Cloud Top Pressure

Cloud Top Pressure

Draft 30 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 top pressure (CTP) and cloud top height (CTH) for cloudy pixels. These indicate the vertical location in the atmosphere of the top of the cloud. CTP is provided in hPa (equivalent to mbar), and CTH in km. CTP is retrieved and CTH is derived from it using an ancillary pressure profile.

Many heritage approaches use thermal infrared bands to retrieve cloud altitude (e.g. Baum et al., 2012; Poulsen et al., 2012), although such channels will not be available on the PACE OCI. Because of this, a new research algorithm, using oxygen (O2) A-band measurements, has been developed. A-band CTP approaches have been applied to several instruments, e.g. GOME (Rozanov and Kokhanovsky, 2004), TropOMI (Loyola et al., 2018), MERIS (Fischer et al., 1997), and EPIC (Yang et al., 2019). Their capabilities are determined chiefly by the spectral and spatial resolution of the data (Heidinger and Stephens, 2000). The research algorithm has been implemented on Sentinel-3 OLCI as a proxy, as this has the most similar spectral and spatial characteristics to OCI. At present there is no broadly available OLCI CTP product that could be adapted or compared.

2 - Algorithm Description

Across the visible spectrum, clouds are bright features with fairly spectrally flat reflectance characteristics. Oxygen is well-mixed in the atmosphere (i.e. the column amount is proportional to total atmospheric pressure) and has strong absorption features at several locations, including the A-band centered near 760 nm. These absorption features cause a darkening of the signal seen by a satellite sensor at the top of atmosphere (TOA). The darkening is dependent primarily upon the altitude and brightness (determined by particle size, phase, and water content) of a cloud; a higher, brighter cloud will block more of the below-cloud absorption, resulting in a brighter and spectrally-flatter TOA signal (Heidinger and Stephens, 2000). Other factors such as surface albedo, surface pressure, cloud vertical structure, and the presence of aerosol features, can also be important.

The research algorithm uses four bands from the OLCI sensor, centered just outside (753.75 nm) and at various locations inside (761.25, 764.375, and 767.4 nm) the O2 A-band with differential absorption strengths. OCI will have bands with similar positions and widths. These are used to simultaneously retrieve the cloud optical depth (COD), CTP, and surface albedo using an Optimal Estimation approach. Surface albedo is subject to a strong a priori constraint from an ancillary data base. Other properties, such as cloud effective radius, vertical structure, and aerosol profiles, are held fixed.

The present application uses the operational cloud mask (IdePix) from the Sentinel Application Platform (SNAP, https://step.esa.int/main/toolboxes/snap/) and runs retrievals assuming each of liquid and ice phase clouds. At present phase is determined by examination of the retrieval cost function. In eventual OCI application, it is expected that OCI-based cloud mask and phase products will be used as inputs. In addition, processing requires MERRA2 surface pressure and pressure profile products. The former is required as this determines the total O2 absorption, and the latter to convert the retrieved CTP to CTH. Note that by convention CTH is reported relative to mean sea level, rather than to surface altitude.

3 - Implementation

The research algorithm is being refined, and will be implemented into SDS for OCI proxy processing at a later date. More information will be provided at that time.

4 - Assessment

Several techniques exist to provide robust observations of CTH, while validation-grade CTP measurements are less common. It is anticipated that most assessment will be against CTH data, although CTP products will be compared against other collocated satellite retrievals (from e.g. VIIRS or GOES).

Spaceborne and ground-based lidar and radar systems (Winker et al., 2010; Marchand et al., 2016) are precise and effective tools to measure CTH. These provide the height of the very top of the cloud, while absorption-based techniques such as the A-band are sensitive to the optically-weighted effective altitude so may be in error if the assumed cloud vertical structure is incorrect (Heidinger and Stephens, 2000). While it is possible that CALIOP will no longer be active during the PACE mission, IceSat-2 and EarthCare may be suitable spaceborne alternatives, dependent on the extent of orbital colocation with PACE. Ground-based systems within the ARM and CloudNet networks provide relevant data products and are expected to remain available.

Geometric techniques based on parallax have also been applied to retrieve CTH operationally from multiangle data (e.g. Muller et al., 2002). These could conceptually be applied to the measurements made by the HARP2 and SPEXOne sensors aboard PACE OCI, although given the coarser spatial resolution of these sensors compared to previous parallax retrievals it is unclear what the uncertainty of such an application would be.

