The Detection of Weekly Preferential Occurrences with an Application to Rainfall

Abstract The detection of weekly preferential occurrences in atmospheric and hydrologic processes has recently attracted much attention as a way to identify the signature of anthropogenic climatic changes. The interpretation of previous analyses, however, is not unequivocal, in part as a result of a lack of widely accepted statistical criteria. Here, a general and exact method to detect the presence of weekly preferential occurrences is developed and applied to long rainfall observations in Marghera, Italy; Philadelphia, Pennsylvania; and Portland, Maine. The method makes use of the fact that, under the null hypothesis of stationarity, the process of event occurrence in the different days of the week is equivalent to the random distribution of a number of balls (the wet days) in a set of boxes (the days of the week). The departure from a homogeneous distribution is then characterized through the probability of the maximum number of balls in a box, which can be computed exactly with no ad hoc assumptions. The new method shows that (i) preferential rainfall weekly occurrences emerge in all cases in the most recent period analyzed (1990–2006), while they are absent—or are too weak to be detected—in previous years (before 1989); and (ii) the balls-in-boxes approach appears to be more sensitive than Pearson's test when deviations from homogeneity are associated with just one day of the week, a common occurrence in connection with day-of-the-week effects. The results presented help to reconcile previous contrasting studies and to contribute compelling evidence that anthropogenic changes in the local climate have occurred over the past century in urban and industrial areas

Doubly stochastic distributions of extreme events

The distribution of block maxima of sequences of independent and identically-distributed random variables is used to model extreme values in many disciplines. The traditional extreme value (EV) theory derives a closed-form expression for the distribution of block maxima under asymptotic assumptions, and is generally fitted using annual maxima or excesses over a high threshold, thereby discarding a large fraction of the available observations. The recently-introduced Metastatistical Extreme Value Distribution (MEVD), a non-asymptotic formulation based on doubly stochastic distributions, has been shown to offer several advantages compared to the traditional EV theory. In particular, MEVD explicitly accounts for the variability of the process generating the extreme values, and uses all the available information to perform high-quantile inferences. Here we review the derivation of the MEVD, analyzing its assumptions in detail, and show that its general formulation includes other doubly stochastic approaches to extreme value analysis that have been recently proposed

Field migration rates of tidal meanders recapitulate fluvial morphodynamics

The majority of tidal channels display marked meandering features. Despite their importance in oil-reservoir formation and tidal landscape morphology, questions remain on whether tidalmeander dynamics could be understood in terms of fluvial processes and theory. Key differences suggest otherwise, like the periodic reversal of landscape-forming tidal flows and the widely accepted empirical notion that tidal meanders are stable landscape features, in stark contrast with their migrating fluvial counterparts. On the contrary, here we show that, once properly normalized, observed migration rates of tidal and fluvial meanders are remarkably similar. Key to normalization is the role of tidal channel width that responds to the strong spatial gradients of landscape-forming flow rates and tidal prisms. We find that migration dynamics of tidal meanders agree with nonlinear theories for river meander evolution. Our results challenge the conventional view of tidal channels as stable landscape features and suggest that meandering tidal channels recapitulate many fluvial counterparts owing to large gradients of tidal prisms across meander wavelengths

On the O'Brien-Jarrett-Marchi law

The relationship between the total water volume entering a lagoon during a characteristic tidal cycle (i.e., the prism) and the size of its inlet is well established empirically since the classic work of O'Brien and Jarrett widely cited in the geomorphic and hydrodynamic literature. Less known is a rather deep theoretical explanation proposed by Marchi. This paper reviews the empirical and theoretical evidence on which the relation is based, setting the various theoretical approaches so far pursued within the general framework ensured by Marchi's theoretical treatment of the problem. We conclude that the depth of the empirical and theoretical validations and the breadth and the importance of its implications suggest that the O'Brien-Jarrett-Marchi law relating the minimum inlet cross-sectional area and the tidal prism flowing through it may be referred to thereinafte

