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Highly populated coastal environments receive large quantities of treated and untreated wastewater from human and industrial sources. Bivalve molluscs accumulate and retain contaminants, and their analysis provides evidence of past contamination. Rivers and precipitation are major routes of bacteriological pollution from surface or sub-surface runoff flowing into coastal areas. However, relationships between runoff, precipitation, and bacterial contamination are site-specific and dependent on the physiographical characteristics of each catchment. In this work, we evaluated the influence of precipitation and river discharge on molluscs' Escherichia coli concentrations at three sites in Central Italy, aiming at quantifying how hydrometeorological conditions affect bacteriological contamination of selected bivalve production areas. Rank-order correlation analysis indicated a stronger association between E. coli concentrations and the modelled Pescara River discharge maxima (r = 0.69) than between E. coli concentration and rainfall maxima (r = 0.35). Discharge peaks from the Pescara River caused an increase in E. coli concentration in bivalves in 87% of cases, provided that the runoff peak occurred 1–6 days prior to the sampling date. Precipitation in coastal area was linked to almost 60% of cases of E. coli high concentrations and may enhance bacterial transportation offshore, when associated with a larger-scale weather system, which causes overflow occurrence. HIGHLIGHTS Using a grid-distributed hydrological model to assess hydrometeorological conditions in the absence of direct measurements.; Analysis of a previous uninvestigated area.; New discharge threshold determining Escherichia coli concentration peaks found in Central Italy in order to allow the development of an early warning system for risk assessment.

An extreme weather event hit the coastal city of Chennai, India, in November-December 2015 causing severe damage to infrastructure worth billions of dollars, people’s lives and their livelihood. Nearby districts to Chennai, such as Cuddalore, Kancheepuram and Tiruvallur were also affected by rainfall over 300mm during the first week of December. This was caused by the unusual wind surges in the troposphere providing favorable environmental conditions for the extensive rainfall and the formation of a deep depression in the Bay of Bengal on 30 November 2015, which was blocked by Eastern Ghats that inhibited the movement of the synoptic system. Electricity and telecommunication lines were suspended and some hospitals were shut down for a few days. It brought the whole city into a state of emergency and National Disaster Rescue Force were deployed in an effort to take care of the evacuation of people.In this work, we present the estimation of the hydrological stress caused by the extreme rainfall event in Chennai and the nearby river basins during the course of this northeastern monsoon event in India. The hydrological stress is given through the application of Best Discharge based Drainage (BDD) Index, calculated by the CETEMPS Hydrological Model (CHyM). Hydrological simulation is carried out by forcing the model with the 3-hourly NASA IMERG 0.1x0.1 grid precipitation dataset. Preliminary results show a spatial coherence between the hydrological stress detected by the index and the most impacted river segments, due to heavy precipitation. The application of hydrological stress indices is helpful for forecasting fluvial floods in the river network with minimum calibration requirements, providing a useful tool for warning the respective authorities for minimal losses due to natural calamities.

Flood and flash floods are complex events, depending on weather dynamics, basin physiographical characteristics, land use cover and water management. For this reason, prediction of such events usually deals with very accurate model tuning and validation, which is usually site-specific and based on climatological data, such as discharge time series or flood databases. In this work, we developed and tested two hydrological stress indices for flood detection in the Central Italy Apennine District: a heterogeneous geographical area, characterized by complex topography and medium-to-small catchment extension. Proposed indices are threshold-based and developed taking into account operational requirements of civil protection end-users. They are calibrated and tested through the application of signal theory, in order to overcome data scarcity over ungauged areas, as well as incomplete discharge time series. The validation has been carried out on a case study basis, through the use of flood reports from various source of information, as well as hydrometric level time series, which represent the actual hydrological quantity monitored by civil protection operators. Obtained results shows as the overall accuracy of flood prediction is greater than 0.8, with false alarm rates below 0.5 and probability of detection ranging from 0.51 to 0.80. A slight anticipation of peak occurrence was found, however, the time shift of indices signal peak it is strongly dependent on the presence of dams that regulates the flood propagation. Moreover, the different nature of the proposed indices suggests their application in a complementary way, as the index based on drained precipitation appears to be more sensible to rapid flood propagation in small tributaries, while the discharge-based index is particularly responsive to main channel dynamics.

