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The Antarctic Ice Sheet plays a major role in regional and global climate variability and represents, probably, the most critical factor of future sea-level rise. Snow and solid precipitation more broadly have been recognized as primary mass input for ice sheet. However, despite its fundamental role in the surface mass balance estimation, precipitation over Polar region and in the Antarctica particularly, remains largely unknown, being not well assessed by numerical weather/climate models, by ground observations and satellite measurements as well. More accurate estimations of precipitation in the Antarctic continent are desirable not only in understanding the behavior of the Antarctic Ice Sheet, but also in validating global climate and numerical weather prediction models and also in order to constrain measurements from space during validation/calibration satellite campaigns.Recently, several observatories in Antarctica have been equipped with equipment for cloud and precipitation measurements, such as the two Italian stations “Mario Zucchelli”, Terra Nova Bay, and Concordia, in the Antarctic Plateau. At “Mario Zucchelli”, instrumentation includes 24-GHz vertical pointing radar Micro Rain Radar (MRR) and optical disdrometer. The synergetic use of such set of instruments allows for characterizing and quantifying precipitation, even if quantitative estimate of precipitation from radar is extremely demanding, especially in snowfall, because of variability microphysical features of hydrometeors.Usually precipitation estimation methods with weather radar are based on relationships between radar equivalent reflectivity factor (Ze) and liquid equivalent snowfall rate (SR). Several relationships are reported in literature, derived from comparison between radar and ground sensors but very few are suitable for the Antarctic continent and none also considers the microphysical characterization of hydrometeors.This work shows quantitative estimate of the Antarctic precipitation for several snow episodes at the Mario Zucchelli station using specific ZE-SR relationships also taking into account the snowfall classification according to dominating hydrometeor type (e.g. pristine, aggregate, dendrite, plate). Microphysical properties of precipitation are inferred by comparing radar measurements with simulations obtained from disdrometer measurements in terms of reflectivity factor. Specifically, the Ze directly derived by radar has been compared with the Ze calculated by disdrometer observations coupling particle size distributions and NASA database of hydrometeor backscattering values based on the Discrete Dipole Approximation. More challenging are estimations at Concordia, where ice particles have very small sizes and are hardly detectable by laser disdrometer, and where MRR lacks of adequate sensitivity.

An assessment of the performance of a self-consistent numerical method for dual-frequency radar based on the retrieval of microphysical precipitation parameter profiles is presented. From the surface reference technique (SRT), the estimation of path-integrated attenuation (PIA) is performed at both wavelengths and reflectivity factors are corrected for attenuation. Then, solving numerically a system of two nonlinear differential equations, the drop size distribution (DSD) parameters are obtained. The method is applied only in the stratiform rain region, from the surface along the path upward to the brightband bottom.Assuming a gamma DSD model to describe the distribution of precipitation found in nature, a methodology has been developed to transform the estimated DSD provided by a vertically pointing Micro Rain Radar to a profile given by a ground-based Ku- and Ka-band radar, and then in a spaceborne dual-frequency radar measurement profile.Under ideal conditions in which the different errors that simultaneously affect the retrieval of precipitation microphysical parameters may be individually studied, particular emphasis has been placed on the incidence of variability due to the DSD shape parameter μ, the presence of uncertainties in PIA estimates, and radar signal fluctuations.To achieve an appropriate level of confidence in the simulation outputs, a qualitative indirect method of validation was realized by comparing the results obtained by the simulation with the experimental ones and weighing how consistent they are with what the theory implies. GPM near-real-time data from an entire year (October 2014–September 2015) were used for this purpose.

