Cloud Phase

Cloud Phase

Draft 28 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 thermodynamic phase of a cloudy pixel: that is, whether the clouds are composed of liquid water, or of ice crystals. While some clouds may be mixed phase, this product reports the optically-effective phase, which is generally that of the uppermost portion of the cloud.

Because liquid droplets and ice crystals have very different scattering and absorption properties, this quantity is an input to several other downstream algorithms (e.g. cloud optical properties, cloud top pressure).

The development and implementation of this data product is still in progress, and this page will be amended as the approach is finalized.

2 - Algorithm Description

The consensus report of the first PACE Science Team (Platnick et al., 2018) discussed pathways toward an OCI cloud phase retrieval algorithm. This contained results of sensitivity analyses of OCI information content to cloud properties, as an extension of previous work by Coddington et al (2012), with similar approaches also being applied to ground based observational data (Jäkel et al. 2013).

The anticipated algorithm will use OCI near-infrared (nIR) and shortwave infrared (swIR) bands from 865 to 2260 nm channels. These show characteristic spectral features based on particle size and phase. In particular, the combination of bands at 1615, 2135 and 2260 nm significantly increases the skill at discriminating phase, due to differences in the magnitudes and slopes of spectral absorption of liquid water vs. ice across this spectral range. Specifically, relative to a liquid droplet, an ice particle of a given size absorbs 200% more at 1615 nm, 30% more at 2135 nm, and 50% less at 2260 nm.

It is still to be determined whether this will be processed as a stand-alone algorithm, or the appropriate spectral channels will be used to augment different cloud property retrieval algorithm(s) to determine the appropriate cloud phase.

3 - Implementation


4 - Assessment

This product will be evaluated against suitable ground-based and spaceborne data sets, such as lidar with depolarization capabilities and/or other sensors in combination (e.g. Riedi et al. 2001; Hu et al., 2009; Marchant et al., 2016).

It is expected that PACE’s multiangle polarimeters will be able to provide cloud phase information (van Diedenhoven et al., 2012), which will also be used as a comparison data source. Thermal phase retrievals from polar-orbiting and geostationary sensors (e.g. VIIRS, GOES) will also be used.

5 - References

Coddington, O., P. Pilewskie, and T. Vukicevic (2012), The Shannon information content of hyperspectral shortwave cloud albedo measurements: Quantification and practical applications, J. Geophys. Res., 117, D04205, doi: 10.1029/2011JD016771

Jäkel, E., J. Walter, and M. Wendisch (2013), Thermodynamic phase retrieval of convective clouds: impact of sensor viewing geometry and vertical distribution of cloud properties, Atmos. Meas. Tech., 6, 539–547, doi: 10.5194/amt-6-539-2013

Hu, Y., D. Winker, M. Vaughan, B. Lin, A. Omar, C. Trepte, D. Flittner, P. Yang, S.L. Nasiri, B. Baum, R. Holz, W. Sun, Z. Liu, Z. Wang, S. Young, K. Stamnes, J. Huang, and R. Kuehn (2009), CALIPSO/CALIOP Cloud Phase Discrimination Algorithm. J. Atmos. Oceanic Technol., 26, 2293–2309, doi: 10.1175/2009JTECHA1280.1

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

Platnick, S., O. Coddington, S. A. Ackerman, R. Frey, A. Heidinger, A. Walter, K. G. Meyer, Z. Zhang, and B. van Diedenhoven (2018), Cloud Retrievals in the PACE Mission: PACE Science Team Consensus Document, PACE Technical Report Series, NASA/TM–2018-219027/ Vol. 4, https://pace.oceansciences.org/docs/TM2018219027Vol.4.pdf">TM2018219027Vol.4.pdf

Riedi, J., P. Goloub, and R. T. Marchand (2001), Comparison of POLDER cloud phase retrievals to active remote sensors measurements at the ARM SGP site, Geophys. Res. Lett, 28 (11), 2185-2188, doi: 10.1029/2000GL012758

van Diedenhoven, B., A. M. Fridlind, A. S. Ackerman, and B. Cairns (2012), Evaluation of Hydrometeor Phase and Ice Properties in Cloud-Resolving Model Simulations of Tropical Deep Convection Using Radiance and Polarization Measurements. J. Atmos. Sci., 69, 3290–3314, doi: 10.1175/JAS-D-11-0314.1

6 - Data Access

Sample data products will be available on request when a proxy algorithm has been implemented.