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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 19, Iss. 19 — Sep. 12, 2011
  • pp: 18602–18613
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Comparison of full-sky polarization and radiance observations to radiative transfer simulations which employ AERONET products

Nathan J. Pust, Andrew R. Dahlberg, Michael J. Thomas, and Joseph A. Shaw  »View Author Affiliations


Optics Express, Vol. 19, Issue 19, pp. 18602-18613 (2011)
http://dx.doi.org/10.1364/OE.19.018602


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Abstract

Visible-band and near infrared polarization and radiance images measured with a ground-based full-sky polarimeter are compared against a successive orders of scattering (SOS) radiative transfer model for 2009 summer cloud-free days in Bozeman, Montana, USA. The polarimeter measures radiance and polarization in 10-nm bands centered at 450 nm, 490 nm, 530 nm, 630 nm, and 700 nm. AERONET products are used to represent aerosols in the SOS model, while MISR satellite BRF products are used for the surface reflectance. While model results generally agree well with observation, the simulated degree of polarization is typically higher than observed data. Potential sources of this difference may include cloud contamination and/or underestimation of the AERONET-retrieved aerosol real refractive index. Problems with the retrieved parameters are not unexpected given the low aerosol optical depth range (0.025 to 0.17 at 500 nm) during the study and the corresponding difficulties that these conditions pose to the AERONET inversion algorithm.

© 2011 OSA

1. Introduction

Over the last decade, important progress has been made toward a better understanding of the radiative effects of aerosols on climate [1

1. M. Chin, R. A. Kahn, and S. E. Schwartz, CCSP 2009: Atmospheric Aerosol Properties and Climate Impacts, A Report by the U.S. Climate Change Science (NASA, Washington, D.C., USA 2009).

]. To develop a more accurate aerosol climatology, several surface-based [2

2. B. N. Holben, T. F. Eck, I. Slutsker, D. Tanre, J. P. Buis, A. Setzer, E. Vermote, J. A. Reagan, Y. J. Kaufman, T. Nakajima, F. Lavenu, I. Jankowiak, and A. Smirnov, “AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization,” Remote Sens. Environ. 66(1), 1–16 (1998). [CrossRef]

] and satellite-based optical remote sensing platforms (e.g. [3

3. D. J. Diner, J. C. Beckert, T. H. Reilly, C. J. Bruegge, J. E. Conel, R. A. Kahn, J. V. Martonchik, T. P. Ackerman, R. Davies, S. A. W. Gerstl, H. R. Gordon, J. P. Muller, R. B. Myneni, P. J. Sellers, B. Pinty, and M. M. Verstraete, “Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview,” IEEE Trans. Geosci. Rem. Sens. 36(4), 1072–1087 (1998). [CrossRef]

] and [4

4. L. A. Remer, Y. J. Kaufman, D. Tanré, S. Mattoo, D. A. Chu, J. V. Martins, R. R. Li, C. Ichoku, R. C. Levy, R. G. Kleidman, T. F. Eck, E. Vermote, and B. N. Holben, “The MODIS Aerosol Algorithm, Products, and Validation,” J. Atmos. Sci. 62(4), 947–973 (2005). [CrossRef]

]) have been deployed. Despite these advances, it is increasingly apparent that more highly constrained aerosol retrievals require the addition of polarization information to the current radiance-only methods [5

5. M. I. Mishchenko, B. Cairns, G. Kopp, C. F. Schueler, B. A. Fafaul, J. E. Hansen, R. J. Hooker, T. Itchkawich, H. B. Maring, and L. D. Travis, “Accurate monitoring of terrestrial aerosols and total solar irradiance: Introducing the Glory mission,” Bul. Amer. Met. Soc. 88(5), 677–691 (2007). [CrossRef]

8

8. E. Boesche, P. Stammes, T. Ruhtz, R. Preusker, and J. Fischer, “Effect of aerosol microphysical properties on polarization of skylight: sensitivity study and measurements,” Appl. Opt. 45(34), 8790–8805 (2006). [CrossRef] [PubMed]

]. The performance improvement gained by including polarization in retrievals owes to its higher sensitivity to select aerosol properties [8

8. E. Boesche, P. Stammes, T. Ruhtz, R. Preusker, and J. Fischer, “Effect of aerosol microphysical properties on polarization of skylight: sensitivity study and measurements,” Appl. Opt. 45(34), 8790–8805 (2006). [CrossRef] [PubMed]

]. This sensitivity also enables sky polarization measurements to be used to independently verify the accuracy of retrieved aerosol parameters from radiance-only methods.