Atmospheric particulate matter (PM) is one of the pollutant that may have a significant impact on human health. Data collected during three years in an urban area of the Adriatic coast are analysed using three models: a multiple linear regression model, a neural network model with and without recursive architecture. Measured meteorological parameters and PM10 concentration are used as input to forecast the daily averaged concentration of PM10 from one to three days ahead. All simulations show that the neural network with recursive architecture has better performances compared to both the multiple linear regression model and the neural network model without the recursive architecture. Results of PM forecasts are compared with the air quality limits for health protection to test the performance as operational tool. The inclusion of carbon monoxide (CO) concentration as further input parameter in the model, has been evaluated in terms of forecast improvements. Finally, all models are used to forecast the PM2.5 concentration, using as input the meteorological data, the PM10 and CO concentration, to simulate the situation when PM2.5 is not observed. The comparison between observed and forecasted PM2.5 shows that the neural network is able to forecast the PM2.5 concentrations even if PM2.5 is not included among the input parameters.

The weather forecasts for precipitation have considerably improved in recent years thanks to the increase of computational power. This allows to use both a higher spatial resolutions and the newly developed parameterization schemes for representing sub-grid scale physical processes. However, precipitation estimation is still affected by errors that can impact on the response of hydrological models. To the aim of considering the uncertainties in the precipitation forecast and how they propagate in the hydrological model, an ensemble approach is investigated. A meteo-hydro ensemble system is built to forecast events in a complex orography terrain where catchments of different size are present. In this context, the meteo-hydrological forecast system is implemented and tested for a severe hydrological event occurred over Central Italy on November 15, 2017. During this period, a flash flood hit the Abruzzo region causing precipitation up to 200 mm/24 hours and producing damages with a high impact on social and economic activities.The newly developed meteo-hydro ensemble system is compared with a high resolution deterministic forecast and with the observations over the same area, showing a very good response. In addition, the ensemble allows for an estimation of the predictability of the event a few days in advance and of the uncertainty of this flood. Although the modelling framework is implemented on the basins of Abruzzo region, it is portable and applicable to other areas.

International audience; The Western Mediterranean Sea is often affected by heavy precipitation which frequently generates floods or even flash floods. These events generally produce brief but major freshwater inputs in the ocean. In order to evaluate the sensitivity to the representation of river freshwater input, three different runoff forcing dataset are used to drive the NEMO ocean model: a monthly climatology, an observational dataset with a daily or a hourly frequency. The sensitivity is investigated over the first Special Observation Period (SOP1) of the HyMeX program that took place in autumn 2012, in two configurations of NEMO: the first is WMED36 over the Western Mediterranean Sea at 1/36°-resolution and the second is a new configuration covering the NorthWestern Mediterranean Sea with a 1/72°-resolution named NWMED72. With NWMED72, the impact of the representation of the river freshwater flux, i.e. moving from a surface flux to a vertical distribution of the flux, is also evaluated. The results show that the ocean stratification is significantly modified locally in simulations where runoff observations are used compared to those using the climatology. The sea surface salinity is modified as well as the mixed layer which is thinner as bounded by a well marked halocline. The sea surface temperature is also impacted by the change in runoff frequency. Moreover, the current intensity in river plume during flood is increased. Vertical profiles of salinity and temperature and thus the mixed layer depth are changed when the runoff forcing is distributed over a depth. Those changes are limited and very local but the realism of the river runoff input is improved.

The response of Mediterranean small catchments hydrology to climate change is still relatively unexplored. Regional Climate Models (RCMs) are an established tool for evaluating the expected climate change impact on hydrology. Due to the relatively low resolution and systematic errors, RCM outputs are routinely and statistically post-processed before being used in impact studies. Nevertheless, these techniques can impact the original simulated trends and then impact model results. In this work, we characterize future changes of a small Apennines (Central Italy) catchment hydrology, according to two radiative forcing scenarios (Representative Concentration Pathways, RCPs, 4.5 and 8.5). We also investigate the impact of a widely used bias correction technique, the empirical Quantile Mapping (QM) on the original Climate Change Signal (CCS), and the subsequent alteration of the original Hydrological Change Signal (HCS). Original and bias-corrected simulations of five RCMs from Euro-CORDEX are used to drive the CETEMPS hydrological model CHyM. HCS is assessed by using monthly mean discharge and a hydrological-stress index. HCS shows a large spatial and seasonal variability where the summer results are affected by the largest decrease of mean discharge (down to &minus, 50%). QM produces a small alteration of the original CCS, which generates a generally wetter HCS, especially during the spring season.

