Correlation analysis between variability pattern of TPW and climate variables

Darae Lee; Kyung-Soo Han; Chaeyoung Kwon; Minji Seo; Kyeong-sang Lee

doi:10.1117/12.2241772

19 October 2016 Correlation analysis between variability pattern of TPW and climate variables

Darae Lee, Kyung-Soo Han, Chaeyoung Kwon, Minji Seo, Kyeong-sang Lee

Proceedings Volume 10001, Remote Sensing of Clouds and the Atmosphere XXI; 1000111 (2016) https://doi.org/10.1117/12.2241772
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

Water vapor is main absorption factor of outgoing longwave radiation. Because increase of water vapor accelerate to become high land surface temperature, it is essential to monitoring the changes in the amount of water vapor and to investigating the causes of such changes. This paper, we monitor variability pattern of Total Precipitable Water (TPW) which observed by satellite. But long-term investigation of climate over Korea peninsula is very difficult due to climatic characteristic in middle latitude of instable atmospheric. El Nino that is one of climate variables appears regularly when compared to the others. Also, precipitation of all climate variables play an important part to analyze variability pattern of water vapor because it is produced by water vapor. Therefore, if we know climatic variability by them, correlation analysis between TPW and climate variables can be improved. In this study, we analyze long-term change of TPW from Moderate-Resolution Imaging Spectroadiometer (MODIS) and precipitation change in middle area of Korea peninsula quantitatively and El Nino was compared to relation of TPW and precipitation. The aim of study is to investigate precipitation and El Nino has an impact on variability pattern of TPW. First, time series analysis is used to calculate TPW and precipitation quantitatively, and anomaly analysis is performed to analyze their correlation. From the results obtained, TPW and precipitation has correlation mostly but the part had inverse correlation was found. We compare it with El Nino of anomaly results. As a result, after El Nino occurred, TPW and precipitation had inverse correlation.

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Darae Lee, Kyung-Soo Han, Chaeyoung Kwon, Minji Seo, and Kyeong-sang Lee "Correlation analysis between variability pattern of TPW and climate variables", Proc. SPIE 10001, Remote Sensing of Clouds and the Atmosphere XXI, 1000111 (19 October 2016); https://doi.org/10.1117/12.2241772

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KEYWORDS

Electroluminescence

Climatology

Satellites

MODIS

Analytical research

Time series analysis

Climate change

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