In the past few years, we have developed a full-sky polarimetric imager [9

9. N. J. Pust and J. A. Shaw, “Dual-field imaging polarimeter using liquid crystal variable retarders,” Appl. Opt. 45(22), 5470–5478 (2006). [CrossRef] [PubMed]

12

12. N. J. Pust and J. A. Shaw, “Comparison of Skylight Polarization Measurements and MODTRAN-P Calculations,” J. Appl. Remote Sens. 5(1), 053529 (2011). [CrossRef]

]. In this study, we compare measurements of full-sky polarization against a polarized (vector) successive order of scattering (SOS) radiative transfer model [13

13. J. Lenoble, M. Herman, J. L. Deuzé, B. Lafrance, R. Santer, and D. Tanré, “A successive order of scattering code for solving the vector equation of transfer in the earth's atmosphere with aerosols,” J. Quant. Spect. Rad. Trans. 107(3), 479–507 (2007). [CrossRef]

] which uses aerosol products from the Aerosol Robotic Network (AERONET) [2

2. B. N. Holben, T. F. Eck, I. Slutsker, D. Tanre, J. P. Buis, A. Setzer, E. Vermote, J. A. Reagan, Y. J. Kaufman, T. Nakajima, F. Lavenu, I. Jankowiak, and A. Smirnov, “AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization,” Remote Sens. Environ. 66(1), 1–16 (1998). [CrossRef]

]. Since AERONET does not currently measure polarization (at most stations) and consequently does not use polarization in its aerosol retrievals, the accuracy of AERONET aerosol parameters can be indirectly assessed by comparing sky polarization observations with results from radiative transfer simulations which employ AERONET products. If the retrieved aerosol parameters from AERONET accurately represent the aerosols, the polarization simulated using these parameters is expected to agree with the observed polarization. In this paper, we discuss comparisons between a radiative transfer model which uses AERONET aerosol products and observations from a full-sky polarimeter.

1.1 Visible and NIR full-sky polarization and radiance measurements

The observation data reported here is generated by a visible, near-infrared (VNIR) imaging polarimeter that was developed for studying both sky polarization and ground-based object polarization signatures [9

9. N. J. Pust and J. A. Shaw, “Dual-field imaging polarimeter using liquid crystal variable retarders,” Appl. Opt. 45(22), 5470–5478 (2006). [CrossRef] [PubMed]

12

12. N. J. Pust and J. A. Shaw, “Comparison of Skylight Polarization Measurements and MODTRAN-P Calculations,” J. Appl. Remote Sens. 5(1), 053529 (2011). [CrossRef]

]. The polarimeter is capable of switching between two fields of view—a wide-angle fisheye for imaging the full sky (used exclusively here) and a narrow-angle telephoto for imaging smaller objects. At the time of study, the instrument operated in five 10-nm bands centered at 450 nm, 490 nm, 530 nm, 630 nm, and 700 nm. Two liquid crystal variable retarders (LCVRs) are used to electronically vary the retardance seen by incoming light so that a full Stokes image is measured in less than a few tenths of a second. (Intensity images from the camera are inverted by a calibration matrix to form the 4-element Stokes images.) Quick acquisition allows reliable measurements in partly cloudy skies without polarization artifacts that would arise if the clouds were to move between frames. This imager obtains polarized sky measurements with uncertainty less than (usually much less than) ± 3% in the degree of linear polarization (DoLP).

All observations here are for seven cloud-free days in late August and September 2009 in Bozeman, Montana, USA.

1.2 AERONET

The Aerosol Robotic Network (AERONET) of solar radiometers measures direct solar irradiance and sky radiance across the globe [2

2. B. N. Holben, T. F. Eck, I. Slutsker, D. Tanre, J. P. Buis, A. Setzer, E. Vermote, J. A. Reagan, Y. J. Kaufman, T. Nakajima, F. Lavenu, I. Jankowiak, and A. Smirnov, “AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization,” Remote Sens. Environ. 66(1), 1–16 (1998). [CrossRef]

]. These measurements are used to calculate aerosol optical depth from direct solar irradiance measurements and to derive aerosol properties, such as size distribution, refractive index, and single scatter albedo, from a sky radiance inversion scheme [14

14. O. Dubovik and M. D. King, “A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements,” J. Geophys Lett. 105(D16), 20673–20696 (2000). [CrossRef]

]. We used aerosol products provided from our AERONET instrument (which is co-located with the polarimeter) to represent aerosols in the radiative transfer model.

1.3 Successive orders of scattering (SOS) radiative transfer model

2. Setting up the comparison

Figure 1
Fig. 1 Overview of the radiance and polarization comparison methodology.
shows the methods used to make the comparisons discussed here: model vs. polarimeter polarization, model vs. polarimeter radiance, and AERONET vs. polarimeter radiance. The validity of the model results used in the comparisons depends heavily on the accuracy of the model parameters. The parameters that most influence the modeled sky radiance and polarization are the aerosol and molecular optical depths, the aerosol and molecular single scatter albedos, the aerosol scattering phase matrix, and the surface reflectance parameters. The methods for generating and including these variables into the SOS model are discussed in the following sections.