The Microwave Infrared Combined Rainfall Algorithm (MICRA) consists in a statistical integration method using the satellite microwave-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne infrared measurements on limited sub-regions and time windows. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The actual implementation is explained, although the basic concepts of MICRA are very general and the method is easy to be extended for considering innovative statistical techniques or measurements from additional space-borne platforms. In order to demonstrate the potentiality of MICRA, case studies over central Italy are also discussed. Finally, preliminary results of MICRA validation by ground based remote and in situ measurements are shown and a comparison with a Neural Network (NN) based technique is briefly illustrated.

We assess the statistics of different daily precipitation indices in ensembles of Med-CORDEX and EURO-CORDEX experiments at high resolution (grid spacing of ~0.11°, or RCM11) and medium resolution (grid spacing of ~0.44°, or RCM44) with regional climate models (RCMs) driven by the ERA-Interim reanalysis of observations for the period 1989–2008. The assessment is carried out by comparison with a set of high resolution observation datasets for nine European subregions. The statistics analyzed include quantitative metrics for mean precipitation, daily precipitation probability density functions (PDFs), daily precipitation intensity, frequency, 95th percentile and 95th percentile of dry spell length. We assess an ensemble including all Med-CORDEX and EURO-CORDEX models together and others including the Med-CORDEX and EURO-CORDEX separately. For the All Models ensembles, the RCM11 one shows a remarkable performance in reproducing the spatial patterns and seasonal cycle of mean precipitation over all regions, with a consistent and marked improvement compared to the RCM44 ensemble and the ERA-Interim reanalysis. A good consistency with observations by the RCM11 ensemble (and a substantial improvement compared to RCM44 and ERA-Interim) is found also for the daily precipitation PDFs, mean intensity and, to a lesser extent, the 95th percentile. A general improvement by the RCM11 models is also found when the data are upscaled and intercompared at the 0.44° and 1.5° resolutions. For some regions the RCM11 ensemble overestimates the occurrence of very high intensity events while for one region the models underestimate the occurrence of the most intense extremes. The RCM11 ensemble still shows a general tendency to underestimate the dry day frequency and 95th percentile of dry spell length over wetter regions, with only a marginal improvement compared to the lower resolution models. This indicates that the problem of the excessive production of low precipitation events found in many climate models persists also at relatively high resolutions, at least in wet climate regimes. Concerning the Med-CORDEX and EURO-CORDEX ensembles we find that their performance is of similar quality over the Mediterranean regions analyzed. Finally, we stress the need of consistent and quality checked fine scale observation datasets for the assessment of RCMs run at increasingly high horizontal resolutions.

Hourly concentrations of ozone (O 3 ) and nitrogen dioxide (NO 2 ) have been measured for 16 years, from 1998 to 2013, in a seaside town in central Italy. The seasonal trends of O 3 and NO 2 recorded in this period have been studied. Furthermore, we used the data collected during one year (2005), to define the characteristics of a multiple linear regression model and a neural network model. Both models are used to model the hourly O 3 concentration, using, two scenarios: 1) in the first as inputs, only meteorological parameters and 2) in the second adding photochemical parameters at those of the first scenario. In order to evaluate the performance of the model four statistical criteria are used: correlation coefficient, fractional bias, normalized mean squared error and a factor of two. All the criteria show that the neural network gives better results, compared to the regression model, in all the model scenarios. Predictions of O 3 have been carried out by many authors using a feed forward neural architecture. In this paper we show that a recurrent architecture significantly improves the performances of neural predictors. Using only the meteorological parameters as input, the recurrent architecture shows performance better than the multiple linear regression model that uses meteorological and photochemical data as input, making the neural network model with recurrent architecture a more useful tool in areas where only weather measurements are available. Finally, we used the neural network model to forecast the O 3 hourly concentrations 1, 3, 6, 12, 24 and 48 h ahead. The performances of the model in predicting O 3 levels are discussed. Emphasis is given to the possibility of using the neural network model in operational ways in areas where only meteorological data are available, in order to predict O 3 also in sites where it has not been measured yet.

