Abstract-Temperature warnings are important forecasts because they are used to protect life and property. Temperature forecasting is the application of science and technology to predict the state of the temperature for a future time and a given location. Temperature forecasts are made by collecting quantitative data about the current state of the atmosphere. In this paper, a neural network-based algorithm for predicting the temperature is presented. The Neural Networks package supports different types of training or learning algorithms. One such algorithm is Back Propagation Neural Network (BPN) technique. The main advantage of the BPN neural network method is that it can fairly approximate a large class of functions. This method is more efficient than numerical differentiation. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain temperature. The proposed idea is tested using the real time dataset. The results are compared with practical working of meteorological department and these results confirm that our model have the potential for successful application to temperature forecasting. Real time processing of weather data indicate that the BPN based weather forecast have shown improvement not only over guidance forecasts from numerical models, but over official local weather service forecasts as well.

Abstract-Temperature warnings are important forecasts because they are used to protect life and property. Temperature forecasting is the application of science and technology to predict the state of the temperature for a future time and a given location. Temperature forecasts are made by collecting quantitative data about the current state of the atmosphere. In this paper, we present a neural network-based algorithm for predicting the temperature. The Neural Networks package supports different types of training or learning algorithms. One such algorithm is Back Propagation Neural Network (BPN) technique. The main advantage of the BPN neural network method is that it can fairly approximate a large class of functions. This method is more efficient than numerical differentiation. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain temperature. The results are compared with actual working of meteorological department and these results confirm that our model have the potential for successful application to temperature forecasting. Real time processing of weather data indicate that the BPN based weather forecast have shown improvement not only over guidance forecasts from numerical models, but over official local weather service forecasts as well.