2.1 Aerosol scattering and absorption

AERONET products provided all aerosol properties included in the SOS radiative transfer model. Since polarimeter observation wavelengths and measurement times did not always correspond directly to associated AERONET values, AERONET parameters were linearly interpolated in wavelength and time to match the polarimeter. This interpolation was applied to aerosol optical depths, size distributions, and complex refractive indices. (The model used the “direct sun” aerosol optical depth measurements, as opposed to the retrieval-derived aerosol optical depth.) Similarly, phase functions and single-scatter albedos (SSA) provided by AERONET were not always available at the polarimeter wavelengths. Furthermore, the full aerosol scattering phase matrix–as opposed to the phase function provided by AERONET–was needed for the polarized radiative transfer model. At each polarimeter wavelength, a Mie code and T-matrix kernel lookup table provided by AERONET [16

16. O. Dubovik, A. Sinyuk, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Munoz, B. Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin, and I. Slutsker, “Application of spheroid models to account for aerosol particle nonsphericity in sensing of desert dust,” J. Geophys. Lett. 111(D11), D11208 (2006). [CrossRef]

] were used to generate these parameters directly from the interpolated size distribution, sphericity, and complex refractive index. For AERONET size distributions below 100% sphericity, the parameters were handled in a consistent manner to AERONET. That is, the parameters were calculated for a mixture of spherical particles and spheroid particles with a fixed shape (aspect ratio) distribution–the same spheroid shape distribution used in the AERONET operational algorithm [16

16. O. Dubovik, A. Sinyuk, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Munoz, B. Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin, and I. Slutsker, “Application of spheroid models to account for aerosol particle nonsphericity in sensing of desert dust,” J. Geophys. Lett. 111(D11), D11208 (2006). [CrossRef]

,19].

The aerosols were assumed to have a vertical extinction distribution described by a Gaussian centered on a 2 km height and were otherwise assumed to be homogenous throughout the column. (Physically realizable modifications to this vertical distribution did not appreciably affect the model results.)

The aerosols parameters varied significantly over the seven days studied. Figure 2
Fig. 2 Representative sample of aerosol retrievals for all study days.
shows a representative set of retrieval aerosol parameters for each day. Roughly half of the retrieved AERONET sphericity parameters (not shown) were over 90% with the remainder being distributed roughly evenly from 0% to 90%.

2.2 Molecular scattering and absorption

While AERONET wavelengths were selected to minimize molecular absorption, the polarimeter bands were originally chosen to uniformly sample the visible/NIR spectrum without regard to molecular absorption features. The polarimeter 630 and 700 nm bands observe portions of the atmospheric spectrum with significant oxygen and water vapor absorption features, respectively. The presence of these features necessitated an accurate representation of the molecular absorption in the models.

To simulate the molecular absorption, aerosol- and cloud-free MODTRAN [20

20. A. Berk, G. P. Anderson, P. K. Acharya, L. S. Bernstein, L. Muratov, J. Lee, M. Fox, S. M. Adler-Golden, J. H. Chetwynd, M. L. Hoke, R. B. Lockwood, J. A. Gardner, T. W. Cooley, C. C. Borel, P. E. Lewis, and E. P. Shettle, “MODTRAN5: 2006 Update,” in Proceedings of the SPIE6233, 508–515 (2006).

] transmission simulations were executed using a MODTRAN mid-latitude summer standard atmosphere. These models included the MODTRAN default atmospheric constituents (nitrogen, oxygen, ozone, nitrogen dioxide, etc.) as well as ozone and precipital water vapor products supplied by AERONET. Using the resulting MODTRAN spectral transmission, the effective total molecular optical depth of each polarimeter band was calculated from a spectral average of the MODTRAN transmittance weighted by the polarimeter band transmission. Then, the effective molecular single scatter albedo (SSA) was calculated as a ratio of the Rayleigh (molecular scattering) optical depth and the effective total molecular optical depth. While this effective molecular absorption method may not be physically exact, no appreciable differences were seen in the model results between this effective molecular absorption method, and the physically exact method which first executed the SOS model across all spectral features and then band-averaged the resulting polarized radiances. Using the former method reduced the computation time by a large factor. We found that the molecular optical depths and SSAs generated by MODTRAN for the 450 and 490 nm polarimeter bands agreed well with the AERONET-provided parameters for those bands. The SOS molecular vertical extinction distribution was set to be an exponential with an 8 km scale height in the model.

2.3 Surface reflection

Multiple scattering of surface-reflected light from aerosols and molecules greatly reduces the DoLP (while increasing the radiance) observed in ground-based sky measurements [21

21. A. R. Dahlberg, N. J. Pust, and J. A. Shaw, “Effects of surface reflectance on skylight polarization measurements,” Opt. Express 19(17), 16008-16021 (2011). [CrossRef] [PubMed]

]. Therefore, accurate surface reflectance parameters are needed for valid simulations. To fulfill this need, we modified the SOS code to directly incorporate BRF model parameters from the MISR satellite land surface product [22

22. D. J. Diner, J. V. Martonchik, C. Borel, S. A. W. Gerstl, H. R. Gordon, Y. Knyazikhin, R. Myneni, B. Pinty, and M. M. Verstraete, “MISR. Level 2 Surface Retrieval Algorithm Theoretical Basis,” “http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/MISR/ATB_L2Surface43.pdfhttp://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/MISR/ATB_L2Surface43.pdf.

]. MISR provides BRF parameters for a modified version of the RPV (Rahman-Pinty-Verstraete) model [23

23. H. Rahman, B. Pinty, and M. M. Verstraete, “Coupled surface-atmosphere reflectance (CSAR) model. 2: Semiempirical surface model usable with NOAA advanced very high resolution radiometer data,” J. Geophys. Lett. 98(D11), 20791–20801 (1993). [CrossRef]

]. (The SOS code as provided to us by AERONET implemented the standard RPV model.)

