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QUANTITATIVE PRECIPITATION ESTIMATES FROM DUAL-POLARIZATION WEATHER RADAR IN LAZIO REGION
Get PDFMany phenomena (such as attenuation and range degradation) can influence the accuracy of rainfall radar estimates. They introduce errors that increase as the distance from the radar increases, thereby decreasing the reliability of radar estimates for applications that require quantitative precipitation estimation. The aim of the present work is to develop a range dependent error model called adjustment factor, that can be used as a range error pattern for allowing to correct the mean error which affects long-term quantitative precipitation estimates. A range dependent gauge adjustment technique was applied in combination with other processing of radar data in order to correct the range dependent error affecting radar measurements. Issues like beam blocking, path attenuation, vertical structure of precipitation related error, bright band, and incorrect Z-R relationship are implicitly treated with this type of method. In order to develop the adjustment factor, radar error was determined with respect to rain gauges measurements through a comparison between the two devices, based on the assumption that gauge rain was real. Therefore, the G/R ratio between the yearly rainfall amount measured in each rain gauge position during 2008 and the corresponding radar rainfall amount was calculated against the distance from radar. Trend of the G/R ratio shows two behaviors: a concave part due to the melting layer effect close to the radar location, and an almost linear increasing trend at greater distance. Then, a linear best fitting was used to find an adjustment factor, which estimates the radar error at a given range. The effectiveness of the methodology was verified by comparing pairs of rainfall time series that were observed simultaneously by collocated rain gauges and radar. Furthermore, the variability of the adjustment factor was investigated at the scale of event, both for convective and stratiform events. The main result is that there is not an univocal range error pattern, as it is also a function of the event characteristics. On the other hand, the adjustment factor tends to stabilize over long periods of observation as in the case of a whole year of measures
On precipitation measurements collected by a weather radar and a rain gauge network
No full textThe present work aims to analyze the correspondence between the rainfall radar estimates and the rain gauges measurements collected at different distances from radar. The radar data utilized in this work have been collected by the Polar 55C weather radar, located in Roma Tor Vergata research area, from 2008 to 2009. The scanning strategy adopted by Polar 55C prefigured the cyclical repetition of eight sweeps in all directions with constant elevation. Each 5 minutes 8 Plan Position Indicators are acquired, each one with a different elevation angle, ranging from 0.5 to 7.5_. This study considers measurements collected at 1.5_ elevation. The noise level is determined by supposing that at great distance the sampling volume is likely situated in an atmospheric region above the precipitation. In this way the modal value in the last two range-bins has been chosen as a reference to determine the noise level at the receiver. The range-bins whose reflectivity doesn’t exceed noise level were considered affected by noise. The modality developed for the ground clutter removal is based on the existence of typical values for the standard deviations of the differential reflectivity and of the differential phase when the radar return is caused by precipitation. In fact in the presence of meteorological echoes at the receiver, these standard deviations can be expressed by the width of the Doppler spectrum and the co-polar correlation coefficient, about which is well-known the variation range characteristic of rainfall. Only the radar reflectivity which corresponds to meteorological signal was converted into rainfall intensity by using a parametric algorithm. Finally, the radar rainfall intensity values were remapped onto a 1 square kilometre Cartesian grid, by assigning to each pixel the mean of the rainfall values estimated in the radar range-bins belonging to the pixel. Rain gauges located at different distances from Polar 55C were selected so that most of ranges in the scanning circle are covered. Moreover only rain gauges not placed in areas where the radar beam is blocked were considered. The rain gauges data were compared with rainfall radar estimates in the pixels where are located the rain gauges considered. The ratio G/R between rainfall amounts rain gauges measurements and rainfall amounts radar estimates was calculated against the distance from radar, by considering all the events utilized in this work. A trend was found; the greater the distance from the radar, the higher the ratio G/R. Once the trend has been found, a best fitting line was used to find the radar error at a given range and the radar rainfall estimates were consequently corrected
Comparison between radar and rain gauges data at different distances from radar and correlation existing between the rainfall values in the adjacent pixels
No full textA test bed for verification of a methodology to correct the effects of range dependent errors on radar estimates
No full textQuantitative radar precipitation estimates are affected by error determined by many causes that include, among others,
radar miscalibration, range degradation (including beam broadening and sampling of precipitation at increasing altitude),
attenuation, ground clutter, variability of Z-R relation, variability of drop size distribution, hydrometeor water phase
distribution within the sampled volume, vertical variability of the precipitation system, vertical air motion, precipitation
drift, anomalous propagation and beam-blocking (Zawadzki, 1984; Koistinen and Puhakka, 1986; Saltikoff et al., 2000;
Villarini et al., 2008; Berenguer and Zawadzki, 2009; Villarini and Krajewski, 2010). Several sources, such as attenuation,
range degradation and radar sampling above the clouds, determine a range dependent behaviour of error. The aim of this
work is to quantify the range-dependent influence of the above-mentioned sources of uncertainties on rainfall radar
estimates, through comparison between radar and rain gauge network precipitation fields. To reach this objective, the G/R
ratio was calculated against range, where G and R are the corresponding rain gauge and radar rainfall amount, respectively,
computed at each rain gauge location. Radar data are processed to compensate calibration and attenuation effects.
