The photon signal-to-noise ratio (SNR) is defined in terms of statistical quantities, and the Poisson and Gaussian probability distribution functions are defined and described. Those distributions are applied to lidar measurements, and the effect of background light on lidar SNR is quantified. The signal-limited and background-limited SNR regimes are defined. The lidar equation is then introduced as a model of the range-dependent lidar signal, and the background model is a constant additional term. All the variables in both models are introduced and defined. They include the number of photons in each laser pulse, the optical efficiencies of the transmitter and receiver, the geometrical function, the receiver solid angle, the range bin length, the volume backscatter coefficient, the extinction coefficient, the spectral radiance of the background, the receiver field of view, the receiver optical bandpass, and the sampling interval of the data system. Finally, a lidar system known as the Eye safe Atmospheric Research Lidar (EARL) is introduced because it is used as an example throughout the rest of the book.

This chapter covers some applications of the atmospheric optics and the engineering principles in the previous chapters as they are employed in operational and proposed lidars. Many of the previous examples involved elastic backscatter aerosol lidars, so this chapter also includes many of the other most common types: wind lidars of several kinds; Rayleigh temperature lidar; differential absorption lidar (DIAL); Raman lidar for profiling trace gases, aerosols, and temperature; high spectral resolution lidar (HSRL); and resonance fluorescence lidar. Descriptions of these techniques are presented here with appropriate references, along with comments on the engineering challenges of these various types of lidars and the ways that they illustrate the principles laid out in the previous chapters. The data analysis algorithms for most of these types of lidar are derived. The laser remote sensing technique known as integrated path differential absorption (IPDA) is also described, along with its data analysis.

An overview of optical scattering in the atmosphere includes the sizes and concentration of scatterers, the mathematical formalism of scattering, and definitions of the lidar scattering and extinction coefficients. The Rayleigh, Mie, and geometric scattering regimes are defined by the scattering parameter, and implications of Rayleigh scattering on lidar measurements are elucidated for both signals and background. Molecules store energy in translational, rotational, and vibrational motions, and atoms store energy in electronic excitations. These energy storage mechanisms cause the lidar observables of Doppler shifts, molecular and Raman spectra, and atomic spectra, which, along with Rayleigh scattering, enable lidar measurements of temperatures and winds; water vapor and trace gas concentrations; and aerosol extinction coefficients at altitudes from the surface up to the mesosphere and lower thermosphere. The lidar techniques that exploit all these phenomena operate over a range of wavelengths from the long wave infrared to the ultraviolet and the reasons for the differing wavelengths of the various techniques are explained with a graphic that summarizes the chapter.

A brief overview and description of the atmospheric lidar measurement technique is followed by the structure of the atmosphere in terms of the troposphere, stratosphere, and mesosphere, as it is usually presented in atmospheric science and meteorology. The atmosphere is then described in terms of lidar observables at all altitudes, including water vapor; trace gases; clouds; several other kinds of particulate matter; and metal atoms, as well as density, temperature, and winds. Examples of lidar measurements include tropospheric and stratospheric ozone, greenhouse gases, other pollutants, tropospheric and stratospheric aerosols, polar stratospheric clouds, and atoms of sodium, potassium, calcium, and iron in the mesosphere. Finally, the structure and contents of the book are described, and suggestions for further reading are given.

This chapter begins with a brief review of electronic circuitry and terminology because optical detection and signal processing are in the realm of electrical engineering. A detailed discussion of analog detection follows, with circuitry including transimpedance amplifiers and equivalent circuits for analyzing noise and bandwidth. Two electronic noise sources are introduced, Johnson noise and amplifier noise, and their effects on SNR are modeled. Photon counting is then discussed in terms of its instrumentation, advantages, and limitations. The basic principles of coherent detection are elucidated through a mathematical derivation, and the advantages of coherent detection are shown: high SNR, optical background discrimination, and the measurement of Doppler shifts to sense winds. The main types of detectors used in lidar systems are then discussed, including intrinsic and PIN photodiodes, photomultipliers, avalanche photodiodes, and single-photon avalanche diodes. The advantages of internal detector gain for optimizing SNR are quantified.

Data analysis starts with preprocessing raw lidar data. Algorithms are presented and explained for digital filtering, background subtraction, range correction, and merging profiles from multiple receiver channels or from hybrid analog/digital data systems. Analysis techniques for cloud and aerosol lidar data are then illustrated, with examples of raw and range-corrected data followed by the scattering ratio, which can be used to find the transmittance of a cloud or aerosol layer. Analysis of depolarization data from co-polar and cross-polar receiver channels is discussed, and an algorithm is included for separating aerosol depolarization from the total atmospheric depolarization. Other simple techniques that do not require data inversion are then covered, including the slope method and multi-angle lidar. Finally, elastic backscatter lidar inversions are described, with a derivation of the Klett method for a single-component atmosphere (aerosols). The algorithms for a two-component atmosphere (molecules and aerosols) are presented, along with the limitations of this method.

