Lidar-based remote sensing of atmospheric boundary layer height over land and ocean

Luo, Tao; Yuan, Renmin; Wang, Zhien

doi:10.5194/amt-7-173-2014

Cited by 68 publications

(61 citation statements)

References 52 publications

(56 reference statements)

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“…In contrast to earlier studies (e.g. Heese et al, 2010;Wiegner and Geiß, 2012;Martucci et al, 2010) the latest instruments software version used in this study also comprises a correction for the sensitivity fluctuations of the photo avalanche diode of the receiver. This correction significantly increases the temporal stability of retrieved backscatter profiles.…”

Section: Jenoptik Chm15kmentioning

confidence: 99%

Mixing-layer height retrieval with ceilometer and Doppler lidar: from case studies to long-term assessment

Hirsikko²,

et al. 2014

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Abstract. Aerosol signatures observed by ceilometers are frequently used to derive mixing-layer height (MLH) which is an essential variable for air quality modelling. However, Doppler wind lidar measurements of vertical velocity can provide a more direct estimation of MLH via simple thresholding. A case study reveals difficulties in the aerosol-based MLH retrieval during transition times when the mixing layer builds up in the morning and when turbulence decays in the afternoon. The difficulties can be explained by the fact that the aerosol distribution is related to the history of the mixing process and aerosol characteristics are modified by humidification. The results of the case study are generalized by evaluating one year of joint measurements by a Vaisala CT25K and a HALO Photonics Streamline wind lidar. On average the aerosol-based retrieval gives higher MLH than the wind lidar with an overestimation of MLH by about 300 m (600 m) in the morning (late afternoon). Also, the daily aerosol-based maximum MLH is larger and occurs later during the day and the average morning growth rates are smaller than those derived from the vertical wind. In fair weather conditions classified by less than 4 octa cloud cover the mean diurnal cycle of cloud base height corresponds well to the mixing-layer height showing potential for a simplified MLH estimation.

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Section: Jenoptik Chm15kmentioning

confidence: 99%

Mixing-layer height retrieval with ceilometer and Doppler lidar: from case studies to long-term assessment

Hirsikko²,

et al. 2014

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“…The gradient method is one of the mathematical methods, gives a mean of deriving boundary layer height by performing the first derivative of normalized range corrected signal [13]- [15]. It was explained mathematically by equation 5.…”

Section: ) Gradient Methodmentioning

confidence: 99%

Signal processing algorithm for autodetection of atmospheric boundary layer (ABL) height from lidar signals

Pradeep¹,

Kumar²,

Venkateswarlu³

2017

IJET

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Abstract-The atmospheric boundary layer (ABL), the lowest layer of the atmosphere, is a highly dynamic layer that is influenced byseveral parameters such as surface heating, turbulence, moisture transport etc.,. The detection of the height of ABL plays a crucial role in aviation, pollution monitoring,meteorology and agriculture sectors. At present, several methods are available to identify the height of the boundary layer (BL), however with coarse temporal and spatial resolutions. LIDAR offers high resolution measurements oncontinuous basis. LIDAR is one of the active remote sensors of atmosphere works on the principle of radar but employs laser with fine pulse widths. LIDAR technique has been employed to study the altitude profiles of aerosols, clouds, winds,temperature and humidity layers in the atmosphere. In this paper, we show a signal processing methodology to derive the ABL height from LIDAR signals. The signal processing methodology uses different analytical techniques such as Gradient, IPM, LGM, Variance and Wavelet as its modules to derive the height of ABL on an automatic basis. The automatic detection of ABL height from LIDAR signals employs an algorithm that employs a correlation process that works on inter-comparison results between different analytical methods. All these processes undergo in a systematic manner to present the ABL height detection automatically over a period of time frame set. The minimum time period required for detection of ABL height on automatic basis needs half-hour time period. In this presentation, we show the results of LIDAR measurements corresponds to convective period and present the detection of ABL height using the above signal processing algorithm.Keywords-Atmosphere, ABL, Remote sensing, aerosols, LIDAR I. INTRODUCTION ABL is a thin dusty layer that exists near the earth's surface. This layer contains a large part of particulate matter (PM) and other pollutants. Everyday several tons of PM is injected into the ABL by natural and manmade sources exist on the Earth's surface. Differential heating of landmasses generates winds in this layer. Winds play a key role in the transport and mixing of PM. The vertical column of PMchanges during the ABL evaluation. The ABL thickness varies with time and location on land surfaces. The thickness of ABL depends on the solar energy reaching the earth's surface. Land areas associated with strong convection and large winds mark deep ABL [1]. Shallow ABL exists over low temperate zones. This dusty layer acts as a dynamical fluid and the top portion of the layer undergo severe fluctuations during convective periods. Determination of ABL thickness during convective periods is difficult. In-situ and remote sensing methods are available to estimate ABL thickness. The atmospheric parameter, ABL thickness, is required for agriculture, meteorology, and aviation sectors. Radiosondes connected to Hydrogen filled balloons provide the in-situ or direct measurements of ABL thickness [2], [3].These units equipped with sensors for measurement of pre...