The SOS model simulates a homogenous surface. Since an individual pixel in a MISR image is not expected to properly represent an entire area, we calculated an effective BRF model for the 50 km radius area surrounding Bozeman as follows. Using the MISR BRF model parameters, the BRF values for every possible source and view angle geometry at every pixel were calculated. Then, the BRF values were averaged across all pixels according to geometry. The averaged BRF data were then fit to an effective MRPV model. The effective model was calculated for all bands in each MISR product available for the Bozeman 2009 summer. Finally, the effective MRPV model parameters were linearly interpolated to the polarimeter both spectrally and temporally before inclusion in the SOS model. (This method was also repeated for an 8 km radius area, but the SOS simulation results were not significantly different from the 50 km case.)

The SOS model allowed specification of the polarized surface using the Nadal and Breon model parameters [24

24. F. Nadal and F. M. Breon, “Parameterization of surface polarized reflectance derived from POLDER spaceborne measurements,” IEEE Trans. Geosci. Rem. Sens. 37(3), 1709–1718 (1999). [CrossRef]

]. Since the area surrounding Bozeman is largely forested, we tried the “high NDVI forest” parameters specified in the table on pg. 1715 of [24

24. F. Nadal and F. M. Breon, “Parameterization of surface polarized reflectance derived from POLDER spaceborne measurements,” IEEE Trans. Geosci. Rem. Sens. 37(3), 1709–1718 (1999). [CrossRef]

], but the model results were not significantly different from the case where a completely unpolarized surface was defined.

3. Comparison results and discussion

3.1 Full sky time lapse comparisons

For each polarimeter observation, sky radiance and polarization values were simulated by the SOS model for a sparse grid of zenith and azimuth angles that covered the entire sky. Using two-dimensional interpolation, values for the remaining sky were generated. For comparison purposes, these simulated data were projected identically to the polarimeter fisheye projection. An example of both the observed data and the projected simulation data is shown in Fig. 3
Fig. 3 Excerpt from a time lapse (Media 1) of observation and model comparisons for 02 Sept 2009, Bozeman, Montana, USA. The RGB composite images were formed from the 450, 530, and 630 nm images. Error and degree of linear polarization (DoLP) images are shown only for the 450 nm band. Radiance error is defined as (observed - modeled)/observed. The arc with a centered-disk is an automated sun occulter. The Babinet (above the sun) and Brewster (below the sun) neutral points are seen in both the observed and modeled DoLP.
. As the animation in Fig. 3 (Media 1) shows, the polarimeter and the model typically agree well, although some biases do exist.

3.2 Maximum degree of polarization comparisons

The maximum degree of linear polarization (DoLP) and its associated minimum sky radiance were selected as the parameters of interest for the comparison. (The sky region with minimum radiance is near the region with the maximum DoLP.) We have found that differences between the model DoLP and the observation DoLP across the entire sky are typically well correlated with maximum DoLP differences. If the DoLP maxima agree, the DoLPs in the remaining sky regions also generally agree. Figure 4
Fig. 4 Comparison of the observation and model maximum degree of polarization (DoLP) for times when the polarimeter measurement and AERONET retrieval were within 10 minutes of each other. 530 nm results (not shown) are very similar to the 450 and 490 nm results. Polarimeter error bars are shown which vary from ± 0.03 for fully polarized light to ± 0.003 for unpolarized light.
shows comparisons of the observations and the models for these two parameters. Data shown are for times when the polarimeter observations and the AERONET retrievals were within 10 minutes of each other.

3.3 Potential sources of comparison differences

For all wavelengths, the modeled DoLP tends to be higher than observation while the radiance tends to be lower. One potential explanation for this behavior is thin cloud contamination. In our experience, thin clouds will reduce the DoLP significantly. This effect is most pronounced in the maximum DoLP sky regions and at longer wavelengths. Thin clouds also increase the sky radiance. This may explain the higher observed radiances and lower observed DoLPs. Since we would not expect cloud contaminations to be present in all observations (and AERONET quality control seeks to eliminate them), contaminations would be manifested as a one-sided variability in the scatter plot that both reduces the DoLP and increases the radiance for select observations. If correct, this hypothesis explains why a significant fraction of the scatter points in the visible bands are within error bars for both the radiance and the polarization (Figs. 4 and 5), while many are not.

For the 630 and 700 nm bands, the polarization differences between the model and the polarimeter are more extreme than the shorter wavelengths. While cloud contamination affects the longer wavelengths more severely, the occurrence of large polarization differences only in the bands with significant molecular absorption warrants special examination. Several issues must be considered.

First, light from wavelengths with large molecular absorption features have increased polarization when compared to adjacent bands that lack molecular absorption [25

25. E. Boesche, P. Stammes, R. Preusker, R. Bennartz, W. Knap, and J. Fischer, “Polarization of skylight in the O(2)A band: effects of aerosol properties,” Appl. Opt. 47(19), 3467–3480 (2008). [CrossRef] [PubMed]

,26

26. J. Zeng, Q. Han, and J. Wang, “High-spectral resolution simulation of polarization of skylight: Sensitivity to aerosol vertical profile,” Geo. Res. Lett. 35, L20801 (2008). [CrossRef]

]. Therefore, overestimation of the molecular absorption will cause an overestimated DoLP in the model. The higher simulated DoLP (with respect to the observed quantity) may indicate that the molecular absorption included in the model is overestimated. For the 630 nm and 700 nm bands, the primary absorbing molecules are oxygen and water vapor, respectively. We expect that the MODTRAN oxygen model used is accurate since oxygen is a uniformly mixed and temporally consistent atmospheric gas. In contrast, water vapor is temporally variable. To account for this variability, we included AERONET-produced precipital water vapor (pwv) products into the MODTRAN model. We have compared pwv values retrieved by AERONET to values retrieved by a local SUOMINET station and found that the two instruments generally agree quite well. Therefore, we have no reason to suspect that the molecular absorption included in the model is overestimated.