Analyses Through the Metastatistical Extreme Value Distribution Identify Contributions of Tropical Cyclones to Rainfall Extremes in the Eastern United States

AbstractTropical cyclones (TCs) generate extreme precipitation with severe impacts across large coastal and inland areas, calling for accurate frequency estimation methods. Statistical approaches that take into account the physical mechanisms responsible for these extremes can help reduce the estimation uncertainty. Here we formulate a mixed‐population Metastatistical Extreme Value Distribution explicitly incorporating non‐TC and TC‐induced rainfall and evaluate its implications on long series of daily rainfall for six major U.S. urban areas impacted by these storms. We find statistically significant differences between the distributions of TC‐ and non‐TC‐related precipitation; moreover, including mixtures of distributions improves the estimation of the probability of extreme precipitation where TCs occur more frequently. These improvements are greater when rainfall aggregated over durations longer than one day are considered

Plasmonic Bandgaps in 1D Arrays of Slits on Metal Layers Excited by Out-of-Plane Sources

We analyze the effective opening of finite bands of inhibited transmission in realistic systems excited by actual out-of-plane sources. We first observe how the excitation of surface plasmon polaritons in one-dimensional arrays of metal slits depends on the angle of incidence of the source field. Then, the well-known grating-coupling equation is revised in order to find an asymmetric structure with equivalent parameters which, under perfectly normal excitation, is able to exhibit surface plasmon polariton modes at the same wavelengths of the original structure which undergoes a nonorthogonal incidence of the light. In this way we demonstrate through finite-element simulations that a realistic system, probed by a source beam in a finite light-cone, can be effectively decomposed in several equivalent systems with different physical and geometrical parameters, with results in the enlargement of the theoretically expected punctual minimum of transmission

Toward coherent space–time mapping of seagrass cover from satellite data: an example of a Mediterranean lagoon

Seagrass meadows are a highly productive and economically important shallow coastal habitat. Their sensitivity to natural and anthropogenic disturbances, combined with their importance for local biodiversity, carbon stocks, and sediment dynamics, motivate a frequent monitoring of their distribution. However, generating time series of seagrass cover from field observations is costly, and mapping methods based on remote sensing require restrictive conditions on seabed visibility, limiting the frequency of observations. In this contribution, we examine the effect of accounting for environmental factors, such as the bathymetry and median grain size (D50) of the substrate as well as the coordinates of known seagrass patches, on the performance of a random forest (RF) classifier used to determine seagrass cover. Using 148 Landsat images of the Venice Lagoon (Italy) between 1999 and 2020, we trained an RF classifier with only spectral features from Landsat images and seagrass surveys from 2002 and 2017. Then, by adding the features above and applying a time-based correction to predictions, we created multiple RF models with different feature combinations. We tested the quality of the resulting seagrass cover predictions from each model against field surveys, showing that bathymetry, D50, and coordinates of known patches exert an influence that is dependent on the training Landsat image and seagrass survey chosen. In models trained on a survey from 2017, where using only spectral features causes predictions to overestimate seagrass surface area, no significant change in model performance was observed. Conversely, in models trained on a survey from 2002, the addition of the out-of-image features and particularly coordinates of known vegetated patches greatly improves the predictive capacity of the model, while still allowing the detection of seagrass beds absent in the reference field survey. Applying a time-based correction eliminates small temporal variations in predictions, improving predictions that performed well before correction. We conclude that accounting for the coordinates of known seagrass patches, together with applying a time-based correction, has the most potential to produce reliable frequent predictions of seagrass cover. While this case study alone is insufficient to explain how geographic location information influences the classification process, we suggest that it is linked to the inherent spatial auto-correlation of seagrass meadow distribution. In the interest of improving remote-sensing classification and particularly to develop our capacity to map vegetation across time, we identify this phenomenon as warranting further research.</p