In a previous work a 1-year time series of fungal spore concentrations was used to calibrate an artificial neural network for the estimation of Alternaria and Pleospora concentrations associated with observed meteorological variables in the atmosphere of L’Aquila, Italy. In this article the possibility to use the neural model calibrated with observed meteorological variables to predict the future fungal spore concentration from meteorological forecast is investigated. The results show that the proposed technique appears to be a suitable device to operationally predict the Alternaria and Pleospora concentrations a few days in advance. Emphasis is given to the actual use of these predictions for establishing a preventive strategy for allergy sufferers and for an appropriate use of fungicide treatments in agricultural activities, avoiding unsafe and useless pollution of the atmosphere, crops and fields.

Summary A collection of one year daily forecasts with the MM5 mesoscale model is used to investigate the possibility to downscale hourly precipitation fields from a horizontal grid spacing of 27 km to one at 3 km. The downscaling is performed using a multi-layer Neural Network built with information of terrain, land use and predicted precipitation at the four adjacent grid points of the MM5 coarse grid. Results obtained for a domain of complex topography show that the proposed technique produces realistic downscaled precipitation fields. Emphasis is given to the possible application of the methodology to the coupling of hydrological and meteorological models or for downscaling coarse scale climate model precipitation fields to hydrological catchment scales.

Since 2002, within the framework of the Cetemps Centre of Excellence at the University of L'Aquila, a distributed grid-based hydrological model (CHYM) has been developed to provide a general purpose model for operational flood warning activity. This paper presents two new cellular automata (CA) algorithms used respectively for drainage network extraction and rainfall data assimilation. The first is a cellular automaton-based algorithm for the extraction of a drainage network from an arbitrary digital elevation model. It has been implemented and tested on a large number of different domains. This algorithm is able to define the flow direction at every point on the digital elevation model where singular points are present (pits or flat areas). The second is a CA-based numerical technique for assimilating different data sources of rainfall to rebuild the rainfall field on a grid. This technique has been shown to produce a reasonable rainfield shape without any geometrical artefacts that often produc...

Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network algorithm for operational applications. The algorithm combines numerical weather model information with the satellite data. Using this algorithm, daily rainfall estimates were derived for 4 yr of the Ethiopian and Zambian main rainy seasons and were compared with two other algorithms—a multiple linear regression making use of the same information as that of the neural network and a satellite-only method. All algorithms were validated against rain gauge data. Overall, the neural network performs best, but the extent to which it does so depends on the calibration/validation protocol. The advantages of the neural network are most evident when calibration data are numerous and close in space and time to the validation data. This result emphasizes the importance of a real-time calibration system.

The diurnal variation of regional wind patterns in the complex terrain of Central Italy was investigated for summer fair-weather conditions and winter time periods using a radar wind profiler. The profiler is located on a site where interaction between the complex topography and land-surface produces a variety of thermally and dynamically driven wind systems. The observational data set, collected for a period of one year, was used first to describe the diurnal evolution of thermal driven winds, second to validate the Mesoscale Model5 (MM5) that is a three-dimensional numerical model. This type of analysis was focused on the near-surface wind observation, since thermally driven winds occur in the lower atmosphere. According to the valley wind theory expectations, the site – located on the left sidewall of the valley (looking up valley) – experiences a clockwise turning with time. Same characteristics in the behavior were established in both the experimental and numerical results. Because the thermally driven flows can have some depth and may be influenced mainly by model errors, as a third step the analysis focuses on a subset of cases to explore four different MM5 Planetary Boundary Layer (PBL) parameterizations. The reason is to test how the results are sensitive to the selected PBL parameterization, and to identify the better parameterization if it is possible. For this purpose we analysed the MM5 output for the whole PBL levels. The chosen PBL parameterizations are: 1) Gayno-Seaman; 2) Medium-Range Forecast; 3) Mellor-Yamada scheme as used in the ETA model; and 4) Blackadar.

