The roadmap to enable operational soil moisture (SM) monitoring for meteorologic and hydrological early warning depends on the capabilities of the available remote sensing and modelling products. Since each type of soil moisture product shows specific strengths and limitations due to their technical restrictions over certain environments, the detection of impactful anomalies across a wide range of conditions and scales is often challenging and incomplete without a combination of complemental data types of sufficient resolution, revisit time and coverage. This study evaluates the capabilities of SM products of different nature and their compatibility for combination, with special attention to their uncertainties in spatial consistency and in residual trends. While the first has been often revisited to validate remote sensing and modelling products against in situ data, the last is often overlooked in studies addressing SM changes despite its potential to disrupt the outcomes.To meet the demands of operational monitoring this study evaluated three SM products: (1) the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) active Advanced SCATterometer (ASCAT)-derived dataset, (2) the passive subset of the European Space Agency (ESA) - Climate Change Initiative (CCIp), and (3) the modelled dataset from the European Drought Observatory (EDO). The analysis was carried out over Europe in the period 2007-2022 at 10 d temporal scales and 5 km x 5 km spatial sampling. First, Pearson's correlation coefficient (R) is used to measure the correspondence between H120, H121, CCIp and EDO SM products. Then triplets of the active, passive and model-based products are applied triple collocation analysis (TCA) to assess their performance based on TCA metrics such as the correlation, error variance, sensitivity and signal-to-noise ratio.We obtained that these popular well-validated datasets are increasingly capable in view of the notable TCA scores obtained but still subject to patches of spatial inconsistency and residual trends when compared against in situ SM data of the International Soil Moisture Network (ISMN). These uncertainties have minimal impact on drought monitoring in most of Europe, except in snow prone regions and for the assessment of long-term soil moisture trends used to design climate adaptation policies. Furthermore, each type of soil moisture product prevails in terms of triple collocation scores over the others under specific environmental conditions of the European continent. In view of the synergies shown by the active and passive remote sensing and the modelled SM estimates, two merged products are proposed and tested against the in situ data. The merging of the products is conducted by combining the various products based on weights calculated proportionally to the R_TCA scores of the triplets equalized in dynamic range matching their cumulative distribution functions. Results indicate that combining H SAF ASCAT, CCIp and EDO equals or surpasses the spatial and temporal consistency of the individual SM products alone, even when only the near-real-time products of H SAF ASCAT and EDO are combined. The evaluation of the trends of the individual products also indicates that small residual trends remain despite the improved filtering of the uncertainties, but given their differing sign of the trend, once combined into merged products can provide improved temporal stability of the series. Thus, merging remote sensing and modelled SM products enhances spatial consistency, resolution, temporal coverage and near-real-time capabilities for better European-scale drought monitoring, strengthening the early warning and risk management systems devoted to improving societal and environmental resilience.

Soil moisture is a crucial state variable for understanding the water cycle. The increasingly available soil moisture data from remote sensing and models is rapidly facilitating improved hydrological analysis and evaluation of climate change impacts. To discern the degree of alteration of soil moisture, the patterns of spatiotemporal anomalies must be considered, but often product-specific uncertainties are overlooked. Such limitations are of particular concern for the operational monitoring and long-term evaluation of soil moisture. Among the sources of uncertainty jeopardizing remotely sensed and modeled soil moisture, this study evaluates over Europe (1) the heterogeneous spatial patches of validity, (2) the residual trends in the series, and (3) the sensitivity of anomaly detection to the baseline period of popular soil moisture products such as the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF), the passive subset of the Climate Change initiative on SM (CCIp) and the European Drought Observatory (EDO) datasets. The inter-comparison of these remotely sensed and modeled soil moisture products by triple collocation analysis and against data of the international soil moisture network (ISMN) provides insightful results regarding (1) the contrasting patches of accurate soil moisture estimates, (2) the existence of residual temporal trends in the series, and (3) the differing sensitivity of the products to the baseline period for anomaly analysis. The factors impacting products are subject to debate, particularly concerning spatial and temporal consistency. Merged products combining H SAF, EDO and CCIp are also assessed to elucidate their potential and limitations for operational monitoring in comparison to individual products. Overall, the combined products equal or exceed the performance of individual products while incorporating specific benefits and drawbacks. Outcomes also inform about the best-performing product by area and period. All in all, the study illustrates the notable degree of consistency of commonly available soil moisture databases for multiple applications, despite some constraints, while highlighting the potential of merged soil moisture products for the operational monitoring of droughts within the European Drought Observatory (EDO) system.

