Questions & explanations
1. What is the role of the lidar ratio in the HSRL retrieval algorithm?
In HSRL, the lidar ratio (extinction-to-backscatter ratio) is directly measured because the algorithm can retrieve both the aerosol backscatter coefficient (from the filter channel) and the aerosol extinction coefficient (from the molecular signal's attenuation). The molecular signal decreases with range due to aerosol extinction; by comparing the measured molecular signal with a theoretical clear-air signal, the algorithm computes the aerosol extinction. Then the lidar ratio is simply extinction divided by backscatter. This eliminates the need to assume a lidar ratio, which is the main source of error in standard LiDAR. The lidar ratio itself can be used to infer aerosol type (e.g., pollution vs. dust).
2. What is a common method to correct for multiple scattering in LiDAR retrievals?
A common method uses a theoretical model, such as the Monte Carlo simulation, that simulates the multiple scattering for given cloud or aerosol properties. The algorithm compares the measured signal with the model to estimate the multiple scattering contribution. Another approach is to use a parameterization based on the lidar field-of-view (FOV) and the scattering phase function. For example, a correction factor 'η' (multiple scattering factor) is introduced to modify the single-scattering lidar equation. The algorithm iteratively adjusts η to match the observed signal. This correction improves the accuracy of extinction and backscatter profiles, especially in dense clouds.
3. What is high-spectral-resolution LiDAR (HSRL)?
High-spectral-resolution LiDAR (HSRL) is a LiDAR technique that separates the backscattered light into a narrow molecular (Rayleigh) component and a broader particle (Mie) component using a very high-resolution spectral filter. Unlike standard LiDAR, which measures total backscatter, HSRL gives separate signals from molecules and aerosols. This allows direct measurement of aerosol optical properties like backscatter and extinction coefficients without assumptions. The technique uses a laser with a very narrow linewidth and a filter such as a Fabry-Perot interferometer or atomic vapor filter. HSRL is used for aerosol and cloud studies, improving climate models.
4. How does the HSRL algorithm separate molecular and aerosol backscatter contributions?
The HSRL algorithm uses the fact that molecular backscatter has a broad spectral width due to thermal motion (Doppler broadening), while aerosol backscatter is narrow (because large particles move slowly). The high-resolution filter passes only a very narrow band around the laser wavelength. The signal that passes through the filter comes only from aerosols because the molecular signal is shifted out of the passband. The total backscatter includes both. The algorithm then subtracts the aerosol component (from the filter channel) from the total to get the molecular component. This separation is clean, allowing direct retrieval of aerosol and molecular profiles.
5. What are the advantages of Raman LiDAR over elastic backscatter LiDAR for water vapor profiling?
Raman LiDAR directly measures water vapor by its unique Raman wavelength, so it does not rely on assumptions about aerosol properties. Elastic backscatter LiDAR can only estimate water vapor indirectly through differential absorption or by assuming aerosol properties, which adds large uncertainties. Raman LiDAR works even in clear air where there are few aerosols, while elastic backscatter LiDAR requires sufficient particle scattering. However, Raman LiDAR signals are much weaker, requiring powerful lasers and sensitive detectors. The Raman technique provides more accurate and independent water vapor profiles, making it valuable for atmospheric research.
6. Give an example of how DIAL is used to measure atmospheric ozone concentration.
To measure ozone, the DIAL system uses an on-line wavelength near 300 nm, where ozone absorbs strongly, and an off-line wavelength near 350 nm where ozone absorbs weakly. The ultraviolet laser pulses are sent upward, and the backscattered light is collected by a telescope. The algorithm compares the signals from the two wavelengths to compute the ozone density at different altitudes. This yields a vertical profile of ozone concentration from the ground to about 40 km. The data are used to study ozone depletion and the health of the stratospheric ozone layer. Balloon-borne sensors can also provide such profiles, but DIAL offers better time resolution.
7. How does a Raman LiDAR algorithm derive a water vapor mixing ratio from the measured signals?
The algorithm measures the intensity of the Raman backscatter from water vapor (H2O) and from nitrogen (N2) or oxygen (O2) at different wavelengths. The ratio of H2O Raman signal to N2 Raman signal is proportional to the water vapor mixing ratio. The algorithm corrects for the differences in Raman scattering cross-sections and atmospheric transmission at the two wavelengths. It also subtracts background and noise. A calibration factor, obtained by comparing with a reference instrument, converts the ratio to absolute humidity. The result is a vertical profile of water vapor concentration. This is important for weather prediction and climate studies.
8. Explain how a rotational Raman LiDAR retrieves atmospheric temperature.
A rotational Raman LiDAR measures the intensity of the rotational Raman spectrum of nitrogen or oxygen. The spectrum's shape depends on temperature because the population of rotational states follows the Boltzmann distribution. The algorithm uses two or more narrow wavelength channels to sample different parts of the rotational Raman spectrum. The ratio of signals from two channels is a function of temperature. The algorithm applies a calibration curve to convert the ratio to temperature. This technique works even when there are no aerosols, as Raman scattering is from molecules. Temperature profiles from the ground to about 50 km can be obtained.
9. Explain how HSRL can be used to discriminate between thin clouds and aerosol layers.
HSRL can discriminate between clouds and aerosols because clouds have a much stronger backscatter and a different lidar ratio. The algorithm looks at the measured aerosol backscatter and extinction profiles. For clouds, the backscatter signal is high and the lidar ratio (extinction/backscatter) is different compared to typical aerosols. Also, the depolarization ratio (a separate channel) can show that cloud particles are non-spherical, causing more depolarization. The HSRL algorithm can set a threshold on the lidar ratio or backscatter to identify cloud layers. This helps separate the contributions of clouds and aerosols in climate studies.
10. Give an example of a situation where ignoring multiple scattering would lead to a significant error in the LiDAR retrieval.
In a thick water cloud with optical depth greater than 3, multiple scattering dominates. If ignored, the algorithm would retrieve an apparent extinction coefficient that is much smaller than the true value, and the backscatter coefficient would be overestimated. For example, the cloud top might appear less dense and the cloud bottom more attenuated. This leads to an underestimate of cloud optical depth and an overestimate of cloud droplet effective radius in some retrieval methods. Accurate cloud properties (important for climate models) require multiple scattering correction. Thus, for dense clouds, multiple scattering cannot be neglected.
11. Why does the DIAL algorithm need to account for the presence of aerosols?
Aerosols scatter and absorb light, and their effect can differ between the on-line and off-line wavelengths due to differences in the optical properties. If not corrected, the algorithm would mistake aerosol attenuation for gas absorption, leading to wrong concentration values. The algorithm uses models of aerosol extinction and backscatter, or measurements from the off-line channel alone, to estimate the aerosol contribution. It also assumes that the aerosol optical properties vary slowly with wavelength. In heavily polluted air, this correction becomes more difficult. Accurate aerosol handling is essential for reliable DIAL measurements.
12. What are the advantages of HSRL over standard elastic backscatter LiDAR for aerosol studies?
Standard elastic backscatter LiDAR cannot separate molecular and aerosol contributions without an assumption about the aerosol extinction-to-backscatter ratio (lidar ratio). This assumption introduces large uncertainties. HSRL directly measures the molecular and aerosol signals, so it provides a direct measurement of the aerosol extinction coefficient and lidar ratio. This makes HSRL more accurate for characterizing aerosol types (e.g., dust, smoke, pollution). HSRL also works better in low aerosol loading because the molecular signal can be accurately measured. The drawback is that HSRL systems are more complex and expensive.