A document by Nanditha J S. Click on the document to view its contents.

The time scale of usual hydrological applications can vary from a few minutes to a few days. An accurate description of the precipitation probability distributions at the appropriate time scale is needed to compute meaningful summaries like return levels and periods. However few statistical parametric models can handle the full rainfall distribution at these different temporal scales. In this context, we propose and study a new meta-Gaussian model which leads to a parametric model with four parameters for the full distribution of precipitation. The main advantage of our model is that it can be applied to a wide range of accumulation periods. In particular, it still performs well below the hourly scale. In addition, each parameter is linked to a different part of the distribution: one of them describes the probability of rainfall occurrence, two parameters are related respectively to the shape of lower and upper tails of the distribution, and the last one is a multiplicative scale parameter. The building block of our model is the use of a latent Gaussian process that offers flexibility and simple inference algorithms. The model is fitted to rain gauge data recorded in Guipavas (France). It is shown that the proposed distribution handles accumulation periods from 6 minutes to several days. The model outperforms other meta-Gaussian models which have been proposed in the literature.

A comparison of moderate to extreme daily precipitation from the ERA-5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF) against two observational gridded data sets, EOBS and CMORPH, is presented. We assess the co-occurrence of precipitation days and compare the full precipitation distributions. The co-occurrence is quantified by the hit rate. An extended generalized Pareto distribution is fitted to the positive precipitation distribution at every grid point and confidence intervals of quantiles compared. The Kullback-Leibler divergence is used to quantify the distance between the entire extended generalized Pareto distributions obtained from ERA-5 and the observations. For days exceeding the local 90th percentile, the mean hit rate is 65% between ERA-5 and EOBS (over Europe) and 60% between ERA-5 and CMORPH (globally). Generally, we find a decrease of the co-occurrence with increasing precipitation intensity. The agreement between ERA-5 and EOBS is weaker over the southern Mediterranean region and Iceland compared to the rest of Europe. Differences between ERA-5 and CMORPH are smallest over the oceans. Differences are largest over North-West America, Central Asia and land areas between 15°S and 15°N. The confidence intervals on quantiles are overlapping between ERA-5 and the observational data sets for more than 80% of the grid points on average. The intensity comparisons indicate an excellent agreement between ERA-5 and EOBS over Germany, Ireland, Sweden and Finland, and a disagreement over areas where EOBS uses sparse input stations. ERA-5 and CMORPH precipitation intensity agree well over the mid-latitudes and disagree over the tropics.