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Detailed Data Quality Summary for the CALIPSO Lidar Level 2 PSC Mask Version 2.00 Data Product

Created: March 04, 2022 - Updated: March 09, 2022



Data Version: 2.00
Data Release Date: March 04, 2022
Data Date Range: June 13, 2006 to March 21, 2021

 

PSC Detection

As described in Pitts et al. (2018), PSCs are detected using a successive horizontal averaging (5, 15, 45, 135 km) procedure similar to the approach used in the standard CALIOP feature detection algorithm (SIBYL). This approach ensures that optically thicker clouds (e.g., ice and fully developed supercooled ternary solution, STS) are found at the finest possible spatial resolution while also enhancing the detection of tenuous PSCs (e.g., low number density NAT mixtures) that are found only through additional averaging. Prior to PSC detection, CALIOP data between 8.3 and 30.1 km are averaged to a common 5-km horizontal by 180-m vertical grid to account for the change in spatial resolution of the Level 1B data products at the 20.2 km altitude. PSCs are then identified as statistical outliers in either 532-nm scattering ratio, R′532, or 532-nm perpendicular attenuated backscatter, Β′, relative to the background stratospheric aerosol population. The inclusion of Β′ is a significant enhancement over the detection approach used in SIBYL that increases the sensitivity to depolarizing, low scattering ratio PSCs and results in an increase in total PSC area of 15% (Pitts et al., 2009). Individual profiles of R′532 and Β′ are scanned from the top altitude (~ 30.1 km) downward, and a CALIOP observation is assumed to be a PSC if either R′532 or Β′ exceeds a statistical threshold. The thresholds for the background aerosol - assumed to be those data at MERRA-2 temperatures above 200 K - are defined as the daily median plus one median absolute deviation of Β′ and R′532. These are computed in overlapping 100 K-thick potential temperature (θ) layers over the range from θ = 250-750 K. Then for a candidate CALIOP data point to be identified as a PSC, its value of Β′ (or R′532) must exceed the background aerosol threshold by at least its uncertainty, u(Β′) (or u(R′532)). We also impose a spatial coherence test that requires that more than 11 of the points in a 5-point horizontal by 3-point vertical box centered on the candidate feature exceed the current PSC detection threshold or to have been identified as a PSC at a previous (finer) averaging scale. This revised approach does a better job overall of capturing PSC clusters identified by the naked eye in CALIOP orbital images while continuing to eliminate false PSC identifications stemming from positive noise spikes in the data. Spot checks of the V2 Antarctic PSC database from early May - when no PSCs observations are expected - indicate that the V2 false positive rate is much less than 0.01%.

Note, the tropopause is often difficult to locate in the polar regions and we make no explicit attempt to distinguish tropospheric cloud from stratospheric cloud in the 8.5-30 km altitude range over which we produce the CALIOP PSC cloud mask. However, we do tag each observation in the database with a feature flag that identifies its altitude location relative to the reported MERRA-2 tropopause as one of three possibilities: (1) below the tropopause, (2) between the tropopause and tropopause + 4 km, or (3) above the tropopause + 4 km. This allows data users to perform some crude separation between tropospheric and stratospheric cloud as desired.

PSCs form in the cold, polar winter stratosphere when the temperatures fall below ~195 K. Over the Antarctic, the PSC season extends from May through October. Over the Arctic, the PSC season is more variable, but may extend from December through March. The PSC Mask V2 data files are therefore only produced for the months May-October (Antarctic) and December-March (Arctic).



