Surface fair-weather potential gradient measurements from a small tropical Island station Suva, Fiji

Systematic analysis of the surface fair-weather potential gradient (PG) measured for 13 months (July 2005-July 2006) at 10 s resolution over a small tropical island station Suva (18.08°S, 178.45°E), Fiji is presented. Based on the solar radiation (>800 W m-2) and surface wind speed (<4.5 m s-1) conditions, 63 fair-weather days were selected. After sorting the data into a range of 0-1000 V m-1, the average PG was ∼139 V m-1. The measured fair-weather PG had a semi-diurnal structure, with a more pronounced peak at 0730 LT (1930 UT) and a reduced peak at 2200 LT (1000 UT). The time of occurrence of the morning peak and the noontime minima agreed well with the Carnegie curve. However, the variation about the global mean in the measured PG was 150% whereas for the Carnegie curve it was only 35%. The effects of the local meteorological parameters on the PG measurements were found to be small. On average, the PG during the dry season (May-October) was always greater than in the wet season (November-April). In contrast, analysis of regional (0-60°S and 100°E-160°W) lightning activity on fair-weather days showed a peak at ∼2000 LT and higher lightning activity during the wet season. These results indicate that the regional thunderstorm activity has no direct connection with the local fair-weather PG at the site

Self organizing data mining for weather forecasting

The rate at which organizations are acquiring data is exploding and managing such data so as to infer useful knowledge that can be put to use is increasingly becoming important. Data Mining (DM) is one such technology that is employed in inferring useful knowledge that can be put to use from a vast amount of data. This paper presents the data mining activity that was employed in weather data prediction or forecasting. The self-organizing data mining approach employed is the enhanced Group Method of Data Handling (e-GMDH). The weather data used for the DM research include daily temperature, daily pressure and monthly rainfall. Experimental results indicate that the proposed approach is useful for data mining technique for forecasting weather data

Development on the move: measuring and optimising migration’s economic and social impacts in Fiji

Although migration come to play an increasingly significant role in Fiji’s economy and society over the past few decades, its impacts on the country’s development have been mixed. While skilled migrants contribute a great deal to Fiji through remittances and other forms of social capital they send home, their departure also deprives the country of vital human resources and appears to be leading to ‘brain drain’. However, to date, the data on these impacts has been fairly patchy. This report therefore aims to fill some of the gaps in the evidence base by providing the first nationally representative dataset on migration and development in Fiji, and by using econometric methodologies to assess some of the economic and social impacts that migration appears to be having on individuals and households who remain in the country. Below we present some of the key findings of the report, and the implications of these for Fijian policymakers