddy-amplitude errors, which originate from a misrepresentation of the planetary-scale wave amplitude (i.e., polar vortex strength), play an important role in determining the SSW prediction skill with a non-negligible contribution from eddy-phase errors. These errors are mostly caused by the misdetection of short-term wave activities immediately before the SSW onset. Furthermore, an improved SSW prediction through well-represented planetary-scale wave activities is related to an improved prediction in the troposphere on time scales of 10 days and longer. This result confirms that better representation of the stratosphere could lead to improved subseasonal- to-seasonal predictions in the northern extratropics