PSC Composition Classification

CALIOP PSC composition is inferred from the knowledge that enhancements in Β are produced by non-spherical PSC particles (NAT or ice), whereas spherical STS PSC droplets produce enhancements in R532 but not in Β. Based on a comparison of Β and R532 data with temperature-dependent theoretical optical calculations for non-equilibrium mixtures of liquid droplets and prolate NAT or ice spheroids with an aspect (diameter-to-length) ratio of 0.9, CALIOP PSC observations are separated into three major composition classes: STS, liquid-NAT mixtures (“NAT mixtures”), and liquid-ice mixtures (“ice”). There are two additional subclasses: “enhanced NAT mixtures,” which capture higher NAT number density; and “wave ice”, which are high number density ice PSCs likely triggered by mountain waves. As an example, Fig. 1a shows the distribution of CALIOP Antarctic PSC measurements of 10-18 July 2008 in the Β vs. R532 coordinate system, with PSC composition class/sub-class boundaries. Figure 1b shows optical calculations presented in the same reference frame assuming 50 hPa atmospheric pressure, 5 ppmv H2O, and 3 ppbv HNO3, with instrument noise, which is predominantly shot noise. The V2 algorithm includes improvements to correct a number of known deficiencies in the previous V1 composition classification scheme. The V2 algorithm also incorporates a retrieval of 532-nm particulate backscatter, Βparticulate, through which Β′ and R′532 are later corrected for attenuation due to overlying particulate layers (i.e. the “primes” are removed), allowing for a more robust comparison with the theoretical results. These improvements are discussed below in context of Fig. 1b, where the theoretical optical results are plotted in the coordinate system Β vs. R532, surrogates for the measured attenuated CALIOP quantities Β′ and R′532 used for PSC detection. The following points are to be noted in our revised V2 algorithm:

  • The former V1 Mix1 and Mix2 classes of liquid-NAT mixtures have been combined into a single class named “NAT mixtures” for brevity.
     
  • The former V1 Mix2-enhanced class has been renamed “enhanced NAT mixtures” and it is now defined as the sub-class of NAT mixtures with R532 > 2 and Β > 2×10-5 km-1sr-1. This conservative boundary was determined empirically by comparing CALIOP Antarctic PSC data to contemporaneous MIPAS observations with and without a belt of NAT clouds formed by heterogeneous nucleation on wave ice PSCs over the Antarctic Peninsula (Höpfner et al., 2006). MIPAS data from 2008 May 27/28/30 (M. Höpfner, Karlsruhe Institute of Technology, private communication) showed no evidence of these NAT clouds, and only about 2% of CALIOP NAT mixture data from those days had R532 > 2 and Β > 2×10-5 km-1sr-1. In contrast, NAT belt clouds were clearly evident in MIPAS data on 2008 May 29 and 2008 June 01/02, and their locations were matched extremely well by CALIOP NAT mixtures with R532 > 2 and Β > 2×10-5 km-1sr-1. In theoretical terms, CALIOP enhanced NAT mixture points correspond roughly to those NAT mixtures with particle radius rNAT < 3 µm and NAT volume density (VD) > 1.0 µm3cm-3, which match the MIPAS NAT detection limits (rNAT < 3 µm and NAT VD > 0.3 µm3cm-3) reasonably well. Since our criteria defining enhanced NAT mixtures are conservative, the enhanced NAT mixtures sub-class is not all-inclusive, i.e., it does not capture all NAT mixture PSCs heterogeneously nucleated in wave ice PSCs.
     
  • The wave ice class remains the same as in V1, i.e. ice PSCs with R532 > 50. We reemphasize that this definition of wave ice is not all-inclusive, i.e. some additional ice PSCs are likely associated with mountain waves, but do not meet our stringent wave-ice classification criterion.
     
  • The dashed horizontal line labeled Β⊥,thresh represents qualitatively the CALIOP statistical threshold for detection of PSCs containing non-spherical particles. In practice, this threshold changes with horizontal averaging scale and differs from point to point due to its dependency on u(Β). Each data point is assigned a non-spherical particle confidence index CINS = [Β-u(Β)]/u(Β). Points with CINS > 1 are presumed to be PSCs containing non-spherical particles.
     
  • The dashed magenta vertical line labeled Rthresh represents qualitatively the CALIOP statistical threshold for detection of liquid PSCs. In practice, Rthresh also changes with horizontal averaging scale and differs from point to point due to its dependency on u(R532). Data points classified as STS are those with CINS ≤ 1, but with R532 > Rthresh. Each is assigned an STS confidence index CISTS = [R532 - u(R532)])/u(R532); CISTS > 1.
     
  • Note that in practice, there is not a distinct separation between histograms of Β for V2 STS and NAT mixtures. We estimate that 10-15% of data points in either class may fall in the overlap region and thus could be misclassified.
     
