We measure the cross-polarized backscattered light from a linearly polarized laser beam penetrating a cloud made of spherical particles with a gated intensified CCD camera. In accordance with previously published results, we observe a clear azimuthal pattern in the recorded images. We show that the pattern originates from second order scattering, and that higher-order scattering causes blurring that increases with optical depth. We also find that the contrast of the symmetrical features can be related to the measure of the optical depth. Moreover, by identifying and subtracting the blurring contributions, the resulting pattern provides a "pure" second-order scattering measurement that can be used for the retrieval of droplet size. We apply this technique on a stratus cloud located at 1400 m. The extinction values retrieved on the basis of the laboratory quantification of the blurring of the multiple scattering secondary polarization patterns measured from the ICCD images are then compared with the profile of the extinction coefficient obtained using Bissonnette's algorithm, which is based on the multiple-field-of-view (MFOV) lidar returns.
A multiple-scattering lidar technique is used to retrieve simultaneously the extinction coefficient and the effective particle diameter of clouds and precipitation. In addition, the linear depolarization ratio is measured to determine the liquid or solid phase of the particles. The reported measurements were made with a ground-based multiple-field-of-view (MFOV) lidar pointed at zenith. The lidar was fired in 10-s bursts, every minute for periods ranging from 30 min to 3 hours. The vertical resolution was selectable from 1.5 to 6 m but typically set at 3 m. The retrieved profiles are collected in the form of time-height maps of 1 min x 3 m resolution of the extinction coefficient, effective particle diameter, and depolarization ratio. Here, we analyze only the cloud layer and identify, from the statistics of the retrieved extinction coefficient and effective droplet diameter, important physical behaviors of water clouds. The results not only demonstrate the validity of the lidar retrievals but show that systematic lidar probings can yield significant information on cloud physics.
Lidar probing of dense water clouds gives rise to significant multiple scattering contributions. Instead of trying to mitigate the effect, we have developed methods to collect and angularly resolve the multiple scattering returns. The proposed retrieval method combines these measurements with a rapid semi-empirical computation method of the lidar multiple scattering contributions. Solutions are calculated for the extinction coefficient, the effective droplet diameter, the liquid water content and the rain rate. The paper reviews the main measurement methods, discusses briefly the concept and implementation of the retrieval technique, presents validation results obtained from Monte Carlo simulations, and compares lidar solutions for liquid water content and effective droplet diameter with in-cloud aircraft measurements. Good correlation is demonstrated for both simulation and field data. We conclude that multiple-scattering lidar is a practical option for the remote sensing of water cloud microphysical parameters up to the lidar penetration depths of typically 200 m.
The lidar has long been proposed as a potential remote sensor of cloud microphysical and optical parameters. The conventional lidar has had only mixed success because retrieval in a medium of such density requires an independently measured boundary value deep in the cloud and a relation between backscatter and extinction. The solution we propose is to make detection at multiple fields of view (MFOV) and exploit the additional information provided by multiple scattering. In this paper, we compare MFOV-based lidar retrievals with in situ measurements of the liquid water content and droplet diameters in liquid-phase stratus clouds. The results show good correlation between the lidar solutions and the in situ data, but a constant bias of 20-30% depending on the parameter. The bias is reduced to 10% if comparisons are restricted to the cloud base region accessible to the lidar. Another significant conclusion is that the lidar solutions are, within 20-30%, statistically representative of the complete layer in spite of the limited penetration range.
Supercooled cloud and precipitation water droplets cause in-flight icing of aircraft lifting and control surfaces and
thus constitute a safety hazard to aviation. There is a growing interest in the development of remote sensors to warn
of the danger zones. A known characteristic of these zones is that they are spatially and temporally variable, hence
the need for real time detection. We have tested in two coordinated field experiments the DREV multiple-fieldof-
view (MFOV) lidar as a means of characterizing icing conditions. The required information is the temperature,
the phase, the liquid water content arid the droplet size of clouds and precipitation. The last three quantities are
obtainable, within limits, with the MFOV lidar. The paper briefly describes the MFOV measurement and solution
methods, and reports on sample retrieval results of liquid water content and droplet effective diameter. These data
are directly applicable to the remote characterization of in-flight icing conditions. The accuracy of these lidar solutions
is currently estimated at 30-40%.
Currently, the commercial use of atmospheric lidar is limited to the measurement of cloud ceiling.
