Search

In-cloud motion vector retrieval is of great interest in several atmospheric science research fields. Short time extrapolation of radar data (precipitation nowcasting), assimilation into numerical weather prediction models, study of atmospheric circulation, as well as reference scenarios for future satellite missions, are glaring example where the knowledge of in-cloud motion vectors can play a relevant role. In this work, a dataset of nearly one-year and half of measurements collected by ground-based weather radars over the Italian peninsula, is used to perform the reconstruction of horizontal in-cloud rain motion vectors (RMVs) using both optical flow-based solutions from literature and an innovative extension of the multiple Doppler solution that make use of mosaicked Doppler radar data. The outcomes of the techniques imple-mented are analyzed in terms of reference Doppler measurements, reanalysis wind fields from ERA5 and evaluating the impact of the RMVs in a semilagrangian precipitation nowcasting framework. To the author knowledge, this is the first attempt in quantitatively evaluating RMVs. Results show that the use of Doppler information enhances the dynamic range of the retrieved RMV intensity with respect to optical flow-based solutions giving a better agreement with the ERA5 too. In terms of precipitation nowcasting, the use of Doppler-driven RMV does not give significant improvements due to gradients shown by RMV intensity when constrained with the measured Doppler.

Imaging disdrometers are widely used in field campaigns to provide information on the shape of hydrometeors, together with the diameter and the fall velocity, which can be used to derive information on the shape–size relations of hydrometeors. However, due to their higher price compared to laser disdrometers, their use is limited to scientific research purposes. The 3D stereo (3DS) is a commercial imaging disdrometer recently made available by Thies Clima and on which there are currently no scientific studies in the literature. The most innovative feature of the 3DS is its ability in capturing images of the particles passing through the measurement volume, crucial to provide an accurate classification of hydrometeors based on information about their shape, especially in the case of solid precipitation. In this paper. the performance of the new device is analyzed by comparing 3DS with the Laser Precipitation Monitor (LPM) from the same manufacturer, which is a known laser disdrometer used in many research works. The data used in this paper were obtained from measurements of the two instruments carried out at the Casale Calore site in L’Aquila during the CORE-LAQ (Combined Observations of Radar Experiments in L’Aquila) campaign. The objective of the comparison analysis is to analyze the differences between the two disdrometers in terms of hydrometeor classification, number and falling speed of particles, precipitation intensity, and total cumulative precipitation on an event basis. As regards the classification of precipitation, the two instruments are in excellent agreement in identifying rain and snow; greater differences are observed in the case of particles in mixed phase (rain and snow) or frozen phase (hail). Due to the different measurement area of the two disdrometers, the 3DS generally detects more particles than the LPM. The performance differences also depend on the size of the hydrometeors and are more significant in the case of small particles, i.e., D < 1 mm. In the case of rain events, the two instruments are in agreement with respect to the terminal velocity in still air predicted by the Gunn and Kinzer model for drops with a diameter of less than 3 mm, while, for larger particles, terminal velocity is underestimated by both the disdrometers. The agreement between the two instruments in terms of total cumulative precipitation per event is very good. Regarding the 3DS ability to capture images of hydrometeors, the raw data provide, each minute, from one to four images of single particles and information on their size and type. Their number and coarse resolution make them suitable to support only qualitative analysis of the shape of precipitating particles.

Abstract Assessing the uncertainty of precipitation measurements is a challenging problem because precipitation estimates are inevitably influenced by various errors and environmental conditions. A way to characterize the error structure of coincident measurements is to use the triple colocation (TC) statistical method. Unlike more typical approaches, where measures are compared in pairs and one of the two is assumed error‐free, TC has the enviable advantage to succeed in characterizing the uncertainties of co‐located measurements being compared to each other, without requiring the knowledge of the true value which is often unknown. However, TC requires to have at least three co‐located measuring systems and the compliance with several initial assumptions. In this work, for the first time, TC is applied to in‐situ measurements of rain precipitation acquired by three co‐located devices: a weighing rain gauge, a laser disdrometer and a bidimensional video disdrometer. Both parametric and nonparametric formulations of TC are implemented to derive the rainfall product precision associated with the three devices. While the parametric TC technique requires tighter constraints and explicit assumptions which may be violated causing some artifacts, the nonparametric formulation is more flexible and requires less strict constrains. For this reason, a comparison between the two TC formulations is also presented to investigate the impact of TC constrains and their possible violations. The results are obtained using a statistically robust dataset spanning a 1.5 year period collected in Switzerland and presented in terms of traditional metrics. According to triple colocation analysis, the two disdrometers outperform the classical weighing rain gauge and they have similar measurement error structure regardless of the integration time intervals.

