Statistical framework to simulate daily rainfall series conditional on upper-air predictor variables

Abstract

We propose a statistical framework to generate synthetic rainfall time series at daily resolution, conditional on predictor variables indicative of the atmospheric circulation at the mesoscale. We do so by first introducing a dimensionless measure to assess the relative influence of upper-air variables at different pressure levels on ground-level rainfall statistics, and then simulating rainfall occurrence and amount by proper conditioning on the selected atmospheric predictors. The proposed scheme for conditional rainfall simulation operates at a daily time step (avoiding discrete approaches for identification of weather states), can incorporate any possible number and combination of predictor variables, while it is capable of reproducing rainfall seasonality directly from the variation of upper-air variables, without any type of seasonal analysis or modeling. The suggested downscaling approach is tested using atmospheric data from the ERAInterim archive and daily rainfall measurements from western Greece. The model is found to accurately reproduce several statistics of actual rainfall time series, at both annual and seasonal levels, including wet day fractions, the alternation of wet and dry intervals, the distributions of dry and wet spell lengths, the distribution of rainfall intensities in wet days, short-range dependencies present in historical rainfall records, the distribution of yearly rainfall maxima, dependencies of rainfall statistics on the observation scale, and long-term climatic features present in historical rainfall records. The suggested approach is expected to serve as a useful tool for stochastic rainfall simulation conditional on climate model outputs at a regional level, where climate change impacts and risks are assessed.

Large scale climate and rainfall seasonality in a Mediterranean Area: Insights from a non‐homogeneous Markov model applied to the Agro‐Pontino plain

“…The literature about the climatology of Mediterranean region (Lionello et al, 2006a;Alpert et al, 2006;Ulbrich et al, 2006;Giorgi & Lionello, 2008;Sheffield & Wood, 2008;Langousis & Kaleris, 2014), suggests that atmospheric fields from a region bounded by latitude: 20 N to 80 N and longitude: 80 W to 60E, should be wide enough for capturing the influence of large scale atmospheric circulation on local rainfall in the region. Most of the climate indices cited above refers to dipolar atmospheric features within this region.…”

A parametric approach for simultaneous bias correction and high‐resolution downscaling of climate model rainfall

“…Informed societal adjustment to potential climatic changes requires in depth understanding of Earth's atmospheric response to various natural and man‐made forcings, including greenhouse gas emission scenarios associated with different rates of economic and population growth [see e.g., IPCC, ; Vrac et al ., ; Fowler et al ., ; Wetterhall et al ., ; Johnson and Sharma , ; Raje and Mujumdar , ; Palatella et al ., ; Annan et al ., ; Hasson et al ., , among others]. For the latter purpose, one needs accurate estimates of hydrological variables at a regional level and fine temporal scales (e.g., daily), suitable to run hydrologic models, and conduct impact studies [see e.g., Wilby and Harris , ; Perkins et al ., ; Kjellström et al ., ; Wilby , ; Evans and McCabe , ; Stoll et al ., ; Teutschbein et al ., ; Sulis et al ., ; Deidda et al ., ; Langousis and Kaleris , ; Camici et al ., ; Piras et al ., ; La Jeunesse et al ., ; Dentoni et al ., ; Majone et al ., ; Maurer et al ., ; Herrmann et al ., ].…”

“…Despite the large number of GCM/RCM simulations currently available, and the many years of research dedicated in refining models' assumptions and conceptualization, CM results still exhibit considerable biases. This is especially true in the case of rainfall, as its intermittent character and highly variable nature at hydrologically relevant spatiotemporal scales challenge modeling attempts [see e.g., Mearns et al ., ; Walsh and McGregor , ; Bates et al ., ; Charles et al ., ; Prudhomme et al ., ; Busuioc et al ., ; Kiktev et al ., ; Dibike et al ., ; Baguis et al ., ; Smiatek et al ., ; Urrutia and Vuille , ; Kjellström et al ., ; Willems and Vrac , ; Stoll et al ., ; Gagnon and Rousseau , ; Teutschbein and Seibert , ; Hasson et al ., ; Langousis and Kaleris , ].…”

ApproximateBayesian computation methods for daily spatiotemporal precipitation occurrence simulation

“…More recently, stochastic precipitation generators (SPGs) have played a key role in statistical downscaling [ Maraun et al ., ; Wilks , ]. Moreover, current advances on conditional rainfall simulation incorporate atmospheric circulation variables as predictors for weather generation [ Langousis and Kaleris , ; Langousis et al ., ]. The popularity, scope, and multifaceted nature of SWGs as has led to several review studies [ Hewitson and Crane , ; Zorita and von Storch , ; Wilby et al ., ; Fowler et al ., ].…”