  • Points in the grey box at the lower left fall below both CALIOP PSC detection thresholds and are classified as non-features. It should be noted that all measured and derived quantities for non-features are also retained in the V2 data product. A comprehensive discussion of so-called “sub-visible” PSCs can be found in the paper by Lambert et al. (2016), who show that they often can be detected through gas-phase uptake of HNO3 as observed by MLS even though they are not detectable as PSCs by CALIOP.
     
  • The position of the boundary separating NAT mixtures and enhanced NAT mixtures from ice (labeled RNAT|ice) now is calculated dynamically according to the total abundances of HNO3 and H2O vapors. RNAT|ice is based on a parameterization of theoretical calculations of R532 for fully developed STS (assumed to be points between Tice and Tice-1 K) over a wide range of atmospheric pressures and HNO3 and H2O mixing ratios. Total HNO3 and H2O abundances are determined on a daily basis as a function of altitude and DMP equivalent latitude based on nearly coincident “cloud-free” Aura MLS data, where the CALIOP PSC data themselves are used to filter out MLS data affected by uptake in the cloud particles. Then each point with CINS > 1 is assigned a NAT|ice confidence index CINAT|ice = (R532-RNAT|ice)/u(R532). For points classified as ice or wave ice, CINAT|ice > 0. For NAT mixtures or enhanced NAT mixtures, CINAT|ice < 0.
     
  • The V2 composition classification extends downward in altitude to the 215 hPa pressure level (˜10 km), the lowest reliable level for Aura MLS HNO3 data that is required to define the location of the NAT mixture/ice boundary (RNAT|ice) in our classification scheme. All clouds at altitudes below this pressure level are assumed to be ice.
     
  • Assessments of the Quality of CALIOP V2 PSC Data Products

    Generally favorable comparisons between CALIOP PSC results and measurements from the MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument on the Envisat spacecraft MIPAS and earlier (V1) CALIOP PSC composition results were found by Höpfner et al. (2009) and more recently by Spang et al. (2016) using a new Bayesian classifier approach. Most recently, Höpfner et al. (2018) found that for coincident PSC scenes classified as predominantly (> 50%) STS by CALIOP, there was good agreement between the magnitudes and vertical profile shapes of CALIOP and MIPAS particle VD. Recent comparisons of CALIOP V2 and MIPAS Bayesian PSC composition classifications for the 2006-2011 overlap period found a high degree of consistency between the datasets.

    The near-simultaneous and collocated measurements of gas phase HNO3 and H2O by Aura MLS have provided additional validation of the PSC composition inferred from CALIOP data. The basic approach was introduced by Lambert et al. (2012) and involves comparing the observed temperature-dependent uptake of HNO3 by CALIOP-detected PSCs of different compositions with modeled uptake of HNO3 for equilibrium STS and NAT. The technique was refined by Pitts et al. (2013), who restricted the CALIOP-MLS comparisons to homogeneous scenes: those with > 75% PSC coverage over the effective MLS measurement volume, with 2/3 of those PSCs identified as a single CALIOP PSC composition. More recent analysis of CALIOP V2 PSC observations and MLS HNO3 and H2O data from 2006-2018 (see Fig. 2) show that both STS and ice PSCs are near thermodynamic equilibrium with the gas phase, as expected from theoretical considerations. The ice and STS histogram mode peaks occurring below Teq are consistent with a small cold bias in the MERRA-2 temperature analyses as noted by Lambert et al. (2012) and Lambert and Santee (2018). The NAT mixture distributions are broader and roughly bimodal with one mode slightly below the NAT equilibrium temperature and a second more populous mode at 3-4 K below NAT equilibrium, which corresponds approximately to the STS equilibrium temperature. As discussed in Pitts et al. (2013), this bimodality is likely a consequence of different exposure times of air parcels to temperatures below TNAT. The mode near the STS equilibrium temperature represents air parcels with relatively brief exposure to temperatures below TNAT. These parcels contain non-equilibrium liquid-NAT mixtures with a detectable enhancement in Β, but the uptake of HNO3 is dominated by the much more numerous liquid droplets at the lower temperatures. The NAT mixture mode near the NAT equilibrium temperature corresponds to parcels that have been exposed to temperatures below TNAT for extended periods of time, allowing a larger fraction of the gas-phase HNO3 to condense onto the thermodynamically-favored NAT particles and bringing the mixture closer to NAT equilibrium. These composite histograms, which incorporate over 12 years of CALIOP PSC measurements, demonstrate behavior consistent with theoretical expectations for each composition class, providing confidence that the V2 composition classification scheme is robust.