Clouds cover substantial parts of the Earth’s surface and they are one of the most essential components of the global climate system impacting the Earth’s radiation balance as well as the water cycle redistributing water around the globe as precipitation. Therefore, continuous observation of clouds is of primary interest in climate and hydrological studies. This work documents the first efforts in Italy in remote sensing clouds and precipitation using a combination of K- and W-band (24 and 94 GHz, respectively) radar profilers. Such a dual-frequency radar configuration has not been widely used yet, but it could catch on in the near future given its lower initial cost and ease of deployment for commercially available systems at 24 GHz, with respect to more established configurations. A field campaign running at the Casale Calore observatory at the University of L’Aquila, Italy, nestled in the Apennine mountain range is described. The campaign features are preceded by a review of the literature and the underpinning theoretical background that might help newcomers, especially in the Italian community, to approach cloud and precipitation remote sensing. This activity takes place in interesting time for radar sensing clouds and precipitation, stimulated both by the launch of the ESA/JAXA EarthCARE satellite missions scheduled in 2024, which will have on-board, among other instruments, a W-band Doppler cloud radar and the proposal of new missions using cloud radars currently undergoing their feasibility studies (e.g., WIVERN and AOS in Europe and Canada, and U.S., respectively).

Accurate knowledge of the rain amount is a crucial driver in several hydrometeorological applications. This is especially true in complex orography territories, which are typically impervious, thus, leaving most mountain areas ungauged. Due to their spatial and temporal coverage, weather radars can potentially overcome such an issue. However, weather radar, if not accurately processed, can suffer from several limitations (e.g., beam blocking, altitude of the observation, path attenuation, and indirectness of the measurement) that can hamper the reliability of the rain estimates performed. In this study, a comparison between rain gauge and weather radar retrievals is performed in the target area of the Abruzzo region in Italy, which is characterized by a heterogeneous orography ranging from the seaside to Apennine ridge. Consequently, the Abruzzo region has an inhomogeneous distribution of the rain gauges, with station density decreasing with the altitude reaching approximately 1500 m a.s.l. Notwithstanding, pluviometric inflow spatial distribution shows a subregional dependency as a function of four climatic and altimetric factors: coastal, hilly, mountain, and inner plain areas (i.e., Marsica). Such areas are used in this analysis to characterize the radar retrieval vs. rain gauge amounts in each of those zones. Compared to previous studies on the topic, the analysis presented the importance of an accurate selection of the climatic and altimetric subregional areas where the radar vs. rain gauge comparison is undertaken. This aspect is not only of great importance to correct biases in radar retrieval in a more selective way, but it also paves the way for more accurate hydrometeorological applications (e.g., hydrological model initialization and quantification of aquifer recharge), which, in general, require the accurate knowledge of rain amounts upstream of a basin. To fill the gap caused by the uneven rain gauge distribution, ordinary Kriging (OK) was applied on a regional scale to obtain 2D maps of rainfall data, which were cumulated on a monthly and yearly basis. Weather radar data from the Italian mosaic were also considered, in terms of rain rate retrievals and cumulations performed on the same time frame used for rain gauges. The period considered for the analysis was two continuous years: 2017 and 2018. The output of the elaborations included raster maps for both radar and interpolated rain gauges, where each pixel contained a rainfall quantity. Although the results showed a general underestimation of the weather radar data, especially in mountain and Marsica areas, they were within the 95% confidence interval of the OK estimation. Our analysis highlighted that the average bias between radar and rain gauges, in terms of precipitation amounts, was a function of altitude and was almost constant in each of the selected areas. This achievement suggests that after a proper selection of homogeneous target areas, radar retrieval can be corrected using the denser network of rain gauges typically distributed at lower altitudes, and such correction can be extended at higher altitudes without loss of generality.

On 10 July 2019, a giant hail-bearing supercell hit the Adriatic coast of central Italy. Hailstones with a maximum diameter of 14 cm were reported in the city of Pescara between 10:00 and 11:00 UTC. In this work, the main synoptic and mesoscale features, responsible for the triggering and the development of the supercell, are analyzed using the WRF model. The intrusion of Bora wind over the northern and central Adriatic was relevant for two reasons: on the one side, the arrival of low-level cold air produced an uplift of the pre-existing warm air and favored the triggering of convection; on the other side, the strong vertical wind shear, also due to the presence of intense upper-level southwesterlies, created conditions favorable to the formation of the supercell. The predictability of the event is also discussed, comparing simulations starting at different initial times and forced with GFS and IFS forecasts. The model results show that the runs initialized at earlier times reproduced more accurately the track and the time evolution of the supercell. The HAILCAST module of WRF was also used to simulate hailstorm characteristics, such as the average hailstone diameter. WRF-HAILCAST simulations proved to be in fair agreement with the radar reflectivity retrievals and with local reports.

