A linear Markov model has been developed to simulated and predict the short-term climate change in the Antarctic, with particular emphasis on sea ice variability. Seven atmospheric variables along with sea ice were chosen to define the state of the Antarctic climate, and the multivariate empirical orthogonal functions of these variables were used as the building blocks of the model. The predict...

There are at present two major governmental seasonal rainfall forecasting programs in Australia. The first of these programs is run by the Australian Government through the Bureau of Meteorology and commenced in 1989. The second is run by the Queensland Government (QG) through its Department of Environment and Resource Management (and its predecessors) and commenced in 1994. Both programs issue...

[1] The vulnerability of society on extreme weather has resulted in extensive research on the statistics of extremes. Although the theoretical framework of extreme value statistics is well developed, meteorological applications are often limited by the relative shortness of the available datasets. In order to overcome this problem, we use archived data from all past seasonal forecast ensemble r...

This paper presents an Artificial Intelligence approach for regional rainfall forecasting for Orissa state, India on monthly and seasonal time scales. The possible relation between regional rainfall over Orissa and the large scale climate indices like El-Niño Southern Oscillation (ENSO), EQUitorial INdian Ocean Oscillation (EQUINOO) and a local climate index of Ocean-Land Temperature Contrast (...

Revenue forecasting is difficult but essential for companies that want to create high-quality revenue budgets, especially in an uncertain economic environment with changing government policies. Under these conditions, the subjective judgment of decision makers is a crucial factor in making accurate forecasts. This investigation develops a fuzzy least-squares support vector regression model with...

This paper uses half-hourly electricity demand data in South Australia as an empirical study of nonparametric modeling and forecasting methods for prediction from half-hour ahead to one year ahead. A notable feature of the univariate time series of electricity demand is the presence of both intraweek and intraday seasonalities. An intraday seasonal cycle is apparent from the similarity of the d...

Across Europe, organizations in different sectors are sensitive to climate variability and change, at a range of temporal scales from the seasonal to the interannual to the multi-decadal. Climate forecast providers face the challenge of communicating the uncertainty inherent in these forecasts to these decision-makers in a way that is transparent, understandable and does not lead to a false sen...

The subseasonal to seasonal timescale provides a unique opportunity to capitalise on the expertise of the weather and climate research communities, and to bring them together to improve predictions on a timescale of particular relevance to the Global Framework for Climate Services (GFCS). A planning group which included representatives from WWRP/THORPEX, WCRP, CBS and CCl drafted the implementa...

The subseasonal to seasonal timescale provides a unique opportunity to capitalise on the expertise of the weather and climate research communities, and to bring them together to improve predictions on a timescale of particular relevance to the Global Framework for Climate Services (GFCS). A planning group which included representatives from WWRP/THORPEX, WCRP, CBS and CCl drafted the implementa...

The subseasonal to seasonal timescale provides a unique opportunity to capitalise on the expertise of the weather and climate research communities, and to bring them together to improve predictions on a timescale of particular relevance to the Global Framework for Climate Services (GFCS). A planning group which included representatives from WWRP/THORPEX, WCRP, CBS and CCl drafted the implementa...