Osservare le nuvole in giro per i cieli di tutto il mondo è un'avventura entusiasmante: le nuvole cambiano aspetto, hanno nomi diversi, assumono le forme più strane, accompagnandoci ovunque andiamo o speriamo di arrivare. Per orientarsi nelle sterminate geografie celesti, però, una guida risulta indispensabile: ed è per questo motivo che Vincenzo Levizzani, fisico italiano del CNR, ha deciso di realizzare il Piccolo manuale per cercatori di nuvole e svelarci i segreti della vita delle nubi, terrestri ed extraterrestri, dalle quote più alte a quelle più basse, e le loro trasformazioni in piogge, temporali, nebbie.

In questa presentazione viene analizzato l'andamento meteo, legato al deflusso e l'erosione, registrato nel sito sperimentale di Tenuta Cannona (centro sperimentale vitivinicolo di Agrion, a Carpeneto) nel periodo 2000 - 2020 e nel dettaglio l'annata 2021, con l'analisi dei principali fenomeni erosivi verificatisi.

Campagna glaciologica 2017. Osservazioni generali per il gruppo Miravidi-Lechaud

La modellizzazione spaziale di processi complessi, come quelli idrologici, è di assoluto rilievo in molte scienze ambientali e sta assumendo particolare importanza negli ultimi decenni anche alla luce di quei cambiamenti climatici che diversi studi e rapporti di organismi internazionali stanno mettendo sempre più in evidenza (IPCC, 2013). Di particolare rilievo è lo studio della variabilità delle precipitazioni che può fornire utili valutazioni relativamente all'impatto sulla disponibilità e gestione delle risorse idriche, sull'uso del suolo, sulle attività economiche e sulla società. La distribuzione spaziale delle piogge è condizionata da numerosi fattori ed è quindi necessario condurre uno studio dettagliato per migliorare l'accuratezza delle analisi (Chen et al., 2017). Negli ultimi anni sono stati proposti diversi metodi per l'interpolazione dei dati pluviometrici, e molti lavori sono stati dedicati al confronto tra i diversi modelli proposti. Infatti, l'applicazione del metodo di interpolazione appropriato può variare da regione a regione (Buttafuoco et al., 2016). Un approccio comunemente eseguito è il confronto tra tecniche deterministiche e geostatistiche. Dai risultati di questi confronti, molti autori hanno evidenziato come le tecniche geostatistiche forniscano una migliore stima delle precipitazioni rispetto alle tecniche deterministiche (ad esempio, Goovaerts, 2000). Tuttavia, altri autori hanno concluso che i risultati dipendono dalla densità di campionamento (ad esempio, Dirks et al., 1998). Di conseguenza, nella letteratura non esiste consenso unanime su quale sia il metodo migliore per interpolare le precipitazioni mensili. Per quanto riguarda la Regione Calabria, negli ultimi anni sono stati condotti diversi studi sull'interpolazione spaziale delle precipitazioni mensili (Caloiero et al., 2015) e annuali (Longobardi et al., 2016), ma senza effettuare un confronto tra le diverse metodologie. Inoltre, studi pregressi hanno rilevato una certa dipendenza della pioggia dalla distanza dal mare (Buttafuoco et al., 2011; Caloiero et al., 2015), che può essere considerata in queste analisi. In questo studio, al fine di determinare il miglior metodo per l'interpolazione delle precipitazioni mensili in Calabria, una regione che per orografia e posizione geografica presenta una marcata variabilità spaziale delle piogge, sono stati confrontati i risultati derivanti dall'applicazione di approcci geostatistici e deterministici.

Archivio storico di dati di precipitazione su tutta l'Europa stimati con l'algoritmo PEMW da noi sviluppato. a partire da dati acquisiti da sensori a micronde. Attività svolta nell'ambito del progetto OrientGate, finanziato da South East Europe Transnational Cooperation Programme. http://www.orientgateproject.org/

software multi-sensore per il monitoraggio delle precipitazioni convettive, mediante tecniche di morphing e calibrazione dinamiche dei campi di precipitazione convettiva stimati da AMSU/MHS con osservazioni IR telerlilevate sensore Spinning Enhanced Visible and Infrared Imager (SEVIRI) su Metosat Second Generation (MSG).

Il documento illustra le attività per il quinto anno dell'Intesa Operativa (IO) n.619 [DA-003] fra il Dipartimento della Protezione Civile (DPC) e il Centro di Competenza dell'Istituto di Ricerca per la Protezione Idrogeologica (CC-IRPI), del Consiglio Nazionale delle Ricerche (CNR). Le attività previste si inquadrano nell'ambito dell'Accordo di Programma Quadro firmato dal Capo del Dipartimento della Protezione Civile della Presidenza del Consiglio dei Ministri, e dal Presidente del Consiglio Nazionale delle Ricerche [DA-002]. Nell'ambito della presente IO è stato realizzato il Sistema d'Allertamento Nazionale per il possibile innesco di Fenomeni franosi indotti da piogge (SANF). Tale sistema è basato sul confronto tra soglie empiriche per il possibile innesco di fenomeni franosi e dati di pioggia misurata (dalla rete pluviometrica nazionale) e prevista (dal modello LAMI) messi a disposizione dal DPC attraverso la piattaforma. Il presente documento illustra le analisi effettuate e descrive i prodotti generati a partire dai dati di pioggia immagazzinati nel database del sistema SANF. La finalità delle analisi è quella di valutare la qualità dei dati pluviometrici utilizzati in SANF.