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.

Nowadays, laser disdrometers constitute a very appealing tool for measuring surface precipitation properties, by virtue of their capability to estimate not only the rainfall amount and intensity, but also the number, the size and the velocity of falling drops. However, disdrometric measures are affected by various sources of error being some of them related to environmental conditions. This work presents an assessment of Thies Clima laser disdrometer performance with a focus on the relationship between wind and the accuracy of the disdrometer output products. The 10-min average rainfall rate and total rainfall accumulation obtained by the disdrometer are systematically compared with the collocated measures of a standard tipping bucket rain gauge, the FAK010AA sensor, in terms of familiar statistical scores. A total of 42 rainy events, collected in a mountainous site of Southern Italy (Montevergine observatory), are used to support our analysis. The results show that the introduction of a new adaptive filtering in the disdrometric data processing can reduce the impact of sampling errors due to strong winds and heavy rain conditions. From a quantitative perspective, the novel filtering procedure improves by 8% the precipitation estimates with respect to the standard approach widely used in the literature. A deeper examination revealed that the signature of wind speed on raw velocity-diameter spectrographs gradually emerges with the rise of wind strength, thus causing a progressive increase of the wrongly allocated hydrometeors (which reaches 70% for wind speed greater than 8 m s−1). With the aid of reference rain-gauge rainfall data, we designed a second simple methodology that makes use of a correction factor to mitigate the wind-induced bias in disdrometric rainfall estimates. The resulting correction factor could be applied as an alternative to the adaptive filtering suggested by this study and may be of practical use when dealing with disdrometric data processing.

The high relevance of satellites for collecting information regarding precipitation at global scale implies the need of a continuous validation of satellite products to ensure good data quality over time and to provide feedback for updating and improving retrieval algorithms. However, validating satellite products using measurements collected by sensors at ground is still a challenging task. To date, the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission is the only active sensor able to provide, at global scale, vertical profiles of rainfall rate, radar reflectivity, and Drop Size Distribution (DSD) parameters from space. In this study, we compare near surface GPM retrievals with long time series of measurements collected by seven laser disdrometers in Italy since the launch of the GPM mission. The comparison shows limited differences in the performances of the different GPM algorithms, be they dual- or single-frequency, although in most cases, the dual-frequency algorithms present the better performances. Furthermore, the agreement between satellite and ground-based estimates depends on the considered precipitation variable. The agreement is very promising for rain rate, reflectivity factor, and the mass-weighted mean diameter (Dm), while the satellite retrievals need to be improved for the normalized gamma DSD intercept parameter (Nw).

This study shows how satellite-based passive and active microwave (MW) sensors can be used in conjunction with high-resolution Numerical Weather Prediction (NWP) simulations to provide insights of the precipitation structure of the tropical-like cyclone (TLC) Numa, which occurred on 15−19 November 2017. The goal of the paper is to characterize and monitor the precipitation at the different stages of its evolution from development to TLC phase, throughout the storm transition over the Mediterranean Sea. Observations by the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) and by the GPM constellation of MW radiometers are used, in conjunction with the Regional Atmospheric Modeling System (RAMS) simulations. The GPM-CO measurements are used to analyze the passive MW radiometric response to the microphysical structure of the storm, while the comparison between successive MW radiometer overpasses shows the evolution of Numa precipitation structure from its early development stage on the Ionian Sea into its TLC phase, as it persists over southern coast of Italy (Apulia region) for several hours. Measurements evidence stronger convective activity at the development phase compared to the TLC phase, when strengthening or weakening phases in the eye development, and the occurrence of warm rain processes in the areas surrounding the eye, are identified. The weak scattering and polarization signal at and above 89 GHz, the lack of scattering signal at 37 GHz, and the absence of electrical activity in correspondence of the rainbands during the TLC phase, indicate weak convection and the presence of supercooled cloud droplets at high levels. RAMS high-resolution simulations support what inferred from the observations, evidencing Numa TLC characteristics (closed circulation around a warm core, low vertical wind shear, intense surface winds, heavy precipitation), persisting for more than 24 h. Moreover, the implementation of DPR 3D reflectivity field in the RAMS data assimilation system shows a small (but non negligible) impact on the precipitation forecast over the sea up to a few hours after the DPR overpass.

