Data rescue and analysis of historical wind direction data in Malta

Abstract

Malta as an island archipelago, is highly susceptible to even the slightest of fluctuations in meteorological parameters, with the main influencing variable being wind, especially wind direction. Adaptation to such a natural phenomena and the harnessing of its potential riches as a renewable source of energy needs a well informed decision, based on long term analyses of time series data. The greater Mediterranean region is considered as a climate change hotspot and despite boasting of extensive historical meteorological data recordings has very limited long time series digital data available. The insufficient availability of extended and complete climate records on wind direction was the main motivator behind this dissertation, as historical data is the connecting link to the paleoclimatic community. The main aim of this dissertation was the data rescue of wind direction and its analyses. A reliable time series involves the painstaking task of shifting and checking through thousands of hard copies of meteorological observations covering as wide a time span as possible. The wider the time span, the better the results. The data inputted for this thesis and the acquired results were compared with an already available time series for the Maltese Islands. Although this process was set, it was also very dynamic in nature and open to changes along the way. The monthly mean values from this inputted data produced the finalised wind roses by using a WRPLOT Lakes Environmental software programme. The data rescued yielded various statistics, the most significant one relating to data from 1940-1964 which when compared to data from a previous study of 1997-2006 registered a gradual but steady shift in wind direction towards the west, thus confirming the alternative hypothesis. Readily accessible long time series data on online portals is important for subsequent climatological studies; it can be used for climate change studies and challenges, in improving existing climate services and for future weather predictions.