Development of SuperEnsemble Techniques for the Mediterranean Ocean Forecasting System

This research activity studied how the uncertainties are concerned and interrelated through the multi-model approach, since it seems to be the bigger challenge of ocean and weather forecasting. Moreover, we tried to reduce model error throughout the superensemble approach. In order to provide this aim, we created different dataset and by means of proper algorithms we obtained the superensamble estimate. We studied the sensitivity of this algorithm in function of its characteristics parameters. Clearly, it is not possible to evaluate a reasonable estimation of the error neglecting the importance of the grid size of ocean model, for the large amount of all the sub grid-phenomena embedded in space discretizations that can be only roughly parametrized instead of an explicit evaluation. For this reason we also developed a high resolution model, in order to calculate for the first time the impact of grid resolution on model error.

A coupled ocean-atmosphere system for short term forecasting in the southern european seas

This coupled model combines two state-of-the-art numerical models, NEMO for the oceanic component and WRF for the atmospheric component and implements them at an appropriate resolution. The oceanic model has been implemented starting from the Mediterranean Forecasting System with a resolution of 1/24° and the domain was extended to exactly match the grid of a newly implemented atmospheric model for the same area. The uncoupled ocean model has been validated against SST observed data, both in the simulation of an extreme event and in the short-term forecast of two seasonal periods. A new setup of the model was successfully tested in which the downward radiative fluxes were prescribed from atmospheric forecasts. Various physical schemes, domain, boundary, and initial conditions were tested with the atmospheric model to obtain the best representation of medicane Ianos. The heat fluxes calculated by the uncoupled models were compared to determine which setup gave the best energy balance between the components of the coupled model. The coupling strategy used is the traditional one, where the ocean is driven by the surface stress, heat fluxes, and radiative fluxes computed in the atmospheric component, which in turn receives the SST and surface currents. As expected, the overall skills of the coupled model are slightly degraded compared to the uncoupled models, even though the positioning and timing of the cyclone at the time of the landfall is enhanced. The mean heat fluxes do not change compared to the uncoupled model, whereas the pattern of the shortwave radiation and latent heat is changed. Moreover, the two energy fluxes are larger in absolute values than those calculated with the MFS formulas. The fact that they have opposite signs give raise to a compensation error that limits the overall degradation of the coupled simulation.

Ensemble forecasting in the Mediterranean sea

A new methodology is being devised for ensemble ocean forecasting using distributions of the surface wind field derived from a Bayesian Hierarchical Model (BHM). The ocean members are forced with samples from the posterior distribution of the wind during the assimilation of satellite and in-situ ocean data. The initial condition perturbations are then consistent with the best available knowledge of the ocean state at the beginning of the forecast and amplify the ocean response to uncertainty only in the forcing. The ECMWF Ensemble Prediction System (EPS) surface winds are also used to generate a reference ocean ensemble to evaluate the performance of the BHM method that proves to be eective in concentrating the forecast uncertainty at the ocean meso-scale. An height month experiment of weekly BHM ensemble forecasts was performed in the framework of the operational Mediterranean Forecasting System. The statistical properties of the ensemble are compared with model errors throughout the seasonal cycle proving the existence of a strong relationship between forecast uncertainties due to atmospheric forcing and the seasonal cycle.

Towards Rapid Environmental Assessment and Coastal Forecasting in the Northern Adriatic Sea

