A multivariate circular-linear hidden Markov model and site-specific assessment of wind predictions by an atmospheric simulation system

Il contenuto (Full text) non è disponibile all'interno di questo archivio. Spedisci una richiesta all'autore per una copia del documento
Tipo di pubblicazione: Articolo in atti di convegno
Tipologia MIUR: Contributo in Atti di Convegno (Proceeding) > Contributo in atti di convegno
Titolo: A multivariate circular-linear hidden Markov model and site-specific assessment of wind predictions by an atmospheric simulation system
Autori: Gianluca Mastrantonio, Alessio Pollice, Francesca Fedele
Autori di ateneo:
Intervallo pagine: pp. 1-6
Tipo di referee: Esperti anonimi
Editore: 9788861970618
Titolo del convegno: 48th scientific meeting of the Italian Statistical Society
Abstract: Winds from the North-West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations in the city of Taranto. In 2012 the Apulia Government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 hours in advance. Wind prediction is addressed using the Weather Research and Forecasting (WRF) at- mospheric simulation system by the Regional Environmental Protection Agency (ARPA Puglia). We investigate the ability of the WRF system to properly predict the local wind speed and direction allowing different performances for unknown weather regimes. Observed and WRF-predicted wind speed and direction at a rele- vant location are jointly modeled as a 4-dimensional time series with a finite number of states (wind regimes) characterized by homogeneous distributional behavior. Ob- served and simulated wind data are made of two circular (direction) and two linear (speed) variables, then the 4-dimensional time series is jointly modeled by a mix- ture of projected-skew normal distributions with time-dependent states, where the temporal evolution of the state membership follows a first order Markov process. Parameter estimates are obtained by a Bayesian MCMC-based method and results provide useful insights on wind regimes corresponding to different performances of WRF predictions.
Data: 2016
Status: Pubblicato
Lingua della pubblicazione: Inglese
Parole chiave:
Dipartimenti (originale): DISMA - Dipartimento di Scienze Matematiche
Dipartimenti: DISMA - Dipartimento di Scienze Matematiche
URL correlate:
    Area disciplinare: Area 13 - Scienze economiche e statistiche > STATISTICA
    Data di deposito: 29 Lug 2017 14:54
    Data ultima modifica (IRIS): 29 Lug 2017 15:03:58
    Data inserimento (PORTO): 31 Lug 2017 02:00
    Permalink: http://porto.polito.it/id/eprint/2677744
    Link resolver URL: Link resolver link

    Azioni (richiesto il login)

    Visualizza il documento (riservato amministratori) Visualizza il documento (riservato amministratori)