Lightning can influence many human activities, being a threat also for human lives. The Mediterranean area is prone to thunderstorms and lightning. In this context, lightning forecast plays a fundamental role. We studied the impact of lightning data assimilation (LDA) on lightning and precipitation forecast over Italy and over part of the Central Mediterranean Basin. First, we highlight some characteristics of strokes over Italy and the Central Mediterranean [1], considering data over 13 years recorded by LINET (LIghtning NETwork). The analyses of the records show that lightning activity occurs mainly in summer and fall; moreover, a substantial change of convection characteristics between the two season is apparent. In summer, convection occurs over the land, in fall it is mainly over the sea.Then, we consider a two-seasons data assimilation experiment [2] running the Weather Research and Forecasting (WRF) model coupled with the Dynamic Lightning Scheme (DLS) at 3km horizontal resolution for summer 2020 and fall 2021. Each simulation produced the forecast for the following 6h. Therefore, the representation of a whole day needs four different simulations. Verification is done over two sub-periods, 0-3h and 3-6h after assimilation. Results for the 0-3h phase show a positive impact of LDA on strokes forecast, both improving correct forecasts and reducing false alarms. Depending on the case, LDA can trigger convection missed by control forecast and can correct strokes’ patterns, leading to predictions more in agreement with observations. An improvement compared to the previous day forecast, without LDA, is also obtained. Therefore, the forecast over the 0-3h phase with LDA is applicable to issue warnings and alerts as the storm is approaching. LDA forces convection where lightning is observed. Consequently, lightning forecast improvement given by LDA, is more evident over the land in summer and over the sea in fall. The 3-6h phase show a negligible impact of LDA on strokes forecast.References[1] Marco Petracca, Stefano Federico, Nicoletta Roberto, Silvia Puca, Leo Pio D'Adderio, Rosa Claudia Torcasio, Stefano Dietrich. A 13-year long strokes statistical analysis over the Central Mediterranean area, Atmospheric Research, Volume 304, 2024, 107368, ISSN 0169-8095, https://doi.org/10.1016/j.atmosres.2024.107368.[2] Stefano Federico, Rosa Claudia Torcasio, Jana Popova, Zbyněk Sokol, Lukáš Pop, Martina Lagasio, Barry H. Lynn, Silvia Puca, Stefano Dietrich, Improving the lightning forecast with the WRF model and lightning data assimilation: Results of a two-seasons numerical experiment over Italy, Atmospheric Research, Volume 304, 2024, 107382, ISSN 0169-8095, https://doi.org/10.1016/j.atmosres.2024.107382.

This paper presents the first detailed analysis of cloud-to-ground (CG) and intra-cloud (IC) strokes characteristics from the Lightning Detection Network (LINET) over Italy and the Central Mediterranean area, a lightning active area in south Europe. We study the strokes over a 13-year period from 2010 to 2022, aiming to understand how it varies with different temporal scales (hourly, monthly, seasonally, and yearly), surface types (sea and land), and ground levels (0-100 m; 100-200 m; 200-400 m; 400-800 m; 800-1200 m; 1200-2000 m and above 2000 m). We found that the stroke's maximum activity was observed in August; specifically, July has the maximum activity over the land with a maximum diurnal peak in the afternoon, while in September, the convection shifts over the sea with a secondary daily maximum in the morning. The largest current intensities are observed in January, over sea and during nighttime. Moreover we found that stroke current intensities, polarity and IC height emissions are influenced by ground altitude level. Our paper provides new insights into the spatio-temporal patterns and characteristics of lightning over Italy and the Central Mediterranean area, which can be useful for improving weather forecasting, climate modeling, risk assessment, and damage mitigation strategies in this area.