The Po River is a crucial resource for the Italian economy, since 40% of the gross domestic product comes from this area. It is thus crucial to quantify the impact of climate change on this water resource in order to plan for future water use. In this paper a mini ensemble of 8 hydrological simulations is completed from 1960 to 2050 under the A1B emission scenario, by using the output of two regional climate models as input (REMO and RegCM) at two different resolutions (25 km–10 km and 25 km–3 km). The river discharge at the outlet point of the basin shows a change in the spring peak of the annual cycle, with a one month shift from May to April. This shift is entirely due to the change in snowmelt timing which drives most of the discharge during this period. Two other important changes are an increase of discharge in the wintertime and a decrease in the fall from September to November. The uncertainty associated with the winter change is larger compared to that in the fall. The spring shift and the fall decrease of discharge imply an extension of the hydrological dry season and thus an increase in water stress over the basin. The spatial distributions of the discharge changes are in agreement with what is observed at the outlet point and the uncertainty associated with these changes is proportional to the amplitude of the signal. The analysis of the changes in the anomaly distribution of discharge shows that both the increases and decreases in seasonal discharge are tied to the changes in the tails of the distribution, i.e. to the increase or decrease of extreme events.

Airborne particles (pollens and fungal spores) are recognized as important causes of allergies and many other pathologies whose main symptoms are usually associated with respiratory problems. In addition, these particles seem to be responsible for clinical symptoms of oculorhinitis and bronchial asthma. Many authors showed how pollen and spore concentrations are critically linked to meteorological conditions, while other studies investigated the possibility to estimate these concentrations through meteorological parameters. So, many different approaches have been proposed, and one of the most sophisticated is based on the use of a complex artificial neural network architecture. Once the neural device is calibrated using simultaneous time series of observed meteorological parameters and airborne biological particles, it is straightforward to use the Neural Network to predict spore concentrations using operational Limited Area Meteorological Model. In a previous work, it has been shown that the MM5 meteorological model developed by National Center for Atmospheric Research and Pennsylvania State University can be coupled with the above-cited neural predictor to provide a good prediction of Alternaria and Pleospora spore in the location of L’Aquila (Central Italy). Following the same approach, this work aims to provide the mapping of spore concentration over a wide area covered by high-resolution meteorological prediction in Central Italy. The complex patterns of fungal spore concentrations in selected areas will be described, and the high temporal variability of such fields will be discussed as well. The possibility to infer useful information from the predicted pattern of spore concentrations is discussed, as an example it appears that for people suffering from allergy to fungal spores is more comfortable to spend summertime close to the east coast of Italian Peninsula respect to the west coast. A further step of this work may easily lead to an operational use of the model for supporting the clinical management of allergies and for establishing a preventive strategy in agriculture to avoid unsafe and useless pollution of atmosphere, crops and fields.

In this study, we investigate the potential impact on local air quality of a biomass power plant, which is planned for installation near L'Aquila, a city of 70,000 people located in a mountain valley in Central Italy. The assessment is carried out by applying a one year simulation with the CALPUFF model, following the recommendations of the U. S. Environmental Protection Agency. Meteorological input is produced with CALMET model, fed with both MM5 meteorological fields at 3 km resolution and wind observations from a surface weather station. We estimate small (

An analysis of the results of a climate simulation for a doubling of atmospheric carbon dioxide concentration over the European region is reported. Physical mechanisms are sought which could explain possible changes in the diurnal temperature range (DTR) under conditions of increased atmospheric greenhouse gas content. We show that an important contribution to changes in DTR is given by soil moisture. In areas where soil moisture increases due to an increase in precipitation there is a positive change in latent heat flux and a decrease in sensible heat flux. As a result, in areas with increasing soil moisture, the increase in maximum daytime temperature will be smaller than that in minimum temperature, thereby causing a decrease in the DTR. The opposite occurs for areas which undergo soil drying. This process amplifies the effect of cloud changes on surface solar and infrared radiation and dominates the direct effect of downward infrared radiation associated with increasing greenhouse gas concentration. Because the soil water content is largely controlled by precipitation, our results are consistent with early observational findings of negative correlation between changes in precipitation and in diurnal temperature range.

Lidar observations at Sodankyla (Finland) on January 9 and 11 1992, made as part of the European Arctic Stratospheric Ozone Experiment (EASOE), showed very different profiles of backscattering ratio. A prominent layer was seen between potential temperatures of 420 and 445K on January 9, which was nearly absent in the backscattering profile between these isentropic surfaces on the 11th. Examination of the trajectories of the airmasses reaching Sodankyla on these days lead us to suggest that the larger returns on January 9th could arise from the formation of a nitric acid coating on volcanic aerosols. Simulations using a microphysical model produce results consistent with this suggestion. We conclude that nitric acid condensation on January 9 can explain the measurements, but that independent information would be needed to quantify the characteristics of the aerosol substrate.

