Comparison of different fittings of experimental DSD

Retrieval of a distribution of raindrop sizes from measured drop spectra is critically influenced by the tail of the distribution. The influence of various tail-types is studied with reference to four parameterisations fitted both to the large dropsonlyand to the entire sample of the disdrometer-measured spectra. Results of this preliminary analysis show that the Weibull distribution with a shape parameter greater than oneseems to fit the highest percentages of the measured drop spectra. © 2013 AIP Publishing LLC

Influence of wind-induced effects on laser disdrometer measurements: Analysis and compensation strategies

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

Validation of rainfall estimation derived from commercial DVB received signal with disdrometer, rain gauges and ground based radar

An accurate measurement and monitoring of precipitation events is closely linked with different applications that have an impact on human welfare such as water resources management, and floods, landslides or wildfire risk assessments. Currently rain gauges, disdrometers, ground-based weather radars and satellite sensors (both active and passive) can be considered the conventional devices for precipitation measurements that are worldwide adopted. These devices have different measurement principles, time and space resolution, and accuracy (Gebremichael and Testik, 2013). In the last decade, a new technology that exploits the microwave satellite links has been investigated to retrieve precipitation information. The idea is to estimate the precipitation starting from the attenuation of the signal along its propagation path. Few studies have been carried out in this direction (such as Barthès and Mallet, 2013 and Mercier et al., 2015), showing promising results. In that regards, recently, an Italian project called NEFOCAST, funded by Tuscany Region (Italy), has been carried out with the aim of estimating rainfall rate from attenuation measurements made available by commercial interactive digital video broadcasting (DVB) receivers, called smartLNBs. During the NEFOCAST project, an ad hoc rainfall retrieval algorithm has been developed, tuned and tested. It allows to estimate, with 1-minute rate, the instantaneous rainfall rate (R, in mm/h) from the ratio η = Es/N0 between the received energy-per-symbol Es and the one-sided power spectral density of the additive white Gaussian noise N0, (Giannetti et al. 2017). To validate the algorithm, a 1-year field campaign (from January 2018 to January 2019) was conducted. The collected data allow to compare the SmartLNB precipitation estimates with the measurements gathered by ‘conventional’ meteorological devices such as rain gauges, weather radar and disdrometer. A network of 24 smartLNBs was deployed in Tuscany, along with 11 rain gauges and one X-band dual-polarization weather radar. Furthermore, the performance of the NEFOCAST algorithm has been preliminarily tested by comparing data provided from one SmartLNB installed at the Institute of Atmospheric Sciences and Climate (ISAC) of CNR in Rome (Italy) with a co-located laser disdrometer. For this site, data from a dual polarization C-band weather radar (Polar55C) could be compared with SmartLNB measurements along the Earth-satellite link. In fact, during the project the Polar55C has been aimed in the same direction as the SmartLNB, with the same elevation angle, thus scanning the same portion of atmosphere where the SmartLNB signal was propagating. Preliminary results show a good agreement between the total cumulative precipitation (in mm) obtained from SmartLNB data and the one collected by the co-located disdrometer during different rainfall events. The corresponding values of Normalized Mean Absolute Error (NMAE) and Root Mean Square Error (RMSE) obtained comparing the total cumulative precipitations obtained from SmartLNB and disdrometer are 41% and 4.71 mm, respectively. Encouraging results come also from the comparison of the total precipitation amounts as measured by the network of SmartLNBs and rain gauges, with values of NMAE (RMSE) that range between 39% and 53% (2.8 mm and 8.0 mm), depending on the specific site

A Multisensor Investigation of Convection During HyMeX SOP1 IOP13

A multisensor analysis of the convective precipitation event occurred over Rome during the IOP13 (October 15th, 2012) of the HyMeX (Hydrological cycle in the Mediterranean eXperiment) Special Observation Period (SOP) 1 is presented. Thanks to the cooperation among Italian meteorological services and scientific community and a specific agreement with NASA-GSFC, different types of devices for meteorological measurements were made available during the HyMeX SOP.1. For investigating this event, used are the 3-D lightning data provided by the LINET, the CNR ISAC dual-pol C-band radar (Polar 55C), located in Rome, the Drop Size Distributions (DSD) collected by the 2D Video Disdrometer (2DVD) and the collocated Micro Rain Radar (MRR) installed at the Radio Meteorology Lab. of "Sapienza" University of Rome, located 14 km from the Polar 55C radar. The relation between microphysical structure and electrical activity during the convective phase of the event was investigated using LINET lightning data and Polar 55C (working both in PPI and RHI scanning mode) observations. Location of regions of high horizontal reflectivity (Zh) values ( > 50 dBz), indicating convective precipitation, were found to be associated to a high number of LINET strokes. In addition, an hydrometeor classification scheme applied to the Polar 55C scans was used to detect graupel and to identify a relation between number of LINET strokes and integrated IWC of graupel along the event. Properties of DSDs measured by the 2DVD and vertical DSD profiles estimated by MRR and their relation with the lighting activity registered by LINET were investigated with specific focus on the transition from convective to stratiform regimes. A good agreement was found between convection detected by these instruments and the number of strokes detected by LINET

