Precipitation measurements are crucial for improving climate models and weather forecasting, with satellite-based radar systems, such as the Dual Precipitation Radar (DPR) on board the Global Precipitation Measurement Core Observatory (GPM CO), playing a pivotal role in enhancing observational coverage, especially in remote regions. In November 2023, an orbit boost maneuver raised the GPM CO’s altitude from 400 km to 435 km, potentially influencing the quality of DPR data. This study evaluates the impact of this altitude increase on the accuracy of DPR-derived precipitation products by comparing data acquired before and after the orbit boost. We assess the performance of DPR measurements against ground-based laser disdrometer data collected in Italy between May 2018 and November 2024. The results highlight the effects of the altitude change on rainfall retrievals, contributing to a better understanding of how orbital adjustments can affect satellite-based precipitation observations.

Physical characteristics of precipitation, like temporal and spatial variability, jointly with coverage and costs of conventional meteorological devices for quantitative rainfall estimation (i.e., rain gauges, disdrometers, weather radars) make the precipitation monitoring a complex task. However, real time rainfall maps are an important tool for many applications, dealing with environment, social activities, and business. Recently, the use of “opportunistic” methods to estimate rainfall has been investigated, highlighting the possibility to exploit inexpensive opportunities to augment information about precipitation. This paper deals with SmartLNBs (Smart Low-Noise Block converters), which are commercially available interactive digital video broadcasting (DVB) receivers designed to be used as bidirectional modems for commercial interactive TV applications. In the last few years an algorithm that converts the SmartLNB raw data into attenuation values, from which the rainfall rate is obtained, has been developed and evaluated. The aim of this paper is to describe the improvements of the rainfall estimation from SmartLNBs brought by significant changes in the data acquisition from SmartLNB and by algorithms’ update. One year of data collected in Rome and Tuscany (Italy) are analyzed to test the performance of SmartLNB in estimating rainfall accumulation with respect to co-located rain gauges and disdrometer in the new configuration. Comparing SmartLNB and disdrometer data in Rome we obtained Root Mean Square Error equal to 7.7 mm, Normalized Mean Absolute Error equal to 44%, with a correlation coefficient of 0.91, that can point out the maturity of the technique.

Millimitere (mm) and sub-millimiter (sub-mm) radiometer observation of the atmosphere from space is an appealing topic given the variety of information obtainable. The exploitation of window frequencies and various gaseous absorption bands at 50/60, 118, 183 allow for a better representation of tropospheric temperature profiles, water vapor and cloud liquid contents, as well as for hail detection, and to some extent, rainfall and snowfall estimates. These observations have shown tangible impacts on numerical weather prediction and data assimilation, climate benchmarking, hydrometeorology, extreme weather nowcasting, and civil protection. Further benefits for ice cloud retrievals are expected from observations at higher frequency, such as 243 and 664 GHz channels foreseen in the upcoming EUMETSAT Polar System-Secon Generation (EPS-SG) Ice cloud imager (ICI) sensor [1] , [2] . The increase in frequency, and consequently the reduction in wavelength, from mm to sub-mm also gives the technological advantage of reduced size of the overall system, maintaining performances unchanged, thus making it easier to implement constellation of radiometers with the glaring benefit of incrementing the repetition time of the satellite overpasses. A precursor on this topic was proposed by Prof. Marzano in 2009 [3] with the FLOwer constellation of MM-wave RADiometers (FLORAD) mission. The FLORAD concept consisted in tree small satellites (<500 kg each) in a pseudo-stationary orbit (also termed as resonant or floreal orbit) to have a repetition rate of 1 hour over the Mediterranean area with a cross-track scanner sensor named FLOMIS (FLORAD microwave imager-sounder) with channels ranging from 90 to 230 GHz. Two evolutions of FLORAD were proposed later, adding radio occultation [4] or cloud radar [5] . Ten years later, technological progress allowed the deployment of a proof-of-concept radiometer on a cubesat (1.23 kg), named TEMPEST-D [6] , as well as TROPICS [7] , a six-radiometer constellation (5.34 kg each). These missions exploit satellites that are orders of magnitude smaller and cheaper than traditional satellites operated by federal agencies, revolutionizing the next-generations of Earth-observations [8] . In Europe, the ESA/EUMETSAT prototype satellite of the Arctic Weather Satellite (AWS) mission has been recently approved. The AWS Microwave Radiometer (MWR) is a 19 channel cross-track scanning radiometer consisting of a rotating antenna focusing the incoming radiation onto four feedhorns (one for each group of channels) and four receivers, covering the frequency range 50–325 GHz. The AWS will be the forerunner of the potential EPS-Sterna mission, a constellation of small (120 kg) polar-orbiting satellites based on the AWS, each carrying a single microwave radiometer providing frequent coverage of the Earth and full coverage of the polar zones with no gaps. The EPS-Sterna would complement the MetOp series as well as the US NOAA’s Joint Polar Satellite System by providing more frequent observations mainly for temperature and humidity sounding but also for improving precipitation monitoring at high latitudes.

