Time series modelling of surface pressure data

✍ Scribed by Al-Awadhi, Shafeeqah; Jolliffe, Ian

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 114 KB
• Volume: 18
• Category: Article
• ISSN: 0899-8418
• DOI: 10.1002/(sici)1097-0088(19980330)18:4<443::aid-joc260>3.0.co;2-g

✦ Synopsis

In this paper we examine time series modelling of surface pressure data, as measured by a barograph, at Herne Bay, England, during the years 1981-1989. Autoregressive moving average (ARMA) models have been popular in many fields over the past 20 years, although applications in climatology have been rather less widespread than in some disciplines. Some recent examples are Milionis and Davies (Int. J. Climatol., 14, 569 -579) and Seleshi et al. (Int. J. Climatol., 14,[911][912][913][914][915][916][917][918][919][920][921][922][923]. We fit standard ARMA models to the pressure data separately for each of six 2-month natural seasons. Differences between the best fitting models for different seasons are discussed. Barograph data are recorded continuously, whereas ARMA models are fitted to discretely recorded data. The effect of different spacings between the fitted data on the models chosen is discussed briefly.

Often, ARMA models can give a parsimonious and interpretable representation of a time series, but for many series the assumptions underlying such models are not fully satisfied, and more complex models may be considered. A specific feature of surface pressure data in the UK is that its behaviour is different at high and at low pressures: day-to-day changes are typically larger at low pressure levels than at higher levels. This means that standard assumptions used in fitting ARMA models are not valid, and two ways of overcoming this problem are investigated. Transformation of the data to better satisfy the usual assumptions is considered, as is the use of non-linear, specifically threshold autoregressive (TAR), models.