    References

    Höpfner, M., Larsen, N., Spang, R., Luo, B. P., Ma, J., Svendsen, S. H., Eckermann, S. D., Knudsen, B., Massoli, P., Cairo, F., Stiller, G., v. Clarmann, T., and Fischer, H., “MIPAS detects Antarctic stratospheric belt of NAT PSCs caused by mountain waves”, Atmos. Chem. Phys., 6, 1221- 1230, https://doi.org/10.5194/acp-6-1221-2006, 2006.

    Höpfner, M., Pitts, M. C., Poole, L. R., “Comparison between CALIPSO and MIPAS observations of polar stratospheric clouds”, J. Geophys. Res., 114, D00H05, doi:10.1029/2009JD012114, 2009.

    Höpfner, M., T. Deshler, M. Pitts, L. Poole, R. Spang, G. Stiller, and T. von Clarmann, “The MIPAS/Envisat climatology (2002-2012) of polar stratospheric cloud volume density profiles”, Atmos. Meas. Tech., 11 (10), 5901-5923, doi:10.5194/amt-11-5901-2018, 2018.

    Lambert, A., and M. L. Santee, “Accuracy and precision of polar lower stratospheric temperatures from reanalyses evaluated from A-Train CALIOP and MLS, COSMIC GPS RO, and the equilibrium thermodynamics of supercooled ternary solutions and ice clouds”, Atmos. Chem. Phys., 18 (3), 1945-1975, doi:10.5194/acp-18-1945-2018, 2018.

    Lambert, A., M. L. Santee, D. L. Wu, and J. H. Chae, “A-train CALIOP and MLS observations of early winter Antarctic polar stratospheric clouds and nitric acid in 2008”, Atmos. Chem. Phys., 12 (6), 2899-2931, doi:10.5194/acp-12-2899-2012, 2012.

    Pitts, M. C., Poole, L. R., Lambert, A., Thomason, L. W., “An assessment of CALIOP polar stratospheric cloud composition classification”, Atmos. Chem. Phys., 13, 2975-2988, doi:10.5194/acp-13-2975-2013, 2013.

    Pitts, M. C., L. R. Poole, and R. Gonzalez, “Polar stratospheric cloud climatology based on CALIPSO spaceborne lidar measurements from 2006 to 2017”, Atmos. Chem. Phys., 18 (15), 10,881-10,913, doi:10.5194/acp-18-10881-2018, 2018.

    Spang, R., Hoffmann, L., Höpfner, M., Griessbach, S., Müller, R., Pitts, M. C., Orr, A. M. W., and Riese, M.: “A multi-wavelength classification method for polar stratospheric cloud types using infrared limb spectra”, Atmos. Meas. Tech., 9, 3619-3639, https://doi.org/10.5194/amt-9-3619-2016, 2016.

     
     
     

    Figure 1. (a) 2-D histogram of CALIOP Antarctic PSC data for 10-18 July 2008 at latitudes 65-75° S and potential temperatures (θ) 475-525 K. (b) Theoretical optical calculations for non-equilibrium liquid–NAT and liquid–ice mixtures. The dashed and grey boxes at the lower left represent points that fall below both CALIOP V2 PSC detection thresholds and are classified as non-PSCs, i.e. supercooled binary solution (SBS) background aerosol.

     
     
     

    Figure 2. Histograms of CALIOP V2 PSC observations by composition at 21 km altitude from 13 Antarctic and 12 Arctic winters as a function of (a, c) T - Tice and (b, d) T - Teq, where T is the ambient temperature at CALIOP/MLS observation points interpolated from MERRA-2 gridded analyses and Teq is equal to Tice, TNAT or TSTS (depending on the respective composition class) calculated using MLS gas-phase H2O and HNO3 data. Green = STS; red = NAT mixtures + enhanced NAT mixtures; and dark blue = ice + wave ice. Updated from Pitts et al. (2018).


    NASA
    Last Updated: May 09, 2022
    Curator: Charles R. Trepte
    NASA Official: Charles R. Trepte

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