Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation
Abstract
:1. Introduction
2. Multi-Instrument Single-Platform Concept for Aerosol Retrieval
2.1. The PMAp Approach
2.2. Sensors Contributing to PMAp
2.2.1. The Global Ozone Monitoring Experiment (GOME-2)
2.2.2. The Advanced Very High-Resolution Radiometer (AVHRR/3)
2.2.3. The Infrared Atmospheric Sounding Interferometer (IASI)
2.3. The Colocation Algorithm
2.3.1. AVHRR/GOME-2 Colocation
2.3.2. IASI/GOME-2 Colocation
2.4. Consistency between Co-Registered AVHRR and GOME-2 Information
3. The PMAp Multi-Sensor Aerosol Retrieval Algorithm
3.1. General Structure
- Step 1 (Multi-Sensor): The multi-sensor part of the retrieval is dedicated to aerosol/cloud discrimination, deriving cloud information, and preliminary classification of the suspended aerosol (through an aerosol class). The result of this step is:
- ○
- A cloud flag to exclude pixels with a large cloud contribution;
- ○
- Cloud decontaminated GOME-2 PMD reflectances for partly cloudy conditions;
- ○
- A flag for thick dust/ash aerosol (also used in Step 2 to constrain the aerosol class);
- ○
- An aerosol class including fine/coarse discrimination;
- Step 2 (GOME-2): For every candidate aerosol model from the aerosol class identified in Step 1, an AOD is derived from the GOME-2 unpolarized reflectance using one single PMD band (the most favorable one: in the red spectral range over the ocean, and in the blue part of the spectrum over land). For cloud-free pixels over the ocean, the surface contribution (parametrised with the chlorophyll concentration) is fitted using the reflectance in two additional bands. The result of this step is:
- ○
- A set of AOD estimates (i.e., one AOD for every candidate aerosol model);
- Step 3 (GOME-2): Reflectance and polarized reflectance from several PMD bands are used in a minimization process to identify the best aerosol model (default aerosol model otherwise). Based on this model, the final AOD is extracted from the set retrieved in step 2. The polarized reflectance (q-Stokes fraction) is used for cloud-free pixels only, and the uncertainty is calculated for the retrieved AOD. The result of this step is:
- ○
- The aerosol model (estimated or default);
- ○
- A final AOD and an estimate of the associated uncertainty;
3.2. Definition of Aerosol Models and Look-Up-Tables
3.2.1. Aerosol LUT
- The aerosol optical thickness (from 0.1 to 4.0);
- The observation geometry: viewing zenith angle (VZA with 5°step from 0 to 60°), solar zenith angle (SZA with 5°step from 25 to 75°), and cosine of relative azimuth angle (cos(RAZI) with step 0.1);
- The surface pressure (1013.0 hPa for ocean and 700.0, 1013.0 hPa for land);
- The surface contribution for land: surface albedo (from 0 to 0.8);
- The surface contribution for ocean: surface chlorophyll concentration (from 1.0e−5 to 10.0 mg.m−3) and surface wind speed (from 3.0 to 11.0 m.s−1);
3.2.2. UV LUT
- The elevation (0–8 km);
- The observation geometry: viewing zenith angle (from 0 to 60°), solar zenith angle (SZA from 20 to 85°), and relative azimuth angle (step 20°, from 0 to 180°);
- The surface albedo (from 0.01 to 0.8);
3.2.3. Cloud LUT
- The cloud optical depth (from 0 to 100);
- The observation geometry: viewing zenith angle (from 0 to 60°), solar zenith angle (SZA from 20 to 85°), and relative azimuth angle (step 20°, from 0 to 180°);
- The cloud albedo (from 0.01 to 0.8);
3.3. Step 1: Cloud/Aerosol Discrimination from Multi-Sensor Pre-Classification
3.3.1. Desert Dust Detection
3.3.2. Volcanic Ash/Thick Dust Detection
- A threshold (−2.2 °K) on the brightness temperature difference (BTD) between 10.8 µm and 12 µm AVHRR bands is widely used because dust and ash events cause a negative BTD due to the dust absorption at 10.8 µm (with a different threshold value for ocean and land) [51]. The separation between aerosol and clouds works best for ice clouds due to ice absorption at 12 µm. However, the test also delivers a lot of misclassifications due to low-level water clouds. Therefore, an additional test on the spatial homogeneity of AVHRR brightness temperature at 10.8 µm is also applied in a 3 × 3 pixel vicinity (ash/dust when the variance is lower than typically 0.1);