Accurate measurement and monitoring of precipitation is crucial for many applications, such as flood and drought risk assessment and management. Conventional meteorological devices for estimating precipitation (i.e., rain gauges, disdrometers, active and passive remote sensors, be they ground-based, spaceborne, or airborne) have their own strengths and weaknesses. The latter are often related to time and space resolution, coverage, and cost. In the last two decades, several studies have been carried out to exploit opportunistic signals of terrestrial microwave communication links to improve precipitation estimation capability. This study describes and evaluates a method to retrieve rainfall rate from the signal-to-noise ratio obtained from commercial interactive digital video broadcasting (DVB) receivers [referred as to smart low-noise blocks (smartLNBs)]. During a 1-year measurement campaign carried out in Tuscany (Italy) with purposely deployed instruments, the precipitation values estimated from a set of SmartLNBs were compared with measurements from co-located rain gauges and disdrometer. The normalized mean absolute error (NMAE) and root-mean-square error (RMSE) obtained comparing the total cumulative precipitation from a SmartLNB and a disdrometer are 48.8% and 7.46 mm, respectively. Encouraging results also come from comparing the total precipitation amounts as measured by the SmartLNBs and by the rain gauges, with values of NMAE (respectively, RMSE) ranging between 44% and 82% (respectively, 5.2 and 11.5 mm).

In recent years, severe weather phenomena have occurred with increasing frequency throughout the Mediterranean area. Because of the extreme intensity of the phenomena and of their small spatio-temporal scales, an early warning of severe rainfall through a timely and accurate estimation is crucial for reducing the hydrological risk and for disaster mitigation. On the other hand, the rain-gauge networks are often not able to detect rainfall due to their limited sampling capability, as occurred in the two case studies presented in this work, characterized by severe weather conditions. For both case studies, we utilized a data-fusion procedure aimed at real-time estimation of cumulative rainfall fields, based on the reflectivity factor Z provided by ground weather radar and rain-gauge estimates of the rainfall intensity R. The use of Z-R relationships determined a priori, as typically done by operational weather services, is not appropriate for obtaining accurate rainfall estimates, as needed, for instance, to forecast flash flood events. Consequently, additional information is needed. The procedure utilized is based on an adaptive data-fusion technique, relying on the dynamic adjustment of the coefficients of the Z-R relationship to the observed phenomenon. Its application to two severe weather case studies demonstrated the capability of the methodology to correctly identify and monitor areas of high potential risk as well as to provide rainfall estimates in such areas.

This work describes a new self-contained methodology for separating the vertical wind component and hydrometeor signature from Doppler spectra acquired by vertically pointing microwave radars during rain precipitation events. Forward electromagnetic model simulations and statistical inversion of the quantities derived by the Doppler spectrum are used to achieve the aforementioned goal. Some field campaign measurements are preliminarily used to support the analysis. Results from numerical simulations show a capability of detecting vertical wind orientation with a success rate of approximatively 90%, whereas the estimation error of the wind strength was found of the order of 37%.

