1,720,977 research outputs found
Evaluation of Gamma Raindrop Size Distribution Assumption through Comparison of Rain Rates of Measured and Radar-Equivalent Gamma DSD
No full textTo date, one of the most widely used parametric forms for modeling raindrop size distribution (DSD) is the
three-parameter gamma. The aim of this paper is to analyze the error of assuming such parametric form to
model the natural DSDs. To achieve this goal, a methodology is set up to compare the rain rate obtained from
a disdrometer-measured drop size distribution with the rain rate of a gamma drop size distribution that
produces the same triplets of dual-polarization radar measurements, namely reflectivity factor, differential
reflectivity, and specific differential phase shift. In such a way, any differences between the values of the two
rain rates will provide information about how well the gamma distribution fits the measured precipitation. The
difference between rain rates is analyzed in terms of normalized standard error and normalized bias using
different radar frequencies, drop shape–size relations, and disdrometer integration time. The study is performed
using four datasets of DSDs collected by two-dimensional video disdrometers deployed in Huntsville (Alabama)
and in three different prelaunch campaigns of the NASA–Japan Aerospace Exploration Agency
(JAXA) Global Precipitation Measurement (GPM) ground validation program including the Hydrological
Cycle inMediterraneanExperiment (HyMeX) special observation period (SOP) 1 field campaign inRome. The
results show that differences in rain rates of the disdrometer DSD and the gamma DSD determining the same
dual-polarization radar measurements exist and exceed those related to the methodology itself and to the disdrometer
sampling error, supporting the finding that there is an error associatedwith the gammaDSDassumption
Seismic signature of an extreme hydro-meteorological event in Italy
No full textAbstract Flash floods are a major threat for Mediterranean countries and their frequency is expected to increase in the next years due to the climatic change. Civil protection agencies are called to deal with increasing hydrological risk, but existing hydro-meteorological monitoring networks might not be enough for detecting, tracking, and characterizing rapidly evolving floods produced by severe convective storms. Nowadays, hydro-meteorological information in several watersheds particularly in small and mid-size in orographically complex regions or in third-world countries, is still not available or insufficient. To improve our observational capability of these events, we propose to exploit the seismic recordings, which act as opportunistic signals and can complement well-established procedures to early detect the occurrence of flash floods at regional scale. Here, we investigate the hydro-meteorological event that hit central Italy in September 2022 and resulted in a devastating flash flood. We compare seismic data from a national monitoring network with raingauges and hydrometer data. Our evidence suggests that the main stages of the hydro-meteorological events can be tracked by the spatio-temporal evolution of the seismic noise confirming the capability of this multi-sensor approach in detecting and characterizing such kind of events
Using raindrop size distributions from different types of disdrometer to establish weather radar algorithms
No full textRadar precipitation retrieval uses several relationships that parameterize precipitation properties (like rainfall rate
and liquid water content and attenuation (in case of radars at attenuated frequencies such as those at C- and Xband)
as a function of combinations of radar measurements. The uncertainty in such relations highly affects the
uncertainty precipitation and attenuation estimates. A commonly used method to derive such relationships is to
apply regression methods to precipitation measurements and radar observables simulated from datasets of drop
size distributions (DSD) using microphysical and electromagnetic assumptions. DSD datasets are determined both
by theoretical considerations (i.e. based on the assumption that the radar always samples raindrops whose sizes follow
a gamma distribution) or from experimental measurements collected throughout the years by disdrometers. In
principle, using long-term disdrometer measurements provide parameterizations more representative of a specific
climatology. However, instrumental errors, specific of a disdrometer, can affect the results. In this study, different
weather radar algorithms resulting from DSDs collected by diverse types of disdrometers, namely 2D video
disdrometer, first and second generation of OTT Parsivel laser disdrometer, and Thies Clima laser disdrometer, in
the area of Rome (Italy) are presented and discussed to establish at what extent dual-polarization radar algorithms
derived from experimental DSD datasets are influenced by the different error structure of the different type of
disdrometers used to collect the data
C/S algorithm based on properties of dual-polarization radar measurements derived from disdrometer data
No full textThe paper proposes a convective/stratiform (C/S) classification algorithm based on dual-polarization radar measurements obtained from disdrometer measurements through an electromagnetic scattering/extinction model. The drop size distributions, collected by a 2D video disdrometer (2DVD) installed in Rome during HyMeX SOP1, have been used to define the algorithm, and the C-band radar measurements, collected by a Polar 55C, have been analyzed to assess the performance of the proposed method
Comparison of different fittings of experimental DSDs
No full textThe knowledge of drop size distribution (DSD) of rain, namely the frequency distribution of drop equivolume
diameters, has a wide range of applications in earth sciences such as precipitation physics, hydrology and agricultural
and soil sciences, it is also important in precipitation remote sensing, especially in radar meteorology for
relationships among rainfall rate and radar measurements such as the radar reflectivity factor. In general, retrieval
of parametric DSDs would aim to best model the largest portion of measured drop spectra, as a consequence, there
is no guarantee that the selected distribution will adequately model some DSD portions, such as the tail. However,
for characterising physical quantities such as the liquid water content and radar reflectivity, the right tail is critical
because large drops play a much more important role than small droplets. In order to study the influence of various
tail-types, four different one-sided continuous distributions (the Pareto, the Lognormal, the Gamma and the
Weibull distributions) have been fitted both to the large drops only and to the entire sample of the measured spectra.
Observational data consist of 1-min spectra collected by two-dimensional video disdrometer (2DVD). One
dataset was measured during the first special observation period of the hydrological cycle in the Mediterranean
experiment (HyMeX) field campaign in Rome from September to November 2012, while the second one during
the Mid-latitude Continental Convective Clouds Experiment (MC3E) field campaign in Oklahoma from April to
June 2011. The results obtained for the two different datasets are consistent. Results of this preliminary analysis
show that considering the whole fitting the Weibull distribution seems to fit the highest percentages of the measured
drop spectra (37% for HyMex and 42% for MC3E), on the other end this distribution is closely followed
by the Gamma and the Lognormal distribution, with approximately 30% of success. While for the tail fitting the
performances of the Weibull and Lognormal distributions increase to the detriment of the Gamma distribution;
the Weibull distribution has the highest percentage of success for the Hymex dataset, while for the MC3E dataset
the Lognormal distribution fits the highest number of measured spectra. For both the datasets, when the Weibull
distribution performs the best fitting, the shape parameter of the distribution is greater than one
A Support Vector Machine Hydrometeor Classification Algorithm for Dual-Polarization Radar
Full text linkAn algorithm based on a support vector machine (SVM) is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively) and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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