A new Coastal Rapid Environmental Assessment (CREA) strategy has been developed and successfully applied to the Northern Adriatic Sea. CREA strategy exploits the recent advent of operational oceanography to establish a CREA system based on an operational regional forecasting system and coastal monitoring networks of opportunity. The methodology wishes to initialize a coastal high resolution model, nested within the regional forecasting system, blending the large scale parent model fields with the available coastal observations to generate the requisite field estimates. CREA modeling system consists of a high resolution, O(800m), Adriatic SHELF model (ASHELF) implemented into the Northern Adriatic basin and nested within the Adriatic Forecasting System (AFS) (Oddo et al. 2006). The observational system is composed by the coastal networks established in the framework of ADRICOSM (ADRiatic sea integrated COastal areaS and river basin Managment system) Pilot Project. An assimilation technique exerts a correction of the initial field provided by AFS on the basis of the available observations. The blending of the two data sets has been carried out through a multi-scale optimal interpolation technique developed by Mariano and Brown (1992). Two CREA weekly exercises have been conducted: the first, at the beginning of May (spring experiment); the second in middle August (summer experiment). The weeks have been chosen looking at the availability of all coastal observations in the initialization day and one week later to validate model results, verifying our predictive skills. ASHELF spin up time has been investigated too, through a dedicated experiment, in order to obtain the maximum forecast accuracy within a minimum time. Energetic evaluations show that for the Northern Adriatic Sea and for the forcing applied, a spin-up period of one week allows ASHELF to generate new circulation features enabled by the increased resolution and its total kinetic energy to establish a new dynamical balance. CREA results, evaluated by mean of standard statistics between ASHELF and coastal CTDs, show improvement deriving from the initialization technique and a good model performance in the coastal areas of the Northern Adriatic basin, characterized by a shallow and wide continental shelf subject to substantial freshwater influence from rivers. Results demonstrate the feasibility of our CREA strategy to support coastal zone management and wish an additional establishment of operational coastal monitoring activities to advance it.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

Coastal modelling studies for forecasting and remediation solutions

The coastal area along the Emilia-Romagna (ER), in the Italian side of the northern Adriatic Sea, is considered to implement an unstructured numerical ocean model with the aim to develop innovative tools for the coastal management and a forecasting system for the storm surge risk reduction. The Adriatic Sea has been the focus of several studies because of its peculiar dynamics driven by many forcings acting at basin and local scales. The ER coast is particularly exposed to storm surge events. In particular conditions, winds, tides and seicehs may combine and contribute to the flooding of the coastal area. The global sea level rise expected in the next decades will increase even more the hazard along the ER and Adriatic coast. Reliable Adriatic and Mediterranean scale numerical ocean models are now available to allow the dynamical downscaling of very high-resolution models in limited coastal areas. In this work the numerical ocean model SHYFEM is implemented in the Goro lagoon (named GOLFEM) and along the ER coast (ShyfER) to test innovative solutions against sea related coastal hazards. GOLFEM was succesfully applied to analyze the Goro lagoon dynamics and to assess the dynamical effects of human interventions through the analysis of what-if scenarios. The assessment of storm surge hazard in the Goro lagoon was carried out through the development of an ensemble storm surge forecasting system with GOLFEM using forcing from different operational meteorological and ocean models showing the fundamental importance of the boundary conditions. The ShyfER domain is used to investigate innovative solutions against storm surge related hazard along the ER coast. The seagrass is assessed as a nature-based solution (NBS) for coastal protection under present and future climate conditions. The results show negligible effects on sea level but sensible effects in reducing bottom current velocity.

Stima dei parametri di modelli idrologici mediante ottimizzazione dell’utilità

Tradizionalmente, l'obiettivo della calibrazione di un modello afflussi-deflussi è sempre stato quello di ottenere un set di parametri (o una distribuzione di probabilità dei parametri) che massimizzasse l'adattamento dei dati simulati alla realtà osservata, trattando parzialmente le finalità applicative del modello. Nel lavoro di tesi viene proposta una metodologia di calibrazione che trae spunto dell'evidenza che non sempre la corrispondenza tra dati osservati e simulati rappresenti il criterio più appropriato per calibrare un modello idrologico. Ai fini applicativi infatti, può risultare maggiormente utile una miglior rappresentazione di un determinato aspetto dell'idrogramma piuttosto che un altro. Il metodo di calibrazione che viene proposto mira a valutare le prestazioni del modello stimandone l'utilità nell'applicazione prevista. Tramite l'utilizzo di opportune funzioni, ad ogni passo temporale viene valutata l'utilità della simulazione ottenuta. La calibrazione viene quindi eseguita attraverso la massimizzazione di una funzione obiettivo costituita dalla somma delle utilità stimate nei singoli passi temporali. Le analisi mostrano come attraverso l'impiego di tali funzioni obiettivo sia possibile migliorare le prestazioni del modello laddove ritenute di maggior interesse per per le finalità applicative previste.