High -resolution soil moisture data is crucial in the development of hydrological applications as it provides detailed insights into the spatiotemporal variability of soil moisture. The emergence of advanced remote sensing technologies, alongside the widespread adoption of machine learning, has facilitated the creation of continental and global soil moisture products both at fine spatial (1 km) and temporal (daily) scales. Some of these products rely on several data sources as input (satellite, in situ, modelling), and therefore an evaluation of their actual spatial and temporal resolution is required. Nevertheless, the absence of appropriate ground monitoring networks poses a significant challenge for this assessment. In this study, five high -resolution (1 km) soil moisture products (S1-RT1, S1 -COP, SMAP-Planet, SMAP-NSIDC, and ESACCI-Zheng) were analysed and evaluated throughout the Italian territory, together with a coarse resolution (12.5 km) dataset for comparison (ASCAT-HSAF). The main objective is to investigate their actual spatial and temporal resolution, and accuracy. Firstly, a cross -comparison of the products in space and time is carried out, including the use of triple collocation analysis. Secondly, an application -based assessment is implemented, considering irrigation, fire, drought, and precipitation case studies. The results clearly indicate the limitations and the potential of each product. Sentinel -1 based products (S1COP and S1-RT1) are found able to reproduce high -resolution spatial patterns by detecting localised events for irrigation, fire, and precipitation. Their lower temporal resolution leads to accuracies lower than that of the SMAP-Planet product, and comparable with SMAP-NSIDC and ESACCI-Zheng products. However, SMAP-Planet is found to have an actual spatial resolution coarser than 1 km. The study highlights the need for further research to improve the high -resolution soil moisture products, and particularly to determine accurately the spatial resolution represented in soil moisture products. At the same time, the analysed products are found able to address high -resolution applications for the first time, opening promising activities for their operational use in hydrology and water resources management.

Snow is the main positive component of surface mass balance in Antarctica. Therefore, accurate snow measurements of snowfall play a crucial role in characterizing the Antarctic ice sheet's variability and its impact on the sea-level rise. The remote sensing of precipitation and in situ measurements are, in general, challenging tasks and even more difficult in an environment like Antarctica. Radar profilers are increasingly used in Antarctic research stations to highlight snowfall processes through vertical reflectivity profiles and improve the quantitative precipitation estimation, also exploiting the synergy with surface measurements. This work summarizes the field campaign experience at the Italian Antarctic station "Mario Zucchelli," analyzing the vertical profiles of reflectivity collected by a Micro Rain Radar (MRR), set with a vertical resolution of 35 m and a temporal resolution of 1 min. Such an MRR set up allowed us to use a trustworthy range gate just 105 m above the ground, thus avoiding contamination of clutter. Factors influencing the behavior of vertical profiles are analyzed, emphasizing the sublimation process and its implications on surface snowfall estimation at the ground.

