Atmospheric particulate matters (PM) are tiny pieces of solid or liquid matter associated with the Earth’s atmosphere. They are suspended in the atmosphere as atmospheric aerosol. Recently, density of fine particles PM2.5, diameter of 2.5 micrometers or less, from China is serious environmental issue in East part of Asia. In this study, the authors have developed a PM2.5 density distribution visualization system using ground-level sensor network dataset and Mie lidar dataset. The former dataset is used for visualization of horizontal PM2.5 density distribution and movement analysis, the latter dataset is used for visualization of vertical PM2.5 density distribution and movement analysis.
KEYWORDS: Data acquisition, LIDAR, Meteorology, Data modeling, Ozone, Atmospheric modeling, Data analysis, Cameras, Satellites, Magnetic resonance imaging
A web-base data acquisition and management system for GOSAT (Greenhouse gases Observation SATellite) validation lidar data-analysis has been developed. The system consists of data acquisition sub-system (DAS) and data management sub-system (DMS). DAS written in Perl language acquires AMeDAS (Automated Meteorological Data Acquisition System) ground-level local meteorological data, GPS Radiosonde upper-air meteorological data, ground-level oxidant data, skyradiometer data, skyview camera images, meteorological satellite IR image data and GOSAT validation lidar data. DMS written in PHP language demonstrates satellite-pass date and all acquired data. In this article, we briefly describe some improvement for higher performance and higher data usability. GPS Radiosonde upper-air meteorological data and U.S. standard atmospheric model in DAS automatically calculate molecule number density profiles. Predicted ozone density prole images above Saga city are also calculated by using Meteorological Research Institute (MRI) chemistry-climate model version 2 for comparison to actual ozone DIAL data.
Greenhouse gases Observation SATellite (GOSAT) was launched to enable the precise monitoring of the density
of carbon dioxide by combining global observation data sent from space with data obtained on land, and with
simulation models. In addition, observation of methane, another greenhouse gas, has been considered. For
validation of GOSAT data products, ground-base observation with Fourier Transform Spectrometer (FTS),
aerosol lidar and ozone-DIAL (DIfferencial Absorption Lidar) at Saga University, JAPAN has been continued
since March, 2011. In this article, observation results obtained from aerosol lidar are reported.
An web-base data acquisition and management system for GOSAT (Greenhouse gases Observation SATellite)
validation lidar data analysis is developed. The system consists of data acquisition sub-system (DAS) and data
management sub-system (DMS). DAS written in Perl language acquires AMeDAS ground-level meteorological
data, Rawinsonde upper-air meteorological data, ground-level oxidant data, skyradiometer data, skyview camera
images, meteorological satellite IR image data and GOSAT validation lidar data. DMS written in PHP language
demonstrates satellite-pass date and all acquired data.
The vertical distributions of the water vapor mixing ratio (w) were measured by Raman lidar at the Meteorological Research Institute, Japan, in 2000 to 2004. The measured values were compared with those obtained with radiosondes, hygrometers on the meteorological observation tower, and Global Positional System (GPS) antennas. The values of w obtained with the lidar agreed within 9% with those obtained with the Meisei RS2-91 radiosonde for w > 0.5 g/kg-1. However, they were systematically higher than those obtained with the Vaisala RS80-A radiosonde for that region. The vertical variations of w obtained with the lidar were similar to those obtained with the Meisei RS-01G and Meteolabor Snow White radiosondes for w > 0.3 g/kg-1. The temporal variations of w obtained with the lidar were similar to those obtained with the hygrometers at heights between 50 and 213 m on the tower, although the absolute values differed systematically due to the incomplete overlap of the laser beam and the receiver's field of view at the lower heights. The precipitable water vapor content obtained with the lidar generally agreed with those obtained with GPS, except for the period when the large spatial inhomogeneity of w was present.
Conference Committee Involvement (3)
Lidar and Optical Remote Sensing for Environmental Monitoring XVII
2 December 2024 | Kaohsiung, Taiwan
Lidar Remote Sensing for Environmental Monitoring XVI
24 September 2018 | Honolulu, Hawaii, United States
Lidar Remote Sensing for Environmental Monitoring XV
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