Satellites offer an unrivalled vantage point to observe and measure Earth system processes and parameters. Precipitation (rain and snow) in particular, benefit from such observations since precipitation is spatially and temporally highly variable and with satellites overcoming some of the deficiencies of conventional gauge and radar measurements. This paper provides an overall review of quantitative precipitation estimation, covering the basis of the satellite systems used in the observation of precipitation, the dissemination and processing of this data, and the generation, availability and validation of these precipitation estimates. A selection of applications utilising these precipitation estimates are then outlined to exemplify the utility of such products.

The Water vapour Strong Lines at 183 GHz (183-WSL) fast retrieval method retrieves rain rates and classifies precipitation types for applications in nowcasting and weather monitoring. The retrieval scheme consists of two fast algorithms, over land and over ocean, that use the water vapour absorption lines at 183.31 GHz corresponding to the channels 3 (183.31±1 GHz), 4 (183.31±3 GHz) and 5 (183.31±7 GHz) of the Advanced Microwave Sounding Unit module B (AMSU-B) and of the Microwave Humidity Sounder (MHS) flying on NOAA-15-18 and Metop-A satellite series, respectively. The method retrieves rain rates by exploiting the extinction of radiation due to rain drops following four subsequent steps. After ingesting the satellite data stream, the window channels at 89 and 150 GHz are used to compute scattering-based thresholds and the 183-WSLW module for rainfall area discrimination and precipitation type classification as stratiform or convective on the basis of the thresholds calculated for land/mixed and sea surfaces. The thresholds, initially empirically derived from observations, are corroborated by the simulations of the RTTOV radiative transfer model applied to 20000 ECMWF atmospheric profiles at midlatitudes and the use of data from the Nimrod radar network. A snow cover mask and a digital elevation model are used to eliminate false rain area attribution, especially over elevated terrain. A probability of detection logistic function is also applied in the transition region from no-rain to rain adjacent to the clouds to ensure continuity of the rainfall field. Finally, the last step is dedicated to the rain rate retrieval with the modules 183-WSLS (stratiform) and 183WSLC (convective), and the module 183-WSL for total rainfall intensity derivation. A comparison with rainfall retrievals from the Goddard Profiling (GPROF) TRMM 2A12 algorithm is done with good results on a stratiform and hurricane case studies. A comparison is also conducted with the MSG-based precipitation Index (PI) and the Scattering Index (SI) for a convective-stratiform event showing good agreement with the 183-WSLC retrieval. A complete validation of the product is the subject of Part II of the paper.

Orographic precipitation is a result of very complex processes and its study using numerical models is of utmost importance since it can open an important avenue to the improvement of precipitation forecasts, especially during the warm season. Mainland Portugal is characterised by a very variable terrain between the north and south regions, the latter being much smoother, with sparse mountains that barely reach 1000 m. Conversely, several mountain ranges are distributed over Spain with heights often exceeding 1500 m altitude. A mesoscale non-hydrostatic atmospheric model (MesoNH) is used to study the orographic precipitation during a limited period in spring of 2002 over the Iberian Peninsula. In order to assess the effects of the mountains, case study simulations are done, with and without the orography. MesoNH is initialized and forced by the ECMWF analyses. The effects of orography on precipitation over neighbouring regions are also analyzed. Simulations show that orography is a key factor in determining the spatial distribution of precipitation over the Iberian Peninsula, with enhancements in the regions with mountain ranges and diminution or suppression over certain valleys. The simulated precipitation fields were visually compared with radar observations in central Portugal and quantitatively compared with rain gauge data all over Portugal in order to assess the model performance in the analyzed cases.