Quantitative precipitation estimation over antarctica using different ze-sr relationships based on snowfall classification combining ground observations

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

Multi-sensor analysis of convective activity in central Italy during the HyMeX SOP 1.1

Abstract. A multi-sensor analysis of convective precipitation events that occurred in central Italy in autumn 2012 during the HyMeX (Hydrological cycle in the Mediterranean experiment) Special Observation Period (SOP) 1.1 is presented. Various microphysical properties of liquid and solid hydrometeors are examined to assess their relationship with lightning activity. The instrumentation used consisted of a C-band dual-polarization weather radar, a 2-D video disdrometer, and the LINET lightning network. Results of T-matrix simulation for graupel were used to (i) tune a fuzzy logic hydrometeor classification algorithm based on Liu and Chandrasekar (2000) for the detection of graupel from C-band dual-polarization radar measurements and (ii) to retrieve graupel ice water content. Graupel mass from radar measurements was related to lightning activity. Three significant case studies were analyzed and linear relations between the total mass of graupel and number of LINET strokes were found with different slopes depending on the nature of the convective event (such as updraft strength and freezing level height) and the radar observational geometry. A high coefficient of determination (R2 = 0.856) and a slope in agreement with satellite measurements and model results for one of the case studies (15 October 2012) were found. Results confirm that one of the key features in the electrical charging of convective clouds is the ice content, although it is not the only one. Parameters of the gamma raindrop size distribution measured by a 2-D video disdrometer revealed the transition from a convective to a stratiform regime. The raindrop size spectra measured by a 2-D video disdrometer were used to partition rain into stratiform and convective classes. These results are further analyzed in relation to radar measurements and to the number of strokes. Lightning activity was not always recorded when the precipitation regime was classified as convective rain. High statistical scores were found for relationships relating lightning activity to graupel aloft

Database of the Italian disdrometer network

In 2021, a group of seven Italian institutions decided to bring together their know-how, experience, and instruments for measuring the drop size distribution (DSD) of atmospheric precipitation, giving birth to the Italian Group of Disdrometry (in Italian named Gruppo Italiano Disdrometria, GID, https://www.gid-net.it/, last access: 16 May 2023). GID has made freely available a database of 1 min records of DSD collected by the disdrometer network along the Italian peninsula. At the time of writing, the disdrometer network was composed of eight laser disdrometers belonging to six different Italian institutions (including research centres, universities, and environmental regional agencies). This work aims to document the technical aspects of the Italian DSD database consisting of 1 min sampling data from 2012 to 2021 in a uniform standard format defined within GID. Although not all the disdrometers have the same data record length, the DSD data collection effort is the first of its kind in Italy, and from here onwards, it opens up new opportunities in the surface characterization of microphysical properties of precipitation in the perspective of climate records and beyond. The Version 01 GID database can be downloaded at https://doi.org/10.5281/zenodo.6875801 (Adirosi et al., 2022), while Version 02 can be downloaded at https://doi.org/10.5281/zenodo.7708563 (Adirosi et al., 2023). The difference among the two versions is the diameter–fall velocity relation used for the DSD computation

Validazione di una innovativa rete di rilevamento pluviometrica basata sulla misura opportunistica di segnali televisivi diffusi da satellite