Two lidar systems, an aerosol lidar and an O3 Differential Absorption Lidar (DIAL), have been routinely operated at the same site (L‧Aquila, Italy; 42°N, 13°E) since August 1991. The multiwavelength analysis of the lidar signals allows to retrieve parameters related to equivalent aerosol size distributions and their optical properties. These are needed to correct the ozone DIAL profiles from the disturbance introduced by the stratospheric volcanic aerosols. The method and the confidence of the retrieved ozone profiles are discussed in a companion paper. Here we present the whole measurement series of ozone and backscattering ratio profiles during the period from August 1991 to December 1992. In addition, for some observations, the mode radius and the dispersion of the representative aerosol size distribution are reported. The time evolutions of aerosol surface area density and mass mixing ratio are also discussed within the uncertainties of the retrieval algorithm.

The ozone depletion over the Antarctic region is now attributed to processes involving heterogeneous chemistry on polar stratospheric clouds. Similar mechanisms are probably working also in the Northern hemisphere high latitudes [ Douglass and Stolarski (1989) Geophys. Res. Lett. 16, 131] and may be important in explaining the secular trend of ozone in the last twenty years above 50° North [ Pitari and Visconti (1991) J. geophys. Res. 96, 10,931]. Hofmann and Solomon [(1989) J. geophys. Res. 94, 5029] have shown that the local observed decrease in the ozone amount following the eruption of El Chichon could be explained in terms of heterogeneous chemistry on the volcanic aerosol surface. In this paper we use a two dimensional model to study the effects on ozone introduced by the El Chichon aerosols through a perturbation in the radiation field; both the temperature and the photolysis rates are affected. We show that up to half of the observed decrease may be attributed to radiative effects at mid latitudes.

We report calculation results which could impose some constraints for the proposed chemical and dynamical theories on the ozone hole. One set of calculations is about the consistency between the observed optical thickness of polar stratospheric clouds (PSC) and the surface required for the heterogeneous reactions by the chemical theories. Assuming a reaction rate for ClNO3+H2O we find the sticking coefficient to be larger than 0.1 for consistency with observed PSC extinctions. The other set of calculations is about the perturbation to the residual circulation due to the presence of additional heating produced by PSCs. The perturbed residual circulation is of the opposite sign (i.e. downward) with respect to that necessary for the dynamical generation of an ozone hole.

Within the activities of Cetemps Center of Excellence of University of L’Aquila, a distributed grid based hydrological model has been developed with the aim to provide a general purpose tool for flood alert mapping. One of the main characteristic of this model is the ability to assimilate different data sources for rebuilding two dimensional rainfall distribution. The model can be used for any geographical domain with any resolution up to the resolution of the implemented Digital Elevation Model, namely about 300 meters in the current implementation. A Cellular Automata based algorithm has been implemented to extract a coherent drainage network scheme for any geographical domain. The algorithm for flow scheme extraction and for the assimilation of different rainfall data sources are described in details too. Several applications of such algorithms are also shown.

A flood forecast chain, developed at the Centre of Excellence for Remote Sensing and Hydro-Meteorology (CETEMPS) and based on coupled mesoscale atmospheric and a newly developed distributed hydrological model with in-situ and remote sensing data integration, is illustrated. The focus is on small-catchment flood forecast in complex topography in Central Italy, but the developed modelling and processing integrated tools may be easily applied to any geographical and orographic scenario. Emphasis is put on the integration of numerical models and retrieval algorithms with aim to provide an overview of an objective system for hydro-meteorological alert-map emission. As an example, the forecast hydro-meteorological chain is applied to a case study of an extreme event on 23–25 January 2003 and results are discussed.

The term now cast in hydro meteorology reflects the need for timely and accurate predictions of risky environmental situations, which are related to the development of severe meteorological events at short time scales. The objective of this paper is to apply a fully neural-network approach to the rainfall field now casting from infrared (IR) and microwave (MW) passive-sensor imagery aboard, respectively, geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellites. The multisatellite space-time prediction procedure, which is named Neural Combined Algorithm for Storm Tracking (NeuCAST), consists of two consecutive steps. First, the IR radiance field measured from a geostationary satellite radiometer (e.g., Meteosat) is projected ahead in time (e.g., 30 min); second, the projected radiance field is used in estimating the rainfall field by means of an MW-IR combined rain retrieval algorithm exploiting GEO-LEO observations. The NeuCAST methodology is extensively illustrated and discussed in this paper. Its accuracy is quantified by means of quantitative error indexes, which are evaluated on selected case studies of rainfall events in Southern Europe in 2003 and 2005.

