Method for recognizing cloud based on mie-scattering laser radar with equalized value distribution

Abstract

The invention discloses a method for recognizing a cloud based on a mie-scattering laser radar with equalized value distribution. The method comprises the steps that a mie-scattering laser radar signal is input; semi-discretisation data processing based on noise estimation is carried out on the mie-scattering laser radar signal; data distribution equalization is carried out on the mie-scattering laser radar signal; the background base line of the mie-scattering laser radar signal is generated; the mie-scattering laser radar signal is layered and recognized based on the background base line. Through the method, the importance of a smoothness problem in the cloud recognition process can be weakened, meanwhile, the background base line adapting to signal changes by itself is found out, and therefore the problem that a current algorithm overly depends on smoothness or missing detection happens to weak cloud echo signals is solved, the recognition rate of cloud signals in the observation process is guaranteed, and the data quality of cloud observation is improved, and more accurate observation data are provided for weather and climate study and forecasting.

Description

technical field [0001] The invention relates to the technical field of laser radar, in particular to a meter scattering laser radar cloud recognition method based on numerical distribution equalization. Background technique [0002] In meteorological and climate research, the existence and change of clouds have an important impact on radiation balance and water vapor transmission. Accurate cloud appearance rate and cloud height distribution information is the key to related research, and this information can only come from long-term accurate observations . At present, lidar, as the main tool for long-term continuous observation of cloud information, has been widely used in a large number of research and observation sites at home and abroad. Due to the rapid development of laser equipment and photoelectric detection equipment, the performance of atmospheric detection lidar has been rapidly improved in recent years. Especially with the emergence of Micro-pulse Lidar (MPL), L...

AI Technical Summary

Problems solved by technology

[0021] 2. Missing detection of multi-layer cloud signals: When there are more than two layers of cloud layers and the signal strength of different cloud layers varies greatly, the baseline of linear fitting will be higher than the weak cloud layer signals, resulting in failure to detect

[0022] 3. Missing detection of near-field signals: Lidar geometric factors cause near-field signals to be incompletely detected. Therefore, the signal has the characteristic of increasing with distance to the vicinity of the transition zone and then attenuating with distance.

This method seems simple, but its shortcomings are also obvious: 1. Smoothing the lidar signal is a problem that cannot be solved well in lidar data processing.

2. When the smoothness is not enough, the misjudgment caused by noise fluctuation will greatly increase

3. When the smoothing is excessive, it is difficult to distinguish the cloud signal from the aerosol signal, and at the same time, it is easy to cause the judgment error of the cloud base position during the smoothing process

4. When the signal in the cloud layer is uneven, it will lead to misjudgment of the cloud base, and there will be clouds in the cloud

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