However, very pertinent meteorological information (such as the structure and phase of clouds and
precipitation) can be obtained by recording both polarization components of the returns while operating
the lidar at a fixed repetition rate and scanning its axis at a constant angular elevation speed. We present
results obtained with our dual-polarization scanning lidar system during two measurement campaigns:
MWISP (Mount Washington Icing Sensors Project) in April 1999, and AIRS (Alliance Icing Research
Study) in the 1999-2000 winter
Supercooled cloud and precipitation water droplets constitute a safety hazard to aviation. Concerned agencies are funding research on methods of remotely sensing the icing potential of such clouds and precipitation. The parameters needed are the temperature, the liquid water content (LWC) and the average droplet size. We report here on preliminary results obtained with a multiple-field-of- view (MFOV) lidar in an experimental program carried out at Mount Washington during April 1999. The MFOV technique consists in the measurement of the forward multiple scattering contributions to lidar returns coupled with a solution method that makes use of this additional information to calculate the extinction coefficient and the effective droplet diameter as the dependent functions, and the LWC as a by-product. The paper describes the MFOV retrieval method and gives sample results obtained in the Mount Washington experiment. The retrieved solutions demonstrate the lidar capability of remotely sensing droplet size and LWC profiles of clouds and precipitation. Solution accuracy is determined to be 30 - 40% but the analysis of the data from all fielded sensors will be needed to validate these numbers.
This paper discusses adverse and favorable effects of multiple scattering in lidar measurements. Adverse effects arise because the conventional lidar equation ignores multiple scattering radiation. Hence, the parameters retrieved under this approximation are in error by an amount that depends on the multiple scattering strength. This is illustrated by numerical simulations of space-based DIAL measurements of atmospheric ozone profiles in the presence of cirrus clouds and ground layer aerosols. On the other hand, multiple scattering contributes additional information on the aerosol extinction coefficient and particle size. The paper shows that this can be exploited to derive simultaneous solutions for the extinction coefficient and mean particle diameter independently of external data as required in conventional solution methods.
Despite recent technical advances, adverse weather still constitutes an important decision factor in the efficient use of IR sensors. The presence of fog, clouds or precipitation affects both the IR transmission and background properties of the atmosphere. Taking these effects into account requires the knowledge of the optical parameters of fog, clouds or precipitation which, in general, fluctuate too much on a scale of a few kilometers to be predictable with acceptable accuracy. Therefore, systems performance calculations based on modeling alone cannot provide all the necessary information for real time, on-site decision making. A promising alternative is continuous monitoring of atmospheric aerosol properties with a lidar. The method use in this study is the multiple-field- of-view technique which takes advantage of the information contained in the multiple scattering contributions to solve for both the droplet concentration and effective diameter. We can then use these solutions to derive the atmospheric radiance and transmittance, and calculate from there the contrast-to-noise ratio of IR images of small targets. Using actual lidar probings, examples of performance curves of a generic surveillance sensor are obtained for two types of targets. Results show that performance can drastically change over an interval as short as one minute, which emphasizes the need for real time, on site monitoring in adverse weather.
A method for the evaluation of cloud temperature and transmittance from ground-based measurements of the downwelling spectral radiance is proposed. The method uses the strong emission band of ozone at 9.6 micrometers as a natural source of IR radiation for probing clouds. Temperature and transmittance are derived from the cloudy sky radiance measured in two narrow spectral channels. Cloud parameters are found by solving a system of equations requiring an estimate of the cloud height and thickness obtained from lidar returns and of atmospheric temperature and humidity profiles. Tests performed on experimental spectra verify the method with typical temperature errors smaller than 2 K for the spectral measurements coincident in time with the lidar measurements. The main sources of limitation are identified and analyzed.
Lidar sounding of clouds give rise to multiple scattering contributions that contain useful information on cloud parameters. The challenge is to extract and make use of this information for the retrieval of the cloud droplet density and effective size. This paper is on the continuation of our group's effort for the development of a reliable inversion method of lidar returns measured at multiple fields of view. It is proposed to use the multiple scattering information to calibrate each lidar signal and derive from there the extinction coefficient at the lidar wavelength and the effective droplet diameter. The method is applied to two stratus cloud events. The solutions obtained are consistent with independent depolarization measurements and with general knowledge on cloud properties.
A lidar-transmissometer intercomparison was made during an international experiment held in the German Alps to characterize the vertical structure of aerosols and clouds. The transmission path was 2325-m long and inclined at 30 degrees along the slope of a steep mountain ridge. the transmissometer consisted of a Nd:YAG and a CO2 laser located in the valley and a large-mirror receiver that captured the full beams on the mountain top. Two lidars, one at 1.06 micrometer and one at 1.054 micrometer, were operated with their axes approximately parallel to the transmissometer axis but separated by a horizontal distance on the order of 20 - 40 m. The first one was operated in retroreflector mode and the relative transmittance was determined from the reflection off the mountain ridge above the cloud layer. The second one had a special receiver designed to make simultaneous recordings at four fields of view. The range-resolved scattering coefficient and effective cloud droplet radius are calculated from these four-field-of-view measurements by solving a simplified model (Appl. Opt. 34, 6959-6975, 1995) of the multiply scattered returns. The two simultaneous solutions for the scattering coefficient and effective droplet size make possible extrapolation at wavelengths other than the lidar wavelength of 1.054 micrometer. The main measurement event analyzed in this paper lasted 1.5 hours and produced transmittances ranging from less than 5% to more then 90%. The comparisons show good correlation between the transmissometer data and all lidar solutions including extrapolation at 10.59 micrometer.
