Abstract
Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network-based temperature prediction model time-series weather data obtained from an automatic weather station and image data from a regional data assimilation and prediction system (RDAPS). To accommodate such different types of data into a single model, a bidirectional long short-term memory (BLSTM) model and a convolutional neural network (CNN) model are chosen to represent the features from the time-series observed data and the RDAPS image data. The two types of features are combined to produce temperature predictions for up to 14 days in the future. The performance of the proposed temperature prediction model is evaluated by objective measures, including the root mean squared error and mean bias error. The experiments demonstrated that the proposed model combining both the observed and RDAPS image data is better in all performance measures for all prediction periods compared with the BLSTM-based model using observed data and the CNN-BLSTM-based model using RDAPS image data alone.
Highlights
Since the beginning of human history, human beings have experienced various weather and climate changes, some of which have driven them to change their place of residence
One is a set of automatic weather station (AWS)-observed data provided by Korean Meteorological Agency (KMA) [21], in which the observed data are grouped into a five-dimensional vector at one-hour intervals, including the relative humidity (RH), wind speed (WS), wind direction (WD), rainfall (RF), and temperature in degrees
This paper proposed a deep neural network-based temperature prediction model using both time-series observed weather data and regional data assimilation and prediction system (RDAPS) image data
Summary
Since the beginning of human history, human beings have experienced various weather and climate changes, some of which have driven them to change their place of residence. The authors of a recent study [7] pointed out a problem with poor prediction accuracy due to missing observed data from sensors installed at the observatory To solve this problem, a deep learning-based refinement model was proposed in [7], and the prediction model using the refined data provided better prediction accuracy than the model using data approximated using linear interpolation. There have been several studies indicating that combining numerical forecast data with observed data improved the accuracy of temperature prediction [9] and aerosol prediction [10]. The proposed temperature prediction model applies two different deep neural networks, RNNs and convolutional neural networks (CNNs), to the observed time-series data of an AWS and the numerally forecast image data, respectively.
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