Atmospheric lidar
Part of the transmitted radiation is scattered by atmospheric components (i.e., gases, molecules, aerosols, clouds) backward to the lidar, where it is collected by a telescope.
Cloud-base height can be identified by the time difference between the transmittance of the laser pulse to the sky and the detection of the backscattered light by the telescope.
Some polarization lidar systems can measure the entire backscatter phase matrix, thereby avoiding the ambiguity of the depolarization ratio when oriented particles are present.
The large diversity of aerosol optical properties, including their sources and the meteorological processes they are subjected to, requires vertically resolved measurements, which can only be performed with routine lidar observations.
However the lidar ratio, as an intensive aerosol property, strongly depends on the size, morphology and chemical composition of the particles and is highly variable with respect to height, which often risks the extinction profile credibility.
The process for calculating backscatter- and extinction coefficient profiles from EBL returns is widely known as the Klett method [22] and was originally formalised by Hitschfeld and Bordan in 1954.
Extracting the microphysical properties of particles is motivated by the need for a deeper understanding of the effect of aerosols on climate by investigating their spatial and temporal variability.
Other microphysical parameters involving the characterization of aerosols are the mean (effective) radius, the total volume and surface-area concentration, the complex refractive index and the single-scattering albedo (climate forcing).
While knowing the aerosol properties (forward problem) and predicting the lidar signal is a straightforward calculation, the inverse process is mathematically ill-posed (i.e., non-unique and incomplete solution space), showing a strong sensitivity on input uncertainties.
) lidar is related to the number size distribution via the Fredholm integral equation of the first kind: where r is particle radius, m is the complex refractive index, and ?
The theory of inverse ill-posed problems demonstrates that potential noisy components in the lidar data will cause the solution to blow up, regardless of the error level magnitude.
(1) offers a reasonable approximation for almost-spherical particles (e.g. biomass burning aerosols), it no longer provides a viable description for the non-spherical case.
[32] Lidar systems can be used to measure concentration profiles of atmospheric gases (i.e., water vapor, ozone), and industrial emissions (i.e., SO2, NO2, HCl).
By examining the intensity difference of the scattered light at the two frequencies, DIAL systems can separate the contribution of the specific molecule in the atmosphere.
Lidar systems can measure atmospheric temperature from the ground up to approximately 120 km using a variety of techniques, each adapted for a specific altitude range .
[34] Measuring temperature in the lower part of the atmosphere is typically done by taking advantage of temperature-dependent changes in molecular scattering or absorption properties.
Rotational Raman lidar has been a useful active remote atmospheric temperature profiling technique, but implementations have required external calibration.
The concept of using Differential Absorption Lidar (DIAL) for profiling temperature in the lower atmosphere (surface to 6 km) was proposed throughout the 1980s.
However the effect of spectral broadening by molecular scatterers made the problem of measuring oxygen absorption with lidar intractable for several decades.
The received signal is proportional to molecular numerical density, which is in turn connected to temperature based on the ideal gas law.
Temperature profiles at higher altitudes, up to 120 km, can be derived by measuring the broadening of absorption spectra of atoms of metals such as Na, Ca, K, and Fe.
Nevertheless, the lidar signal gets more sensitive to air molecules in the UV band, and an expected aerosol-to-molecule backscatter ratio is harder to be met.
Meteorological variables (i.e. temperature, humidity, wind) in the PBL are critically important as inputs for reliable simulations in air quality models.
From an observational perspective, PBL height has historically been measured with radiosondes[42][43] but in recent years remote sensing instruments such as lidar have been utilized.
The concept of using lidar to detect PBL height relies on the assumption that there is a strong gradient in the concentration of aerosols in the ML versus the free atmosphere.
Continuous monitoring of PBL height will allow for a better understanding of the depth of convective turbulent processes in the ML which are a primary driver of air pollutants.
[41] Normally at the top of the PBL, buoyancy flux reaches a minimum and large gradients of potential temperature, water vapor, and aerosols are observed.
Ceilometers are a ground based Lidar optimised for measurement of cloud on the approach path of aircraft, they can also be used for PBL studies.
Range-resolved NO2 concentrations on a near-horizontal path are obtained by the NO2 DIAL system in the range of 0.3–3 km and show good agreement with those measured by a conventional air pollution monitoring station.
A detection sensitivity of ± 0.9 ppbv at 95% confidence level in the region of 0.3–1 km is achieved with 15-minute averaging and 700 m range resolution during hours of darkness, which allows accurate concentration measurement of ambient NO2.