Finally, the range dependent error was modeled through an adjustment factor, derived for different elevation angles.
Radar data were collected by the Polar 55C weather radar located in Rome (Italy) managed by the Institute of Atmospheric
Sciences and Climate of the National Research Council (ISAC-CNR) of Italy in 2008. Rain gauges data were collected by
the network of the Lazio regional administration located inside the radar scanning area. A subset of rain gauges appears as
aligned along a given direction from the radar along a range of almost 120 km free from beam blocking effects is used to
verify the effectiveness of the methodology. A set of five events is used to this purpose. This direction, which is almost
parallel to the Tyrrhenian coast line, is also that along which intense convective cells tend often to organize themselves as a
squall line. Such condition is verified in the considered dataset.
In the next section the data selection methodology is detailed. In Sect. 3 characteristics of Polar 55C weather radar are
described, as well as the methodologies followed to calibrate weather radar with rain gauges, to perform radar rainfall
estimates and to correct radar sampling errors and attenuation. In Sect. 4 logarithm of G/R trends with range, obtained before
and after each processing of radar data, were compared, referring to different elevation angles. In Sect. 5 the influence of the
melting layer on radar estimates is treated. Finally Sect. 6 completes the paper with conclusions
Effetti dell’errore variabile in range sulle stime radar di pioggia
No full textMolteplici sorgenti di incertezze inficiano le stime di pioggia radar causando un
errore dipendente dalla distanza dal radar. In questo studio la dipendenza spaziale
dell’errore dovuto all’attenuazione del segnale e alla degradazione in range è stata
valutata attraverso un confronto fra i campi di precipitazione della rete
pluviometrica e del radar. I dati radar si riferiscono al biennio 2008-2009 e
provengono dal radar meteorologico Polar 55C ubicato a Roma. La corrispondenza
fra dati radar e pluviometrici è stata esaminata attraverso lo studio dell’andamento
del rapporto tra le cumulate pluviometrica e radar in funzione della distanza del
volume campionato. I risultati mostrano che entro 40 km l’errore è indipendente
dalla distanza, mentre oltre tale range le stime radar peggiorano richiedendo
l’introduzione di un fattore di correzione. Infine, il confronto fra gli andamenti del
rapporto tra le cumulate ottenuti per eventi convettivi e stratiformi ha evidenziato la
diversa influenza della degradazione in range a seconda della tipologia dell’evento
di pioggia.Multiple sources of uncertainty disrupt radar estimates of rain causing unerrore dependent on the distance from the radar. In this study the dependence spazialedell'errore due to signal attenuation and degradation in range is statavalutata through a comparison between the precipitation fields of retepluviometrica and radar. The radar data refer to the period 2008-2009 eprovengono from weather radar Polar 55C located in Rome. The corrispondenzafra radar data and precipitation was examined by studying the relationship between the cumulated dell'andamentodel pluviometric and radar function of the distance the Volume championship. The results show that the error is within 40 km distance indipendentedalla, while above this range radar estimates worsen richiedendol'introduzione of a correction factor. Finally, the comparison between the performance delrapporto between the cumulated obtained for events convective and stratiform showed ladiversa influence of degradation in range depending on the type dell'eventodi rain
Assessment of the watershed DEM mesh size influence on a large dam design hydrograph
No full textThe present study aims to assess the digital elevation model (DEM) resolution influence on the peak flow estimation for the design hydrograph of a large dam. This was executed by comparing the design hydrograph peak flows, with respect to a 2000years return period, which wereestimated for the Pietrarossa dam, in the South-East of Sicily, for different DEM spatial resolutions. The methodology consisted of the watershed extraction from the catchment basin in which the directly wired area belongs. Furthermore, the intensity duration frequency (IDF) curves were estimated starting from the observational time seriescollected by two rain gauges located near the dam. Finally, through a rainfall-runoff transformation, the design hydrographs were obtained by using both the watershed and the IDF curves. Considering different spatial resolutions, it was found that both the peak flow and the total volume decreases as the DEM spatial resolution decreases
