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Constraining aerosol sources using MODIS backscattered radiances Easan Drury - G2

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Presentation on theme: "Constraining aerosol sources using MODIS backscattered radiances Easan Drury - G2"— Presentation transcript:

1 Constraining aerosol sources using MODIS backscattered radiances Easan Drury - G2

2 Outline: 1.Briefly describe MODIS Level 2 algorithm and products. 2.Describe the assumptions made in the MODIS AOD algorithm {V4.2.2, Remer et al., 2005} ( 3.Describe assumptions made in the GEOS-CHEM AOD product 4.Outline method for relating aerosol sources to TOA backscattered radiances. How can one best use MODIS to provide observational constraints on GEOS-CHEM aerosol sources?

3 Deriving Level 2 archived products:MODIS algorithm: Level 2 aerosol products have a resolution of 10km x 10km corresponding to 400 Level 1b pixels, and compress the information from a granule to ~80KB. Level 2 products are derived as follows: Level 1b granules contain ~5 min swath data and are ~ 0.5 gigabytes  too much data to constrain global aerosol models! 1.Cloud screening:  different algorithm for Land and Ocean  filters use spatial variability, and wavelength dependent brightness to determine cloudy pixels 2.Land  filter pixels with snow/ice and pixels with water 3.Ocean  filter pixels with sunglint and high sediment loading 4.Further isolate the pixels we can reasonably quantify surface reflectance. Different retrievals over Ocean and Land based on the different nature of surface reflectance

4 MODIS algorithm: Deriving surface contributions Over LAND, surface reflectance is highly variable both spatially and temporally. To minimize the error in quantifying surface reflectance ‘dark pixels’ are found at 2130nm (IR is used because the atmosphere is nearly transparent). A ‘dark pixel’ is given by  The MODIS algorithm exploits the relationship between surface reflectance at 2130nm and the 469 & 645nm’s. Lastly, the darkest 20% and brightest 50% of the remaining pixels are discarded to filter out cloud shadows and residual cloud contamination. Kaufmann et al., IEEE Trans. Geo., 1997 Over OCEAN the surface reflectance is less variable and six wavelengths are archived  555, 645, 858, 1240, 1640, 2130 nm. The pixels remaining after cloud, glint and sediment filtering are further filtered by discarding the darkest 25% and brightest 25%. This is again done to filter cloud shadows and residual cloud contamination. [1] MODIS Level 2 data archives TOA backscattered reflectance & surface contributions for filtered pixels. [2] These parameters are used to derive MODIS AODs

5 Deriving AOD products: 2 - Assume single-scattering to expand total reflectance into components: 1 - Define backscattered reflectance based on Lambertian surface reflection: 3 – The AOD algorithm is different for aerosols over OCEAN or LAND because the treatment of surface reflectance left 2 wavelengths for LAND retrieval and 6 wavelengths for the OCEAN retrieval. MODIS algorithm: Level 2 TOA backscattered reflectances & surface reflectances drive the MODIS AOD retrieval as follows:

6 LAND: The idea is to calculate AODs by fitting aerosol path radiance to an optical depth using the single scattering approximation: MODIS algorithm: Calculating LAND AOD: The single scattering albedo and aerosol phase functions are assumed using ‘pure dust’, ‘pure non-dust’ and ‘mixed-type’ based on the ratio of blue to red channel brightness. The non-dust single scattering albedos are shown geographically below: [1] Assumed aerosol properties have a very rough spatial and temporal grid [2] Derived AODs products are significantly influenced by these assumptions

7 MODIS LUTs: Land Remer et al., 2005

8 MODIS algorithm: The idea is to generate look up tables (LUTs) of optical properties based on a bimodal aerosol distribution with 4 small mode sizes and 5 large mode sizes. An interpolating least-squares algorithm determines the small and large mode sizes (and refractive indices) based on minimizing the error in the 6 channels weighted heavily to 858nm. Calculating OCEAN AOD: Remer et al., 2005 [1] AOD product is sensitive to the assumed refractive indices and size parameters [2] For polluted continental outflow, we’d expect the imaginary part of the refractive index to be greater than 0.0035. AODs would be underestimated by the MODIS algorithm

9 GEOS-CHEM AOD algorithm: 1 - Assign wavelength and humidity dependent refractive indices and size distributions to modeled aerosol types: (1) sulfate, (2) nitrate, (3) OC, (4) BC, (5) Sea Salt [2 modes] and (6) dust [3 modes]. 2 - Calculate a look up table (LUT) of optical properties using Mie Code. 3 - Calculate column optical properties using modeled aerosol concentrations and sum to get column AODs σ = scattering efficiency α = absorption efficiency [1] The aerosol optical properties (e.g. refractive indices, size distributions and hydroscopic growth) are different from those assumed in the MODIS AOD algorithm. [2] Inverting aerosol sources in AOD space introduces unnecessary error based these assumptions. [3] Can we do better by comparing with TOA backscattered reflectances?

10 New approach  Calculate synthetic backscattered radiances: Use GEOS-CHEM aerosol concentrations for each model layer. Convert aerosol concentrations to layer optical properties at MODIS wavelengths using Mie code: Output  τ (λ), ω (λ), Φ (λ) Run GEOS-CHEM optical properties and MODIS Level 2 surface reflectance through radiative transfer code (LIDORT) to generate synthetic upwelling spectral reflectance at MODIS wavelengths and geometries. Output  L TOA (λ) Invert TOA radiance comparison to generate constraints on aerosol sources.

11 What’s gained? For OCEAN, we don’t have to assume refractive index of m~1.45 – 0.0035i & fixed bimodal size distribution. For LAND, we don’t have to assume pre-tabulated aerosol types for a given location. We don’t have to make a single scattering approximation measurement error significantly decreased: Δτ = ±0.05 ± 0.15τ [Land] Δτ = ±0.03 ± 0.05τ [Ocean] ΔL < ±0.02 [both ] Remer et al., 2005 (however there are significant errors remaining in residual cloud & surface contamination) What assumptions do we make? We assume the same surface reflectance as the MODIS retrieval algorithm. This is the best approximation we have for the comparison. We assume refractive indices, size distributions & hydroscopicity (from GADs, DEAD model, etc. ). However, these optical properties will be improved with further understanding. We assume spherical particles in our Mie code. We assume aerosols are externally mixed – no aerosol microphysics.

12 Additional Slides:

13 Direction of further research: Finish code for aerosol source inversions using backscattered reflectances over the US: Integrate inversions from surface concentrations (CASTNET, IMPROVE), surface transmittance (AERONET) and in situ aircraft observations (ICARTT). Extend method to South and East Asia, integrating AERONET, TRACE-P and ACE-ASIA data. Quantify the ability of different observational platforms to constrain aerosol sources (satellites, surface optical measurements, surface direct measurements, aircraft direct measurements).

14 MODIS LUTs: Ocean Remer et al., 2005

15 Data previously used for constraining aerosol sources: Comparison of modeled AODs w/ satellite-derived AODs Comparison of modeled AODs w/ surface-derived AODs [AERONET, INDOEX, ACE-ASIA] Comparison of modeled surface aerosol concentrations w/ measured surface aerosol concentrations [IMPROVE, CASTNET] In Situ measurements of aerosol concentration during flight campaigns [INDOEX, TRACE-P, ACE-ASIA, ICARTT, etc.] LIDAR [ACE-ASIA, ICARTT, etc.]

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