With the advent of the Global Precipitation Measurement (GPM) mission and the associated Ground Validation campaigns, there has been a strong development of studies related to dual-frequency and more recently to triple-frequency radar. In this context, one requirement is that at least one of the radar frequencies operates in the Rayleigh regime while the others have to ensure a measurable difference in reflectivities. A common radar coupling for triple frequency systems is the Ku-, Ka-, and W-band. Multi-frequency radars, in addition to the classic single-frequency reflectivity (SFR) measurement for each frequency, allow a further parameter, the dual-frequency ratio (DFR) defined as the ratio between two reflectivities at two frequencies. Referring to the same measurement volume, and for a fixed microphysical ice particle model, SFR and DFR allow to better constraint parameters of the particle size distribution, such as the mass-weighted mean diameter (Dm) and the normalized intercept parameter (Nw) when a normalized gamma distribution is assumed. This paper deals with various topics with the preliminary purpose of assessing the accuracy of the ice water content (IWC) estimate obtained using SFR and DFR methods to evaluate the improvements brought by the use of DFR. To pursue this goal, a simple microphysical model was used to choose the form of the SFR and DFR estimation algorithms and to evaluate their performances in a simulated framework. The most important aspect revealed by the study is that the cloud water content (CWC) plays a very important role both in the mass vs. diameter relationship as well as in the IWC estimation. The combined use of specific radar algorithms according to the different CWC values has shown notable improvements for the IWC estimation. Since CWC is not an operational measure, a substitute parameter was sought in the (DFRaou, DFRwoa) domain defined by the Ka- and Ku-band and by the W- and Ka-band measurements. This new parameter provides improvements similar to those obtained with the use of CWC. Data from the OLYMPEX field campaign that include an airborne triple-frequency radar at Ku-, Ka-, and W-band, as well as airborne measurements of in-situ bulk microphysics and meteorological parameters were used to validate the robustness of the methodology.

The quantitative estimation of rain rates using meteorological radar has been a major theme in radar meteorology and radar hydrology. The increase of interest in polarimetric radar is in part because polarization diversity can reduce the effect on radar precipitation estimates caused by raindrop size variability, which has allowed progress on radar rainfall estimation and on hydrometeorological applications. From an operational point of view, the promises regarding the improvement of radar rainfall accuracy have not yet been completely proven. The main reason behind these limits is the geometry of radar measurements combined with the variability of the spatial structure of the precipitation systems. To overcome these difficulties, a methodology has been developed to transform the estimated drop size distribution (DSD) provided by a vertically pointing micro rain radar to a profile given by a ground-based polarimetric radar. As a result, the rainfall rate at the ground is fixed at all ranges, whereas the broadening beam encompasses a large variability of DSDs. The resulting DSD profile is used to simulate the corresponding profile of radar measurements at C band. Rainfall algorithms based on polarimetric radar measurements were taken into account to estimate the rainfall into the radar beam. Finally, merit factors were used to achieve a quantitative analysis of the performance of the rainfall algorithm in comparison with the corresponding measurements at the ground obtained from a 2D video disdrometer (2DVD) that was positioned beside the micro rain radar. In this method, the behavior change of the merit factors in the range is directly attributable to the DSD variability inside the radar measurement volume, thus providing an assessment of the effects due to beam broadening.

final version published on line:https://www.sciencedirect.com/science/article/abs/pii/S0022169415007659 Abstract:Rainfall is one of the most crucial processes in hydrology, and the direct and indirect rainfall measurement methods are constantly being updated and improved. The standard instrument used to measure rainfall rate and accumulation is the rain gauge, which provides direct observations. Though the small dimension of the orifice allows rain gauges to be installed anywhere, it also causes errors due to the splash and wind effects. To investigate the role of the orifice dimension, this study, for the first time, introduces and demonstrates an apparatus for observing rainfall called a giant-rain gauge that is characterised by a collecting surface of 100 m2. To discuss the new instrument and its technical details, a preliminary analysis of 26 rainfall events is provided. The results suggest that there are significant differences between the standard and proposed rain gauges. Specifically, major discrepancies are evident for low time aggregation scale (5, 10, and 15 min) and for high rainfall intensity values.

A method is proposed to retrieve raindrop shape–size relations from the radar measurements of reflectivity factor Zh, differential reflectivity Zdr, and specific differential phase Kdp at S band. This procedure is obtained using a domain defined by the two variables Kdp/Zh and Zdr where the drop size distribution (DSD) variability is collapsed onto a line and any variation is essentially due to the drop shape variability. To obtain information on the raindrop shape–size relation underlying a set of radar observations, this domain is studied in conjunction with another domain describing the relation between the drop axial ratio (or shape) and its equivolumetric diameter. Using an initial drop shape and choosing a set of DSDs described by a normalized gamma model, polarimetric radar measurements are produced by simulation. An averaged curve of Kdp/Zh versus Zdr is obtained and compared with the same curve obtained from the radar data. By changing the initial axial ratio relation, a procedure of minimization between the two curves is developed to derive the underlying drop shape–size relation governing the radar measurements under consideration. Three sets of radar data collected in different climatic regions are analyzed to evaluate whether there is a unique shape–size relation.