The primary concern of this paper is to evaluate the accuracy of drop size distribution (DSD) retrievals for the rain region using global precipitation measurement (GPM) dual-frequency precipitation radar (DPR). Direct comparison between ground and spaceborne radar might answer the question, but unfortunately, this is a difficult and challenging task that can result in largely unmeasurable uncertainty. In this paper, the self-consistent numerical method (SNM) was used to estimate microphysical parameters along rain profiles. The assessment of the SNM performance characterizes its accuracy in the microphysics retrieval. It follows that the profile data set used for the evaluation plays an important role in this process. The ideal solution would be to use real conditions. For this purpose, a new approach for providing vertical profiles closer to the natural vertical variability of rainfall microphysical characteristics and its global vertical structure can be derived from the observations of NASA dual-frequency, dual-polarization, Doppler radar (D3R)—a matched-beam,$\textit {Ku}$- and$\textit {Ka}$-band dual-frequency radar that is similar to the precipitation radar (DPR) on board the GPM core satellite. The profile data set was generated using reflectivity measurements collected in RHI mode, after correcting them for attenuation using the polarimetric measurements of the$\textit {Ku}$-band. With this data set, the behavior of SNM is evaluated in the presence of the inaccuracy introduced by taking the constant value of$\mu $and the measurement errors. The entire version 4 of the GPM DPR precipitation products obtained with data collected from September 1, 2014 to February 28, 2017 was used. In the presence of bright-band features, the rain region of matched scans, for which measurements were simultaneously collected by$\textit {Ku}$- and$\textit {Ka}$-band radars, was employed to retrieve DSD parameters with SNM and GPM algorithms. A direct comparison between the two procedures was performed in terms of merit factors. The results show a significant difference between the performance of the two methods by not taking into consideration the SNM procedures that, instead, are being performed by the GPM algorithms.

A new data processing chain has been applied to polarimetric radar observations at X band. The article describes the chain and provides an analysis of the quality of the polarimetric variables. Different algorithms have been studied based on simple Zh-R, on Zhand Zdr, on Kdpand Kdpand Zdr. Evaluation against rain-gauges, confirms the superiority of polarimetric algorithms. It also shows that the benefits brought by algorithms based on power variables, Zhand Zdrare critically dependent on the capability of a correct calibration.

This paper presents and discusses some results of a specific Weather Radar Post-Processor software that provides weather classification maps and related binary risk maps after the processing of dual polarization airborne X band radar outputs. Weather classification maps and binary risk maps are used as input to decision support systems for aircraft pilots in case of bad weather conditions. The software runs inside an Electronic Flight Bag and the results shown here are obtained using true flight plans coming from the flight management system of an ATR72 flight simulator. Dual polarization avionic X band radar outputs are generated assuming true weather scenarios with a radar signal simulation software developed by the research team of the Radar and Surveillance Systems Laboratory of CNIT.

In this study, precipitation retrievals exploiting the available overpasses of passive microwave (PMW) cross-track and conically scanning radiometers in the GPM era are used to monitor the evolution of heavy precipitation systems occurred during the fall 2014 in Italy. Two different physically-based retrieval algorithms (CDRD for SSMIS and PNPR for AMSU/MHS and ATMS) are used in conjunction with official NASA/JAXA GPM instantaneous precipitation products (for AMSR-2 and GMI). The comparison with dual-polarization radar observations at ground evidences the ability of the different sensors to identify different precipitation areas and regimes. This is particularly relevant in presence of complex orography, often found in proximity of coastal areas for the analyzed cases. Analysis of the accuracy and consistency of the retrievals is carried out taking into account the different spatial resolution and viewing geometry of the different radiometers and the different approaches used for the precipitation retrieval.

