Cloud Optical Properties

Cloud Optical Properties

Draft 27 Apr 2020, Andrew Sayer

Table of Contents

  1. Product Summary
  2. Algorithm Description
  3. Implementation
  4. Assessment
  5. References
  6. Data Access

1 - Product Summary

This product provides the cloud optical depth (COD) and cloud effective radius (CER). COD is a dimensionless optical measure related to the total extinction of light by cloud droplets at visible wavelengths. CER (in microns) is a description of cloud particle size – specifically, the ratio of the third to the second moment of the droplet size distribution.

From these (and knowledge of cloud thermodynamic phase, i.e. whether the cloud is composed of liquid water or ice crystals) the cloud water path (CWP) – known as liquid water path (LWP) or ice water path (IWP) dependent on phase – is also provided. CWP (in gm-2) is the total mass of cloud water in the atmospheric column. These quantities are colloquially referred to as the "cloud optical properties" data products, although only COD is an optical metric. Further, COD is synonymous with cloud optical thickness (COT)

The algorithm is an implementation of the optical properties component (Platnick et al., 2003, 2017) of the CHIMAERA retrieval code (Wind et al., 2020), which has been applied widely to retrieve cloud optical properties from multispectral satellite measurements. The current implementation is to MODIS measurements as a proxy for the PACE OCI.

2 - Algorithm Description

Satellite instruments do not measure cloud optical properties directly, but rather reflected or emitted radiation at the top of atmosphere (TOA) at various wavelengths. While clouds appear bright and white at visible wavelengths, in the shortwave infrared (swIR) they show wavelength-dependent absorption as a function of particle size. Therefore, a combination of a "nonabsorbing" channel in the visible and an "absorbing" channel in the swIR is an effective and reasonably orthogonal way to retrieve COD and CER from satellite measurements (Nakajima and King, 1990). As COD increases the nonabsorbing channel gets brighter due to increasing reflection, while as CER increases the absorbing channel gets darker due to increasing absorption.

In practical terms, the choice of absorbing and nonabsorbing bands is determined by the surface type, as the magnitude of the response depends on the underlying surface reflectance (Wind et al., 2020). Additionally, the retrieval is performed and reported for multiple choices of swIR absorbing band, as offsets between the results for different swIR bands are related to cloud vertical structure (Platnick. 2000).

Retrievals are performed assuming each of liquid and ice cloud phases. Often, because liquid droplets and ice crystals can exhibit quite different size ranges, only one of the models will provide an acceptable fit to the TOA observations. A best estimate of retrieval phase is provided combining information on these fits with tests from the PACE cloud phase data product (currently as in Marchant et al., 2016, without the tests based on thermal bands).

While for opaque clouds the sensitivity to surface reflectance is small, an ancillary estimate of the surface reflectance is needed, to determine COD/CER accurately in cases of optically-thin clouds such as cirrus. Similarly, above-cloud trace gas absorption is corrected for using reanalysis data and ancillary cloud top height information (the output of a separate retrieval), and the PACE cloud mask is needed to determine which pixels to process.

Finally, LWP and IWP are proportional to the product of COD and CER. The constant of proportionality is dependent on the cloud vertical structure; the implementation assumes vertical homogeneity (Platnick et al., 2003). For IWP, the assumed ice crystal habit (shape) is also a factor.

3 - Implementation


4 - Assessment

Direct validation of cloud optical properties is difficult, because of rapid variation in their optical properties in space and time. A variety of approaches have been used to assess these optical properties, and due to these difficulties many are an intercomparison rather than a true validation. Some examples are given below; other data sets exist or may become available, and will be assessed for their suitability as appropriate.

Typically, COD, CER, and L/IWP are assessed by examining consistency against other satellite data sets (e.g. Sayer et al., 2011; King et al., 2013). Several suitable sensors (e.g., VIIRS, ATMS and ICI on polar orbiting platforms and the new generation of imagers such as ABI on geostationary platforms) are expected to be operational in the PACE era and will be used as comparative data sources. Such satellite-to-satellite comparisons are the most readily-available and large-scale option, despite the fact that it does not provide a true validation.

Multiangle polarimetry is able to provide highly accurate retrievals of cloud droplet size distribution parameters including CER (Alexandrov et al., 2018) with a weighting strongly to cloud top (and other differences; Miller et al., 2018), and it is expected that such retrievals will be available from the PACE’s HARP2 instrument. Otherwise, CER is typically evaluated against airborne in situ measurements of size distribution from various instrument types (e.g., Painemal and Zuidema, 2011, Witte et al., 2018). This will be done if overflights of relevant field campaigns, such as a dedicated PACE validation campaign, are available. Note that in situ cloud size distribution measurements can be used to estimate COD as well. COD can also be compared against ground-based estimates from e.g. Sun photometry, although these have some uncertainties and are not typically considered validation grade (Chiu et al. 2010).

LWP will be evaluated against ground-based and airborne microwave radiometer retrievals, which are commonly used for this purpose (e.g. Seevala and Horváth, 2010; Sporre et al., 2016). IWP comparisons can be made with ground-based (Mace et al., 2005; Tian et al., 2018) radar, airborne microwave/sub-mm observations (Evans et al. 2005) and spaceborne radar (Deng et al., 2010).

