the climate and weather patterns of buffalo (new york, u.s.a.) are strongly influenced by thecity’s proximity to lake erie. total monthly snowfall in buffalo is forecasted using neural network techniques(multi-layer perceptron = mlp) and a multiple linear regression (lr) model. the period of analysis comprises 28 years from january 1982 to december 2009. input data include: zonal wind speed (u-...

An ensemble local hydrologic forecast derived from the seasonal forecasts of the International Research Institute for Climate Prediction (IRI) is presented. Three-month seasonal forecasts were used to resample historical meteorological conditions and generate ensemble forcing datasets for a TOPMODEL-based hydrology model. Eleven retrospective forecasts were run at Florida and New York sites. Fo...

s Statistical seasonal climate forecasting in Australia: An historical overview Dr Roger Stone......................................................................................................................................................................................18 The scientific basis of seasonal climate prediction Dr Scott Power.......................................................

Introduction Conclusions References

Disease transmission forecasts can help minimize human and domestic animal health risks by indicating where disease control and prevention efforts should be focused. For disease systems in which weather-related variables affect pathogen proliferation, dispersal, or transmission, the potential for disease forecasting exists. We present a seasonal forecast of St. Louis encephalitis virus transmis...

agriculture as one of the major economic sectors of iran, has an important role in gross domestic production by providing about 14% of gdp. this study attempts to forecast the value of the agriculture gdp using periodic autoregressive model (par), as the new seasonal time series techniques. to address this aim, the quarterly data were collected from march 1988 to july 1989. the collected data w...

It has recently been argued that single-model seasonal forecast ensembles are overdispersive, implying that the real world is more predictable than indicated by estimates of so-called perfect model predictability, particularly over the North Atlantic. However, such estimates are based on relatively short forecast data sets comprising just 20 years of seasonal predictions. Here we study longer 4...

Data mining techniques are frequently used to extract the disease related factors from the huge datasets. Data mining is the task of discovering formerly unknown, appropriate patterns and relationships in huge datasets. Generally, each data mining task differs in the type of knowledge it extracts and the kind of data demonstration it uses to convey the discovered information. Forecasting is a p...

[1] This study uses a Bayesian approach to merge ensemble seasonal climate forecasts generated by multiple climate models for better probabilistic and deterministic forecasting. Within the Bayesian framework, the climatological distribution of the variable of interest serves as the prior, and the likelihood function is developed with a weighted linear regression between the climate model hindca...

We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual fore...