Analog data systems use fast digitizers to convert continuous voltage waveforms into digital signals with discrete values in both time and voltage. The voltage resolution is limited by the digitizer’s number of bits, so the output is in discrete steps, which causes an uncertainty called digitization noise, characterized by the ideal SNR for a given number of bits. The SNR improvement caused by averaging is quantified. Static figures of merit include offset and gain errors and nonlinearities. Dynamic figures of merit include signal-to-noise and distortion ratio, total harmonic distortion, effective number of bits, and spurious free dynamic range, all measured by a fast Fourier transform, and aperture error. Testing methods include the histogram test, which is illustrated with an example. A testing summary table is provided. Photon counting data systems have a discriminator, a shaper, and a counter. The shaper has a dead time between pulses that causes a maximum count rate. Models of the true versus measured count rates are given for both paralyzable and non-paralyzable photon counting systems. Hybrid analog/photon counting data systems are described.

The need for optomechanics arises from the fact that light wavelengths are extremely small compared to the dimensions of optical elements, so the tolerances in locating elements in a lidar system are often small compared to those of more usual mechanical manufacturing. The effects of deformations and tilts on optical wavefronts are illustrated with diagrams for both mirrors and lenses. The elastic modulus and the coefficient of thermal expansions are defined, and these properties are summarized in a table for the most common optical and mechanical materials, along with their densities. Techniques are described for supporting and mounting optical elements to avoid sag due to gravity and distortions due to inappropriate clamping. Kinematic principles for optical mounts are defined. Commercial mechanisms that provide precision motion are described, and the design process of athermalization is mentioned. Finally, design principles for the overall structure of a lidar system are presented, and the structure of an eye safe elastic backscatter lidar is used as an example.

The main components of a lidar receiver include the telescope, a field stop, a collimating lens, an optical filter, a field lens, and a detector. These elements are discussed sequentially. Incidence angle and temperature effects on filter response are quantified, and techniques for filter tuning are described. Common spectral shapes of interference filters are given along with their equivalent rectangular widths. Filter aging effects are described, with examples. Polarization sensitivity in receivers is discussed, along with a review of common lidar polarization analyzers. An actual lidar receiver design is described as an example. The shape of the geometrical function (also called the crossover or overlap function) is shown, and a review of geometric optics is given. Engineering the geometrical function is then discussed at length, with many diagrams. Formulas for finding the start and end of crossover are derived, and a graphical technique is introduced for visualizing the results. A two-receiver method for monitoring much of the geometrical function is illustrated with an example. Finally, comments on methods for achieving lidar transmitter–receiver alignment are presented.

The range of atmospheric scattering particle sizes is compared graphically with the range of wavelengths used in lidar to show that common particles are both much smaller and much larger than the wavelengths. Scattering must therefore be considered in all three regimes, so formulations and important results are described for Rayleigh, Mie, and geometric optics scattering. Aerosols in the troposphere and stratosphere are then described, with sources, sinks, and size ranges. The lidar ratio is defined. Other particle types are found in water and ice clouds, polar stratospheric clouds, and noctilucent clouds. Depolarization by nonspherical particles is described with Stokes vectors and Mueller matrices, and algorithms are given for finding the degree of depolarization from lidar measurement data. Particle classifiers are described, with examples, illustrating classification techniques using lidar ratios, depolarization ratios, and color ratios. The theory of sun photometry is then reviewed, and AERONET data products are described, with examples.

The three most common lidar transmitter-receiver configurations are illustrated, along with the basic transmitter components. The components are described sequentially, starting with the laser, with a table of the most important laser characteristics for lidar systems. Lidar beam expanders are described. Lasers are then discussed in terms of their basic requirements (an active medium, a population inversion, and optical feedback), and the properties of laser light (monochromaticity, directionality, and often polarization). Beam parameters and beam quality are then described starting with the Gaussian beam model. Measures of laser beam quality include the beam propagation ratio, spectral purity, and polarization purity. Methods for changing the wavelength are discussed, including stimulated Raman scattering, harmonic generators, and optical parametric oscillators. Laser safety, eye safety standards in terms of maximum permissible exposure, and laser classes are then covered, and the transmitter of an eye safe elastic backscatter lidar is described as an example, with an illustration and a table of its parameters.