Second, while we are confident in the ability of the SOS model to simulate situations with low molecular absorption, we are uncertain to what degree the model has been tested in spectral regions with high molecular absorption.

Third, the spectral-weighted average of the molecular optical depth that we use depends greatly on the accuracy of the filter transmission profiles and the accurate representation of the fine absorption features by MODTRAN. In these highly structured absorption complexes, small errors in the filter profiles or absorption features may cause errors in the weighted molecular optical depth and molecular SSA. These issues make interpretation of results in the absorption bands difficult.

3.4 Maximum DoLP comparisons with artificially increased real refractive index

While real refractive index biases are only one of several potential error sources, we investigated whether a refractive index bias could realistically account for the differences seen between the observations and the models. To test the sensitivity of the model to a potential bias, we introduced an artificial 5% increase to the AERONET real refractive index before calculating the aerosol scattering phase matrix and aerosol SSA included in the model. (Note that we continued to use the AERONET “direct sun” aerosol optical depths, so the model aerosol optical depth was not altered by this increase.) The magnitude of this increase was chosen arbitrarily. Figures 6
Fig. 6 Same as results from Fig. 4, but for models with the AERONET real refractive index artificially increased by 5%.
and 7
Fig. 7 Same as results from Fig. 5, but for models with the AERONET real refractive index artificially increased by 5%.
(which are similar to Figs. 4 and 5) show the results from the models with the artificially enhanced real refractive index when compared to the observed data.

It is important to note that the better agreement obtained from artificially increasing the real refractive index is specific to this data set and should not be interpreted to apply to all AERONET data. Our purpose here is only to show the potential for a real refractive index bias. Also, further adjustments could be made to the real refractive index on a per wavelength basis until agreement is ideal, but manipulating the aerosol parameters to this degree would only show the sensitivity of polarization to real refractive index, not indicate the true real refractive index. Results from artificially increasing an isolated parameter (as done here) are not indicative of inversion behavior. Inversions retrieve all aerosol microphysical parameters (size distribution, sphericity, complex refractive index, etc.) simultaneously. An inversion that included the polarization with the radiance would most likely generate both size distributions and refractive indices that differed from radiance-only retrieved values for this data set. Therefore, our artificially adjusted real refractive index may be overcompensating for errors in other aerosol parameters and the exact value of our adjustment may not be meaningful.

4. Conclusion

We have shown polarization and radiance observations compared with results from radiative transfer model simulations which include aerosol parameters generated by AERONET. While the models generally agree well with the observations, some differences exist. Several potential error sources in the models were identified. Large molecular absorption features in some bands (630 and 700 nm) complicate the interpretation of the results. While several sources of error may exist, simulations suggest that the real part of the refractive index may be underestimated for the aerosol data set in question. This conclusion is not unwarranted given the low aerosol conditions over the course of the observations and the corresponding difficulties that these conditions pose to the AERONET inversion algorithm.

Recently, we moved the 630 and 700 nm polarimeter bands to 675 and 780 nm, respectively, to reduce the difficulties encountered with interpreting data in highly structured molecular absorption bands. In future studies, we should be able to better assess model vs. observation differences.

Appendix 1. AERONET vs. polarimeter radiance comparisons

Both the AERONET CIMEL instrument and the polarimeter produce radiance data products. The polarimeter measures the full sky, while the CIMEL measures sky radiances in the solar almucantar and principal plane. The similarity of these radiance products provides an opportunity for an indirect calibration crosscheck. While direct comparisons can be made for the 450 and 500 nm bands of the two instruments, other bands can only be compared by interpolating the radiance between the bands. This approach is useful only if the inherent limitations of interpolation between spectra with molecular absorption bands are considered. In the following comparisons, the minimum principal plane radiances measured by CIMEL were interpolated to the polarimeter wavelengths and compared to the associated polarimeter radiance (Fig. 8
Fig. 8 Comparisons of minimum radiance measured in the principal plane by the polarimeter and the AERONET CIMEL. Radiance units are uW/(cm2*Sr). Error bars reflect the ± 5% error advertised by AERONET [14]. 700 nm is not shown but shows similar results to the other wavelengths. Only data for which the two instruments took measurements within 10 minutes of each other are shown.
). Because of the large oxygen absorption in the 630 nm band, the comparison at this wavelength should be taken cautiously. (For similar reasons, the 700 nm band, which is located in a water vapor band, is not shown.)

Considering that the two instruments are very different–one is a full sky imager and one is a photometer–and are calibrated with completely different standards, the achieved agreement is quite good. Since many calibration standards (including our own) struggle to output sufficient light at short visible wavelengths, we expect systematic errors in the calibration standard radiances to be most pronounced at these wavelengths. Differences seen at 450 nm may be attributable to these errors.

