Analysis of Long-term Variability through Temperature and Humidity Data in Urban Meteorological Observation Network
- 1Division of Earth Environmental System Science Major of Environmental Atmosphere Sciences, Pukyong National University
- 2Interdisciplinary Program of Earth Environmental Engineering The Graduate School, Pukyong National University
- 3Department of Environmental Atmospheric Sciences, Pukyong National University, Busan 608-737, Korea
In the process of producing grid data using observation data, the density of the stations were found to have the greatest influence on spatial (Hwang and Ham, 2013). Currently, the resolution of Korea’s ground detection network is about 12 to 15km additional stations need to be set up to improve spatial accuracy. However, indiscriminate installation of observatories is an objective challenge because of the enormous cost and the various factors to consider. It is important to select major observation points on an objective basis based on the existing KMA (Korea Meteorological Administration)'s AWS(Automatic Weather System), ASOS(Automated Synoptic Observing System) data to increase the representative and reliability of the observation data. However, the establishment of an observatory so far has been chosen for subjective observation purposes, which may make it difficult to derive scientific data. In this study there is identified the long-term variability of urban meteorological data using the Hurst exponent (H) obtained through Rescaled range analysis (R/S analysis). And additional observation points are proposed for each meteorological element through network analysis.
R/S analysis is an analysis that measures the variability of time series by standardizing observations over time to make them in a dimensionless ratio and analyze the changes according to the length of the data used. H between 0 and 1 provides a criterion for distinguishing the measure of correlation that a time series has. H = 0.5 means that the present event does not affect subsequently, however the other values are correlated, not independent, and continuum of influence (Hwang and Cha 2004). The meteorological factors data were obtained from SK planet, AWS, ASOS installed in Seoul. As a result, long-term relativity between temperature and humidity are shown to be at a minimum of 0.750 and a maximum of 0.941.
Key words : R/S analysis, Hurst exponent, long-term relativity
How to cite: kang, E.-B., Kang, D.-D., and Lee, D.-I.: Analysis of Long-term Variability through Temperature and Humidity Data in Urban Meteorological Observation Network, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11434, https://doi.org/10.5194/egusphere-egu21-11434, 2021.