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.

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.

The 2014–2015 Bárðarbunga fissure eruption at Holuhraun in central Iceland was distinguished by the high emission of gases, in total 9.6 Mt SO2, with almost no tephra. This work collates all ground-based measurements of this extraordinary eruption cloud made under particularly challenging conditions: remote location, optically dense cloud with high SO2 column amounts, low UV intensity, frequent clouds and precipitation, an extensive and hot lava field, developing ramparts, and high-latitude winter conditions. Semi-continuous measurements of SO2 flux with three scanning DOAS instruments were augmented by car traverses along the ring-road and along the lava. The ratios of other gases/SO2 were measured by OP-FTIR, MultiGAS, and filter packs. Ratios of SO2/HCl = 30–110 and SO2/HF = 30–130 show a halogen-poor eruption cloud. Scientists on-site reported extremely minor tephra production during the eruption. OPC and filter packs showed low particle concentrations similar to non-eruption cloud conditions. Three weather radars detected a droplet-rich eruption cloud. Top of eruption cloud heights of 0.3–5.5 km agl were measured with ground- and aircraft-based visual observations, web camera and NicAIR II infrared images, triangulation of scanning DOAS instruments, and the location of SO2 peaks measured by DOAS traverses. Cloud height and emission rate measurements were critical for initializing gas dispersal simulations for hazard forecasting.

Active remote sensing techniques can probe volcanic ash plumes, but their sensitivity at a given distance depends upon the sensor transmitted power, wavelength and polarization capability. Building on a previous numerical study at centimeter wavelength, this work aims at i) simulating the distal ash particles polarimetric response of millimeter-wave radar and multi-wavelength optical lidar; ii) developing and applying a model-based statistical retrieval scheme using a multi-sensor approach. The microphysical electromagnetic forward model of volcanic ash particle distribution, previously set up at microwaves, is extended to include non-spherical particle shapes, vesicular composition, silicate content and orientation phenomena for both millimeter and optical bands. Monte Carlo generation of radar and lidar signatures are driven by random variability of volcanic particle main parameters, using constraints from available data and experimental evidences. The considered case study is related to the ground-based observation of the Eyjafjallajökull (Iceland) volcanic ash plume on May 15, 2010, carried out by the Atmospheric Research Station at Mace Head (Ireland) with a 35-GHz Ka-band Doppler cloud radar and a 1064-nm ceilometer lidar. The detection and estimation of ash layer presence and composition is carried out using a Bayesian approach, which is trained by the Monte Carlo model-based dataset. Retrieval results are corroborated exploiting auxiliary data such as those from a ground-based microwave radiometer also positioned at Mace Head.

Volcanic ash plumes are formed during explosive volcanic eruptions. After advection over several thousands of kilometers, volcanic ash particles are highly fragmented, dispersed and aged with micron- sized sorting. This Annex describes the ash microphysical modeling and the simulated radar and lidar signatures. [...]

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.

Volcanic activity is observed worldwide with a variety of ground and space-based remote sensing instruments, each with advantages and drawbacks. No single system can give a comprehensive description of eruptive activity, and so, a multi-sensor approach is required. This work integrates infrared and microwave volcanic ash retrievals obtained from the geostationary Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI), the polar-orbiting Aqua-MODIS and ground-based weather radar. The expected outcomes are improvements in satellite volcanic ash cloud retrieval (altitude, mass, aerosol optical depth and effective radius), the generation of new satellite products (ash concentration and particle number density in the thermal infrared) and better characterization of volcanic eruptions (plume altitude, total ash mass erupted and particle number density from thermal infrared to microwave). This approach is the core of the multi-platform volcanic ash cloud estimation procedure being developed within the European FP7-APhoRISM project. The Mt. Etna (Sicily, Italy) volcano lava fountaining event of 23 November 2013 was considered as a test case. The results of the integration show the presence of two volcanic cloud layers at different altitudes. The improvement of the volcanic ash cloud altitude leads to a mean difference between the SEVIRI ash mass estimations, before and after the integration, of about the 30%. Moreover, the percentage of the airborne “fine” ash retrieved from the satellite is estimated to be about 1%–2% of the total ash emitted during the eruption. Finally, all of the estimated parameters (volcanic ash cloud altitude, thickness and total mass) were also validated with ground-based visible camera measurements, HYSPLIT forward trajectories, Infrared Atmospheric Sounding Interferometer (IASI) satellite data and tephra deposits.