研究目的
To develop a semi-empirical model using Python to process lidar data for retrieving atmospheric turbulence parameters, PBL height, and other meteorological and astronomical parameters, with applications in environmental and health studies.
研究成果
The semi-empirical model successfully calculates atmospheric turbulence parameters, PBL height, and other profiles from lidar data, with results consistent with theoretical predictions. It offers a cost-effective method for atmospheric monitoring, with applications in weather prediction, environmental health, and astronomy. Future work should focus on experimental validation and improving accuracy under various conditions.
研究不足
Mathematical domain errors occur below 400 m altitude, possibly due to computational issues or division by zero. Cloud formations and rapidly evolving aerosol masses affect scintillation profiles, limiting applicability in non-clear weather. Temperature profiles become unreliable above altitudes where RCS signal is lost. The model relies on approximations and requires experimental confirmation for wind speed and temperature profiles.
1：Experimental Design and Method Selection:
The study uses a semi-empirical model implemented in Python 3.6 to process elastic lidar data. It involves calculating scintillation from range-corrected signals (RCS) to derive structure coefficients and other parameters. Theoretical models like Tatarski's equations are employed.
2：6 to process elastic lidar data. It involves calculating scintillation from range-corrected signals (RCS) to derive structure coefficients and other parameters. Theoretical models like Tatarski's equations are employed.
Sample Selection and Data Sources:
2. Sample Selection and Data Sources: Lidar data collected on 28 May 2017 at the LOASL in Iasi, Romania, using a biaxial elastic lidar platform. Complementary sun-photometer data from the AERONET site is used for inversion.
3：List of Experimental Equipment and Materials:
A standard biaxial elastic lidar platform with a Nd:YAG laser (wavelength 532 nm, pulse energy 100 mJ, frequency 30 Hz, beam diameter 6 mm) and a Newtonian LightBridge telescope (primary mirror diameter 406 mm). Photomultiplier tube (PMT) operated in analogue mode. Sun-photometer part of AERONET network.
4：Experimental Procedures and Operational Workflow:
Data acquisition involves lidar pulses, with RCS profiles computed every 3 minutes or less. Scintillation is calculated from RCS data, and parameters like C2N(z) are derived. Fernald-Klett inversion is applied using sun-photometer data to get extinction and backscatter profiles.
5：Data Analysis Methods:
Statistical analysis using Python scripts, including Savitsky-Golay filtering for noise reduction. Equations from turbulence theory are used to compute profiles, and results are compared with theoretical models.
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