Abstract
The purpose of this study is to analyze the correlation between surface air temperature (SAT) and land surface temperature (LST) based on land use when heat and cold waves occur and to predict the distribution of SAT using the long short-term memory (LSTM) of TensorFlow. For the correlation analysis, the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime LST and maximum, minimum, and mean SAT were measured at 79 weather stations of the Korea Meteorological Administration (KMA) from 2008 to 2018. As a result of the correlation analysis between SAT and LST, the maximum SAT (TMX) had a good correlation with the daytime LST of Terra MODIS, with a Pearson’s correlation coefficient (R) of 0.92 and root mean square error (RMSE) of 4.8 °C, and the minimum SAT (TMN) showed a good correlation with the nighttime LST of Terra MODIS, with an R of 0.93 and RMSE of 4.2 °C. When analyzing temperature characteristics by land use (urban, paddy, upland crop, forest, grass, wetland, bare field, and water), it was confirmed that the climate mitigation effect of the wetland and vegetation area appeared in the LSTs and the observed SAT. In the cold wave period, the average temperatures for urban and wetland areas was the highest, and the average temperature for wetland and forest was not higher than that of other land use classes. As the SAT results predicted through the LSTM model, the accuracy of the TMN during the cold wave period was 0.59 for the coefficient of determination (R2), 3.1 °C for RMSE, and 0.76 for the index of agreement (IoA), while the accuracy of the TMX for the heat wave period was 0.24 for R2, 2.23 °C for RMSE, and 0.63 for IoA.
Highlights
The occurrence of meteorological disasters increases the impacts and risks on the natural environment, society, and economy
The the maximum SAT (TMX) of the day appears in the afternoon, and it might be expected that the daytime land surface temperature (LST) of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (LSTAD) taken at 13:30 has the smallest deviation from the TMX
The characteristics of LST and surface air temperature (SAT) based on land use during heat and cold wave periods were analyzed, and spatial SAT was predicted using TensorFlow-long short-term memory (LSTM) with Terra and Aqua MODIS daytime and nighttime LSTs
Summary
The occurrence of meteorological disasters increases the impacts and risks on the natural environment, society, and economy. Meteorological disasters are diverse, widespread, long-lived and dangerous. They are the largest disasters that cause people to lose their property [1]. Heat waves are among the leading causes of environmentally related deaths worldwide and are expected to increase as a result of climate change. In South Korea, heat wave warnings are in effect when a maximum daily temperature of 33 ◦C or more is expected to last more than 2 days, and heat waves are often accompanied by clear skies, atmospheric subsidence, suppressed air pollutant dispersion, and weak winds. The main characteristics of cold waves are severe cold winds accompanied by snow, rain and frost, which have great adverse effects on agriculture, industry, transportation, and human health, causing great losses to the national economy [1]
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