Dual-polarization weather radars have evolved significantly in the last three decades culminating in operational deployment by the National Weather Service. In addition to operational applications in the weather service, dual-polarization radars have shown significant potential in contributing to the research fields of ground-based remote sensing of rainfall microphysics, the study of precipitation evolution, and hydrometeor classification. Microphysical characterization of precipitation and quantitative precipitation estimation are important applications that are critical in the validation of satellite-borne precipitation measurements and also serve as valuable tools in algorithm development. This paper presents the important role played by dual-polarization radar in validating spaceborne precipitation measurements. Examples of raindrop size distribution retrievals and hydrometeor-type classification are discussed. The quantitative precipitation estimation is a product of direct relevance to spaceborne o...

Investigation of quasi-vertical profiles of C-band radar polarimetric data for QPE in complex orography

Experimental field campaigns of rain precipitation usually require the coexistence of several ground and satellite based observations in order to guarantee a more complete analysis of the collected case studies at the various spa- tial and temporal scales of interest. In the framework of the Ground Validation programme of the NASAA/JAXA Global Precipitation Measurement (GPM) mission, several climate regions of the Earth have been interested by various field campaigns involving experimental setup which include one or more ground based disdrometers and profilers. In such situation a typical implementation of the measurement scheme consists of a pair of K-band verti- cally pointing micro rain radar (MRR) and a 2D video disdrometer (2DVD) installed close each other. Since 2DVD estimates are referred to the ground level, the co-located MRR is supposed to provide complementary vertical pro- files of drop size distribution (DSD) measurements. However, if not properly processed MRR and 2DVD raw data can lead to erroneous interpretations of the underlying microphysics. In this work, we investigate some typical issues occurring when dealing with MRR and 2DVD observations proposing techniques to ensure the adequate data quality required in typical field validation campaigns. More in detail, MRR is an affordable continuous wave frequency-modulated radar (CWFM) typically used at ver- tical incidence. In the MMR configuration used, DSD profiles are estimated from Doppler spectra determined by drops falling at different velocities and at different heights from 1000 meters almost up to the ground level with a vertical resolution of 35 meters and time resolution up to 10 seconds. The importance of the microphysical mea- surements from MRR are related to the effects of the vertical gradients of rain precipitation at the sub-resolution scale of the measurements based remote sensing instruments such as those provided by the dual frequency radar of GPM as well as by ground based weather radars. However, MRR standard processing is based on several assump- tions that, along with some characteristics of the implementation of the CWFM scheme such as spectra aliasing and height-Doppler ambiguity, limit its usefulness for heavy precipitations or in convection. This is particularly true when the influence of air motion on DSD retrieval is not negligible. In this work some of these limitations are investigated through simulations and through a comparison with ground based disdrometers. It is shown that the synergy between 2DVD and MRR measurements, when properly reprocessed, can be exploited to reduce the uncertainty on MRR profiles estimates.

Radar observations of reflectivity and differential reflectivity from a measurement volume are biased due to attenuation and differential attenuation in the presence of rainfall along the path between the measurement volume and the radar. This attenuation is fairly small at S-band frequencies, but not negligible at C-band and higher frequencies such as X-band. The impact of attenuation and differential attenuation on the accuracy of rainfall estimation is studied. It is shown that the biases due to attenuation and differential attenuation nearly cancel each other when used in the rainfall algorithm that uses both reflectivity and differential reflectivity and result in small biases in the rainfall rate estimation. About 2000 X-band radar profiles were simulated based on S-band radar measurements of reflectivity and differential reflectivity, for validation purposes. Theoretical and simulation analyses show that rainfall rate computed at X-band using reflectivity and differential reflectivity without attenuation correction, results in very small bias for certain underlying mean raindrop shape models.

