研究目的
Investigating the estimation of stable boundary layer height (SBLH) using combined lidar and microwave radiometer observations.
研究成果
The synergetic approach using lidar and MWR data with an EKF provides a robust method for SBLH estimation under various atmospheric conditions. The EKF outperforms the nonlinear least squares estimator, especially in nonidealized conditions. Future work involves applying the algorithm to more complex atmospheric scenarios and integrating more sensitive instruments for better aerosol stratification information.
研究不足
The technique is limited by the partial overlap of the lidar at low heights and requires stable atmospheric conditions for aerosol stratification. The existence of stable atmospheric conditions is a prerequisite, and aerosol load affects the quality of filter convergence.
1：Experimental Design and Method Selection
The methodology involves using vertical variance of the backscatter signal from a ceilometer as an indicator of aerosol stratification in the nocturnal stable boundary layer, supported by statistical analysis. Thermodynamic information from MWR-derived potential temperature is incorporated as a coarse estimate of the SBLH. Data from both instruments are adaptively assimilated using an extended Kalman filter (EKF).
2：Sample Selection and Data Sources
Data collected during the HD(CP)2 Observational Prototype Experiment (HOPE) campaign at Jülich, Germany, using collocated Vaisala CT25K ceilometer and humidity and temperature profiler MWR.
3：List of Experimental Equipment and Materials
Vaisala CT25K ceilometer, RPG humidity and temperature profiler (HATPRO) MWR, and Graw DFM-09 radiosonde.
4：Experimental Procedures and Operational Workflow
The approach involves preprocessing of lidar data to estimate the backscatter variance profile, modeling MVRs with an inverted Gaussian-like function, and using EKF for adaptive estimation of SBLH. MWR data is processed to provide coarse SBLH estimates, which are then combined with lidar data for fine estimation.
5：Data Analysis Methods
The EKF minimizes the mean square error over time for optimal estimates. A nonlinear least squares estimator is used for comparison. Statistical analysis is performed to validate the existence of MVRs and their relation to the nocturnal SBL.
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