To recognize application of Artificial Neural Networks (ANNs) in weather forecasting, especially in rainfall forecasting a comprehensive literature review from 1923 to 2012 is done and presented in this paper. And it is found that architectures of ANN such as BPN, RBFN is best established to be forecast chaotic behavior and have efficient enough to forecast monsoon rainfall as well as other wea...

Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural netw...

Weather forecasting has been one of the most challenging problems around the world for more than half a century. Not only because of its practical values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. This paper utilizes artificial neural network (ANN) simulated in MATLAB to predict two important weather parameters i.e. maximum and mini...

In this paper we want to analyse fuzzy weather forecasts, which are computed in our system and used to forecast pollution concentrations. The system works on real data: weather forecasts, meteorological situations and pollution concentrations. We compare defuzzification of the fuzzy weather forecast with weather forecast from Institute of Meteorology and Water Management. This comprehensive ana...

Neural networks have been in use in numerous meteorological applications including weather forecasting. Neural Networks have been used as an effective method for solving many problems in a wide range of application areas. As Neural Networks are being more and more widely used in recent years, the need for their more formal definition becomes increasingly apparent. This paper presents a novel ar...

Abstract This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble postprocessing via Bayesian model averaging (BMA), and ProbForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the compo...

Weather forecasting is based on the use of numerical weather prediction (NWP) models that are able to perform the necessary calculations that describe/predict the major atmospheric processes. One common problem in weather forecasting derives from the uncertainty related to the chaotic behaviour of the atmosphere. A solution to that problem is to perform in addition to “deterministic” forecasts,...

Daily values of forecast scores are evaluated for the students in a weather analysis and forecasting class (ATMS 452) offered by the Department of Atmospheric Sciences of the University of Washington during the spring terms of 1997 through 2007. The objective of this study is to determine the rate at which senior-level undergraduate students develop proficiency at short-term (next-day) weather ...

In the paper we discuss the analysis of multidimensional data. We consider the relationship between them using a special fuzzy number form. Calculations are kept on set of actual and historical meteorological data. Our model using to forecast pollution concentrations is important in today because pollutions have very big influence on our life in particular pollutions PM10 (particulate matter le...

Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broad...