5 - References

Alexandrov, M. D. et al (2018), Retrievals of cloud droplet size from the research scanning polarimeter data: Validation using in situ measurements, Remote Sens. Environ., 210, 76-95, doi: 10.1016/j.rse.2018.03.005

Chiu, J. C., C.-H. Huang, A. Marshak, I. Slutsker, D. M. Giles, B. N. Holben, Y. Knyazikhin, and W. J. Wiscombe (2010), Cloud optical depth retrievals from the Aerosol Robotic Network (AERONET) cloud mode observations, J. Geophys. Res., 115, D14202, doi: 10.1029/2009JD013121

Deng, M., G. G. Mace, Z. Wang, and H. Okamoto (2010), Tropical Composition, Cloud and Climate Coupling Experiment validation for cirrus cloud profiling retrieval using CloudSat radar and CALIPSO lidar, J. Geophys. Res., 115, D00J15, doi: 10.1029/2009JD013104

Evans, K.F., J. R. Wang, P. E. Racette, G. Heymsfield, and L. Li (2005), Ice cloud retrievals and analysis with the compact scanning submillimeter imaging radiometer and the cloud radar system during CRYSTAL FACE. J. Appl. Meteor., 44, 839–859, doi: 10.1175/JAM2250.1

King, M. D., S. Platnick. W. P. Menzel, S. A. Ackerman, and P. A. Hubanks (2013), Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites, IEEE Trans. Geosci. Remote Sens., 51 (7), 3826-3852, doi: 10.1109/TGRS.2012.2227333

Mace, G.G., Y. Zhang, S. Platnick, M.D. King, P. Minnis, and P. Yang (2005), Evaluation of Cirrus Cloud Properties Derived from MODIS Data Using Cloud Properties Derived from Ground-Based Observations Collected at the ARM SGP Site. J. Appl. Meteor., 44, 221–240, doi: 10.1175/JAM2193.1

Marchant, B., S. Platnick, K. Meyer, G. T. Arnold, and J. Riedi (2016), MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, doi: 10.5194/amt-9-1587-2016

Miller, D. J., Z. Zhang, S. Platnick, A. S. Ackerman, F. Werner, C. Cornet, and K. Knobelspiesse (2018), Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES--satellite retrieval simulator, Atmos. Meas. Tech, 11, 6, 3689-3715, doi: 10.5194/amt-11-3689-2018

Nakajima, T. and M.D. King (1990), Determination of the Optical Thickness and Effective Particle Radius of Clouds from Reflected Solar Radiation Measurements. Part I: Theory. J. Atmos. Sci., 47, 1878–1893, doi: 10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2

Painemal, D., and P. Zuidema, (2011), Assessment of MODIS cloud effective radius and optical thickness retrievals over the Southeast Pacific with VOCALS‐REx in situ measurements, J. Geophys. Res., 116, D24206, doi: 10.1029/2011JD016155

Platnick, S. (2000), Vertical photon transport in cloud remote sensing problems, J. Geophys. Res., 105 (D18), 22919–22935, doi: 10.1029/2000JD900333

Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey (2003), The MODIS cloud products: algorithms and examples from Terra IEEE Trans. Geosci. Remote Sens., 41 (2) 459-473, doi: 10.1109/TGRS.2002.808301

Platnick, S., K. G. Meyer, M. D. King, G. Wind, N. Amarasinghe, B. Marchant, G. T. Arnold, Z. B. Zhang, P. A. Hubanks, R. E. Holz, P. Yang, W. L. Ridgway, and J. Riedi (2017), The MODIS cloud optical and microphysical products: collection 6 updates and examples from Terra and Aqua, IEEE Trans. Geosci. Remote Sens., 55 (1), 502-525, doi: 10.1109/TGRS.2016.2610522

Sayer, A. M., C. A. Poulsen, C. Arnold, E. Campmany, S. Dean, G. B. L. Ewen, R. G. Grainger, B. N. Lawrence, R. Siddans, G. E. Thomas, and P. D. Watts (2011), Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment, Atmos. Chem. Phys., 11, 3913–3936, doi: 10.5194/acp-11-3913-2011

Seethala, C., and Horváth, Á. (2010), Global assessment of AMSR‐E and MODIS cloud liquid water path retrievals in warm oceanic clouds, J. Geophys. Res., 115, D13202, doi: 10.1029/2009JD012662

Sporre, M. K., E. J. O'Connor, N. Håkansson, A. Thoss, E. Swietlicki, and T. Petäjä (2016), Comparison of MODIS and VIIRS cloud properties with ARM ground-based observations over Finland, Atmos. Meas. Tech., 9, 3193–3203, doi: 10.5194/amt-9-3193-2016

Tian, J., et al. (2018). Comparisons of ice water path in deep convective systems among ground‐based, GOES, and CERES‐MODIS retrievals. J. Geophys. Res. Atmos., 123, 1708–1723, doi: 10.1002/2017JD027498

Witte, M. K., T. Yuan, P. Y. Chuang, S. Platnick, K. G. Meyer, G. Wind, and H. H. Jonsson (2018), MODIS retrievals of cloud effective radius in marine stratocumulus exhibit no significant bias, Geophys. Res. Lett., 45, 10,656–10,664, doi:

Wind, G., S. Platnick, K. Meyer, T. Arnold, N. Amarasinghe, B. Marchant, and C. Wang (2020), The CHIMAERA system for retrievals of cloud top, optical and microphysical properties from imaging sensors, Computers & Geosciences, 134, 104345, doi: 10.1016/j.cageo.2019.104345

6 - Data Access

Sample data products are available on request.