The Use of Deep Learning in Enhancing the Accuracy of Weather Prediction Model

Abstract

Weather predictions are important in several sectors like farming, transport, flood control and safety. But should it not go further under complex non-linear atmospheric data and dynamic weather patterns? Traditional numerical weather prediction models are reliable but there are often challenges when it comes to dealing with such complexities. Due to the advent of artificial intelligence, there has been the introduction of deep learning as an effective way of increasing the accuracy of weather prediction. In this paper, how deep learning processes like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks are used in improving both short-term and long-term weather forecasting is discussed. The models can process bulk of the time-series and spatial data to reveal concealed trends which are not identified by conventional models. By analyzing current studies and test outcomes, the paper demonstrates the importance of deep learning models as they minimize the estimation error and learn to adapt to real-time data much more easily than many of their alternatives. These findings answer in an affirmative way to the possible potential of deep learning that can complement or even outdo the traditional methods in providing more accurate and timely weather predictions.