Roshydromet supercomputer technologies for numerical weather prediction
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The burgeoning research in the fields of Artificial Intelligence and machine learning has given rise to numerous weather prediction models. But the problem of accurately predicting or forecasting the weather persists. Numerical weather prediction is taking the existing numerical data on weather conditions and applying machine learning algorithms to forecast the weather. This paper is the application of machine learning algorithm-decision Tree to predict the weather based on a few parameters such as temperature, pressure, humidity. Using the stepwise regression process along with the decision tree, weather forecasting is achieved.
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Mathematics plays a fundamental role in forecasting the weather. Numerical weather prediction uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Since the advent of supercomputers & weather satellites, forecasting weather has become more accurate.In this article, it is discussed how the advent of mathematics in weather forecasting has made it precise and reliable on a global scale. This paper tries to bring out a best sample to find reliable output with these parameters. The major factors are considered as predictors for the mathematical model formulation.
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A general introduction to numerical weather prediction is given. The development of the operational forecasting system of the European Centre for Medium-Range Weather Forecasts is summarized, and some results are presented illustrating sensitivity to the horizontal resolution of the atmospheric model, the factor which is most significant in determining computational needs. The spectral method used for the horizontal discretization is described, and computational aspects of its implementation on CRAY-1 and CRAY X-MP machines are discussed. The organization of the multi-tasking employed in the model is presented, and performance figures are given. There is a brief concluding discussion of some likely future developments in medium-range weather prediction.
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In order to meet societal needs for improved weather forecasts, old and new technology must be blended to provide a more detailed and spatially broader view of the atmosphere. Advancing technology, and its associated needs for efficiency, have increased demands for weather information at both short ranges (1-2 days) and extended ranges (1 week-1 month). Over the past two decades the introduction of improved mathematical techniques and electronic technology have allowed a more precise application of the laws of physics to the atmosphere, leading to significant increases in prediction accuracy. There is ample evidence that further advances in predictive ability can be achieved if inherent approximations remaining in the numerical weather prediction (NWP) system are made with more precision.
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Numerical weather prediction models play an increasingly important role in meteorology, both in short- and medium-range forecasting and global climate change studies. The most important components of any numerical weather prediction model are the subgrid-scale parameterization schemes, and the analysis and understanding of these schemes is a key aspect of numerical weather prediction. This book provides in-depth explorations of the most commonly used types of parameterization schemes that influence both short-range weather forecasts and global climate models. Several parameterizations are summarised and compared, followed by a discussion of their limitations. Review questions at the end of each chapter enable readers to monitor their understanding of the topics covered, and solutions are available to instructors at www.cambridge.org/9780521865401. This will be an essential reference for academic researchers, meteorologists, weather forecasters, and graduate students interested in numerical weather prediction and its use in weather forecasting.
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