To design X-band radar systems as well as evaluate algorithm development, it is useful to have simultaneous X-band observation with and without the impact of path attenuation. One way to develop that dataset is through theoretical models. This paper presents a methodology to generate realistic range profiles of radar variables at attenuating frequencies, such as X band, for rain medium. Fundamental microphysical properties of precipitation, namely, size and shape distribution information, are used to generate realistic profiles of X band starting with S-band observation. Conditioning the simulation from S band maintains the natural distribution of rainfall microphysical parameters. Data from the Colorado State University’s University of Chicago–Illinois State Water Survey (CHILL) radar and the National Center for Atmospheric Research S-band dual-polarization Doppler radar (S-POL) are used to simulate X-band radar variables. Three procedures to simulate the radar variables and sample applications are presented.

Monitoring of precipitation using high-frequency radar systems, such as the X band, is becoming increasingly popular because of their lower cost compared to their S-band counterpart. However, at higher frequencies, such as the X band, the precipitation-induced attenuation is significant, and introduces ambiguities in the interpretation of the radar observations. Differential phase measurements have been shown to be very useful for correcting the measured reflectivity for precipitation-induced attenuation. This paper presents a quantitative evaluation of two attenuation correction methodologies with specific emphasis on the X band. A simple differential phase–based algorithm as well as the range-profiling algorithm are studied. The impact of backscatter differential phase on the performance of attenuation correction is evaluated. It is shown that both of the algorithms for attenuation correction work fairly well, yielding attenuation-accurate corrected reflectivities with a negligible bias.

A scale-invariance analysis of space and time rainfall events monitored by meteorological radar over the area of Rome (Italy) is proposed. The study of the scale-invariance properties of intense precipitation storms, particularly important in flood forecast and risk mitigation, allows to transfer rainfall information from the large scale predictive meteorological models to the small scale hydrological rainfall-runoff models. Precipitation events are monitored using data collected by the polarimetric Doppler radar Polar 55C (ISAC-CNR), located 15 km Southeast from downtown. The meteorological radar provides the estimates of rainfall intensity over an area of about 10 000 km2 at a resolution of 2×2 km2 in space and 5 min in time. Many precipitation events have been observed from autumn 2001 up to now. A scale-invariance analysis is performed on some of these events with the aim at exploring the multifractal properties and at understanding their dependence on the meteorological large-scale conditions.

The application of polarimetric radar data to the retrieval of raindrop size distribution parameters and rain rate in samples of convective and stratiform rain types is presented. Data from the Colorado State University (CSU), CHILL, NCAR S-band polarimetric (S-Pol), and NASA Kwajalein radars are analyzed for the statistics and functional relation of these parameters with rain rate. Surface drop size distribution measurements using two different disdrometers (2D video and RD-69) from a number of climatic regimes are analyzed and compared with the radar retrievals in a statistical and functional approach. The composite statistics based on disdrometer and radar retrievals suggest that, on average, the two parameters (generalized intercept and median volume diameter) for stratiform rain distributions lie on a straight line with negative slope, which appears to be consistent with variations in the microphysics of stratiform precipitation (melting of larger, dry snow particles versus smaller, rimed ic...

L'Istituto di Scienze dell'Atmosfera e del Clima (ISAC) del Consiglio Nazionale delle Ricerche (CNR) è Centro di Competenza del Dipartimento della Protezione Civile (DPC) dalla fine del 2006, quando venne firmata una prima Intesa Operativa di durata quinquennale per il periodo Giugno 2007 - Maggio 2012, cui hanno fatto seguito due Intese Operative annuali nel 2013 e la presente nel 2014, cui si riferisce questo report. L'Intesa Operativa 2014, della durata di un anno a partire dal Dicembre 2013, si è svolta con lo scopo di dare continuità alla precedente nello sviluppare e fornire nuovi prodotti operativi finalizzati alla misura ed alla previsione delle situazioni meteorologiche, in particolare delle precipitazioni, sul territorio nazionale, nonché di migliorare e arricchire una piattaforma di visualizzazione e archiviazione (MAMS) per la gestione operativa di tali prodotti, sulla base di indicazioni del DPC.Il tutto accompagnato dal lavoro scientifico garantito dell'ISAC sia sotto forma di pubblicazioni e comunicazioni a congressi, sia sotto forma di report. Le attività sono state suddivise in tre workpackages (WP) riguardanti: 1. Modellistica Meteorologica Previsionale 2. Meteorologia da Satellite 3. Radarmeteorologia Per ogni WP le attività sono suddivise in moduli tematici, in ognuno dei quali, oltre a rappresentare una prosecuzione in continuità con le attività e i prodotti forniti nell'ambito della precedente Intesa Operativa, vi sono aspetti innovativi e di miglioramento in funzione dei progressi della ricerca, dei dati a disposizione e degli strumenti di calcolo. Gli sviluppi dei prodotti operativi e le modalità di accesso e di visualizzazione dei dati sono stati concordati con i responsabili della sala operativa del DPC, in maniera da massimizzarne la loro utilità nelle situazioni di emergenza.