- A threshold on the GOME-2 UV Absorbing Aerosol Index (AAI) was calculated for the whole PMD footprint. This AAI was derived according to de Graff et al. [52]. The PMD-6 band around 380 nm was used to derive an effective surface albedo using the UV LUT (see Section 3.2.2), assuming a purely molecular atmosphere. Then, the PMD-4 band at 340 nm was used to derive the aerosol absorbing index using the effective surface albedo as input according to de Graff et al. [52]. Note this threshold was still under test and was not activated for the generation of the results presented in this paper;
- A test on IASI measurements in the thermal infrared spectral range combining two threshold tests on BTD at 10 µm and 12 µm (lower than −1 °K), and at 7.1 µm and 7.2 µm (higher than 2 °K). The second BTD was calculated between a background channel and the SO2 ν3 absorption band channel, which was by far the strongest absorption band of SO2 in the mid-infrared (as described in [53]). This test was a simultaneous detection of coarse particles and SO2, thus likely suggesting the presence of volcanic ash aerosol;
- Additional tests were applied for ocean only, using thresholds on the reflectance ratios R(1.6 µm)/R(0.87 µm), R(1.6 µm)/R(0.63 µm), and R(0.87 µm)/R(0.63 µm). Pixels were considered as ash/dust when all ratios were higher than 0.70. Dust aerosols usually show a significantly weaker wavelength dependency than clouds or fine mode aerosols [54] in the wavelength range under consideration;
3.3.3. Initial Cloud Mask (First Guess)
- Test Ref: reflectance in visible bands should not show high values (bright clouds);
- Test T4: brightness temperature at 10.8 µm should not have low values, which are a sign of medium and high-level clouds;
- Test T4T5: difference between brightness temperature at 10.8 µm and 12 µm should not give high positive values, which are a sign of thin cirrus presence;
- Test SpHo: spatial homogeneity of the brightness temperature at 10.8 µm within a 3 × 3 AVHRR pixels box should not reveal high values, sign of a cloud edge, a thin cirrus, or a small cumulus (over water surfaces only).
3.3.4. Clear-Sky AVHRR Reflectances for Typical Clear Conditions
- Test BTD: lowest (highest negative) brightness temperature difference between 10.8 µm and 12 µm;
- Test VIS/NIR: iterative removal of the coolest pixels at 10.8 µm as long as heterogeneity is observed in infrared (variance at 10.8 µm > 0.02), but homogeneity is observed in visible (variance at 670 nm < 0.05);
- Test Out: outlier correction through an iterative removal of (i) for the 3 reflective solar bands, the brightest pixel as long as the average of remaining pixels is lower than the median of AVHRR pixels over the GOME-2 footprint, and (ii) for the band 10.8 µm, the coolest pixel as long as the average of remaining pixels is higher than the median of AVHRR pixels over the GOME-2 footprint;
- Test R4: iterative removal of the coolest pixels until the variance of the radiance for 10.8 µm is below a threshold (0.002);
- Test R3: iterative removal of the brightest pixels until the variance of reflectance for 1.6 µm is below a threshold (0.002).
3.3.5. Pre-Classification of Aerosol (Aerosol Class)
3.3.5.1. Desert Dust and Ash
3.3.5.2. Fine/Coarse Mode Discrimination over Ocean
3.3.5.3. Selection of Clear-Sky Condition and Default Aerosol Model
3.3.6. Final Cloud Mask
- Case CS: clear-sky pixel;
- Case SCC: pixel with small cloud contribution for which AOD could be retrieved after cloud decontamination (i.e., reflectance will be corrected for cloud contamination);
- Case LCC: pixel with large cloud contribution and for which it will be impossible to retrieve an AOD;
- The difference is smaller than a threshold: 0.006 for land, 0.0002 for the ocean;
- For ocean only, the relative difference between clear and colocated reflectance is smaller than a threshold (0.05);
3.3.7. Cloud Decontamination
3.4. Step 2: Retrieval of AOD for Every Candidate Aerosol Model
3.4.1. Interpolation of the LUT
3.4.2. Identification of Observations Significantly Perturbed by Sun Glint
3.4.3. Retrieval over Ocean
3.4.4. Retrieval over Land
3.5. Step 3: Selection of the Aerosol Model and its Associated AOD
3.6. Calculation of Uncertainty
- Angular interpolation: nearest neighbours for SZA, VZA, and RAZI are used instead of the values interpolated to the actual measurements;
- Aerosol model: all aerosol models from Table 4 are used for the fit on the retrieval instead of those limited to the aerosol class;
- Cloud decontamination: maximum and minimum values are obtained from the scatter between averaged AVHRR reflectance and the GOME-2 reflectance (standard deviation of the linear fit in Figure 2).
