The INTENSE project: using observations and models to understand the past, present and future of sub-daily rainfall extremes.
Blenkinsop, S., H.J. Fowler, R. Barbero, S.C. Chan, S.B. Guerreiro, E. Kendon, G. Lenderink, E. Lewis, X.-F.Li, S. Westra, L. Alexander, R. Allan, P. Berg, R.J. Dunn, M. Ekstrom, J.P. Evans, G. Holland, R. Jones, E. Kjellstrom, A. Klein-Tank, D. Lettenmaier, V. Mishra, A.F. Prein, J. Sheffield, M.R. Tye
Advances in Science and Research, 15, 117-126, doi: 10.5194/asr-15-117-2018, 2018.
Historical in situ sub-daily rainfall observations are essential for the understanding of short-duration
rainfall extremes but records are typically not readily accessible and data are often subject to errors and inho-
mogeneities. Furthermore, these events are poorly quantified in projections of future climate change making
adaptation to the risk of flash flooding problematic. Consequently, knowledge of the processes contributing to
intense, short-duration rainfall is less complete compared with those on daily timescales. The INTENSE project
is addressing this global challenge by undertaking a data collection initiative that is coupled with advances in
high-resolution climate modelling to better understand key processes and likely future change. The project has so
far acquired data from over 23 000 rain gauges for its global sub-daily rainfall dataset (GSDR) and has provided
evidence of an intensification of hourly extremes over the US. Studies of these observations, combined with
model simulations, will continue to advance our understanding of the role of local-scale thermodynamics and
large-scale atmospheric circulation in the generation of these events and how these might change in the future.
Figure 1. INTENSE project research themes, information flows and outcomes. The collection of in situ sub-daily rain gauge data represents
a core project activity (blue arrows). Thermodynamic and atmospheric/ocean processes are considered independently (red and green arrows)
but also in an integrated manner (yellow arrows). These data and knowledge will be used to evaluate model simulations of extreme rainfall
across different timescales and locations. Model outputs will then be used for the development of process-based downscaling methods and
provide guidance on the use of projections (black arrow). Data in dashed boxes denotes externally produced data used in the project.
This page is maintained by Jason Evans |
Last updated 23 January 2018