1Department of Environmental Science & Engineering, Gwangju Institute of Science & Technology, Republic of Korea
2Department of Atmospheric Sciences, Yonsei University, Republic of Korea
3Science Systems and Applications, Inc., NASA Langley Research Center, Hampton, Virginia, USA
4Department of Satellite Geoinformatics Engineering, Kyungil University, Republic of Korea
5Korea Polar Research Institute, Republic of Korea
6Applied Meteorology Research Lab. National Institute of Meteorological Research
Abstract. The diurnal patterns in pollen vertical distributions in the lower troposphere were investigated by the LIDAR remote sensing technique. Meteorological and pollen concentration data was measured at the surface using a Burkard 7 day recording volumetric spore sampler. An aerosol extinction coefficient and depolarization ratio of 532 nm was obtained from LIDAR measurements in spring (4 May–2 June) 2009 in Gwagnju, Korea. Depolarization ratios from 0.08 to 0.14 were observed only in daytime (09:00–17:00 local time (LT)) during high pollen concentration days from 4 to 9 May. Vertical distributions in the depolarization ratio with time showed a specific diurnal pattern. Depolarization ratios, which varied from 0.08 to 0.14, were measured near the surface in the morning. High depolarization ratios were detected even up to 2.0 km between 12:00 and 14:00 LT but subsequently were observed only close to the surface after 17:00 LT. Low values of depolarization ratios (≤ 0.05) were detected after 18:00 LT until next morning. During the measurement period, the daily variations in the high depolarization ratios close to the surface showed good agreement with those in surface pollen concentrations, which implies that high depolarization ratios can be attributed to high pollen concentrations. The diurnal characteristics in high values of depolarization ratios were closely associated with turbulent transport, which can be caused by increasing temperature and wind speed and decreasing relative humidity. Continuously measured diurnal and vertical characteristics of pollen data can be further used to enhance the accuracy of the pollen-forecasting model via data assimilation studies.