Quantitative estimation of snowfall using radar is a challenging task that is usually accomplished using relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR), typically expressed as power-law (Ze = a × SRb) whose parameters are obtained from long-term measurements. Unfortunately, the changeability of microphysical and scattering characteristics of snowflakes makes them highly variable. The proposed method takes advantage of the estimation of the snowflake microphysical characteristics and develops six Ze-SR relationships depending on particle habit. A classification of particles is obtained by comparing co-located Micro Rain Radar and Parsivel disdrometer observations coupled with a DDA backscattering model in terms of radar reflectivity and is used to select the appropriate Ze-SR relationship. The method was tested using ground-based instruments installed at the Italian Antarctic Station Mario Zucchelli, in the framework of the projects APP (Antarctic Precipitation Properties), MALOX (MAss LOst in wind fluX), and IAMCO (Italian Antarctic Meteo-Climatological Observatory), funded by the Italian National Antarctic Program (PNRA). The Micro Rain Radar was set at the highest vertical resolution (35 m) so that the first trusted range gate was at only 105-m height, close enough to the ground level to be compared with disdrometer particle size distributions. We analyzed data from 52 precipitation days of the 2018–2019 and 2019–2020 summers for a total of 23,566 snowfall minutes. Disdrometer data were corrected from the influence of wind by assigning a reliability weight to each Parsivel bin based on simultaneous disdrometer, MRR, and wind measurements. This method preserves more precipitation data than the more widely used censoring methods that eliminate data collected when wind speed exceeds a given threshold: since strong winds are often associated with significant snow events, censoring methods tend to discard inportant precipitation measurements. The consistency of disdrometer and radar measurements is tested for six snow categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine) in terms of radar reflectivity matched in a 10-min time frame. The related Ze-SR relationship of the selected snow category is used to calculate the cumulated snowfall amount. The comparisons of Ze from disdrometer and MRR at the 105-m height show good agreement, even for nonwind-corrected disdrometere data, although agreement significantly improves if wind-correction is applied. Of the precipitation minutes, we classified 75% of them as aggregate, with a significant percentage of dendrites. Only 5,830 out of 23,566 falling particles showed pristine characteristics. We estimated 84.6 mm w.e. of accumulated snowfall for the 52 events. Such estimates were compared with measurements from a weighing pluviometer available for 32 out of the 52 considered days. Estimation using variable Ze-SR relationships results in a better agreement with the pluviometer (64 mm w.e. vs. 66.5 mm w.e.) with respect to estimates from fixed Ze-SR relationships found in the literature. Results show that combining MRR and disdrometer is undoubtedly valuable for snowfall estimations. In fact, the significant uncertainties in snowfall radar estimates related to the variability of snow microphysical features can be mitigated by using variable Ze-SR relationships.

Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution that make it possible to obtain a 3D structure of precipitating systems or vertical profiles when used in profiling mode. Radars from space have a lower spatial resolution, but they provide a global view. However, radar-based quantitative estimates of solid precipitation are still a challenge due to the variability of the microphysical, geometrical, and electrical features of snow particles. Estimations of snowfall rate are usually accomplished using empirical, long-term relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR). Nevertheless, very few relationships take advantage of the direct estimation of the microphysical characteristics of snowflakes. In this work, we used a K-band vertically pointing radar collocated with a laser disdrometer to develop Ze-SR relationships as a function of snow classification. The two instruments were located at the Italian Antarctic Station Mario Zucchelli. The K-band radar probes the low-level atmospheric layers, recording power spectra at 32 vertical range gates. It was set at a high vertical resolution (35 m), with the first trusted range gate at a height of only 100 m. The disdrometer was able to provide information on the particle size distribution just below the trusted radar gate. Snow particles were classified into six categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine). The method was applied to the snowfall events of the Antarctic summer seasons of 2018–2019 and 2019–2020, with a total of 23,566 min of precipitation, 15.3% of which was recognized as showing aggregate features, 33.3% dendrite aggregate, 7.3% plates aggregate, 12.5% pristine, 24% dendrite pristine, and 7.6% plate pristine. Applying the appropriate Ze-SR relationship in each snow category, we calculated a total of 87 mm water equivalent, differing from the total found by applying a unique Ze-SR. Our estimates were also benchmarked against a colocated Alter-shielded weighing gauge, resulting in a difference of 3% in the analyzed periods.

Today, a consistent segment of the world’s population lives in urban areas, and this proportion will vastly increase in the next decades. Therefore, understanding the key trends in urbanization, likely to unfold over the coming years, is crucial to the implementation of sustainable urban strategies. In parallel, the daily amount of digital data produced will be expanding at an exponential rate during the following years. The analysis of various types of data sets and its derived applications have incredible potential across different crucial sectors such as healthcare, housing, transportation, energy, and education. Nevertheless, in city development, architects and urban planners appear to rely mostly on traditional and analogical techniques of data collection. This paper investigates the prospective of the data science field, appearing to be a formidable resource to assist city managers in identifying strategies to enhance the social, economic, and environmental sustainability of our urban areas. The collection of different new layers of information would definitely enhance planners' capabilities to comprehend more in-depth urban phenomena such as gentrification, land use definition, mobility, or critical infrastructural issues. Specifically, the research results correlate economic, commercial, demographic, and housing data with the purpose of defining the youth economic discomfort index. The statistical composite index provides insights regarding the economic disadvantage of citizens aged between 18 years and 29 years, and results clearly display that central urban zones and more disadvantaged than peripheral ones. The experimental set up selected the city of Rome as the testing ground of the whole investigation. The methodology aims at applying statistical and spatial analysis to construct a composite index supporting informed data-driven decisions for urban planning.

