Combined Approach to the Analysis of Rainfall Super-Extremes in Locations with Limited Observational Records.

Abstract:

The prospect of climatic change and its impacts have brought spatial statistics of extreme events into sharper focus. The so-called “water bombs” are predicted to become more frequent in the extra-tropical regions, and, actually, they raise serious concerns in some regions of the Mediterranean area. However, quantitative statistical methods to properly account for the probability of occurrence of these super-extreme events are still lacking, due to their rare occurrence and to the limited spatial scale at which these events occur.

In order to overcome the lack of data, we propose at first to exploit the information derived from remote sensed datasets. Despite the coarser resolution, these databases are able to provide information continuous in space and time, overcoming the problems related to the discontinuous nature of rainfall measurements. We propose to apply such a kind of approach with the adoption of a Bayesian framework, aimed at combining local measurements with climatic regional information, conditioning the exceedance probability on the large and mesoscale characteristics of the system.

The case study refers to an area located in the North-West of Italy, historically affected by extraordinary precipitation events. We use a dataset of daily at-gauge rainfall measurements extracted from the NOAA GHCN-Daily dataset, combined with the ones provided by some local Environmental Agencies. Daily estimations from the TRMM are adopted too.

First, we identify the most intense events occurred in the area, combining the information from the different datasets. Analysing the related synoptic conditions with the reanalysis of the ECMWF, we then define the conditional variables and the hierarchical relationships between the events and their type. Different climatic configurations that combined with the local morphology and the seasonal condition of the Mediterranean Sea can triggers very intense precipitation events are identified.

The results, compared with those obtained with the classic techniques of frequency analysis and spatial interpolation, demonstrate an increased knowledge coming from satellite, climate and local factors, ensuring more reliable and accurate spatial assessment of extreme thunderstorm probability.