Nonstationary modeling of a long record of rainfall and temperature over Rome

A long record (1862–2004) of seasonal rainfall and temperature from the Rome observatory of Collegio Romano are modeled in a nonstationary framework by means of the Generalized Additive Models in Location, Scale and Shape (GAMLSS). Modeling analyses are used to characterize nonstationarities in rainfall and related climate variables. It is shown that the GAMLSS models are able to represent the magnitude and spread in the seasonal time series with parameters which are a smooth function of time. Covariate analyses highlight the role of seasonal and interannual variability of large-scale climate forcing, as reflected in three teleconnection indexes (Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and Mediterranean Index), for modeling seasonal rainfall and temperature over Rome. In particular, the North Atlantic Oscillation is a significant predictor during the winter, while the Mediterranean Index is a significant predictor for almost all seasons.     

Estimation of radar-rainfall error spatial correlation

The study presents a theoretical framework for estimating the radar-rainfall error spatial correlation (ESC) using data from relatively dense rain gauge networks. The error is defined as the difference between the radar estimate and the corresponding true areal rainfall. The method is analogous to the error variance separation that corrects the error variance of a radar-rainfall product for gauge representativeness errors. The study demonstrates the necessity to consider the area–point uncertainties while estimating the spatial correlation structure in the radar-rainfall errors. To validate the method, the authors conduct a Monte Carlo simulation experiment with synthetic fields with known error spatial correlation structure. These tests reveal that the proposed method, which accounts for the area–point distortions in the estimation of radar-rainfall ESC, performs very effectively. The authors then apply the method to estimate the ESC of the National Weather Service’s standard hourly radar-rainfall products, known as digital precipitation arrays (DPA). Data from the Oklahoma Micronet rain gauge network (with the grid step of about 5 km) are used as the ground reference for the DPAs. This application shows that the radar-rainfall errors are spatially correlated with a correlation distance of about 20 km. The results also demonstrate that the spatial correlations of radar–gauge differences are considerably underestimated, especially at small distances, as the area–point uncertainties are ignored.     

Hydrometeorological analysis of the December 2008 flood in Rome

Abstract

Rome has been plagued by flooding since its foundation, and, in December 2008, the largest flood event over the past 20 years caused a fatality and more than €150 million in economic damage. Meteorological conditions associated with the December 2008 flooding are shown to be typical of flooding in the Tiber. The long record of discharge measurements of the Tiber River at the Ripetta station in downtown Rome was used to examine flood frequency for the Tiber, including assessment of the return interval of the December 2008 flood. Particular attention is given to examination of the stationarity assumption for flood peaks through change-point and trend analyses, quantile regression, and statistical modelling of the flood-peak distribution. Once anthropogenic changes linked to reservoir regulation of the Tiber River have been accounted for, the stationarity assumption holds and can be used for flood frequency analysis. We highlight the difficulties in detecting departures from the stationarity assumption due to climate change. In the current regime, the December 2008 flood event has a return period of the order of 10–20 years.

Citation Villarini, G., Smith, J.A., Napolitano, F. & Baeck, M.L. (2011) Hydrometeorological analyses of the December 2008 flood in Rome. Hydrol. Sci. J. 56(7), 1150–1165.     

Impact of different regression frameworks on the estimation of the scaling properties of radar rainfall

Rainfall is characterized by high variability both in space and time. Despite continuous technological progress, the available instruments that are used to measure rainfall across several spatio-temporal scales remain inaccurate. To remedy this situation, scaling relationships of spatial rainfall offer the potential to link the observed or predicted precipitation quantities at one scale to those of interest at other scales. This paper focuses on the estimation of the spatial rainfall scaling functions. Standard scaling analysis constructed by means of the ordinary least squares method often violates such basic assumptions implicit in its use and interpretation as homoschedasticity, independence, and normality of the errors. Consequently, the authors consider alternative regression frameworks i.e. bootstrapping regression, semi parametric linear model, and multilevel normal linear model to show how these different approaches exert a significant impact on the multifractal analysis of radar rainfall. In addition, the uncertainties associated with the construction of the scaling function due solely to the regression procedure are quantified. The radar data come from the polarimetric C-band weather radar located in Rome, Italy, and the scaling properties are computed for a square domain centred on the radar site with a side length of 128 km and a finest resolution of 1 km².     

An evaluation of the statistical homogeneity of the Twentieth Century Reanalysis

The Twentieth Century Reanalysis (20CR) holds the distinction of having the longest record length (140-year; 1871–2010) of any existing global atmospheric reanalysis. If the record can be shown to be homogenous, then it would be the first reanalysis suitable for long-term trend assessments, including those of the regional hydrologic cycle. On the other hand, if discontinuities exist, then their detection and attribution—either to artificial observational shocks or climate change—is critical to their proper treatment. Previous research suggested that the quintupling of 20CR’s assimilated observation counts over the central United States was the primary cause of inhomogeneities for that region. The same work also revealed that, depending on the season, the complete record could be considered homogenous. In this study, we apply the Bai-Perron structural change point test to extend these analyses globally. A rigorous evaluation of 20CR’s (in)homogeneity is performed, composed of detailed quantitative analyses on regional, seasonal, inter-variable, and intra-ensemble bases. The 20CR record is shown to be homogenous (natural) for 69 (89) years at 50 % of land grids, based on analysis of the July 2 m air temperature. On average 54 % (41 %) of the grids between 60°S and 60°N are free from artificial inhomogenetites in their February (July) time series. Of the more than 853,376 abrupt shifts detected in 26 variable fields over two monthly time series, approximately 72 % are non-climate in origin; 25 % exceed 1.8 standard deviations of the preceding time series. The knock-on effect of inhomogeneities in 20CR’s boundary forcing and surface pressure data inputs to its surface analysis fields is implicated. In the future, reassessing these inhomogeneities will be imperative to achieving a more definitive attribution of 20CR’s abrupt shifts.