Estimation of raindrop size distribution over large spatial and temporal scales has been a long-standing goal of polarimetric radar. Algorithms to estimate the parameters of a gamma raindrop size distribution model from polarimetric radar observations of reflectivity, differential reflectivity, and specific differential phase are developed. Differential reflectivity is the most closely related measurement to a parameter of the drop size distribution, namely, the drop median diameter (D0). The estimator for D0 as well as other parameters are evaluated in the presence of radar measurement errors. It is shown that the drop median diameter can be estimated to an accuracy of 10%, whereas the equivalent intercept parameter can be estimated to an accuracy of 6% in the logarithmic scale. The estimators for the raindrop size distribution parameters are also evaluated using disdrometer data based simulations. The disdrometer based evaluations confirm the accuracy of the algorithms developed herein.

A methodology is proposed for estimating the parameters of a gamma raindrop size distribution model from radar measurements of Zh, Zdr, and Kdp at S band. Previously developed algorithms by Gorgucci et al. are extended to cover low rain-rate events where both Zdr and Kdp are noisy. Polarimetric data from the S-band Dual-Polarization Doppler Radar (S-Pol) during the Tropical Rainfall Measuring Mission (TRMM)/Brazil campaign are analyzed; specifically, the gamma parameters are retrieved for samples of convective and trailing stratiform rain during the 15 February 1999 squall-line event. Histograms of Nw and Do are retrieved from radar for each rain type and compared with related statistics reported in the literature. The functional behavior of Nw and Do versus rain rate retrieved from radar is compared against samples of 2D-video and RD-69 disdrometer data obtained during the campaign. The time variation of Nw, Do, and μ averaged over a 5 km × 5 km area (within which a network of gauges and a profi...

Polarization diversity radar measurements such as reflectivity factor, differential reflectivity, and differential propagation phase are extensively used in rainfall estimation. Algorithms to estimate rainfall from polarimetric radar measurements are based on a model for the raindrop shape as a function of drop diameter. Most of the current algorithms use an equilibrium shape‐size model for raindrops. Variation of the prevailing mean raindrop shapes from an assumed model has a direct impact on the accuracy of radar rainfall estimates. This paper develops composite algorithms to estimate rainfall from polarimetric radar data without an a priori assumption about the specific form of mean raindrop shape‐size model such as equilibrium shape model. The accuracy of rainfall estimates is evaluated in the presence of random measurement errors as well as systematic bias errors. The composite algorithms, independent of a prespecified raindrop shape model, were applied to radar parameters simulated from disdrometer data collected over 3 months, and the corresponding rainfall estimates were found to be in good agreement with disdrometer estimates. The composite algorithms were also tested with Colorado State University CHILL radar observations of the 28 July 1997 Fort Collins (Colorado) flood event. The storm total precipitation estimates based on the composite algorithms developed in this paper were in much better agreement with rain gauge estimates in comparison with conventional algorithms.

Mean raindrop shape–size relationship plays an important role in the interpretation of polarimetric radar measurements. This paper presents the variation of the shape–size relationship, as observed by polarimetric radar measurements. A simple linear model is used to describe the shape–size relation of raindrops. The variation of the slope of this linear model is evaluated using polarimetric radar measurements over Colorado and Florida. While Colorado observations yielded raindrop shape–size relations close to equilibrium, the data from Florida indicated a smaller axis ratio vs. size, possibly caused by raindrop oscillations. Vertical profile of the slope of raindrop shape–size relation is also presented.

