Related papers: Trends in U. S. Storminess 1949--2009

This article proposes a method to quantitatively measure the resilience of transportation systems using GPS data from taxis. The granularity of the GPS data necessary for this analysis is relatively coarse; it only requires coordinates for…

The increasing frequency and intensity of extreme weather events is significantly affecting the power grid, causing large-scale outages and impacting power system resilience. Yet limited work has been done on systematically modeling the…

Waves traveling through random media exhibit random focusing that leads to extremely high wave intensities even in the absence of nonlinearities. Although such extreme events are present in a wide variety of physical systems and the…

This paper introduces a probabilistic framework to quantify community vulnerability towards power losses due to extreme weather events. To analyze the impact of weather events on the power grid, the wind fields of historical hurricanes from…

Forecasting geomagnetic storms is highly important for many space weather applications. In this study we review performance of the geomagnetic storm forecasting service StormFocus during 2011--2016. The service was implemented in 2011 at…

This article analyzes SST remote forcing on the interannual variability of Sahel summer (June-September) moderate (below 75th percentile) and heavy (above 75th percentile) daily precipitation events during the period 1981-2016. Evidence is…

Despite the importance of quantifying how the spatial patterns of extreme precipitation will change with warming, we lack tools to objectively analyze the storm-scale outputs of modern climate models. To address this gap, we develop an…

We use wireless voice-call and text-message volumes to quantify spatiotemporal communication patterns in the New York Metro area before, during, and after the Virginia earthquake and Hurricane Irene in 2011. The earthquake produces an…

The Southern Ocean (in the region 60-180$^\circ$E) south of the Indian Ocean, Australia, and the West Pacific is noted for the frequent occurrence and severity of its storms. These storms give rise to high-amplitude secondary microseisms…

Four episodes of black rainstorms, the highest tier of heavy rain according to the rainstorm warning system in Hong Kong, occurred within eight days from 29 July to 5 August 2025, breaking the record for torrential rain in the territory.…

We estimate the probability that random noise, of several plausible standard distributions, creates a false alarm that a periodicity (or log-periodicity) is found in a time series. We investigate general situations with non-Gaussian…

We present a novel statistical treatment, the "metastatistics of extreme events", for calculating the frequency of extreme events. This approach, which is of general validity, is the proper statistical framework to address the problem of…

The disturbance storm time (Dst) index is an important and useful measurement in space weather research. It has been used to characterize the size and intensity of a geomagnetic storm. A negative Dst value means that the Earth's magnetic…

Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool…

Tornadoes are severe weather phenomena characterized by a violently rotating column of air connecting the ground to a parent storm. Within the United States, hundreds of tornadoes occur every year. Despite this, the dynamics of tornado…

In this paper, we compare the performance of three common deep learning architectures, CNN-LSTM, LSTM, and 3D-CNN, in the context of surrogate storm surge modeling. The study site for this paper is the Tampa Bay area in Florida. Using…

Given the growing environmental challenges, accurate monitoring and prediction of changes in water bodies are essential for sustainable management and conservation. The Continuous Monitoring of Land Disturbance (COLD) algorithm provides a…

Superstatistics is a general method from nonequilibrium statistical physics which has been applied to a variety of complex systems, ranging from hydrodynamic turbulence to traffic delays and air pollution dynamics. Here, we investigate…

We propose Echo State Networks (ESNs) to predict the statistics of extreme events in a turbulent flow. We train the ESNs on small datasets that lack information about the extreme events. We asses whether the networks are able to extrapolate…

Extreme floods pose escalating risks in a changing climate, yet forecasting remains challenging due to peak flow underestimation and high uncertainty. We introduce DRUM, a diffusion-based probabilistic deep learning approach that advances…