3.7. Cloud Optical Depth
4. Results and Validation
4.1. Qualitative Evaluation
4.2. Quantitative Evaluation Using Ground-Based Measurements
- Collect AERONET measurements (level 2.1) within a 30-min time window around a Metop overpass and for stations below 2000 m in altitude;
- Land: identify all corresponding GOME-2 measurements in a 30 km circle around the station;
- Ocean: identify all corresponding GOME-2 measurements in a 30 km circle around the station (located on an island or at the coast);
- Calculate the average PMAp AOD and plot the minimum and maximum value around the station;
- If a quality assured AERONET measurement at 550 nm wavelength is not available, the AERONET value is interpolated at 550 nm from the neighboring spectral measurements;
- Calculate the regression parameters for the whole matchups.
5. Conclusions and Perspectives
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sensor | Channel | CWL (μm) | FWHM (μm) | Wavelength (μm) | Wave Number (cm−1) |
---|---|---|---|---|---|
GOME2 | PMD-4 | 0.3381 | 0.0041 | ||
GOME2 | PMD-5 | 0.3694 | 0.0171 | ||
GOME2 | PMD-6 | 0.3821 | 0.0035 | ||
GOME2 | PMD-7 | 0.4143 | 0.0291 | ||
GOME2 | PMD-8 | 0.4633 | 0.0578 | ||
GOME2 | PMD-9 | 0.5220 | 0.0539 | ||
GOME2 | PMD-10 | 0.5546 | 0.0037 | ||
GOME2 | PMD-11 | 0.5908 | 0.0449 | ||
AVHRR | 1 | 0.630 | 0.100 | ||
GOME2 | PMD-12 | 0.6403 | 0.0441 | ||
GOME2 | PMD-13 | 0.7568 | 0.0241 | ||
GOME2 | PMD-14 | 0.7992 | 0.0089 | ||
AVHRR | 2 | 0.865 | 0.275 | ||
AVHRR | 3a | 1.610 | 0.060 | ||
IASI | 3 | 3.62–5.00 | 2000.0–2760.0 | ||
AVHRR | 3b | 3.740 | 0.380 | ||
IASI | 2 | 5.00–8.26 | 1210.0–2000.0 | ||
AVHRR | 4 | 10.800 | 1.000 | ||
AVHRR | 5 | 12.000 | 1.000 | ||
IASI | 1 | 8.26–15.50 | 645.0–1210.0 |
Sensor | IFOV Size [km] | IFOV Shape | |||
---|---|---|---|---|---|
Nadir | Edge | ||||
ACT | ALT | ACT | ALT | ||
GOME-2 PMD (Metop A) | 5 | 40 | 10 | 60 | rectangular |
GOME-2 PMD (Metop B, C) | 10 | 40 | 20 | 60 | rectangular |
AVHRR (Metop A, B, C) | 1.08 | 1.08 | 6.15 | 2.27 | rectangular |
IASI (Metop A, B, C) | 12 | 12 | 39 | 20 | circular/elliptical |
Aerosol Id | Effective Radius (µm) | Effective Variance (µm) | fl | Refractive Index | Aerosol Type | Altitude | Surface Type | Comment | |||
---|---|---|---|---|---|---|---|---|---|---|---|
small | large | small | large | nr | ni | ||||||
1 | 0.11 | 0.84 | 0.65 | 0.65 | 1.53e−2 | 1.40 | −5.0e−8 | oceanic | 0–2 km | ocean | |
2 | 0.12 | 2.19 | 0.18 | 0.81 | 4.36e−4 | 1.40 | −4.0e−3 | industrial | 0–2 km | land/ocean | |
3 | 0.14 | 2.15 | 0.22 | 0.62 | 7.00e−4 | 1.45 | −1.2e−2 | industrial | 0–2 km | land/ocean | |
4 | 0.12 | 2.43 | 0.20 | 0.87 | 1.70e−4 | 1.50 | −1.0e−2 | biomass | 0–2 km | land/ocean | |
5 | 0.12 | 2.67 | 0.17 | 0.70 | 2.05e−4 | 1.50 | −2.0e−2 | biomass | 0–2 km | land/ocean | |
6 | 0.10 | 1.60 | 0.32 | 0.42 | 4.35e−3 | 1.53 | −3.2e−3/−9.0e−4 | dust | 0–2 km | ocean | weak abs. |
7 | 0.10 | 1.60 | 0.32 | 0.42 | 4.35e−3 | 1.53 | −4.6e−3/−1.2e−3 | dust | 0–2 km | land/ocean | strong abs. |
8 | 0.10 | 1.60 | 0.32 | 0.42 | 4.35e−3 | 1.53 | −1.3e−2/−3.5e−3 | dust | 0–2 km | ocean | weak abs. |
9 | 0.10 | 1.60 | 0.32 | 0.42 | 4.35e−3 | 1.53 | −4.6e−3/−1.2e−3 | dust | 4–6 km | ocean | moderate abs. |