Nonostante la varietà di metodi e strumenti esistenti per la misura di precipitazioni, non esiste un metodo che funzioni meglio di tutti gli altri in tutte le condizioni operative. I pluviometri sono gli strumenti tradizionalmente utilizzati per ottenere misure puntuali con elevata precisione del quantitativo d’acqua che precipita in un determinato intervallo di tempo. I sistemi radar meteorologici consentono invece di stimare la distribuzione spaziale della precipitazione e di monitorarne la dinamica, pur se con precisioni minori. Infine i satelliti consentono in maniera indiretta di stimare la precipitazione su scale molto più ampie, ma al prezzo di un aumento dell’incertezza e di una minor risoluzione spazio temporale. Rispetto a questi strumenti tradizionali ve ne sono alcuni che derivano da un uso opportunistico di sistemi progettati e realizzati per altri scopi, ma che possono comunque fornire informazioni utili alla stima della precipitazione. In particolare, i sistemi di telecomunicazione che impiegano radiocollegamenti con frequenze superiori al GHz possono fornire informazioni rilevanti sulle precipitazioni, attraverso la misurazione dell'attenuazione del segnale trasmesso causata dalle gocce di pioggia nella tratta dal trasmettitore al ricevitore. NEFOCAST è un progetto di ricerca FAR-FAS finanziato dalla Regione Toscana, che sfrutta questa opportunità attraverso innovativi dispositivi satellitari bidirezionali (cioè trasmettitori/ricevitori) denominati Smart Low-Noise Block converter (SmartLNB), concepiti per l’interazione ubiquitaria via satellite ma in grado di rilevare il valore di attenuazione del segnale ricevuto e di trasmetterlo direttamente ad un centro di raccolta. L'utilizzo di SmartLNB presenta significativi vantaggi in termini di costi e facilità di installazione/configurazione e fornisce la possibilità di applicazione in qualsiasi area coperta dal segnale satellitare, offrendo al tempo stesso un'efficiente soluzione “embedded” per la trasmissione dei dati, senza necessità di ricorrere a ricevitori dedicati per la stima della pioggia. L’algoritmo di stima della precipitazione di NEFOCAST è stato studiato basandosi su modelli fisici e su modelli empirico-statistici. Per tale scopo sono state effettuate delle campagne di misura utilizzando uno SmartLNB, un radar meteorologico (puntato nella stessa direzione del satellite) ed un disdrometro co-locati presso la sede del CNR-ISAC di Roma. L’algoritmo di stima dei campi di precipitazione è stato implementato attraverso un filtro di Kalman che utilizza come dati di input le misure degli SmartLNB e dati ausiliari da osservazioni satellitari. Durante il progetto NEFOCAST è stata infine condotta una campagna di test e validazione dell’algoritmo messo a punto per la stima della precipitazione a partire da misure di attenuazione effettuate con gli SmartLNB. Durante tale campagna di misura, della durata di un anno circa, sono stati dislocati sul territorio della Regione Toscana un numero significativo di SmartLNB. Le stime di precipitazione ottenute dalla rete di SmartLNB, opportunamente densa di terminali nell’area di Firenze, sono state analizzate attraverso i confronti con una rete di pluviometri co-locata e un radar polarimetrico X-band Doppler installato per gli obiettivi di calibrazione/validazione

Evaluation of Gamma Raindrop Size Distribution Assumption through Comparison of Rain Rates of Measured and Radar-Equivalent Gamma DSD

To date, one of the most widely used parametric forms for modeling raindrop size distribution (DSD) is the three-parameter gamma. The aim of this paper is to analyze the error of assuming such parametric form to model the natural DSDs. To achieve this goal, a methodology is set up to compare the rain rate obtained from a disdrometer-measured drop size distribution with the rain rate of a gamma drop size distribution that produces the same triplets of dual-polarization radar measurements, namely reflectivity factor, differential reflectivity, and specific differential phase shift. In such a way, any differences between the values of the two rain rates will provide information about how well the gamma distribution fits the measured precipitation. The difference between rain rates is analyzed in terms of normalized standard error and normalized bias using different radar frequencies, drop shape–size relations, and disdrometer integration time. The study is performed using four datasets of DSDs collected by two-dimensional video disdrometers deployed in Huntsville (Alabama) and in three different prelaunch campaigns of the NASA–Japan Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) ground validation program including the Hydrological Cycle inMediterraneanExperiment (HyMeX) special observation period (SOP) 1 field campaign inRome. The results show that differences in rain rates of the disdrometer DSD and the gamma DSD determining the same dual-polarization radar measurements exist and exceed those related to the methodology itself and to the disdrometer sampling error, supporting the finding that there is an error associatedwith the gammaDSDassumption

Raindrop size distribution: Fitting performance of common theoretical models

Modelling raindrop size distribution (DSD) is a fundamental issue to connect remote sensing observations with reliable precipitation products for hydrological applications. To date, various standard probability distributions have been proposed to build DSD models. Relevant questions to ask indeed are how often and how good such models fit empirical data, given that the advances in both data availability and technology used to estimate DSDs have allowed many of the deficiencies of early analyses to be mitigated. Therefore, we present a comprehensive follow-up of a previous study on the comparison of statistical fitting of three common DSD models against 2D-Video Distrometer (2DVD) data, which are unique in that the size of individual drops is determined accurately. By maximum likelihood method, we fit models based on lognormal, gamma and Weibull distributions to more than 42.000 1-minute drop-by-drop data taken from the field campaigns of the NASA Ground Validation program of the Global Precipitation Measurement (GPM) mission. In order to check the adequacy between the models and the measured data, we investigate the goodness of fit of each distribution using the Kolmogorov–Smirnov test. Then, we apply a specific model selection technique to evaluate the relative quality of each model. Results show that the gamma distribution has the lowest KS rejection rate, while the Weibull distribution is the most frequently rejected. Ranking for each minute the statistical models that pass the KS test, it can be argued that the probability distributions whose tails are exponentially bounded, i.e. light-tailed distributions, seem to be adequate to model the natural variability of DSDs. However, in line with our previous study, we also found that frequency distributions of empirical DSDs could be heavy‐tailed in a number of cases, which may result in severe uncertainty in estimating statistical moments and bulk variables