Toward an understanding of the atmospheric forcing uncertainties in the Mediterranean Sea

The thesis has extensively investigated for the first time the statistical distributions of atmospheric surface variables and heat fluxes for the Mediterranean Sea. After retrieving a 30-year atmospheric analysis dataset, we have captured the spatial patterns of the probability distribution of the relevant atmospheric variables for ocean atmospheric forcing: wind components (U,V), wind amplitude, air temperature (T2M), dewpoint temperature (D2M) and mean sea-level pressure (MSL-P). The study reveals that a two-parameter PDF is not a good fit for T2M, D2M, MSL-P and wind components (U,V) and a three parameter skew-normal PDF is better suited. Such distribution captures properly the data asymmetric tails (skewness). After removing the large seasonal cycle, we show the quality of the fit and the geographic structure of the PDF parameters. It is found that the PDF parameters vary between different regions, in particular the shape (connected to the asymmetric tails) and the scale (connected to the spread of the distribution) parameters cluster around two or more values, probably connected to the different dynamics that produces the surface atmospheric fields in the Mediterranean basin. Moreover, using the atmospheric variables, we have computed the air-sea heat fluxes for a 20-years period and estimated the net heat budget over the Mediterranean Sea. Interestingly, the higher resolution analysis dataset provides a negative heat budget of –3 W/m2 which is within the acceptable range for the Mediterranean Sea heat budget closure. The lower resolution atmospheric reanalysis dataset(ERA5) does not satisfy the heat budget closure problem pointing out that a minimal resolution of the atmospheric forcing is crucial for the Mediterranean Sea dynamics. The PDF framework developed in this thesis will be the basis for a future ensemble forecasting system that will use the statistical distributions to create perturbations of the atmospheric ocean forcing.

Dynamics of coastal aquifers: data-driven forecasting and risk analysis

This work is focused on the study of saltwater intrusion in coastal aquifers, and in particular on the realization of conceptual schemes to evaluate the risk associated with it. Saltwater intrusion depends on different natural and anthropic factors, both presenting a strong aleatory behaviour, that should be considered for an optimal management of the territory and water resources. Given the uncertainty of problem parameters, the risk associated with salinization needs to be cast in a probabilistic framework. On the basis of a widely adopted sharp interface formulation, key hydrogeological problem parameters are modeled as random variables, and global sensitivity analysis is used to determine their influence on the position of saltwater interface. The analyses presented in this work rely on an efficient model reduction technique, based on Polynomial Chaos Expansion, able to combine the best description of the model without great computational burden. When the assumptions of classical analytical models are not respected, and this occurs several times in the applications to real cases of study, as in the area analyzed in the present work, one can adopt data-driven techniques, based on the analysis of the data characterizing the system under study. It follows that a model can be defined on the basis of connections between the system state variables, with only a limited number of assumptions about the "physical" behaviour of the system.

Urban air quality: new high-resolution modeling approaches and forecasting tools for citizens