A 1-year time series of fungal spore concentrations has been used to calibrate an artificial neural network for the estimation of Alternaria and Pleospora concentrations associated to observed meteorological variables. Analysis of the results revealed that the daily average values of these meteorological variables are suitable to predict with high confidence the number of fungal spores that are actually observed. The calibrated neural network has also been used randomizing each single input parameter in order to evaluate which meteorological variable contributes more to the formation and the depletion of the selected fungal spores. Emphasis is given to the possibility of using the proposed model for operational activities, predicting the future spore concentrations on the basis of meteorological forecasts.

The term nowcasting reflects the need of timely and accurate predictions of risky situations related to the development of severe meteorological events. In this work the objective is the very short term prediction of the rainfall field from geostationary satellite imagery entirely based on neural network approach. The very short-time prediction (or nowcasting) process consists of two steps: first, the infrared radiance field measured from geostationary satellite (Meteosat 7) is projected ahead in time (30 min or 1 h); secondly, the projected radiances are used to estimate the rainfall field by means of a calibrated microwave-based combined algorithm. The methodology is discussed and its accuracy is quantified by means of error indicators. An application to a satellite observation of a rainfall event over Central Italy is finally shown and evaluated.

The increased number of extreme rainfall events seems to be one of the common feature of climate change signal all over the world (Easterlin et al., 2000; Meehl et al., 2000). In the last few years a large number of floods caused by extreme meteorological events has been observed over the river basins of Mediterranean area and they mainly affected small basins (few hundreds until few thousands of square kilometres of drainage area) . A strategic goal of applied meteorology is now to try to predict with high spatial resolution the segments of drainage network where floods may occur. A possible way to reach this aim is the coupling of meteorological mesoscale model with high resolution hydrological model. In this work few case studies of observed floods in the Italian Mediterranean area will be presented. It is shown how a distributed hydrological model, using the precipitation fields predicted by MM5 meteorological model, is able to highlight the area where the major floods may occur.

Operational, real-time rainfall estimation on a daily timescale is potentially of great benefit for hydrological forecasting in African river basins. Sparseness of ground-based observations often means that only methodologies based predominantly on satellite data are feasible. An approach is presented here in which Cold Cloud Duration (CCD) imagery derived from Meteosat thermal infrared imagery is used in conjunction with numerical weather model analysis data as the input to an artificial neural network. Novel features of this approach are the use of principal component analysis to reduce the data requirements for the weather model analyses and the use of a pruning technique to identify redundant input data. The methodology has been tested using 4 yr of daily rain gauge data from Zambia in central Africa. Calibration and validation were carried out using pixel area rainfall estimates derived from daily rain gauge data. When compared with a standard CCD approach using the same dataset, the neural ...

The drainage of the Fucino Lake of central Italy was completed in 1873, and this possibly caused significant climatic changes over the Fucino basin. In this paper we discuss a set of short-term triple-nested regional model simulations of the meteorological effects of the Fucino Lake on the surrounding region. We find that the model simulates realistic lake-breeze circulations and their response to background winds. The simulations indicate that the lake affects the temperature of the surrounding basin in all seasons and precipitation in the cold season, when cyclonic perturbations move across the region. Some effects of the lake also extend over areas quite far from the Fucino basin. Our results support the hypothesis that the drainage of the lake might have significantly affected the climate of the lake basin. However, longer simulations and further development in some aspects of the model are needed, in order to provide a more statistically robust evaluation of the simulated lake-effects.Key words. Hydrology (anthropogenic effects) – Meteorology and atmospheric dynamics (climatology; mesoscale meteorology)

The Lake Fucino was the largest reservoir of fresh water in the Abruzzo Region until it was drained at the end of last century. The surface of the lake was about 150 square km. Temperature and precipitation historical records show appreciable changes in these variable that could be related to the draining of the lake. Changes in the vegetation around the lake are also recorded especially concerning the existence of plantations of olive trees. The setting of the lake is peculiar being at the center of a closed valley. To assess the possible effect of the lake on the local climate and the meteorology regime we have carried out a simulation using the climate version of the MM5 limited area model. This model includes a code for the soil atmosphere interactions. Also the model has been updated for the land use/land cover at very high resolution (1km). The simulations are carried out at different resolution and use the nesting technique. Preliminary results seem to indicate an overestimation of the changes with respect those present in the historical records.

