Abstract
Temperature forecasting has been a consistent research topic owing to its significant effect on daily lives and various industries. However, it is an ever-challenging task because temperature is affected by various climate factors. Research on temperature forecasting has taken one of two directions: time-series analysis and machine learning algorithms. Recently, a large amount of high-frequent climate data have been well-stored and become available. In this study, we apply three types of neural networks, multilayer perceptron, recurrent, and convolutional, to daily average, minimum, and maximum temperature forecasting with higher-frequency input features than researchers used in previous studies. Applying these neural networks to the observed data from three locations with different climate characteristics, we show that prediction performance with highly frequent hourly input data is better than forecasting performance with less-frequent daily inputs. We observe that a convolutional neural network, which has been mostly employed for processing satellite images rather than numeric weather data for temperature forecasting, outperforms the other models. In addition, we combine state of the art weather forecasting techniques with the convolutional neural network and evaluate their effects on the temperature forecasting performances.
Highlights
Weather forecasting is one of the most steadily researched areas because the weather has a significant impact on humans’ daily lives as well as various industry sectors [1,2,3]
Minimum and maximum temperatures can have adverse impacts on agricultural operations in extreme events, so accurate temperature forecasting plays an important role in preventing agricultural damage [8]
In order to process the detailed and abundant input data, we approached it from the perspective of two-dimensional signal processing using diverse neural network models, multilayer perceptron (MLP), long-short term memory (LSTM), and convolution neural network (CNN)
Summary
Weather forecasting is one of the most steadily researched areas because the weather has a significant impact on humans’ daily lives as well as various industry sectors [1,2,3]. Minimum and maximum temperatures can have adverse impacts on agricultural operations in extreme events, so accurate temperature forecasting plays an important role in preventing agricultural damage [8]. Weather factors including temperature are continuous, data-intensive, multidimensional, dynamic, and chaotic, and it is ever-challenging to accurately predict temperature [9]. Researches on weather forecasting are mainly performed in two ways, physics-based and data-driven models. Physics-based weather forecasting models directly simulate physical processes that numerically analyze the effects of atmospheric dynamics, heat radiation, and impact of green space, lakes, and oceans. Most commercial and public weather forecasting systems still consist of physics-based models [10,11,12]. Data-driven models perform weather forecasting using statistics or machine learning-based algorithms. The data-driven models have the advantage of being able to identify unexpected patterns for the meteorological system without any prior knowledge
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