Acknowledgments

References and links

1.

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2.

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3.

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5.

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E. Boesche, P. Stammes, T. Ruhtz, R. Preusker, and J. Fischer, “Effect of aerosol microphysical properties on polarization of skylight: sensitivity study and measurements,” Appl. Opt. 45(34), 8790–8805 (2006). [CrossRef] [PubMed]

9.

N. J. Pust and J. A. Shaw, “Dual-field imaging polarimeter using liquid crystal variable retarders,” Appl. Opt. 45(22), 5470–5478 (2006). [CrossRef] [PubMed]

10.

N. J. Pust and J. A. Shaw, “Digital all-sky polarization imaging of partly cloudy skies,” Appl. Opt. 47(34), H190–H198 (2008). [CrossRef] [PubMed]

11.

N. J. Pust, “Full Sky Imaging Polarimetry for Initial Polarized MODTRAN Validation,” PhD Thesis, Montana State University (2007).

12.

N. J. Pust and J. A. Shaw, “Comparison of Skylight Polarization Measurements and MODTRAN-P Calculations,” J. Appl. Remote Sens. 5(1), 053529 (2011). [CrossRef]

13.

J. Lenoble, M. Herman, J. L. Deuzé, B. Lafrance, R. Santer, and D. Tanré, “A successive order of scattering code for solving the vector equation of transfer in the earth's atmosphere with aerosols,” J. Quant. Spect. Rad. Trans. 107(3), 479–507 (2007). [CrossRef]

14.

O. Dubovik and M. D. King, “A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements,” J. Geophys Lett. 105(D16), 20673–20696 (2000). [CrossRef]

15.

A. Sinyuk, O. Dubovik, B. Holben, T. F. Eck, F. M. Breon, J. Martonchik, R. Kahn, D. J. Diner, E. F. Vermote, J. C. Roger, T. Lapyonok, and I. Slutsker, “Simultaneous retrieval of aerosol and surface properties from a combination of AERONET and satellite data,” Remote Sens. Environ. 107(1-2), 90–108 (2007). [CrossRef]

16.

O. Dubovik, A. Sinyuk, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Munoz, B. Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin, and I. Slutsker, “Application of spheroid models to account for aerosol particle nonsphericity in sensing of desert dust,” J. Geophys. Lett. 111(D11), D11208 (2006). [CrossRef]

17.

Z. Li, P. Goloub, C. Devaux, X. Gu, J. L. Deuze, Y. Qiao, and F. Zhao, “Retrieval of aerosol optical and physical properties from ground-based spectral, multi-angular, and polarized sun-photometer measurements,” Remote Sens. Environ. 101(4), 519–533 (2006). [CrossRef]

18.

M. Van Weele, T. J. Martin, M. Blumthaler, C. Brogniez, P. N. den Outer, O. Engelsen, J. Lenoble, B. Mayer, G. Pfister, A. Ruggaber, B. Walravens, P. Weihs, B. G. Gardiner, D. Gillotay, D. Haferl, A. Kylling, G. Seckmeyer, and W. M. F. Wauben, “From model intercomparison toward benchmark UV spectra for six real atmospheric cases,” J. Geophys. Lett. 105(D4), 4915–4925 (2000). [CrossRef]

19.

“AERONET Inversion Products,” “http://aeronet.gsfc.nasa.gov/new_web/Documents/Inversion_products_V2.pdfhttp://aeronet.gsfc.nasa.gov/new_web/Documents/Inversion_products_V2.pdf.

20.

A. Berk, G. P. Anderson, P. K. Acharya, L. S. Bernstein, L. Muratov, J. Lee, M. Fox, S. M. Adler-Golden, J. H. Chetwynd, M. L. Hoke, R. B. Lockwood, J. A. Gardner, T. W. Cooley, C. C. Borel, P. E. Lewis, and E. P. Shettle, “MODTRAN5: 2006 Update,” in Proceedings of the SPIE6233, 508–515 (2006).

21.

A. R. Dahlberg, N. J. Pust, and J. A. Shaw, “Effects of surface reflectance on skylight polarization measurements,” Opt. Express 19(17), 16008-16021 (2011). [CrossRef] [PubMed]

22.

D. J. Diner, J. V. Martonchik, C. Borel, S. A. W. Gerstl, H. R. Gordon, Y. Knyazikhin, R. Myneni, B. Pinty, and M. M. Verstraete, “MISR. Level 2 Surface Retrieval Algorithm Theoretical Basis,” “http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/MISR/ATB_L2Surface43.pdfhttp://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/MISR/ATB_L2Surface43.pdf.

23.

H. Rahman, B. Pinty, and M. M. Verstraete, “Coupled surface-atmosphere reflectance (CSAR) model. 2: Semiempirical surface model usable with NOAA advanced very high resolution radiometer data,” J. Geophys. Lett. 98(D11), 20791–20801 (1993). [CrossRef]

24.

F. Nadal and F. M. Breon, “Parameterization of surface polarized reflectance derived from POLDER spaceborne measurements,” IEEE Trans. Geosci. Rem. Sens. 37(3), 1709–1718 (1999). [CrossRef]

25.

