An intensification of short-duration rainfall extremes arising due to climate change could lead to an increase in flash flooding in urban areas and fast-responding catchments. Projections of change from very high resolution climate models are providing new evidence for such increases in a number of areas. However, our understanding of sub-daily rainfall - its drivers and recent and future trends - is limited, primarily through the lack of high quality observations, a lack of understanding of the relevant physical processes and the availability of suitable models and model integrations.
The INTENSE project (https://research.ncl.ac.uk/intense/) is part of a global initiative (GEWEX), collating observations of sub-daily rainfall to produce high quality datasets. It aims to better understand the characteristics, trends and variability of sub-daily extremes, and further, link these with observations of other meteorological variables in order to improve knowledge of the drivers of intense rainfall.
This session focuses on the availability and analysis of sub-daily rain-gauge observations and improving the understanding of drivers of extreme events by linking these observations with other datasets and model simulations. We therefore welcome contributions that:
• Examine the development and availability of new sub-daily rainfall observations and their analysis. This includes novel approaches for the quality control of rain gauge data, climatological analyses of datasets (spatial and temporal characteristics, particularly of extremes) and examining observed trends and variability.
• Identify and assess relevant sub-daily indices or generate other derived sub-daily data products (e.g. gridded rain-gauge products or merged products which combine rain-gauge observations with other types of measurement including remotely sensed data and radar).
• Improve understanding of the drivers of extreme sub-daily events, including large-scale circulation conditions, local thermodynamics (e.g. using the Clausius-Clapeyron relationship) and the impact of global warming on long-term changes.
• Integrate the analysis of outputs from very high resolution climate models with observational datasets, for example, in terms of model validation or critically, improved understanding of atmospheric/meteorological processes and drivers of extreme events.