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.

Analysis and developments of uncertainty processors for real time flood forecasting

The hydrologic risk (and the hydro-geologic one, closely related to it) is, and has always been, a very relevant issue, due to the severe consequences that may be provoked by a flooding or by waters in general in terms of human and economic losses. Floods are natural phenomena, often catastrophic, and cannot be avoided, but their damages can be reduced if they are predicted sufficiently in advance. For this reason, the flood forecasting plays an essential role in the hydro-geological and hydrological risk prevention. Thanks to the development of sophisticated meteorological, hydrologic and hydraulic models, in recent decades the flood forecasting has made a significant progress, nonetheless, models are imperfect, which means that we are still left with a residual uncertainty on what will actually happen. In this thesis, this type of uncertainty is what will be discussed and analyzed. In operational problems, it is possible to affirm that the ultimate aim of forecasting systems is not to reproduce the river behavior, but this is only a means through which reducing the uncertainty associated to what will happen as a consequence of a precipitation event. In other words, the main objective is to assess whether or not preventive interventions should be adopted and which operational strategy may represent the best option. The main problem for a decision maker is to interpret model results and translate them into an effective intervention strategy. To make this possible, it is necessary to clearly define what is meant by uncertainty, since in the literature confusion is often made on this issue. Therefore, the first objective of this thesis is to clarify this concept, starting with a key question: should be the choice of the intervention strategy to adopt based on the evaluation of the model prediction based on its ability to represent the reality or on the evaluation of what actually will happen on the basis of the information given by the model forecast? Once the previous idea is made unambiguous, the other main concern of this work is to develope a tool that can provide an effective decision support, making possible doing objective and realistic risk evaluations. In particular, such tool should be able to provide an uncertainty assessment as accurate as possible. This means primarily three things: it must be able to correctly combine all the available deterministic forecasts, it must assess the probability distribution of the predicted quantity and it must quantify the flooding probability. Furthermore, given that the time to implement prevention strategies is often limited, the flooding probability will have to be linked to the time of occurrence. For this reason, it is necessary to quantify the flooding probability within a horizon time related to that required to implement the intervention strategy and it is also necessary to assess the probability of the flooding time.

Radar based improvements and diagnosis of the convective capabilities of the cosmo-2 model

In the last few years the resolution of numerical weather prediction (nwp) became higher and higher with the progresses of technology and knowledge. As a consequence, a great number of initial data became fundamental for a correct initialization of the models. The potential of radar observations has long been recognized for improving the initial conditions of high-resolution nwp models, while operational application becomes more frequent. The fact that many nwp centres have recently taken into operations convection-permitting forecast models, many of which assimilate radar data, emphasizes the need for an approach to providing quality information which is needed in order to avoid that radar errors degrade the model's initial conditions and, therefore, its forecasts. Environmental risks can can be related with various causes: meteorological, seismical, hydrological/hydraulic. Flash floods have horizontal dimension of 1-20 Km and can be inserted in mesoscale gamma subscale, this scale can be modeled only with nwp model with the highest resolution as the COSMO-2 model. One of the problems of modeling extreme convective events is related with the atmospheric initial conditions, in fact the scale dimension for the assimilation of atmospheric condition in an high resolution model is about 10 Km, a value too high for a correct representation of convection initial conditions. Assimilation of radar data with his resolution of about of Km every 5 or 10 minutes can be a solution for this problem. In this contribution a pragmatic and empirical approach to deriving a radar data quality description is proposed to be used in radar data assimilation and more specifically for the latent heat nudging (lhn) scheme. Later the the nvective capabilities of the cosmo-2 model are investigated through some case studies. Finally, this work shows some preliminary experiments of coupling of a high resolution meteorological model with an Hydrological one.