Polarimetric techniques applied to radars on-board civil aircraft can improve the estimation of risk zones due to dangerous weather during flight. Usually, X-band weather radars are installed at the nose of civil aircrafts. This band is affected by strong attenuation in case of intense precipitation (liquid or mixed phase). The current systems do not compensate backscattered power measurements for attenuation caused by propagation through precipitation, while dual-polarization radars are able to compensate effectively this source of error and to discriminate hydrometeors. In this work, a classification algorithm based on support vector machines (SVM) is proposed. Training is driven by the output of a Fuzzy Logic (FL) classification algorithm (typical classification approach used for ground-based weather radar). SVM high performance in terms of time processing and its flexibility of configuration using all type of inputs variables are important characteristics to be included in some avionic specific equipment, such as the Electronic Flight Bag (EFB). Two datasets have been used to test the SVM classification algorithm. The first dataset is composed of simulated radar polarimetric observations at X-band and the second one is composed of actual dual-polarization radar measurements collected during the Special Observation Period (SOP) 1.1 of HYdrological cycle in MEditerranean EXperiment (Hymex) campaign by the C-band Doppler dual-polarization weather radar (Polar 55C) installed at ISAC-CNR in Rome. Good performance are obtained for SVM classificator. The comparison with FL output shows a good agreement (up to 90%) both in qualitative comparison maps by maps and using a quantitative approach which metric is based on the confusion matrix.

The GPM core satellite will be ready to launch in February 2014, less than 7 months after the end of IGARSS2013 symposium. In the pre-launch era, several international validation experiments such as LPVEX (Light Precipitation Validation Experiment), MC3E (Midlatitude Continental Convective Clouds Experiment), and IFloodS (Iowa Flood Studies) have already generated a substantial set of measurements that continue to contribute to the development and test of pre-launch GPM algorithms. Following launch, it is expected that GPM ground validation will focus on evaluating precipitation data products, generated by a constellation of GPM satellites as well as assumptions made in algorithms. This paper presents simple concepts of dual-polarization radar observation strategies and products that can be generated for the post launch era of the GPM program, especially when there are satellite overpasses. The use of microphysical retrievals from ground-based radars and in-situ observations for validating the retrievals from space based observations is the main focus of this paper.

The 4-satellite constellation Cosmo Sky Med of the Italian Space Agency is providing high quality observations suitable for many applications in “all weather” conditions. However, Cosmo Sky Med SARs use a frequency at X-band (9.6 GHz) for which attenuation due propagation through precipitation is not negligible and can affect several SAR applications. In some conditions, especially heavy thunderstorms, precipitation effects can be significant, masking features of interest of the observed surface. During the two years of operation of Cosmo Sky Med, several images collected in stripmap, pingpong and scansar mode, both over sea and terrain and sea are analyzed and discussed by using simultaneous measurements collected by operational and research weather radar.

Effects of rain in SAR images have been noted since early missions. For SARs at X-band such as the satellites of the Italian Space Agency constellation COSMO SkyMed or the DLR TerraSAR-X, SAR returns from surface are attenuated by precipitation that gives a specific signature in SAR images that depend on polarization. Attenuation, jointly with a reduced space resolution determined by the Doppler spectrum of precipitation returns, limits the capability of detecting surface features during storms. On the other hand, such precipitation signature could be used for atmospheric studies or precipitation measurements. Characteristics induced by precipitation on polarimetric COSMO SkyMed images collected have been detected and analyzed. The analysis methodology exploits also the availability of coincident measurements of precipitation from ground based radars, used to reconstruct the component of SAR return due to precipitation.

Dual polarization radar measurements of rainfall parameters are based on the assumption of a mean shape-size relationship of raindrops. This paper focuses on retrieving and interpreting the mean raindrop shape-size relation from polarimetric radar observations. A procedure to retrieve the drop shape-size relation that governs the polarimetric radar observations of reflectivity (Zh) differential reflectivity (Zdr) and specific differential propagation phase (Kdp) is presented. The mean drop shape-size relations retrieved from measurements collected by the NCAR SPOL radar during three campaigns are analyzed to explore whether the natural raindrop shape-size relation can be described by a unique model.

