A Laboratory Experiment for the Statistical Evaluation of Aerosol Retrieval (STEAR) Algorithms
Abstract
:1. Introduction
2. Rationale
3. Method
3.1. Experimental Apparatus
3.2. AERONET and GRASP Retrieval Algorithms
- subsampling the PI-Neph at the same scattering angles routinely sampled by the AERONET instruments,
- constraining the code to infer real refractive indices between 1.3–1.6 and imaginary refractive indices between 0.0005–0.5, and disallowing refractive index variability with respect to size,
- using 22 radii of equal lognormal spacing between 0.05 and 15 m to infer the volume size distributions, and
- allowing sphere and spheroid shapes.
Monitoring Residuals
3.3. The Aerosol Samples
3.4. Simulating AERONET Scans at Different Solar Zenith Angles
4. Results
4.1. Single-Scatter Albedo
4.2. Bistatic LiDAR Ratio
4.3. Converting Aerodynamic Size to Equivalent Spheres that Are Consistent with Extinction Measurements
4.4. Statistical Comparison of Size Distributions
5. Discussion
5.1. GRASP Imaginary Refractive Index as an Indicator of Aerosol Absorption
5.2. AERONET Absorption bias
5.3. Caveats
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Minerals | Volcanic Ash | Artist Pigments and source locations |
---|---|---|
Hectorite | Mt. St. Helens (USA) | Blue Ridge Hematite (Virginia, USA) |
Hematite | Fuego (Guatemala) | Brown Ocher Goethite (Kaluga, Russia) |
Arizona Test Dust | Mt. Pinatubo (Philippines) | Yellow Ocher Light (Luberon Massif, France) |
Cambrianshale Imt-2 | Eyjafjallajokull (Iceland) | Nicosia Yellow Ocher (Nicosia, Cyprus) |
Saz-2 Ca-rich Montmorillonite | Puyeheu (Chili) | Lemon Ocher (Northern Italy) |
Illite-smectite | Spurr (Alaska) | Ambrogio Yellow Earth (Northern Italy) |
Na-Montmorillonite | Gulagong (Indonesia) | Italian Yellow Earth (Northern Italy) |
Montmorillonite, STx-1b | ||
Montmorillonite SCa-3 | Soot | |
Negev Desert (Israel) | Standards | Ashrae #2 |
Senegal | 600 nm PSL | 120 nm soot |
Ripidolite CCa-2 | 900 nm PSL | 105 nm Soot |
Palygorskite | 100 nm PSL | 60 nm soot |
Arginotec NX Europe | Ammonium Sulfate | 25 nm soot |
A1 ultrafine test dust | Ammonium Nitrate | 70 nm soot |
Silica Dust | Adipic Acid | Fullerene soot |
Method | Slope | Intercept | cc | RMS | N | ||
---|---|---|---|---|---|---|---|
50 | 0.849 | 0.167 | 0.971 | 0.024 | 0.027 | 89 | |
77 | 0.860 | 0.155 | 0.973 | 0.023 | 0.026 | 90 | |
1-deg | 0.741 | 0.270 | 0.916 | 0.023 | 0.028 | 73 | |
50 | 0.821 | 0.191 | 0.967 | 0.023 | 0.028 | 85 | |
77 | 0.784 | 0.227 | 0.966 | 0.024 | 0.028 | 86 | |
1-deg | 0.948 | 0.074 | 0.976 | 0.026 | 0.032 | 72 | |
50 | 0.961 | 0.068 | 0.973 | 0.033 | 0.038 | 117 | |
77 | 0.823 | 0.196 | 0.929 | 0.033 | 0.039 | 95 | |
1-deg | 0.842 | 0.176 | 0.925 | 0.029 | 0.033 | 83 |
Slope | Intercept | cc | RMS | N | ||||
---|---|---|---|---|---|---|---|---|
LiDAR | 50 | 0.774 | 19.80 | 0.714 | 1.79 | 0.02 | 19.94 | 89 |
Ratio | 77 | 1.004 | 7.55 | 0.860 | 7.89 | 0.10 | 15.97 | 90 |
(sr) | 1-deg | 0.991 | 4.76 | 0.973 | 4.11 | 0.06 | 6.54 | 73 |
Effective | 50 | 1.284 | −0.167 | 0.800 | −0.051 | −0.13 | 0.10 | 50 |
Radius | 77 | 1.344 | −0.190 | 0.763 | −0.053 | −0.13 | 0.10 | 45 |
(m) | 1-deg | 1.425 | −0.261 | 0.816 | −0.091 | −0.22 | 0.12 | 38 |
Average | 50 | 1.588 | −0.224 | 0.922 | 0.042 | 0.09 | 0.10 | 50 |
Radius | 77 | 1.556 | −0.216 | 0.853 | 0.030 | 0.07 | 0.11 | 45 |
(m) | 1-deg | 1.698 | −0.318 | 0.875 | −0.009 | −0.02 | 0.10 | 38 |
Effective | 50 | 2.726 | 0.114 | 0.511 | 0.297 | 2.80 | 0.36 | 50 |