Interpretation of polarimetric radar measurements in rainfall such as differential reflectivity and specific differential phase shifts depends on the mean raindrop shape–size relationship. Currently, semiempirical relations between the oblateness and the diameter of the drop are being used. This paper presents an algorithm to obtain the mean shape of the rain drops from polarimetric radar measurements, namely, the reflectivity factor, the differential reflectivity, and the specific differential phase shift. The accuracy of the estimate mean drop shape depends on the measurement accuracies of polarimetric radar observations. Based on asymptotic error analysis and simulations it is shown that the mean raindrop shape can be estimated to an accuracy of 10%. The raindrop shape estimator algorithm developed in this paper is applied to polarimetric radar data collected by the CSU–CHILL radar during the 28 July 1997 Fort Collins, Colorado, flood.

Rainfall estimation using specific differential phase (KDP) involves estimation of KDP over a propagation path. The choice of pathlength plays an important role in the performance of rainfall algorithms using KDP. The path-integrated nature of KDP-based rainfall estimates may involve inhomogeneous paths, thereby having potential implications on the pathlengths used in algorithms for rainfall estimation and measurement errors. The effect of inhomogeneous rainfall paths (over which KDP is estimated) on the choice of rainfall rate algorithms is studied as a function of rain pathlength. Rainfall estimation is biased when algorithms relating rainfall rate and KDP are used over nonuniform paths. This bias is evaluated and compared for different rainfall algorithms. Radar and rain gauge data collected during the Convection and Precipitation/Electrification Experiment (CaPE) are used to evaluate the performance of KDP-based rainfall algorithms, for different pathlengths.

The most commonly used polarimetric radar measurements in rainfall estimation are reflectivity at one polarization (Z H ), differential reflectivity (Z DR ), and specific differential propagation phase (K DP ). The reflectivity measurement requires knowledge of absolute power and is prone to calibration errors. Differential reflectivity is a relative power measurement and is not affected by absolute calibration errors. However, all the algorithms that use differential reflectivity also include reflectivity and therefore are prone to absolute calibration errors. Algorithms to estimate rainfall from specific differential propagation phase are immune to absolute calibration error and attenuation effects [Zrnic and Ryzhkov, 1996]. However, specific differential propagation phase is very noisy at low rain rates. In addition, specific differential propagation phase is estimated as the slope of differential propagation phase measurements over a path. Consequently, there is a trade-off between accuracy and resolution of K DP . Thus there are advantages and disadvantages of each multiparameter radar measurement that translate into the error structure of algorithms involving multiparameter radar measurements. This paper presents a quantitative evaluation of the performance of five different polarimetric radar rainfall algorithms. The performance of the five algorithms is evaluated in the presence of physical variability in rainfall, radar measurement errors, and systematic calibration error.

Specific differential propagation phase (KDP) is estimated as the slope of the differential propagation phase (FDP). The estimate of specific differential propagation phase is typically obtained over a path to minimize measurement error. It is shown that the estimate of specific differential phase over a nonuniform rainfall path is biased, and the bias increases with increased reflectivity variation along the path. It is also shown that the bias can be both positive and negative depending on the nature of nonuniformity in the propagation path. Three models for nonuniform reflectivity variation along the precipitation path are studied. A simple algorithm is presented to correct the bias in the estimation of KDP due to nonuniform rainfall paths. Multiparameter radar data collected over central Florida are analyzed and compared to the theoretical results developed in this paper.

Quantitative application of radar measurements at C band requires correction for attenuation. Algorithms to correct for attenuation and differential attenuation are evaluated based on theoretical analysis as well as radar data. The error structure of three different attenuation correction algorithms based on (a) reflectivity, (b) reflectivity and differential reflectivity, and (c) specific differential propagation phase is analyzed. The error structure of two algorithms to correct the differential attenuation based on (a) reflectivity and differential reflectivity, and (b) specific differential propagation phase is presented. Data from the polarimetric C-band Doppler radar POLDIRAD operated by DLR (Germany) are utilized to intercompare the attenuation and differential attenuation correction algorithms. Radar data and theoretical analysis show that the attenuation correction algorithm using reflectivity and differential reflectivity agrees well with the attenuation correction algorithm based on specific differential phase. Similarly, radar data and theoretical analysis indicate that the algorithms to correct for differential attenuation compare well with each other. In addition the fractional standard error of comparison between the algorithms to correct for attenuation and differential attenuation is in good agreement with theoretical results, providing an indirect verification of the accuracies of the algorithms.