Comparison of methodologies for flood rainfall thresholds evaluation
No full textA flood warning system based on rainfall thresholds allows to overcome classical real time flood forecasting
systems, that generally require to run in real time a hydrological model. This approach is useful when dealing with
medium and small size basins, often characterised by a very rapid response to the storms, leaving only a short
lead time for events mitigation. Rainfall thresholds values specify the precipitation amount for a given duration
that generates a critical discharge in a given cross section. The overcoming of these values could produce a critical
situation in river sites exposed to alluvial risk. Rainfall thresholds values depend on soil moisture conditions and
spatial and temporal distribution of rainfall. In this study a comparison of methodologies for estimating rainfall
threshold values is presented. Critical precipitation amounts are evaluated using both hydrological simulation and
probabilistic methods. The study is focused on three medium-small sized basins (areas ranging from 125 to 800
square kilometres) located in North Lazio Coastal Region, in Central Italy. For each catchment a semi-distributed
hydrological model is calibrated and validated with rain gauge and weather radar data. Then the optimal simulation
models are used to evaluate critical rainfall depths for 1, 3, 6 and 12 h duration. In the probabilistic approach rainfall
thresholds values result from the evaluation of the joint probability function of rainfall depth of a given duration
(1, 3, 6 and 12 h) and the corresponding flow peak value, combined with an utility function minimisation. Two
kind of utility function are examined, one following the Bayesian decision theory, the other the informative entropy
concept. Finally, to assess the performance of each methodology, contingency table are constructed to highlight
the system skill score, i.e. the capacity of correctly issuing warning against false and missed alarms
Comparison of probabilistic methodologies for flood rainfall thresholds evaluation
Get PDFIn medium and small size basins, floods are often characterised by a very rapid response to storms, leaving only a
short lead time for triggering civil protection measures. For a given duration it is possible to identify rainfall values
that generates a critical discharge in a given river cross section. If the rainfall threshold values are exceeded it can
produce a critical situation in river sites exposed to alluvial risk. Comparing directly the observed or forecasted
precipitation with critical reference values, allows to issue a flood warning without running online real-time
forecasting systems.
The critical rainfall threshold values are evaluated by probabilistic methodologies, considering the joint cumulative
distribution of cumulated rainfall and the corresponding peak discharge, for different soil saturation conditions
(represented by AMC classes) and time durations. To estimate the joint distributions three approaches are
examined: firstly, the data are transformed to normality by a Cox-Box Transformation, and the corresponding
joint distribution is a bivariate normal. With the second method the marginal distributions are transformed via the
Normal Quantile Transform, and the corresponding joint distribution is a meta-Gaussian. Finally, the Copula is
applied to obtain joint distributions without assumptions about data or marginal. The joint distributions are then
used to evaluate a risk function based on the informative entropy concept.
The rainfall threshold values are estimated for the Mignone River basin, located in Central Italy. The study
concludes with a system performance analysis, in terms of correctly issued warnings, false alarms and missed
alarms for each proposed approach
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
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