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“…The entrainment layer at the top of the boundary layer exerts a partial blocking effect on the dispersion of surface pollutants; this effect results in a high concentration of near-surface contaminants [4][5][6]. Therefore, accurate determination of PBL height is significant for weather forecasting and studying air quality and climate [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…The IPF method is utilized to obtain RLH by fitting the Lidar signal or particle backscatter coefficient to an idealized profile [12]. The Grad method determines RLH as the gradient maximum of the Lidar signal profile [5,8,13]. The WCT method analyzes the vertical gradients of aerosol and the fast temporal changes in the signal time series as a function of height, and is less affected by signal noise compared to gradient methods [10,14,15].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Iterative Fitting Method to Estimate Nocturnal Residual Layer Height

et al. 2016

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Abstract:The planetary boundary layer (PBL) is an atmospheric region near the Earth's surface. It is significant for weather forecasting and for the study of air quality and climate. In this study, the top of nocturnal residual layers-which are what remain of the daytime mixing layer-are estimated by an elastic backscatter Lidar in Wuhan (30.5 • N, 114.4 • E), a city in Central China. The ideal profile fitting method is widely applied to determine the nocturnal residual layer height (RLH) from Lidar data. However, the method is seriously affected by an optical thick layer. Thus, we propose an improved iterative fitting method to eliminate the optical thick layer effect on RLH detection using Lidar. Two typical case studies observed by elastic Lidar are presented to demonstrate the theory and advantage of the proposed method. Results of case analysis indicate that the improved method is more practical and precise than profile-fitting, gradient, and wavelet covariance transform method in terms of nocturnal RLH evaluation under low cloud conditions. Long-term observations of RLH performed with ideal profile fitting and improved methods were carried out in Wuhan from 28 May 2011 to 17 June 2016. Comparisons of Lidar-derived RLHs with the two types of methods verify that the improved solution is practical. Statistical analysis of a six-year Lidar signal was conducted to reveal the monthly average values of nocturnal RLH in Wuhan. A clear RLH monthly cycle with a maximum mean height of about 1.8 km above ground level was observed in August, and a minimum height of about 0.7 km was observed in January. The variation in monthly mean RLH displays an obvious quarterly dependence, which coincides with the annual variation in local surface temperature.

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Lidar-based remote sensing of atmospheric boundary layer height over land and ocean

Cited by 68 publications

References 52 publications

Mixing-layer height retrieval with ceilometer and Doppler lidar: from case studies to long-term assessment

Mixing-layer height retrieval with ceilometer and Doppler lidar: from case studies to long-term assessment

Signal processing algorithm for autodetection of atmospheric boundary layer (ABL) height from lidar signals

An Improved Iterative Fitting Method to Estimate Nocturnal Residual Layer Height

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