Name | Clear-Sky Condition | Test | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Ref | SpHo | T4 | T4 T5 | BTD | VIS/NIR | Out | R4 | R3 | ||
CS1-DUST | Volcanic Ash and extreme Dust | X | ||||||||
CS2-COARSE | Coarse Mode and Dust | X | X | X | X | X | X | |||
CS3-STD | Unclassified | X | X | X | X | X | X | X | ||
CS4-FINE | Fine Mode | X | X | X | X | X | X | X | X | |
CS5-IR | Thick Aerosol | X | X | X | ||||||
Reference | Unfiltered Clear-sky | X | X | X | X | X | X | X | X | X |
Nr | Class | Surface | Characterization | Aerosol Models (Table 3) | Clear-Sky Condition (Table 4) | Default Model |
---|---|---|---|---|---|---|
0 | no dust/ fine mode | ocean | BTD dust tests negative and strong wavelength dependency of the reflectance between 0.6 µm and 1.6 µm. | 1–5 | CS5 | 1 |
1 | strong coarse mode | ocean | BTD dust tests negative and weak wavelength dependency of the measured signal between 0.6 µm and 1.6 µm | 6–8 | CS3 | 6 |
2 | biomass burning | not used | not used (placeholder) | |||
3 | thick dust/volcanic ash | land/ocean | BTD dust tests positive, weak wavelength dependency in VIS/NIR (sea only), IASI SO2 test negative | 6–8 (ocean) 9 (land) | CS1 | 6 (ocean) 9 (land) |
4 | volcanic ash with SO2 | land/ocean | BTD dust tests positive, weak wavelength dependency in VIS/NIR (sea only), IASI SO2 test positive | 6-8 | CS1 | 8 |
15 | no classification | land/ocean | - | 1–8 (ocean) 2–5, 9 (land) | CS4 (ocean) Min[CS2, CS3, CS4 CS5] (land) | 1 (ocean) 2 (land) |
Metop | Period | Surface | Data | N | Slope | Offset | R | RMSE |
---|---|---|---|---|---|---|---|---|
A | Jun–Sep 2013 | ocean | all | 98 | 0.56 | 0.06 | 0.61 | 0.12 |
B | Jun–Sep 2013 | ocean | all | 119 | 0.43 | 0.10 | 0.49 | 0.25 |
A | Feb–May 2015 | ocean | all | 61 | 0.97 | 0.03 | 0.85 | 0.17 |
B | Feb–May 2015 | ocean | all | 81 | 0.99 | −0.02 | 0.75 | 0.22 |
A | Jun–Sep 2013 | land | all | 853 | 0.92 | 0.04 | 0.58 | 0.30 |
A | Jun–Sep 2013 | land | filtered | 150 | 1.05 | −0.04 | 0.70 | 0.13 |
B | Jun–Sep 2013 | land | all | 986 | 1.15 | 0.00 | 0.56 | 0.32 |
B | Jun–Sep 2013 | land | filtered | 127 | 0.83 | 0.00 | 0.80 | 0.13 |
A | Feb–May 2015 | land | all | 1160 | 0.60 | 0.09 | 0.57 | 0.30 |
A | Feb–May 2015 | land | filtered | 356 | 0.50 | 0.07 | 0.74 | 0.31 |
B | Feb–May 2015 | land | all | 1520 | 0.78 | 0.06 | 0.64 | 0.32 |
B | Feb–May 2015 | land | filtered | 298 | 0.61 | 0.04 | 0.76 | 0.24 |
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Grzegorski, M.; Poli, G.; Cacciari, A.; Jafariserajehlou, S.; Holdak, A.; Lang, R.; Vazquez-Navarro, M.; Munro, R.; Fougnie, B. Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation. Remote Sens. 2022, 14, 85. https://doi.org/10.3390/rs14010085
Grzegorski M, Poli G, Cacciari A, Jafariserajehlou S, Holdak A, Lang R, Vazquez-Navarro M, Munro R, Fougnie B. Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation. Remote Sensing. 2022; 14(1):85. https://doi.org/10.3390/rs14010085
Chicago/Turabian StyleGrzegorski, Michael, Gabriele Poli, Alessandra Cacciari, Soheila Jafariserajehlou, Andriy Holdak, Ruediger Lang, Margarita Vazquez-Navarro, Rosemary Munro, and Bertrand Fougnie. 2022. "Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation" Remote Sensing 14, no. 1: 85. https://doi.org/10.3390/rs14010085