This paper describes a lidar technique for the remote sensing of microphysical and optical properties of fog and clouds. The technique is based on recovering the information contained in the multiple scattering contributions to the lidar signals. The multiple scattering contributions are measured via detection at three or more fields of view ranging from a value slightly greater than the laser beam divergence to a maximum less than the width of the forward peak of the phase function at the lidar wavelength. The inversion is performed by least squares fitting these measurements to a multiple scattering lidar equation obtained in analytic form from a phenomenological model of the scattering processes. The solutions are the scattering coefficient and the effective radius of the fog or cloud droplets. This is sufficient information to determine the parameters of an assumed gamma distribution for the droplet sizes from where cloud properties such as the liquid water content and the extinction coefficients at visible and infrared wavelengths can be calculated. Typical results on slant path optical depth, vertical extinction profiles and fluctuation statistics of clouds are compared with in situ data. The agreement is very satisfactory. Sample appliication results on monitoring the visual and infrared detection ranges through clouds are discussed.
The main aerosol effect on imaging performance is brightness reduction through scattering losses. This is fairly well understood and modeled. However, one phenomenon that is almost always overlooked
is the blurring of images that can result from forward-scattered radiation, i.e., the radiation reaching the image plane after being scattered by airborne particles. The paper describes a new and efficient method of calculation of the forward-scattering effect on the point spread and modulation transfer functions. Solutions are presented that show the dependence of the aerosol blurring effect on particle concentration, particle size, geometry, and size of object features. The paper also reports on a simple experiment for measuring the visible point spread function through fog and rain. In most cases, the measurements are in good agreement with the model predictions. As it turns out, our measurements performed at ranges of 500 and 900 m and optical depths of up to seven show significant aerosol blurring effects only for rain and for some advection fogs with a sufficient number of particles in the size range of about 100 μm.
The use of backscatter lidars as a research tool to remotely sense atmospheric parameters has been well established. But, the use of lidars as an operational tool to predict the performance of electro-optical (EO) systems during periods of adverse weather has not. A model correlating lidar derived atmospheric transmission and FLIR (forward looking infrared) performance has been developed and an international field program, FLAPIR, has been conducted to collect the data necessary to evaluate the model.
The main aerosol effect on imaging performance is brightness reduction through scattering losses. This is fairly well understood and modeled. However, one phenomenon that is almost always overlooked is the blurring of images that can result from forward-scattered radiation, i.e., the radiation reaching the image plane after being scattered by airborne particles. This paper describes a simple experiment to measure the visible point-spread function, or the image of a point source, through fog and rain. The images were recorded with a CCD camera. Frame addition was used to reduce the statistical noise. Series of images were made with different neutral density filters and later recombined to increase the dynamic range beyond the 8-bit gray-level range of the frame grabber. The results show the effects of range, particle density, and particle size. The measurements are generally in good agreement with model predictions. As it turns out, the aerosol blurring effects are important only for rain and for some advection fogs with a sufficient number of particles in the size range of $OM 100 micrometers.
As a beam of light propagates through the atmosphere, scattering by aerosol particles causes the beam profile to broaden. A multi-field-of-view (MFOV) lidar has been developed which makes simultaneous measurements of the energy directly backscattered from the central beam and multiscattered signals arising from the broadened part of the beam. The direct backscatter signal constitutes a conventional Mie lidar signal. Measurements made along a near horizontal path in haze, fog, and rain are presented. The results show that the multiscattered signals are strongly influenced by the extinction coefficient and the size of the aerosols. Thus the multiscattered signals, together with the direct backscatter signal, contain more information about the aerosols than is available from a conventional single field of view lidar.
The paper describes a method of calculating the point spread and modulation transfer functions for imaging through aerosol media. The method is based on the image solution of a point source determined with the multiscattering propagation model of Bissonnette (1988). Results are presented that illustrate the effects of particle size, extinction coefficient and geometry. Comparisons with measurements show good consistency but no systematic validation is performed because the available image and aerosol data are not sufficiently documented or outside the range of validity of the propagation model. The calculated modulation transfer functions are applicable to image enhancement algorithms and could potentially be inverted to provide information on particle sizes.
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