E. Boesche, P. Stammes, R. Preusker, R. Bennartz, W. Knap, and J. Fischer, “Polarization of skylight in the O(2)A band: effects of aerosol properties,” Appl. Opt. 47(19), 3467–3480 (2008). [CrossRef] [PubMed]

26.

J. Zeng, Q. Han, and J. Wang, “High-spectral resolution simulation of polarization of skylight: Sensitivity to aerosol vertical profile,” Geo. Res. Lett. 35, L20801 (2008). [CrossRef]

27.

O. Dubovik, A. Smirnov, B. N. Holben, M. D. King, Y. J. Kaufman, T. F. Eck, and I. Slutsker, “Accuracy assessments of aerosol optical properties retrieved from AERONET sun and sky-radiance measurements,” J. Geophys. Lett. 105(D8), 9791–9806 (2000). [CrossRef]

OCIS Codes
(010.1110) Atmospheric and oceanic optics : Aerosols
(010.1310) Atmospheric and oceanic optics : Atmospheric scattering
(110.5405) Imaging systems : Polarimetric imaging
(010.5620) Atmospheric and oceanic optics : Radiative transfer

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: June 24, 2011
Revised Manuscript: August 1, 2011
Manuscript Accepted: August 2, 2011
Published: September 8, 2011

Citation
Nathan J. Pust, Andrew R. Dahlberg, Michael J. Thomas, and Joseph A. Shaw, "Comparison of full-sky polarization and radiance observations to radiative transfer simulations which employ AERONET products," Opt. Express 19, 18602-18613 (2011)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-19-19-18602


