Search Results

You are looking at 1 - 10 of 42 items for

  • Author or Editor: Gabriele Villarini x
  • Refine by Access: All Content x
Clear All Modify Search
Gabriele Villarini

Abstract

The focus of this study is the evaluation of the research-version Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) rainfall product at its finest spatial and temporal resolutions (3-hourly and 0.25° × 0.25°) over the Rome, Italy, metropolitan area during the period from October 2008 to January 2009. Accurate ground reference rainfall estimates for two satellite pixels are obtained from a dense rain gauge network (22 rain gauges in one pixel and 16 in the other one). The evaluation is based on examination of time series, scatterplots, and survival functions, as well as measures of agreement and disagreement. The results of this study point to the importance of using the TRMM satellite for rainfall estimation. Suggestions in terms of minimum number of rain gauges required to estimate ground reference rainfall are also provided.

Full access
Gabriele Villarini and Witold F. Krajewski

Abstract

It is well acknowledged that there are large uncertainties associated with the operational quantitative precipitation estimates produced by the U.S. national network of the Weather Surveillance Radar-1988 Doppler (WSR-88D). These errors result from the measurement principles, parameter estimation, and the not fully understood physical processes. Even though comprehensive quantitative evaluation of the total radar-rainfall uncertainties has been the object of earlier studies, an open question remains concerning how the error model results are affected by parameter values and correction setups in the radar-rainfall algorithms. This study focuses on the effects of different exponents in the reflectivity–rainfall (ZR) relation [Marshall–Palmer, default Next Generation Weather Radar (NEXRAD), and tropical] and the impact of an anomalous propagation removal algorithm. To address this issue, the authors apply an empirically based model in which the relation between true rainfall and radar rainfall could be described as the product of a systematic distortion function and a random component. Additionally, they extend the error model to describe the radar-rainfall uncertainties in an additive form. This approach is fully empirically based, and rain gauge measurements are considered as an approximation of the true rainfall. The proposed results are based on a large sample (6 yr) of data from the Oklahoma City radar (KTLX) and processed through the Hydro-NEXRAD software system. The radar data are complemented with the corresponding rain gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet.

Full access
Gabriele Villarini and Gabriel A. Vecchi

Abstract

This study focuses on the statistical modeling of the power dissipation index (PDI) and accumulated cyclone energy (ACE) for the North Atlantic basin over the period 1949–2008, which are metrics routinely used to assess tropical storm activity, and their sensitivity to sea surface temperature (SST) changes. To describe the variability exhibited by the data, four different statistical distributions are considered (gamma, Gumbel, lognormal, and Weibull), and tropical Atlantic and tropical mean SSTs are used as predictors. Model selection, both in terms of significant covariates and their functional relation to the parameters of the statistical distribution, is performed using two penalty criteria. Two different SST datasets are considered [the Met Office’s Global Sea Ice and Sea Surface Temperature dataset (HadISSTv1) and NOAA’s extended reconstructed SST dataset (ERSSTv3b)] to examine the sensitivity of the results to the input data.

The statistical models presented in this study are able to well describe the variability in the observations according to several goodness-of-fit diagnostics. Both tropical Atlantic and tropical mean SSTs are significant predictors, independently of the SST input data, penalty criterion, and tropical storm activity metric. The application of these models to centennial reconstructions and seasonal forecasting is illustrated.

The sensitivity of North Atlantic tropical cyclone frequency, duration, and intensity is examined for both uniform and nonuniform SST changes. Under uniform SST warming, these results indicate that there is a modest sensitivity of intensity, and a decrease in tropical storm and hurricane frequencies. On the other hand, increases in tropical Atlantic SST relative to the tropical mean SST suggest an increase in the intensity and frequency of North Atlantic tropical storms and hurricanes.