We propose to use an Artificial Neural Network (ANN) for meteorological blocking recognition. The network output is presented as a number which ranges between 0 (absence of blocking) and 1 (blocked situation). This output is then compared with the step function obtained with a blocking index used in meteorological analysis and in the recognition of synoptic maps. We show that the ANN can pick events which are disregarded by the TM index and that ANN performances are equivalent and in some cases better than those indicated by an analytically computed blocking index.

The secular trend of the Antarctic ozone hole has been studied with a two-dimensional model which can simulate formation of polar stratospheric clouds and includes heterogeneous chemical reactions. Results from the numerical simulation have been validated by comparison with available experimental data. Trends up to the year 2010 using standard (i.e., homogeneous) and heterogeneous chemistry have been compared and show that global ozone depletion reached 5–6% in the last 30 years and will average 8% for the next 20 years. Subtracting a 2% loss due to standard chemistry in the presence of trace gas increase in the last 30 years, we find a 3–4% global ozone loss due to heterogeneous chemistry. The depletion is evident even outside the southern hemisphere spring season and at mid-latitudes, pointing to an increase in global ozone sink.

The possible effects of the QBO on the ozone distribution have been studied including in a 2D model a parameterization of Kelvin and Rossby-gravity wave forcing in the lower equatorial stratosphere. A chemical code complete with heterogeneous reactions allows a simulation of the ozone depletion due to the increase of stratospheric chlorine. With this model, the authors study the possible modulation of the secular trend in the Antarctic ozone hole by the QBO. When heterogeneous chemistry is not included, the model shows a polar ozone oscillation ({plus minus}6 Dobson Units) comparable to that deduced from early measurements (1970-1975). When heterogeneous reactions are taken into account, the model predicts a larger ozone oscillation in the Southern Hemisphere ({plus minus}12 Dobson Units) comparable to that obtained from recent observations. This behavior seems to point out a QBO induced temperature effect and its feedback on PSC with activation of heterogeneous chemistry.

A two-dimensional model has been integrated for two years to study the evolution of the El Chichon aerosol cloud in the stratosphere, starting about three months after the eruption. Initial conditions for the backscattering ratios are taken from airborne lidar measurements, while observations taken at Mauna Loa are used to estimate the initial size distribution for the aerosols. Aerosols have been treated as a passive tracer, because the small changes in the stratospheric dynamics due to the aerosol interaction with solar and longwave radiation can only induce marginal effects on large scale transport. We have also completely neglected microphysical processes like coagulation as well as photochemical effects on chemically reacting species. Results are shown for aerosol extinction mixing ratios, optical thickness, mass column density and size distribution. Extensive comparison with available lidar measurements are also presented and discussed. Major discrepancies are noted between the measured opt...

Forecast, detection and warning of severe weather and related hydro-geological risks is becoming one of the major issues for civil protection. The use of computational intelligence techniques such as artificial neural network and cellular automata algorithm can be suitable for such problems especially for real time forecasting system. Nowcasting (short-term forecasting) of extreme rainfall events is an example that invites to exploit remote sensing systems from satellites, such geostationary and low-orbit radiometers. A rainfall estimation algorithm based on artificial neural network has been developed for this purpose. Satellite data sources can be globally provided but at a quite coarse spatial resolution, therefore, the coupling of rain remote sensing data with regional raingauge networks is also essential for ensuring a calibration of remotely sensed rainfall fields in terms of ground effects. An overwhelming issue is the spatial integration of these rainfall data sources having different space-time resolution and variable accuracies. In this work a cellular automata based algorithm has been used to integrate these heterogeneous data. A flood forecast chain, developed at the Centre of Excellence for Remote Sensing and Hydro-Meteorology Modelling and based on coupled mesoscale atmospheric model and distributed hydrological model with in-situ and remote sensing data integration is presented, with the emphasis on the integration of numerical models and retrieval algorithms using integrated tools based on computational intelligence techniques.