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References

  1. M. Chin, R. A. Kahn, and S. E. Schwartz, CCSP 2009: Atmospheric Aerosol Properties and Climate Impacts, A Report by the U.S. Climate Change Science (NASA, Washington, D.C., USA 2009).
  2. B. N. Holben, T. F. Eck, I. Slutsker, D. Tanre, J. P. Buis, A. Setzer, E. Vermote, J. A. Reagan, Y. J. Kaufman, T. Nakajima, F. Lavenu, I. Jankowiak, and A. Smirnov, “AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization,” Remote Sens. Environ.66(1), 1–16 (1998). [CrossRef]
  3. D. J. Diner, J. C. Beckert, T. H. Reilly, C. J. Bruegge, J. E. Conel, R. A. Kahn, J. V. Martonchik, T. P. Ackerman, R. Davies, S. A. W. Gerstl, H. R. Gordon, J. P. Muller, R. B. Myneni, P. J. Sellers, B. Pinty, and M. M. Verstraete, “Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview,” IEEE Trans. Geosci. Rem. Sens.36(4), 1072–1087 (1998). [CrossRef]
  4. L. A. Remer, Y. J. Kaufman, D. Tanré, S. Mattoo, D. A. Chu, J. V. Martins, R. R. Li, C. Ichoku, R. C. Levy, R. G. Kleidman, T. F. Eck, E. Vermote, and B. N. Holben, “The MODIS Aerosol Algorithm, Products, and Validation,” J. Atmos. Sci.62(4), 947–973 (2005). [CrossRef]
  5. M. I. Mishchenko, B. Cairns, G. Kopp, C. F. Schueler, B. A. Fafaul, J. E. Hansen, R. J. Hooker, T. Itchkawich, H. B. Maring, and L. D. Travis, “Accurate monitoring of terrestrial aerosols and total solar irradiance: Introducing the Glory mission,” Bul. Amer. Met. Soc.88(5), 677–691 (2007). [CrossRef]
  6. Z. Li, P. Goloub, O. Dubovik, L. Blarel, W. Zhang, T. Podvin, A. Sinyuk, M. Sorokin, H. Chen, B. Holben, D. Tanre, M. Canini, and J.-P. Buis, “Improvements for ground-based remote sensing of atmospheric aerosol properties by additional polarimetric measurements,” J. Quan. Spect. Rad. Trans.110(17), 1954–1961 (2009). [CrossRef]
  7. J. L. Deuzé, F. M. Bréon, C. Devaux, P. Goloub, M. Herman, B. Lafrance, F. Maignan, A. Marchand, F. Nadal, G. Perry, and D. Tanré, “Remote sensing of aerosols over land surfaces from POLDER-ADEOS-1 polarized measurements,” J. Geophys. Lett.106(D5), 4913–4926 (2001). [CrossRef]
  8. E. Boesche, P. Stammes, T. Ruhtz, R. Preusker, and J. Fischer, “Effect of aerosol microphysical properties on polarization of skylight: sensitivity study and measurements,” Appl. Opt.45(34), 8790–8805 (2006). [CrossRef] [PubMed]
  9. N. J. Pust and J. A. Shaw, “Dual-field imaging polarimeter using liquid crystal variable retarders,” Appl. Opt.45(22), 5470–5478 (2006). [CrossRef] [PubMed]
  10. N. J. Pust and J. A. Shaw, “Digital all-sky polarization imaging of partly cloudy skies,” Appl. Opt.47(34), H190–H198 (2008). [CrossRef] [PubMed]
  11. N. J. Pust, “Full Sky Imaging Polarimetry for Initial Polarized MODTRAN Validation,” PhD Thesis, Montana State University (2007).
  12. N. J. Pust and J. A. Shaw, “Comparison of Skylight Polarization Measurements and MODTRAN-P Calculations,” J. Appl. Remote Sens.5(1), 053529 (2011). [CrossRef]
  13. J. Lenoble, M. Herman, J. L. Deuzé, B. Lafrance, R. Santer, and D. Tanré, “A successive order of scattering code for solving the vector equation of transfer in the earth's atmosphere with aerosols,” J. Quant. Spect. Rad. Trans.107(3), 479–507 (2007). [CrossRef]
  14. O. Dubovik and M. D. King, “A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements,” J. Geophys Lett.105(D16), 20673–20696 (2000). [CrossRef]
  15. A. Sinyuk, O. Dubovik, B. Holben, T. F. Eck, F. M. Breon, J. Martonchik, R. Kahn, D. J. Diner, E. F. Vermote, J. C. Roger, T. Lapyonok, and I. Slutsker, “Simultaneous retrieval of aerosol and surface properties from a combination of AERONET and satellite data,” Remote Sens. Environ.107(1-2), 90–108 (2007). [CrossRef]
  16. O. Dubovik, A. Sinyuk, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F. Eck, H. Volten, O. Munoz, B. Veihelmann, W. J. van der Zande, J. F. Leon, M. Sorokin, and I. Slutsker, “Application of spheroid models to account for aerosol particle nonsphericity in sensing of desert dust,” J. Geophys. Lett.111(D11), D11208 (2006). [CrossRef]
  17. Z. Li, P. Goloub, C. Devaux, X. Gu, J. L. Deuze, Y. Qiao, and F. Zhao, “Retrieval of aerosol optical and physical properties from ground-based spectral, multi-angular, and polarized sun-photometer measurements,” Remote Sens. Environ.101(4), 519–533 (2006). [CrossRef]
  18. M. Van Weele, T. J. Martin, M. Blumthaler, C. Brogniez, P. N. den Outer, O. Engelsen, J. Lenoble, B. Mayer, G. Pfister, A. Ruggaber, B. Walravens, P. Weihs, B. G. Gardiner, D. Gillotay, D. Haferl, A. Kylling, G. Seckmeyer, and W. M. F. Wauben, “From model intercomparison toward benchmark UV spectra for six real atmospheric cases,” J. Geophys. Lett.105(D4), 4915–4925 (2000). [CrossRef]
  19. “AERONET Inversion Products,” “ http://aeronet.gsfc.nasa.gov/new_web/Documents/Inversion_products_V2.pdf ” http://aeronet.gsfc.nasa.gov/new_web/Documents/Inversion_products_V2.pdf .
  20. A. Berk, G. P. Anderson, P. K. Acharya, L. S. Bernstein, L. Muratov, J. Lee, M. Fox, S. M. Adler-Golden, J. H. Chetwynd, M. L. Hoke, R. B. Lockwood, J. A. Gardner, T. W. Cooley, C. C. Borel, P. E. Lewis, and E. P. Shettle, “MODTRAN5: 2006 Update,” in Proceedings of the SPIE6233, 508–515 (2006).
  21. A. R. Dahlberg, N. J. Pust, and J. A. Shaw, “Effects of surface reflectance on skylight polarization measurements,” Opt. Express19(17), 16008-16021 (2011). [CrossRef] [PubMed]
  22. D. J. Diner, J. V. Martonchik, C. Borel, S. A. W. Gerstl, H. R. Gordon, Y. Knyazikhin, R. Myneni, B. Pinty, and M. M. Verstraete, “MISR. Level 2 Surface Retrieval Algorithm Theoretical Basis,” “ http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/MISR/ATB_L2Surface43.pdf ” http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/MISR/ATB_L2Surface43.pdf .
  23. H. Rahman, B. Pinty, and M. M. Verstraete, “Coupled surface-atmosphere reflectance (CSAR) model. 2: Semiempirical surface model usable with NOAA advanced very high resolution radiometer data,” J. Geophys. Lett.98(D11), 20791–20801 (1993). [CrossRef]
  24. F. Nadal and F. M. Breon, “Parameterization of surface polarized reflectance derived from POLDER spaceborne measurements,” IEEE Trans. Geosci. Rem. Sens.37(3), 1709–1718 (1999). [CrossRef]
  25. E. Boesche, P. Stammes, R. Preusker, R. Bennartz, W. Knap, and J. Fischer, “Polarization of skylight in the O(2)A band: effects of aerosol properties,” Appl. Opt.47(19), 3467–3480 (2008). [CrossRef] [PubMed]
  26. J. Zeng, Q. Han, and J. Wang, “High-spectral resolution simulation of polarization of skylight: Sensitivity to aerosol vertical profile,” Geo. Res. Lett.35, L20801 (2008). [CrossRef]
  27. O. Dubovik, A. Smirnov, B. N. Holben, M. D. King, Y. J. Kaufman, T. F. Eck, and I. Slutsker, “Accuracy assessments of aerosol optical properties retrieved from AERONET sun and sky-radiance measurements,” J. Geophys. Lett.105(D8), 9791–9806 (2000). [CrossRef]

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