Full access
Gabriele Villarini and Gabriel A. Vecchi

Abstract

By considering the intensity, duration, and frequency of tropical cyclones, the power dissipation index (PDI) and accumulated cyclone energy (ACE) are concise metrics routinely used to assess tropical storm activity. This study focuses on the development of a hybrid statistical–dynamical seasonal forecasting system for the North Atlantic Ocean’s PDI and ACE over the period 1982–2011. The statistical model uses only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational record, reflecting the role of both local and nonlocal effects on the genesis and development of tropical cyclones in the North Atlantic basin. SSTs are predicted using a 10-member ensemble of the Geophysical Fluid Dynamics Laboratory Climate Model, version 2.1 (GFDL CM2.1), an experimental dynamical seasonal-to-interannual prediction system. To assess prediction skill, a set of retrospective predictions is initialized for each month from November to April, over the years 1981–2011. The skill assessment indicates that it is possible to make skillful predictions of ACE and PDI starting from November of the previous year: skillful predictions of the seasonally integrated North Atlantic tropical cyclone activity for the coming season could be made even while the current one is still under way. Probabilistic predictions for the 2012 North Atlantic tropical cyclone season are presented.

Full access
David A. Lavers and Gabriele Villarini

Abstract

This paper undertakes a hydrometeorological analysis of flood events in the central United States. Vertically integrated horizontal water vapor transport over 1979–2011 is calculated in the ECMWF Interim Re-Analysis (ERA-Interim) and used in an algorithm to identify episodes of high moisture transport, or atmospheric rivers (ARs), over the central United States. The AR events are almost evenly divided among the seasons (143 during the winter, 144 during the spring, and 124 during the fall), with a minimum (40) during the summer. The annual maxima (AM) floods from 1105 basins over the period 1980–2011 are used as a measure of the hydrologic impact of the AR events. Of these basins, 470 (or 42.5%) had more than 50% of their AM floods linked to ARs. Furthermore, 660 of the 1105 basins (59.7%) had 5 or more of their top 10 AM floods related to ARs, indicating that ARs control the upper tail of the flood peak distribution over large portions of the study area. The seasonal composite average of mean sea level pressure anomalies associated with the ARs shows a trough located over the central United States and a ridge over the U.S. East Coast, leading to southerly winds and the advection of moisture over the study region. Based on the findings of this study, ARs are a major flood agent over the central United States.

Full access
Gabriele Villarini and Gabriel A. Vecchi

Abstract

Tropical cyclones—particularly intense ones—are a hazard to life and property, so an assessment of the changes in North Atlantic tropical cyclone intensity has important socioeconomic implications. In this study, the authors focus on the seasonally integrated power dissipation index (PDI) as a metric to project changes in tropical cyclone intensity. Based on a recently developed statistical model, this study examines projections in North Atlantic PDI using output from 17 state-of-the-art global climate models and three radiative forcing scenarios. Overall, the authors find that North Atlantic PDI is projected to increase with respect to the 1986–2005 period across all scenarios. The difference between the PDI projections and those of the number of North Atlantic tropical cyclones, which are not projected to increase significantly, indicates an intensification of North Atlantic tropical cyclones in response to both greenhouse gas (GHG) increases and aerosol changes over the current century. At the end of the twenty-first century, the magnitude of these increases shows a positive dependence on projected GHG forcing. The projected intensification is significantly enhanced by non-GHG (primarily aerosol) forcing in the first half of the twenty-first century.

Full access
Abdou Khouakhi, Gabriele Villarini, and Gabriel A. Vecchi

Abstract

This study quantifies the relative contribution of tropical cyclones (TCs) to annual, seasonal, and extreme rainfall and examines the connection between El Niño–Southern Oscillation (ENSO) and the occurrence of extreme TC-induced rainfall across the globe. The authors use historical 6-h best-track TC datasets and daily precipitation data from 18 607 global rain gauges with at least 25 complete years of data between 1970 and 2014. The highest TC-induced rainfall totals occur in East Asia (>400 mm yr−1) and northeastern Australia (>200 mm yr−1), followed by the southeastern United States and along the coast of the Gulf of Mexico (100–150 mm yr−1). Fractionally, TCs account for 35%–50% of the mean annual rainfall in northwestern Australia, southeastern China, the northern Philippines, and Baja California, Mexico. Seasonally, between 40% and 50% of TC-induced rain is recorded along the western coast of Australia and in islands of the south Indian Ocean in the austral summer and in East Asia and Mexico in boreal summer and fall. In terms of extremes, using annual maximum and peak-over-threshold approaches, the highest proportions of TC-induced rainfall are found in East Asia, followed by Australia and North and Central America, with fractional contributions generally decreasing farther inland from the coast. The relationship between TC-induced extreme rainfall and ENSO reveals that TC-induced extreme rainfall tends to occur more frequently in Australia and along the U.S. East Coast during La Niña and in East Asia and the northwestern Pacific islands during El Niño.

