Laser Beam Atmospheric Propagation Modelling for Aerospace LIDAR Applications
Abstract
:1. Introduction
1.1. Aerospace Laser Applications
1.2. Structure of the Article
- Section 2 describes the effects on laser beam performance because of atmospheric attenuation. Covering the Beer–Lambert law governing transmittance, this section introduces the atmosphere composition, the dominant linear propagation effects of absorption and scattering, to then describe the non-linear effects concerning turbulence and thermodynamic propagative effects and the subsequent empirical models and theoretical backgrounds individually.
- Section 3 introduces empirical modelling to collectively combine the propagative effects in terms of laser performance. The benefits of empirical modelling in comparison to atmospheric radiative transfer codes are highlighted, and the approaches are subsequently reflected in practical radiometric measurement techniques for atmospheric extinction.
- Section 4 reviews the main atmospheric radiative transfer codes and emphasizes the underpinning methodology of the line-by-line analysis, the inherent assumptions and applications of each model and identifies trends in model development including more extensive use of absorption and scattering models.
2. Atmospheric Extinction and Transmittance
2.1. Atmospheric Properties
2.2. Molecular Line Absorption
2.2.1. Absorption Line Profile
2.2.2. Continuum Absorption
2.2.3. Transmittance Attenuated by Molecular Line Absorption
2.3. Atmospheric Scattering
2.3.1. Aerosols
2.3.2. Rayleigh Scattering
2.3.3. Mie Scattering
2.4. Nonlinear Propagation Effects
2.4.1. Thermal Blooming
2.4.2. Kinetic Cooling
2.4.3. Bleaching
2.4.4. Aerodynamic Effects
2.5. Propagation through Haze, Fog and Rain
2.6. Propagation through Atmospheric Turbulence
2.6.1. Refractive Index Structure Coefficient
2.6.2. Turbulence Effects
2.6.3. Astronomical Refraction
3. Combined and Empirical Propagation Models
3.1. Laser Range Equation
3.2. Signal to Noise Ratio
3.3. Laser Beam Transmittance along a Slant Path
3.4. Particle Retrieval
3.5. Elder–Strong–Langer Model for Absorption
3.6. Elder–Strong–Langer Model for Scattering
3.7. Combined ESLM Model
3.8. Radiometric Measurements of Atmosphere Extinction
3.9. Application of Machine Learning in Laser Propagation
4. Atmospheric Radiative Transfer Models
4.1. 4A/OP
4.2. ARTS
4.3. LIDORT/VLIDORT
4.4. 6S/6SV1
4.5. MODTRAN
4.6. LBLRTM
4.7. COART
4.8. DISORT
4.9. MOSART
4.10. RTTOV
4.11. Summary
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Laser Type | Carrier Wavelength | |
---|---|---|
CO2 | 9.2–11.2 µm | |
Er:YAG | 2 µm | |
Raman Shifted Nd:YAG | 1.54 µm | |
Nd:YAG | 1.06 µm | |
GaAlAs | 0.8–0.904 µm | |
HeNe | 0.63 µm | |
Frequency Doubled Nd:YAG | 0.53 µm | |
Detection Technique | Interferometer Type | Modulation Technique |
Direct Detection | Not applicable | Pulsed |
Amplitude Modulation (AM) | ||
Coherent Detection | Heterodyne | Pulsed |
Homodyne | Amplitude Modulation (AM) | |
Offset Homodyne | Frequency Modulation (FM) | |
Hybrid (AM/FM, Pulse Burst) None (CW) | ||
Functions | Measurements | |
Tracking | Reflectance (Amplitude) | |
Moving Target Indication | Range (Time Delay) | |
Machine Vision | Velocity (Differential Range or Doppler Shift) | |
Velocimetry | Angular Position | |
Wind Shear Detection | Vibration | |
Target Identification | ||
Imaging | ||
Vibration Sensing |
Permanent Constituents | Variable Constituents | ||
---|---|---|---|
% by volume | % by volume | ||
Nitrogen (N2) | 78.084 | Water Vapor (H2O) | 0–0.04 |
Oxygen (O2) | 20.948 | Ozone (O3) | 0–12 × 10−4 |
Argon (Ar) | 0.934 | Sulfur Dioxide (SO2) | 0.001 × 10−4 |
Carbon Dioxide (CO2) | 0.036 | Nitrogen Dioxide (NO2) | 0.001 × 10−4 |
Neon (Ne) | 18.18 × 10−4 | Ammonia (NH3) | 0.004 × 10−4 |
Helium (He) | 5.24 × 10−4 | Nitric Oxide (NO) | 0.0005 × 10−4 |
Krypton (Kr) | 1.14 × 10−4 | Hydrogen Sulfide (H2S) | 0.00005 × 10−4 |
Xenon (Xe) | 0.089 × 10−4 | Nitric acid vapor (HNO3) | Trace |
