Optimized retrieval method for atmospheric temperature profiling based on rotational Raman lidar

Qing, Yan; Wang, Yufeng; Gao, Tianle; Gao, Fei; Di, Huige; Song, Yuming; Hua, Dengxin

doi:10.1364/ao.58.005170

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…), and was collected by telescope to obtain the height distribution of atmospheric parameters, like atmospheric temperature, humidity, wind velocity, aerosol optical properties based on the inversion method of spectral and energy analyses [2,3]. Elastic scattering lidar, hyperspectral lidar, Raman lidar and differential absorption lidar, as the major detection technologies and methods, play an extremely significant role in the atmospheric remote sensing [4][5][6][7]. With the increasing demands of atmospheric remote sensing and environmental monitoring in multi-scale and multi-parameter aspects, lidar tends to develop a comprehensive sensing detection characterized by multiple parameters, long distance, long time, high precision, real time.…”

Section: Introductionmentioning

confidence: 99%

Storage Method of Multi-Channel Lidar Data Based on Tree Structure

Chen

Gao

Zhu

et al. 2022

Preprint

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The multi-channel lidar has the characteristics of fast acquisition speed, large data volume, high dimension, and strong real-time storage, which makes it difficult to be met using the traditional lidar data storage methods. This paper presents a novel approach to store and convert the multi-channel lidar data by traversal method of the tree structure and binary code. In the proposed approach, a tree structure is constructed based on the multi-dimensional characteristics of multi-channel lidar data and the hierarchical relationship between them. The adjacency table storage structure data in the memory is used to generate the sub-tree of the multi-channel lidar data. The results show that the proposed tree structure approach can save the storage capacity and improve the retrieval speed, which can meet the needs of efficient storage and retrieval of multi-channel lidar data.

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Section: Introductionmentioning

confidence: 99%

Storage Method of Multi-Channel Lidar Data Based on Tree Structure

Chen

Gao

Zhu

et al. 2022

Preprint

Self Cite

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“…This new method reduces the temperature error by 50 % compared with commonly used calibration methods in conditions of low signal-to-noise ratio (SNR). Yan et al (2019) has proposed an iterative method for determining Raman temperature. Their method allows independent alternating solutions to the high-and low-quantumnumber PRRs separately, where high-quantum-number PRR lidar returns are used to solve for the channel constant, while low-quantum-number PRR returns with higher SNR are used for retrieving temperature profiles in an iterative fashion.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of temperature from a multiple channel pure rotational Raman backscatter lidar using an optimal estimation method

et al. 2019

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Abstract. We present a new method for retrieving temperature from pure rotational Raman (PRR) lidar measurements. Our optimal estimation method (OEM) used in this study uses the full physics of PRR scattering and does not require any assumption of the form for a calibration function nor does it require fitting of calibration factors over a large range of temperatures. The only calibration required is the estimation of the ratio of the lidar constants of the two PRR channels (coupling constant) that can be evaluated at a single or multiple height bins using a simple analytic expression. The uncertainty budget of our OEM retrieval includes both statistical and systematic uncertainties, including the uncertainty in the determination of the coupling constant on the temperature. We show that the error due to calibration can be reduced significantly using our method, in particular in the upper troposphere when calibration is only possible over a limited temperature range. Some other advantages of our OEM over the traditional Raman lidar temperature retrieval algorithm include not requiring correction or gluing to the raw lidar measurements, providing a cutoff height for the temperature retrievals that specifies the height to which the retrieved profile is independent of the a priori temperature profile, and the retrieval's vertical resolution as a function of height. The new method is tested on PRR temperature measurements from the MeteoSwiss RAman Lidar for Meteorological Observations system in clear and cloudy sky conditions, compared to temperature calculated using the traditional PRR calibration formulas, and validated with coincident radiosonde temperature measurements in clear and cloudy conditions during both daytime and nighttime.

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A simulation comparison of calibration functions for different sets of spectral filter passbands in the traditional pure rotational Raman lidar technique

Gerasimov

2020

Appl. Phys. B

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Optimized retrieval method for atmospheric temperature profiling based on rotational Raman lidar

Cited by 7 publications

References 23 publications

Storage Method of Multi-Channel Lidar Data Based on Tree Structure

Storage Method of Multi-Channel Lidar Data Based on Tree Structure

Retrieval of temperature from a multiple channel pure rotational Raman backscatter lidar using an optimal estimation method

A simulation comparison of calibration functions for different sets of spectral filter passbands in the traditional pure rotational Raman lidar technique

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