Information about the shape of raindrops is critical for estimating rainfall rate with dual polarization radar. As described in the literature, the relation describing drop oblateness as a function of its equivolumetric diameter is nonlinear. There are several relations that express the shape-size dependence as nonlinear fourth-order polynomials. In fact, there is still no consensus regarding the most appropriate equation to use to describe the shape-size relation. Moreover, while non-linear equations are important for studying raindrop shape, it is not clear that they are needed to estimate an integral quantity such as rainfall rate. The validity of using a simple equivalent linear shape-size model based on the principle of the mean value theorem for estimating rain from dual polarization radar measurements is examined. Proposed rain algorithms based on the estimation of the drop shape are compared with algorithms that assume specific a-priori fixed drop shape-size relations expressed by a fourth order polynomial. Reconstructed rain and radar measurement profiles obtained from real radar observations were used to this purpose. Results show that the proposed rain algorithms perform better or at least equal to the algorithms derived assuming a priori fixed shape-size models, demonstrating that the prevailing model directly derived from data is suitable for rainfall retrieval purposes.

Information about the shape of raindrops is critical for the retrieval of rainfall rate from dual-polarization radar measurements. As described in the literature, the relation describing drop oblateness as a function of its equivolumetric diameter is nonlinear. There are several relations that express the shape-size dependence as a nonlinear fourth-order polynomial that has five coefficients or 5 DOF. While these are important for studying raindrop shape, it is not clear that they are needed to estimate an integral quantity such as rainfall rate. This paper examines the validity of using a simple equivalent linear shape-size model based on the principle of the mean-value theorem for estimating rain from dual-polarization radar measurements. Assuming Rayleigh-Gans scattering for spheroids to describe raindrop scattering and drop oblateness described by a linear relation between axis ratio and equivolumetric diameter, a general self-consistency equation relating reflectivity factor, differential reflectivity, specific differential phase shift, and slope of the shape-size relation is obtained for each radar operating frequency. In this 4-D space, relations for estimating rainfall rate without requiring an assumption of a specific drop-shape model from polarimetric radar measurements were obtained. To study all the implications arising from electromagnetic and microphysical aspects, reconstructed rain and radar measurement profiles obtained from real radar observations were used to test the performance of the proposed rain estimation procedure. The performance is compared with algorithms derived assuming specific a priori fixed drop-shape-size relations expressed by a fourth-order polynomial. Results show that, in general, the proposed rain algorithms perform better or at least equal to the algorithms derived assuming a priori fixed shape-size models, demonstrating that the prevailing model directly derived from data is suitable for rainfall retrieval purposes.

X-band weather radar systems present several advantages mainly related to their lower cost and smaller size relative to their S and C band counterparts. The main drawback of X-band is the attenuation suffered by the electromagnetic wave propagating through precipitation that reduces the reliability of rainfall estimates based on power measurements. Developments in dual-polarization techniques have provided solutions to mitigate this problem and have revived the interest on X-band radar systems for operational applications. Because of this growing interest, there is a need to evaluate the performance of X-band polarimetric radars for quantitative rainfall estimation. At X-band, attenuation affects any radar parameter based on backscatter power measurements such as Zh, while differential attenuation affects parameters based on differential power measurement such as Zdr. Consequently, Zhand Zdrmust be corrected prior to use in quantitative applications such as rainfall estimation. However, correction procedures can introduce additional errors that impact on rain estimation and therefore must be applied with caution. When using the X-band rain algorithm based on Zhand Zdr, the biases due to attenuation and differential attenuation nearly cancel each other and result in a small bias of the estimated rainfall rate, so that correction of Zhand Zdrmay not be needed. This paper investigates this property of rain rainfall estimation based on X-band dual-polarization measurements.

Rain microphisycs retrieval has been proposed and demonstrated mainly at S-band where attenuation effects are usually negligible. Recent advances in attenuation correction techniques enable rain microphisycs retrieval also using attenuating frequencies, such as X- and C-band. Most up to date attenuation correction methodologies are based on the use of a constraint represented by the total amount of the attenuation encountered along the path, which is distributed over each range bin contained in the path. The inner self-consistency of radar measurements can be exploited to improve the attenuation estimate obtained using those techniques. In fact, based on the self-consistency principle, an optimization procedure can be devised to obtain the best estimate of specific and cumulative attenuation as well as of specific and cumulative differential attenuation. The main goal of the study is the examination of the drop size distribution retrieval from X-band radar measurements after attenuation correction. The study is based both on simulated and measured data. Simulations based on the use of profiles of gamma drop size distribution parameters obtained from S-band observations are used for quantitative analysis. Examples of the DSD parameter retrievals using radar measurements corrected for attenuation and differential attenuation are also shown.