The purpose of this chapter is to offer an accurate treatment of relevant aspects of satellite remote sensing of precipitation using passive microwave (PMW) radiometers. Microwave observations of the Earth's system differ substantially from those based on optical and infrared wavelengths. Visible (VIS) and infrared (IR) instruments essentially sense the cloud top properties by measuring reflected or emitted radiation. Microwave frequencies, on the contrary, possess greater penetrating capabilities than optical radiation and can thus be exploited to investigate cloud internal properties by retrieving the interaction of hydrometeors with the radiation field. This fact is particularly true in the remote sensing of precipitation, where the impact of the volume of raindrops on the radiative field is directly linked to the total extinction of incident radiation. The following paragraphs will be focused on one hand on theoretical considerations at the foundations of thermal radiation processes and on the other the attention will be centered on a treatment of practical aspects of retrieving precipitation in the microwave bands. Particular attention will be dedicated in the first part of the chapter to the radiative transfer theory in the microwaves by using the Rayleigh-Jeans formulation and a description of the absorption and scattering processes associated with the Mie theory with the approximation of extinction from poly-disperse media as proxies for natural media. This section will create a valid substrate to understand and theoretically evaluate the impact of atmospheric constituents such as precipitation types or hydrometeor phases and sizes on the natural radiation emitted from the Earth. Furthermore, the theoretical considerations of this section will be compared in the second part of the chapter with real measurements. Making use of a wide suite of microwave frequencies of a new generation of PMW sensors flying on board polar orbiting satellites the attenuation of the microwave signal due to rain clouds will be discussed possibly discerning the contribution to the total radiation of the emissivity from various surfaces falling into the satellite field of view. Finally, a new microwave high-frequency method to retrieve and classify precipitation types will be presented.

The relationship between the multi-spectral cloud field characterization from the Advanced Very High Resolution Radiometer (AVHRR) and the rainfall intensities from the Advanced Microwave Sounding Unit-module B (AMSU-B) data were studied for a convective storm event, which occurred during the first 15 days of June 2007 over the Mediterranean. The cloud products exploited in this analysis, cloud mask, type, optical thickness, and effective radius, are obtained from the NOAA-NESDIS operational processing system Clouds from the AVHRR-Extended algorithm (CLAVR-x), whereas the rain intensity values are retrieved from the AMSU-B brightness temperatures via a fast algorithm, using opaque frequencies (centred at 183 GHz) to correct for the presence of water vapour affecting the retrieval results. The algorithm is conceived to discriminate between convective and stratiform rain using a suitable set of thresholds; the retrieval is subsequently carried out separately for the two types. A test for the discrimination of precipitating from non precipitating areas was based on the comparison between the precipitation information and the retrieved cloud parameters. The test produced a cloud optical thickness threshold value, beyond which the precipitation initiates, and an effective radius range for the identification of the precipitating clouds. The results stemming from the application of the test to the June 2007 case study are very encouraging, although still preliminary and restricted to the analyzed Mediterranean storms. In particular, the test shows high potential for delineating non-precipitating areas (more than 90% of successful cases for every considered cloud type) and to identify precipitating ice clouds related to convective rain (confirmed by 82% of hits). On the other hand, the relative inability to address the stratiform cloud systems is proved by the fact that the majority of the missed cases, for each cloud types, is characterized by light rain intensities (< 3mm/h).

The observation of the atmosphere by satellite instrumentation was one of the first uses of remotely sensed data nearly 50 years ago. Since then a range of satellites have carried many different meteorological sensors capable of monitoring the dynamics of the atmosphere and the capture and retrieval of information about atmospheric parameters for use in meteorological and climatological applications. The utilization of satellite observations for meteorology and climatology is essential since the atmosphere is a global feature, and conventional observations of it are primarily land-based. Satellites, with their synoptic view, provide much information benefiting numerical weather prediction models to improve weather forecasting and the ability to monitor weather systems, in particular those that pose a threat to humankind, over the entire Earth. Development of new observational capabilities has led to new insights into atmospheric processes and their interaction, allowing the consequences of anthropogenic activities, such as climate change, to be monitored.

In order to properly utilize remotely sensed precipitation estimates in hydrometeorological applications, knowledge of the accuracy of the estimates are needed. However, relatively few ground validation networks operate with the necessary spatial density and timeresolution required for validation of high-resolution precipitation products (HRPP) generated at fine space and time scales (e.g., hourly accumulations produced on a 0.25 deg spatial scale). In this article, we examine over-land validation statistics for an operationally designed, meteorological satellite-based global rainfall analysis that blends intermittent passive microwave-derived rainfall estimates aboard a variety of low Earth-orbiting satellite platforms with sub-hourly time sampling capabilities of visible and infrared imagers aboard operational geostationary platforms. The validation dataset is comprised of raingauge data collected from the dense, nearly homogeneous, 1-min reporting Automated Weather Station (network of the Korean Meteorological Administration during the June to August 2000 summer monsoon season. The space-time RMS error, mean bias, and correlation matrices were computed using various time windows for the gauge averaging, centered about the satellite observation time. For ±10 min time window, a correlation of 0.6 was achieved at 0.1 deg spatial scale by averaging more than 3 days; coarsening the spatial scale to 1.8 deg produced the same correlation by averaging over 1 h. Finer than approximately 24-h and 1 deg time and space scales, respectively, a rapid decay of the error statistics was obtained by trading-off either spatial or time resolution. Beyond a daily time scale, the blended estimates were nearly unbiased and with an RMS error of no worse than 1 mm day-1.