Variance | 77 | 3.074 | 0.067 | 0.591 | 0.286 | 2.71 | 0.35 | 45 |
1-deg | 1.683 | 0.266 | 0.295 | 0.337 | 3.24 | 0.40 | 38 | |
Average | 50 | 0.953 | 0.229 | 0.588 | 0.223 | 1.56 | 0.23 | 50 |
Standard | 77 | 1.227 | 0.183 | 0.550 | 0.215 | 1.53 | 0.23 | 45 |
Deviation | 1-deg | 1.031 | 0.219 | 0.559 | 0.224 | 1.62 | 0.23 | 38 |
Total | 50 | 1.031 | 5.812 | 0.896 | 7.604 | 0.13 | 11.55 | 50 |
Aerosol | 77 | 1.100 | 3.482 | 0.844 | 9.363 | 0.16 | 14.40 | 45 |
Volume | 1-deg | 1.014 | 10.284 | 0.713 | 11.139 | 0.18 | 18.50 | 38 |
Sample | RH | PM | Sample | RH | PM | ||||
---|---|---|---|---|---|---|---|---|---|
Mt. St. Helens | dry | 2.5 | 2.48 | 2.25 | 1.10 | Mt. St. Helens | wet | 2.5 | 2.30 |
Mt. St. Helens | dry | 1.0 | 2.48 | 1.98 | 1.25 | Mt. St. Helens | wet | 1.0 | 1.80 |
Eyjafjallajokull | dry | 2.5 | 2.16 | 2.16 | 1.00 | Eyjafjallajokull | wet | 2.5 | 2.80 |
Eyjafjallajokull | dry | 1.0 | 2.16 | 2.16 | 1.00 | Eyjafjallajokull | wet | 1.0 | 2.20 |
Puyeheu | dry | 2.5 | 1.84 | 1.84 | 1.00 | Puyeheu | wet | 2.5 | 2.30 |
Puyeheu | dry | 1.0 | 1.84 | 1.84 | 1.00 | Puyeheu | wet | 1.0 | 1.90 |
Fuego Volcano | dry | 2.5 | 2.36 | 2.36 | 1.00 | ||||
Fuego Volcano | dry | 1.0 | 2.36 | 2.15 | 1.10 | ||||
Pinatubo | dry | 2.5 | 2.20 | 2.10 | 1.05 | ||||
Pinatubo | dry | 1.0 | 2.20 | 1.83 | 1.20 | ||||
Gulagong | dry | 2.5 | 2.40 | 2.18 | 1.10 | ||||
Spurr | dry | 2.5 | 2.60 | Spurr | wet | 2.5 | 2.60 | ||
Spurr | dry | 1.0 | 2.10 | ||||||
Negev Desert, Israel | dry | 2.5 | 1.90 | Negev Desert, Israel | wet | 2.5 | 1.80 | ||
Senegal | dry | 2.5 | 1.80 | Montmorillonite SCa-3 | wet | 2.5 | 1.50 | ||
Brown Ocher (Goethite) | dry | 2.5 | 3.67 | 2.22 | 1.65 | ||||
Brown Ocher (Goethite) | dry | 1.0 | 3.67 | 1.93 | 1.90 | ||||
PTI Hematite | dry | 2.5 | 5.86 | 4.19 | 1.40 | ||||
PTI Hematite | dry | 1.0 | 5.86 | 3.45 | 1.70 | ||||
Blue Ridge Hematite | dry | 2.5 | 3.19 | 1.82 | 1.75 | ||||
Nicosia Yellow Ocher | dry | 2.5 | 1.40 | ||||||
Mont. STx, 150 Mm | dry | 2.5 | 1.60 | ||||||
Ashrae #2 | dry | 1.0 | 1.10 | ||||||
Hectorite | dry | 1.0 | 1.50 |
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Schuster, G.L.; Espinosa, W.R.; Ziemba, L.D.; Beyersdorf, A.J.; Rocha-Lima, A.; Anderson, B.E.; Martins, J.V.; Dubovik, O.; Ducos, F.; Fuertes, D.; et al. A Laboratory Experiment for the Statistical Evaluation of Aerosol Retrieval (STEAR) Algorithms. Remote Sens. 2019, 11, 498. https://doi.org/10.3390/rs11050498
Schuster GL, Espinosa WR, Ziemba LD, Beyersdorf AJ, Rocha-Lima A, Anderson BE, Martins JV, Dubovik O, Ducos F, Fuertes D, et al. A Laboratory Experiment for the Statistical Evaluation of Aerosol Retrieval (STEAR) Algorithms. Remote Sensing. 2019; 11(5):498. https://doi.org/10.3390/rs11050498
Chicago/Turabian StyleSchuster, Gregory L., W. Reed Espinosa, Luke D. Ziemba, Andreas J. Beyersdorf, Adriana Rocha-Lima, Bruce E. Anderson, Jose V. Martins, Oleg Dubovik, Fabrice Ducos, David Fuertes, and et al. 2019. "A Laboratory Experiment for the Statistical Evaluation of Aerosol Retrieval (STEAR) Algorithms" Remote Sensing 11, no. 5: 498. https://doi.org/10.3390/rs11050498
APA StyleSchuster, G. L., Espinosa, W. R., Ziemba, L. D., Beyersdorf, A. J., Rocha-Lima, A., Anderson, B. E., Martins, J. V., Dubovik, O., Ducos, F., Fuertes, D., Lapyonok, T., Shook, M., Derimian, Y., & Moore, R. H. (2019). A Laboratory Experiment for the Statistical Evaluation of Aerosol Retrieval (STEAR) Algorithms. Remote Sensing, 11(5), 498. https://doi.org/10.3390/rs11050498