Scenari finanziari e portafogli ottimi: modelli di previsione e strategie per l'asset allocation tattica

Le scelte di asset allocation costituiscono un problema ricorrente per ogni investitore. Quest’ultimo è continuamente impegnato a combinare diverse asset class per giungere ad un investimento coerente con le proprie preferenze. L’esigenza di supportare gli asset manager nello svolgimento delle proprie mansioni ha alimentato nel tempo una vasta letteratura che ha proposto numerose strategie e modelli di portfolio construction. Questa tesi tenta di fornire una rassegna di alcuni modelli innovativi di previsione e di alcune strategie nell’ambito dell’asset allocation tattica, per poi valutarne i risvolti pratici. In primis verificheremo la sussistenza di eventuali relazioni tra la dinamica di alcune variabili macroeconomiche ed i mercati finanziari. Lo scopo è quello di individuare un modello econometrico capace di orientare le strategie dei gestori nella costruzione dei propri portafogli di investimento. L’analisi prende in considerazione il mercato americano, durante un periodo caratterizzato da rapide trasformazioni economiche e da un’elevata volatilità dei prezzi azionari. In secondo luogo verrà esaminata la validità delle strategie di trading momentum e contrarian nei mercati futures, in particolare quelli dell’Eurozona, che ben si prestano all’implementazione delle stesse, grazie all’assenza di vincoli sulle operazioni di shorting ed ai ridotti costi di transazione. Dall’indagine emerge che entrambe le anomalie si presentano con carattere di stabilità. I rendimenti anomali permangono anche qualora vengano utilizzati i tradizionali modelli di asset pricing, quali il CAPM, il modello di Fama e French e quello di Carhart. Infine, utilizzando l’approccio EGARCH-M, verranno formulate previsioni sulla volatilità dei rendimenti dei titoli appartenenti al Dow Jones. Quest’ultime saranno poi utilizzate come input per determinare le views da inserire nel modello di Black e Litterman. I risultati ottenuti, evidenziano, per diversi valori dello scalare tau, extra rendimenti medi del new combined vector superiori al vettore degli extra rendimenti di equilibrio di mercato, seppur con livelli più elevati di rischio.

Digital marketing: le nuove opportunità di business per le aziende nell'era del web 2.0

L’elaborato ha lo scopo di presentare le nuove opportunità di business offerte dal Web. Il rivoluzionario cambiamento che la pervasività della Rete e tutte le attività correlate stanno portando, ha posto le aziende davanti ad un diverso modo di relazionarsi con i propri consumatori, che sono sempre più informati, consapevoli ed esigenti, e con la concorrenza. La sfida da accettare per rimanere competitivi sul mercato è significativa e il mutamento in rapido sviluppo: gli aspetti che contraddistinguono questo nuovo paradigma digitale sono, infatti, velocità, mutevolezza, ma al tempo stesso misurabilità, ponderabilità, previsione. Grazie agli strumenti tecnologici a disposizione e alle dinamiche proprie dei diversi spazi web (siti, social network, blog, forum) è possibile tracciare più facilmente, rispetto al passato, l’impatto di iniziative, lanci di prodotto, promozioni e pubblicità, misurandone il ritorno sull’investimento, oltre che la percezione dell’utente finale. Un approccio datacentrico al marketing, attraverso analisi di monitoraggio della rete, permette quindi al brand investimenti più mirati e ponderati sulla base di stime e previsioni. Tra le più significative strategie di marketing digitale sono citate: social advertising, keyword advertising, digital PR, social media, email marketing e molte altre. Sono riportate anche due case history: una come ottimo esempio di co-creation in cui il brand ha coinvolto direttamente il pubblico nel processo di produzione del prodotto, affidando ai fan della Pagina Facebook ufficiale la scelta dei gusti degli yogurt da mettere in vendita. La seconda, caso internazionale di lead generation, ha permesso al brand di misurare la conversione dei visitatori del sito (previa compilazione di popin) in reali acquirenti, collegando i dati di traffico del sito a quelli delle vendite. Esempio di come online e offline comunichino strettamente.