Full access
Munir A. Nayak, Gabriele Villarini, and A. Allen Bradley

Abstract

Atmospheric rivers (ARs) play a major role in causing extreme precipitation and flooding over the central United States (e.g., Midwest floods of 1993 and 2008). The goal of this study is to characterize rainfall associated with ARs over this region during the Iowa Flood Studies (IFloodS) campaign that took place in April–June 2013. Total precipitation during IFloodS was among the five largest accumulations recorded since the mid-twentieth century over most of this region, with three of the heavy rainfall events associated with ARs. As a preliminary step, the authors evaluate how well different remote sensing–based precipitation products captured the rainfall associated with the ARs and find that stage IV is the product that shows the closest agreement to the reference data. Two of the three ARs during IFloodS occurred within extratropical cyclones, with the moist ascent associated with the presence of cold fronts. In the third AR, mesoscale convective systems resulted in intense rainfall at many locations. In all the three cases, the continued supply of warm water vapor from the tropics and subtropics helped sustain the convective systems. Most of the rainfall during these ARs was concentrated within ~100 km of the AR major axis, and this is the region where the rainfall amounts were highly positively correlated with the vapor transport intensity. Rainfall associated with ARs tends to be larger as these events mature over time. Although no major diurnal variation is detected in the AR occurrences, rainfall amounts during nocturnal ARs were higher than for ARs that occurred during the daytime.

Full access
Alan W. Black, Gabriele Villarini, and Thomas L. Mote

Abstract

Rainfall is one of many types of weather hazard that can lead to motor vehicle crashes. To better understand the link between rainfall and crash rates, daily gridded precipitation data and automobile crash data are gathered for six U.S. states (Arkansas, Georgia, Illinois, Maryland, Minnesota, Ohio) for the period 1996–2010. A matched pair analysis is used to pair rainfall days with dry days to determine the relative risk of crash, injury, and fatality. Overall, there is a statistically significant increase in crash and injury rates during rainfall days of 10% and 8%, respectively, leading to an additional 28 000 crashes and 12 000 injuries in the 1 May–30 September period each year relative to what would be expected if those days were dry. The risk of crashes and injuries increases for increasing daily rainfall totals, with an overall increase in crashes and injuries of 51% and 38% during days with more than 50 mm (2 in.) of rainfall. While urban counties and rural counties with and without interstates each saw increased crash risk during rainfall, urban counties saw the most significant increases in relative risk. There are a number of exceptions to these broad spatial patterns, indicating that relative risk varies in ways that are not explained solely by meteorological factors.

Full access
Gabriele Villarini, Gabriel A. Vecchi, and James A. Smith

Abstract

The authors analyze and model time series of annual counts of tropical storms lasting more than 2 days in the North Atlantic basin and U.S. landfalling tropical storms over the period 1878–2008 in relation to different climate indices. The climate indices considered are the tropical Atlantic sea surface temperature (SST), tropical mean SST, the North Atlantic Oscillation (NAO), and the Southern Oscillation index (SOI). Given the uncertainties associated with a possible tropical storm undercount in the presatellite era, two different time series of counts for the North Atlantic basin are employed: one is the original (uncorrected) tropical storm record maintained by the National Hurricane Center and the other one is with a correction for the estimated undercount associated with a changing observation network. Two different SST time series are considered: the Met Office’s HadISSTv1 and NOAA’s Extended Reconstructed SST.

Given the nature of the data (counts), a Poisson regression model is adopted. The selection of statistically significant covariates is performed by penalizing models for adding extra parameters and two penalty functions are used. Depending on the penalty function, slightly different models, both in terms of covariates and dependence of the model’s parameter, are obtained, showing that there is not a “single best” model. Moreover, results are sensitive to the undercount correction and the SST time series.

Suggestions concerning the model to use are provided, driven by both the outcomes of the statistical analyses and the current understanding of the underlying physical processes responsible for the genesis, development, and tracks of tropical storms in the North Atlantic basin. Although no single model is unequivocally superior to the others, the authors suggest a very parsimonious family of models using as covariates tropical Atlantic and tropical mean SSTs.

Full access