A new methodology for correcting the reflectivity factor and differential reflectivity at C-band for rain attenuation is presented. Following a methodology already proposed for X-band, new algorithms are developed based on a full self-consistency condition, describing the interrelation between polarimetric measurements and attenuation along the rain medium. The performance of the algorithms for application to C-band dual-polarization radars is evaluated extensively using radar data collected by the Polar 55C in the Rome region (Italy). Quantitative evaluation of attenuation and differential attenuation estimates is conducted based on C-band dual-polarization observations generated from S-band radar measurements. Evaluation of the new full self-consistency algorithms shows a significant improvement in performance. A key result of this iterative technique is the capability to remove any systematic bias from attenuation and differential attenuation estimates that could arise from drop size distribution variability

Dual polarization weather radars have brought in significant advancement to precipitation observation, as rainfall rate estimation, microphysical characterization, and hydrometeor classification. The improvements have been mostly demonstrated at S-band frequency where attenuation effects are usually negligible. In Europe C-band is largely adopted in operational and research radars because of larger differential phase measurements, reduced antenna size and an overall lower cost with respect to that of S-band systems. The major disadvantage is that the signal attenuation is not negligible. In the context of GPM Ground Validation, techniques to compensate the reflectivity measurements for propagation effects are thus necessary to obtain GV products from ground-based C-band radars. The attenuation correction methodology using differential phase shift as constraint has shown a good performance. One of the advantages of polarimetric radar measurements is their self-consistency. Starting from the initial guess of attenuation correction provided by the rain profiling algorithm, self-consistency is used in an iterative technique to improve the accuracy of attenuation correction at C-band. The obtained accuracy is evaluated in terms of bias and standard error using C-band profiles generated from S-band dual polarization observations.

This paper reviews the research activities carried out by the radar meteorology group that was established at the Institute of Atmospheric Physics of the National Research Council of Italy. The group started in the late 1970s, and, after the publication of a keystone paper on the use of linear polarization by Seliga and Bringi (1976), its primary focus has been on po- larimetric radar techniques and their meteorological applications. The research efforts, realized through close cooperation first with Pennsylvania State University and later with Colorado State University, have produced positive results that have contributed to a spread in the knowledge of dual- polarization techniques and in learning important information about microphysical processes in clouds and precipitation. This paper highlights some of these research achievements.

Monitoring of precipitation using high frequency radar systems such as X-band is becoming increasingly popular due to their lower cost compared to their counterpart at S-band. Meteorological radar systems operating at S-band frequencies are not affected by attenuation due to precipitation. The S-band radar systems are typically expensive with large antennas, and high power transmitters. Recently, network of meteorological radar systems at higher frequencies such as X band are being pursued, especially for low cost and targeted applications, such as coverage over a city or a small basin. Attenuation correction of the signal returns from these radars is important for quantitative applications. In order to design the 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. The only way to collect such data is from dual frequency radar system with matched beams. This paper presents a methodology to generate realistic range profiles of radar observations 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 form the Colorado State University CHILL radar and the National Center for Atmospheric Research S-POL radar are used to simulate X-band radar observations. The two procedures and sample applications are presented.

Ground validation is a critical part of the European contribution (EGPM) to the Global Precipitation Measurement mission (GPM). According to the EGPM approach, the objective of the ground validation activities extends beyond the traditional measurement of rainfall at ground, to include the generation of a 3-Dimensional data set to be used for validating the microphysical models as well as the assumptions made in the retrieval algorithms. The complexity of the Italian geography and the diversity of its climatology, especially for the distribution of precipitation, suggests the need of a distributed ground validation site which can be effectively realized using the permanent structures where the instrumentation is, or will be in the next future, deployed and operated routinely. The resulting distributed Italian site would be an essential and integral part of the more complex European EGPM supersite. The paper introduces basic concepts to develop plans for the Italian contribution to the ground validation program for EGPM.