The polarization generated in the blue/UV by molecular scattering above cloud top is a direct measure of the cloud top pressure. This has been used to determine CTP both from satellite observations made by the POLDER instruments (Buriez et al.1997) and also from airborne observations (Van Diedenhoven et al. 2013). The SPEXone and HARP2 sensors on PACE both have spectral bands in the blue/UV that allow for this type of retrieval to be performed and there are plans for such products to be provided.

5 - References

Baum, B.A., W. P. Menzel, R. A. Frey, D. C. Tobin, R. E. Holz, S. A. Ackerman, A. K. Heidinger, and P. Yang (2012), MODIS Cloud-Top Property Refinements for Collection 6. J. Appl. Meteor. Climatol., 51, 1145–1163, doi: 10.1175/JAMC-D-11-0203.1

Buriez, J., C. Vanbauce, and F. Parol (1997), Cloud detection and derivation of cloud properties from POLDER, Int. J. Remote Sens., 18, 2785–2813, doi: 10.1080/014311697217332

Fischer, J., R. Preusker, and L. Schüller (1997) ATBD cloud top pressure. European Space Agency Algorithm Theoretical Basis Doc. PO-TN-MEL-GS-0006, 28 pp.

Heidinger, A.K. and G.L. Stephens (2000), Molecular Line Absorption in a Scattering Atmosphere. Part II: Application to Remote Sensing in the O2 A band. J. Atmos. Sci., 57, 1615–1634, doi: 10.1175/1520-0469(2000)057<1615:MLAIAS>2.0.CO;2

Loyola, D. G., S. Gimeno García, R. Lutz, A. Argyrouli, F. Romahn, R. J. D. Spurr, M. Pedergnana, A. Doicu, V. Molina García, and O. Schüssler (2018), The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor, Atmos. Meas. Tech., 11, 409–427, doi: 10.5194/amt-11-409-201

Marchand, R., (2016), ARM and Satellite Cloud Validation. Meteorological Monographs, 57, 30.1–30.11, doi: 10.1175/AMSMONOGRAPHS-D-15-0038.1

Muller, J.‐P., A. Mandanayake, C. Moroney, R. Davies, D. J. Diner, and S. Paradise (2002), Operational retrieval of cloud‐top heights using MISR data, IEEE Trans. Geosci. Remote Sens., 40, 1547–1559, doi: 10.1109/TGRS.2002.801160

Poulsen, C. A., R. Siddans, G. E. Thomas, A. M. Sayer, R. G. Grainger, E. Campmany, S. M. Dean, C. Arnold, and P. D. Watts (2012), Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR, Atmos. Meas. Tech., 5, 1889–1910, doi: 10.5194/amt-5-1889-2012

Rozanov, V. V. and A. A. Kokhanovsky (2004), Semianalytical cloud retrieval algorithm as applied to the cloud top altitude and the cloud geometrical thickness determination from top‐of‐atmosphere reflectance measurements in the oxygen A band, J. Geophys. Res., 109, D05202, doi: 10.1029/2003JD004104

van Diedenhoven, B., B. Cairns, A. M. Fridlind, A. S. Ackerman, and T.J. Garrett (2013), Remote sensing of ice crystal asymmetry parameter using multi-directional polarization measurements — Part 2: Application to the Research Scanning Polarimeter, Atmos. Chem. Phys., 13, 3185-3203, doi: 10.5194/acp-13-3185-2013

Winker, D.M., J. Pelon, J.A. Coakley, S.A. Ackerman, R.J. Charlson, P.R. Colarco, P. Flamant, Q. Fu, R.M. Hoff, C. Kittaka, T.L. Kubar, H. Le Treut, M.P. Mccormick, G. Mégie, L. Poole, K. Powell, C. Trepte, M.A. Vaughan, and B.A. Wielicki (2010), The CALIPSO Mission. Bull. Amer. Meteor. Soc., 91, 1211–1230, doi: 10.1175/2010BAMS3009.1

Yang, Y., K. Meyer, G. Wind, Y. Zhou, A. Marshak, S. Platnick, Q. Min, A. B. Davis, J. Joiner, A. Vasilkov, D. Duda, and W. Su (2019), Cloud products from the Earth Polychromatic Imaging Camera (EPIC): algorithms and initial evaluation, Atmos. Meas. Tech., 12, 2019–2031, doi: 10.5194/amt-12-2019-2019"

6 - Data Access

Sample data products will be available on request when the research algorithm has been integrated into SDS.