AbstractKnowledge of the precipitation contribution to the Antarctic surface mass balance is essential for defining the ice-sheet contribution to sea-level rise. Observations of precipitation are sparse over Antarctica, due to harsh environmental conditions. Precipitation during the summer months (November–December–January) on four expeditions, 2015–16, 2016–17, 2017–18 and 2018–19, in the Terra Nova Bay area, were monitored using a vertically pointing radar, disdrometer, snow gauge, radiosounding and an automatic weather station installed at the Italian Mario Zucchelli Station. The relationship between radar reflectivity and precipitation rate at the site can be estimated using these instruments jointly. The error in calculated precipitation is up to 40%, mostly dependent on reflectivity variability and disdrometer inability to define the real particle fall velocity. Mean derived summer precipitation is ~55 mm water equivalent but with a large variability. During collocated measurements in 2018–19, corrected snow gauge amounts agree with those derived from the relationship, within the estimated errors. European Centre for the Medium-Range Weather Forecasts (ECMWF) and the Antarctic Mesoscale Prediction System (AMPS) analysis and operational outputs are able to forecast the precipitation timing but do not adequately reproduce quantities during the most intense events, with overestimation for ECMWF and underestimation for AMPS.

Relations for retrieving precipitation and attenuation information from radar measurements play a key role in radar meteorology. The uncertainty in such relations highly affects the precipitation and attenuation estimates. Weather radar algorithms are often derived by applying regression methods to precipitation measurements and radar observables simulated from datasets of drop size distributions (DSD) using microphysical and electromagnetic assumptions. DSD datasets can be derived from theoretical considerations or obtained from experimental measurements collected throughout the years by disdrometers. Although the relations obtained from experimental disdrometer datasets can be generally considered more representative of a specific climatology, the measuring errors, which depend on the specific type of disdrometer used, introduce an element of uncertainty to the final retrieval algorithms. Eventually, data quality checks and filtering procedures applied to disdrometer measurements play an important role. In this study, we pursue two main goals: (i) evaluate two different techniques for establishing weather radar algorithms from measured DSD, and (ii) investigate to what extent dual-polarization radar algorithms derived from experimental DSD datasets are influenced by the different error structures introduced by the various disdrometer types (namely 2D video disdrometer, first and second generation of OTT Parsivel disdrometer, and Thies Clima disdrometer) used to collect the data. Furthermore, weather radar algorithms optimized for Italian climatology are presented and discussed.

Weather radars are considered an essential tool to identify and analyze rapidly evolving phenomenon like thunderstorms that are related to threats like high precipitation rates or wind speeds. Depending on the characteristics of radar, different features of a thunderstorm can be detected to highlight important characteristics such its severity, trajectory evolution and particle type distribution within the storm cloud. This chapter describes how different radar measurements (reflectivity, Doppler measurements, and dual-polarization measurements) are usually exploited by radar experts to identify important characteristics of such phenomena. In particular, dual-polarization weather radar allows to identify and quantify the presence of graupel particles that can be present within a cumulonimbus and can be associated with electrical activity, which is the unique characteristic of a thunderstorm.

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%.

This work proposes a multi-parameter method for the detection of cloud-to-ground stroke rate (SRCG) associated to convective cells, based on the measurements of a low-cost single-polarization X-band weather radar. To train and test our procedure, we built up a multi-year dataset, collecting 1575 radar reflectivity volumes that were acquired in the pilot study area of Naples metropolitan environment matched with the LIghtning NETwork (LINET) strokes and meteorological in-situ data. Three radar-based variables are extracted simultaneously for each rain cell and properly merged together, using “ad hoc” classification methods, to produce an estimation of the expected lightning activity for each rain cell. These variables, proxies of mixed-phase particles and ice amount into a convective cell, are combined into a single label to cluster the SRCG into two categories: SRCG = 0 (no production of strokes) or SRCG > 0 (stroke production), respectively. Overall, the main results are comparable with those that were obtained from more advanced radar systems, showing a Critical Success Index of 0.53, an Equitable Threat Score of 0.34, a Frequency Bias Index of 1.00, a Heidke Skill Score of 0.42, a Hanssen-Kuiper Skill Score of 0.42, and an area under the curve of probability of detection as a function of false alarm rate (usually referred as ROC curve) equal to 0.78. The developed technique, although with some limitations, outperforms those based on the use of single stroke proxy parameters.