Air pollution is one of the greatest health risks in the world. At the same time, the strong correlation with climate change, as well as with Urban Heat Island and Heat Waves, make more intense the effects of all these phenomena. A good air quality and high levels of thermal comfort are the big goals to be reached in urban areas in coming years. Air quality forecast help decision makers to improve air quality and public health strategies, mitigating the occurrence of acute air pollution episodes. Air quality forecasting approaches combine an ensemble of models to provide forecasts from global to regional air pollution and downscaling for selected countries and regions. The development of models dedicated to urban air quality issues requires a good set of data regarding the urban morphology and building material characteristics. Only few examples of air quality forecast system at urban scale exist in the literature and often they are limited to selected cities. This thesis develops by setting up a methodology for the development of a forecasting tool. The forecasting tool can be adapted to all cities and uses a new parametrization for vegetated areas. The parametrization method, based on aerodynamic parameters, produce the urban spatially varying roughness. At the core of the forecasting tool there is a dispersion model (urban scale) used in forecasting mode, and the meteorological and background concentration forecasts provided by two regional numerical weather forecasting models. The tool produces the 1-day spatial forecast of NO2, PM10, O3 concentration, the air temperature, the air humidity and BLQ-Air index values. The tool is automatized to run every day, the maps produced are displayed on the e-Globus platform, updated every day. The results obtained indicate that the forecasting output were in good agreement with the observed measurements.

Evaluation of a 2D multileader training system for improving sustainability and precision orchard management applications in italian apple orchards

The presented study aimed to evaluate the productive and physiological behavior of a 2D multileader apple training systems in the Italian environment both investigating the possibility to increase yield and precision crop load management resolution. Another objective was to find valuable thinning thresholds guaranteeing high yields and matching fruit market requirements. The thesis consists in three studies carried out in a Pink Lady®- Rosy Glow apple orchard trained as a planar multileader training system (double guyot). Fruiting leaders (uprights) dimension, crop load, fruit quality, flower and physiological (leaf gas exchanges and fruit growth rate) data were collected and analysed. The obtained results found that uprights present dependence among each other and as well as a mutual support during fruit development. However, individual upright fruit load and upright’s fruit load distribution on the tree (~ plant crop load) seems to define both upright independence from the other, and single upright crop load effects on the final fruit quality production. Correlations between fruit load and harvest fruit size were found and thanks to that valuable thinning thresholds, based on different vegetative parameters, were obtained. Moreover, it comes out that an upright’s fruit load random distribution presents a widening of those thinning thresholds, keeping un-altered fruit quality. For this reason, uprights resulted a partially physiologically-dependent plant unit. Therefore, if considered and managed as independent, then no major problems on final fruit quality and production occurred. This partly confirmed the possibility to shift crop load management to single upright. The finding of the presented studies together with the benefits coming from multileader planar training systems suggest a high potentiality of the 2D multileader training systems to increase apple production sustainability and profitability for Italian apple orchard, while easing the advent of automation in fruit production.

Power flow forecasting and smart charging for electric vehicle charging systems with photovoltaic integration

An essential role in the global energy transition is attributed to Electric Vehicles (EVs) the energy for EV traction can be generated by renewable energy sources (RES), also at a local level through distributed power plants, such as photovoltaic (PV) systems. However, EV integration with electrical systems might not be straightforward. The intermittent RES, combined with the high and uncontrolled aggregate EV charging, require an evolution toward new planning and paradigms of energy systems. In this context, this work aims to provide a practical solution for EV charging integration in electrical systems with RES. A method for predicting the power required by an EV fleet at the charging hub (CH) is developed in this thesis. The proposed forecasting method considers the main parameters on which charging demand depends. The results of the EV charging forecasting method are deeply analyzed under different scenarios. To reduce the EV load intermittency, methods for managing the charging power of EVs are proposed. The main target was to provide Charging Management Systems (CMS) that modulate EV charging to optimize specific performance indicators such as system self-consumption, peak load reduction, and PV exploitation. Controlling the EV charging power to achieve specific optimization goals is also known as Smart Charging (SC). The proposed techniques are applied to real-world scenarios demonstrating performance improvements in using SC strategies. A viable alternative to maximize integration with intermittent RES generation is the integration of energy storage. Battery Energy Storage Systems (BESS) may be a buffer between peak load and RES production. A sizing algorithm for PV+BESS integration in EV charging hubs is provided. The sizing optimization aims to optimize the system's energy and economic performance. The results provide an overview of the optimal size that the PV+BESS plant should have to improve whole system performance in different scenarios.