In this theme session, three speakers will share their work regarding the extreme aspects of rainfall phenomena, in smaller 25-min presentations.
TALK 1: Jennifer Israelsson (University of Reading)
Estimating the dependence structure for extreme tropical rainfall; many issues and some success.
A great deal of research has been done on rainfall extremes over Europe and the US both in a univariate and bivariate setting thanks to the availability of high-quality data. There has however been very limited amount of work done over Africa, and close to none in a multivariate setting, due to the general lack of rain gauge observations and the poor performance of weather models. In this talk, I will present some of my PhD work on estimating the dependence structure in extreme daily rainfall over west Africa and how this connects with the monsoon cycle. I will also talk about some of the many issues and limitations we faced and how some of these might be addressed.
Jennifer Israelsson is a postdoctoral researcher at the University of Reading where she currently works on creating risk scenarios for local hospitals by translating regional climate projections to admissions at a hospital level. Her PhD was in the intersection of Statistics and Meteorology and focused on developing new methods to estimate dependence structures in daily tropical rainfall, and the application of those to better understand differences between rainfall intensities.
TALK 2: Helga Kristín Ólafsdóttir (Gothenburg University/Chalmers)
Frequency changes in extreme rainfall in the Northeastern USA
Extreme daily rainfall can increase with the individual extreme rainfalls becoming more frequent, more intense, or both more intense and more frequent. Based on the Generalized Extreme Value (GEV) distribution for annual maxima series and the General Pareto (GP) distribution for exceedances over threshold for the partial duration series, we develop a new statistical extreme value model, the PGEV model, allowing the use of high quality annual maximum series data instead of less well-checked daily data to estimate trends in intensity and frequency separately. The method is applied to annual maxima data from the NOAA Atlas 14, Volume 10. With increasing mean temperature, the frequency of extreme rainfall events increases as mean temperature increases while the distribution of the intensities of individual extreme rainfall events remains constant in the Northeastern US. We also study three other large areas in the contiguous US, the Midwest, the Southeast, and Texas, where similar but weaker trends than those in the Northeast are found.
Helga is a PhD student at Gothenburg University/Chalmers in Applied Mathematics and Statistics with focus on modelling and model evaluation of extremes with applications on extreme rainfall under climate change.
TALK 3: Jessica Silva Lomba (UL Centre of Statistics and its Applications)
Mixed Moment estimator for pooling spatio-temporal extreme rainfall data in a heteroscedastic context
Extreme Value Theory provides the ideal framework for forecasting the frequency of extreme and hazardous events that are unlikely to occur and hard to predict. In this context, the estimation of the extreme value index is key. Due to accelerating climate change, extreme meteorological phenomena such as heavy precipitation seem to be growing more severe and frequent, but estimation of this evolution remains subject to large uncertainty. Thus, inferential methods for the underlying non-stationary spatio-temporal processes are currently object of widespread interest. A recent development is the concept of scedasis, through which a trend in extremes of space-time indexed observations can be captured and tactfully modelled within a semi-parametric framework. In this talk, we will look at how one can use series of data collected at several isolated locations to model extremes of the whole space-time process, enabling the mixed moment estimator of the extreme value index to seamlessly incorporate space-time non-stationarity and dependence. Application of the extended mixed moment estimator is illustrated with daily rainfall data from a homogeneous region in the UK.
Jessica is a PhD student for Statistics and Operations Research at FCUL, University of Lisbon, on the topic of Extreme Values Theory and Statistics. Currently, her research is focused in estimation for spatio-temporal extreme data under heteroscedasticity.