An analysis to evaluate the impact of multiple radar reflectivity data with a three-dimensional variational (3-D-Var) assimilation system on a heavy precipitation event is presented. The main goal is to build a regionally tuned numerical prediction model and a decision-support system for environmental civil protection services and demonstrate it in the central Italian regions, distinguishing which type of observations, conventional and not (or a combination of them), is more effective in improving the accuracy of the forecasted rainfall. In that respect, during the first special observation period (SOP1) of HyMeX (Hydrological cycle in the Mediterranean Experiment) campaign several intensive observing periods (IOPs) were launched and nine of which occurred in Italy. Among them, IOP4 is chosen for this study because of its low predictability regarding the exact location and amount of precipitation. This event hit central Italy on 14 September 2012 producing heavy precipitation and causing several cases of damage to buildings, infrastructure, and roads. Reflectivity data taken from three C-band Doppler radars running operationally during the event are assimilated using the 3-D-Var technique to improve high-resolution initial conditions. In order to evaluate the impact of the assimilation procedure at different horizontal resolutions and to assess the impact of assimilating reflectivity data from multiple radars, several experiments using the Weather Research and Forecasting (WRF) model are performed. Finally, traditional verification scores such as accuracy, equitable threat score, false alarm ratio, and frequency bias – interpreted by analysing their uncertainty through bootstrap confidence intervals (CIs) – are used to objectively compare the experiments, using rain gauge data as a benchmark.

Near surface quantitative precipitation estimation (QPE) from weather radar measurements is an important task for feeding hydrological models, limiting the impact of severe rain events at the ground as well as aiding validation studies of satellite-based rain products. To date, several works have analyzed the performance of various QPE algorithms using actual and synthetic experiments, possibly trained by measurement of particle size distributions and electromagnetic models. Most of these studies support the use of dual polarization radar variables not only to ensure a good level of data quality but also as a direct input to rain estimation equations. One of the most important limiting factors in radar QPE accuracy is the vertical variability of particle size distribution, which affects all the acquired radar variables as well as estimated rain rates at different levels. This is particularly impactful in mountainous areas, where the sampled altitudes are likely several hundred meters above the surface. In this work, we analyze the impact of the vertical profile variations of rain precipitation on several dual polarization radar QPE algorithms when they are tested in a complex orography scenario. So far, in weather radar studies, more emphasis has been given to the extrapolation strategies that use the signature of the vertical profiles in terms of radar co-polar reflectivity. This may limit the use of the radar vertical profiles when dual polarization QPE algorithms are considered. In that case, all the radar variables used in the rain estimation process should be consistently extrapolated at the surface to try and maintain the correlations among them. To avoid facing such a complexity, especially with a view to operational implementation, we propose looking at the features of the vertical profile of rain (VPR), i.e., after performing the rain estimation. This procedure allows characterization of a single variable (i.e., rain) when dealing with vertical extrapolations. In this work, a definition of complex orography is also given, introducing a radar orography index to objectively quantify the degree of terrain complexity when dealing with radar QPE in heterogeneous environmental scenarios. Three case studies observed by the research C-band polarization agility Doppler radar named Polar 55C, managed by the Institute of Atmospheric Sciences and Climate (ISAC) at the National Research Council of Italy (CNR), were used to prove the concept of VPR. Our results indicate that the combined algorithm, which merges together differential phase shift (Kdp), single polarization reflectivity factor (Zhh), and differential reflectivity (Zdr), once accurately processed, in most cases performs better among those tested and those that make use of Zhh alone, Kdp alone, and Zhh, and Zdr. Improvements greater than 25% are found for the total rain accumulations in terms of normalized bias when the VPR extrapolation is applied.

An 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.