Hydrogen (H2) | 0.5 × 10−4 | Chlorofluorocarbons | Trace |
Methane (CH4) | 1.7 × 10−4 | ||
Nitrous Oxide (N2O) | 0.3 × 10−4 | ||
Carbon Monoxide (CO) | 0.08 × 10−4 |
Window Number | Window Boundaries (µm) |
---|---|
I | 0.72–0.94 |
II | 0.94–1.13 |
III | 1.13–1.38 |
IV | 1.38–1.90 |
V | 1.90–2.70 |
VI | 2.70–4.30 |
VII | 4.30–6.00 |
Type of Scattering | Size of Scatterer |
---|---|
Rayleigh Scattering | Electron Size of Scatterer |
Mie Scattering | Size of Scatterer ≈ |
Non-selective Scattering | Size of Scatterer |
Source | Amount, Tg/yr [106 Metric Tons/yr] | |
---|---|---|
Range | Best Estimate | |
Natural | ||
Soil Dust | 1000–3000 | 1500 |
Sea Salt | 1000–10,000 | 1300 |
Botanical Debris | 26–80 | 50 |
Volcanic Dust | 4–10,000 | 30 |
Forest Fires | 3–1500 | 20 |
Gas-to-particle conversion (total) | 100–260 | 180 |
Sulphate from H2S | 130–200 | |
Ammonium salts from NH3 | 80–270 | |
Nitrate from NOx | 60–430 | |
Hydrocarbons from plant exudations | 75–200 | |
Photochemical | 40–200 | 60 |
Subtotal | 2200–24,000 | 3100 |
Anthropogenic | ||
Direct Emissions | 50–160 | 120 |
Gas-to-particle conversion (total) | 260–460 | 330 |
Sulphate from SO2 | 130–200 | |
Nitrate from NOx | 30–35 | |
Hydrocarbons | 15–90 | |
Photochemical | 5–25 | 10 |
Subtotal | 320–640 | 460 |
Rainfall Rate (cm/h) | Transmittance, τ, for 1800 m Path |
---|---|
0.25 | 0.88 |
1.25 | 0.74 |
2.5 | 0.65 |
10.0 | 0.38 |
Rain Intensity | Rainfall (mm/h) |
---|---|
Mist | 0.025 |
Drizzle | 0.25 |
Light | 1.0 |
Moderate | 4.0 |
Heavy | 16 |
Thundershower | 40 |
Cloudburst | 100 |
Turbulence Strength | Refractive Index Structure Coefficient, |
---|---|
Strong | [m−2/3] |
Intermediate | [m−2/3] |
Weak | [m−2/3] |
Reference | ||||
---|---|---|---|---|
Fried’s Model | 1/3 | 3200 m | [70] | |
Brookner’s Model | 5/6 | 320 m | [71] | |
Tatarski’s Model | 4/3 | ∞ | [72] | |
Hufnagel Condition I | −10 | 1000 m | [73] | |
Hufnagel Condition II | 0 | 1500 m | [73] |
Equation | Ref. | ||
---|---|---|---|
Fried’s Model | (22) | [70] | |
Brookner’s Model | (23) | [71] | |
Tatarski’s Model | (24) | [72] | |
Hufnagel Model | (25) | [73] |
Altitude (km) | |
---|---|
0.001 | 30 |
0.003 | 20 |
0.01 | 15 |
0.03 | 10 |
0.1 | 6 |
0.3 | 4 |
1.0 | 1 |
3.0 | 1 |
Constants | ||||
---|---|---|---|---|
Window | ||||
I | 0.0305 | 0.800 | 0.112 | 54 |
II | 0.0363 | 0.765 | 0.134 | 54 |
III | 0.1303 | 0.830 | 0.093 | 2.0 |
IV | 0.211 | 0.802 | 0.111 | 1.1 |
V | 0.350 | 0.814 | 0.1035 | 0.35 |
VI | 0.373 | 0.827 | 0.095 | 0.26 |
VII | 0.598 | 0.784 | 0.122 | 0.165 |
Case | Condition | Equations | |
---|---|---|---|
A | (69) | ||
B | (70) | ||
C | (71) | ||
D | (72) | ||
R1 | Rain | (73) | |
R2 | Rain | (74) |
Areas of Contribution | ML Algorithms | References | |
---|---|---|---|
Algorithms | Their Functions | ||
SNR improvement | GPML | Filtering | [105] |
SVM, random forest, decision tree, gradient boosting tree | Supervised learning | [106] | |
CNN | Supervised learning | [107] | |
Smoke detection | t-SNE, density-based spatial clustering | Clustering | [106] |
Cirrus cloud detection | CNN | Supervised learning | [107] |
Improving the prediction accuracy | RNN, CNN, RNN-CNN | Supervised learning in sensor fusion | [108] |
Dealing with atmospheric turbulence | ANN | Supervised learning | [109] |
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Fahey, T.; Islam, M.; Gardi, A.; Sabatini, R. Laser Beam Atmospheric Propagation Modelling for Aerospace LIDAR Applications. Atmosphere 2021, 12, 918. https://doi.org/10.3390/atmos12070918
Fahey T, Islam M, Gardi A, Sabatini R. Laser Beam Atmospheric Propagation Modelling for Aerospace LIDAR Applications. Atmosphere. 2021; 12(7):918. https://doi.org/10.3390/atmos12070918
Chicago/Turabian StyleFahey, Thomas, Maidul Islam, Alessandro Gardi, and Roberto Sabatini. 2021. "Laser Beam Atmospheric Propagation Modelling for Aerospace LIDAR Applications" Atmosphere 12, no. 7: 918. https://doi.org/10.3390/atmos12070918