Chiara Lepore

Rainfall-induced landslide susceptibility zonation of Puerto Rico

Landslides are a major geologic hazard with estimated tens of deaths and

Temperature and CAPE dependence of rainfall extremes in the eastern United States

1–2 billion in economic losses per year in the US alone. The island of Puerto Rico experiences one or two large events per year, often triggered in steeply sloped areas by prolonged and heavy rainfall. Identifying areas susceptible to landslides thus has great potential value for Puerto Rico and would allow better management of its territory. Landslide susceptibility zonation (LSZ) procedures identify areas prone to failure based on the characteristics of past events. LSZs are here developed based on two widely applied methodologies: bivariate frequency ratio (FR method) and logistic regression (LR method). With these methodologies, the correlations among eight possible landslide-inducing factors over the island have been investigated in detail. Both methodologies indicate aspect, slope, elevation, geological discontinuities, and geology as highly significant landslide-inducing factors, together with land-cover for the FR method and distance from road for the LR method. The LR method is grounded in rigorous statistical testing and model building but did not improve results over the simpler FR method. Accordingly, the FR method has been selected to generate a landslide susceptibility map for Puerto Rico. The landslide susceptibility predictions were tested against previous landslide analyses and other landslide inventories. This independent evaluation demonstrated that the two methods are consistent with landslide susceptibility zonation from those earlier studies and showed this analysis to have resulted in a robust and verifiable landslide susceptibility zonation map for the whole island of Puerto Rico.

More tornadoes in the most extreme U.S. tornado outbreaks

We analyze how extreme rainfall intensities in the Eastern United States depend on temperature T, dew point temperature Td, and convective available potential energy CAPE, in addition to geographic sub-region, season, and averaging duration. When using data for the entire year, rainfall intensity has a quasi Clausius-Clapeyron (CC) dependence on T, with super-CC slope in a limited temperature range and a maximum around 25°C. While general, these features vary with averaging duration, season, the quantile of rainfall intensity, and to some extent geographic sub-region. By using Td and CAPE as regressors, we separate the effects of temperature on rainfall extremes via increased atmospheric water content and via enhanced atmospheric convection. The two contributions have comparable magnitudes, pointing at the need to consider both Td and atmospheric stability parameters when assessing the impact of climate change on rainfall extremes.

ENSO‐based probabilistic forecasts of March–May U.S. tornado and hail activity

Blowing harder and more often The frequency of tornado outbreaks (clusters of tornadoes) and the number of extremely powerful tornado events have been increasing over nearly the past half-century in the United States. Tippett et al. found that tornado outbreaks have become more common since the 1970s. This increase seems to have been driven by consistent changes in the meteorological environment that make tornadoes more likely to form. However, the changes are not necessarily those that one would expect from climate change, which makes it difficult to predict whether this trend will continue. Science, this issue p. 1419 Clusters of strong tornadoes have become more common in the United States over the past 50 years. Tornadoes and severe thunderstorms kill people and damage property every year. Estimated U.S. insured losses due to severe thunderstorms in the first half of 2016 were

An Extreme Value Model for U.S. Hail Size

8.5 billion (US). The largest U.S. effects of tornadoes result from tornado outbreaks, which are sequences of tornadoes that occur in close succession. Here, using extreme value analysis, we find that the frequency of U.S. outbreaks with many tornadoes is increasing and that it is increasing faster for more extreme outbreaks. We model this behavior by extreme value distributions with parameters that are linear functions of time or of some indicators of multidecadal climatic variability. Extreme meteorological environments associated with severe thunderstorms show consistent upward trends, but the trends do not resemble those currently expected to result from global warming.

Relationships between Hourly Rainfall Intensity and Atmospheric Variables over the Contiguous United States

Extended Logistic Regression is used to predict March-May severe convective storm (SCS) activity based on the preceding December-February (DJF) ENSO state. The spatially-resolved probabilistic forecasts are verified against U.S. tornado counts, hail events and two environmental indices for severe convection. The cross-validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Nina-like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead-time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state.

Understanding the Drivers of Variability in Severe Convection: Bringing Together the Scientific and Insurance Communities

AbstractThe spatial distribution of return intervals for U.S. hail size is explored within the framework of extreme value theory using observations from the period 1979–2013. The center of the continent has experienced hail in excess of 5 in. (127 mm) during the past 30 yr, whereas hail in excess of 1 in. (25 mm) is more common in other regions, including the West Coast. Observed hail sizes show heavy quantization toward fixed-diameter reference objects and are influenced by spatial and temporal biases similar to those noted for hail occurrence. Recorded hail diameters have been growing in recent decades because of improved reporting. These data limitations motivate exploration of extreme value distributions to represent the return periods for various hail diameters. The parameters of a Gumbel distribution are fit to dithered observed annual maxima on a national 1° × 1° grid at locations with sufficient records. Gridded and kernel-smoothed return sizes and quantiles up to the 200-yr return period are dete...

CFSv2 monthly forecasts of tornado and hail activity

AbstractRainfall intensity displays relationships with atmospheric conditions such as surface temperature, and these relationships have implications for how the intensity of rainfall varies with climate. Here, hourly gauge measurements of rainfall over the contiguous United States (CONUS) are related to atmospheric variables taken from the North American Regional Reanalysis for the period 1979–2012. This analysis extends previous work relating the rainfall process to the environment by including a wider range of variables in univariate and bivariate quantile regressions. Known covariate relationships are used to quantify the regional contributions of different weather regimes to rainfall occurrence and to identify preferential atmospheric states for rainfall occurrence. The efficiency of different sets of regressors is evaluated, and the results show that both moisture availability and vertical instability should be taken into account, with CAPE in combination with specific humidity or dewpoint temperatur...

WITHDRAWN: Parameterization of Rainfall Models with 1 Emphasis on Extremes

AFFILIATIONS: allen—Department of Earth and Atmospheric Sciences, Central Michigan University, Mount Pleasant, Michigan, and International Research Institute for Climate and Society, Columbia University, Palisades, New York; tippett—Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York, and Center of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia; Sobel—Department of Applied Physics and Applied Mathematics, and Department of Earth and Environmental Sciences, Columbia University, New York, New York; lepore—Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York CORRESPONDING AUTHOR: John T. Allen, Department of Earth and Atmospheric Sciences, Central Michigan University, Brooks Hall 326, Mount Pleasant, MI 48859 E-mail: johnterrallen@gmail.com

Marginal methods of intensity‐duration‐frequency estimation in scaling and nonscaling rainfall

AbstractClimate Forecast System, version 2, predictions of monthly U.S. severe thunderstorm activity are analyzed for the period 1982–2016. Forecasts are based on a tornado environmental index and ...