Figure 2. (a) Two-dimensional histogram of the frequency of CATS maximum
cloud-top height vs. latitude, when at least one ISS LIS flash is
detected, for March-October 2017. (b) Two-dimensional histogram of
maximum 20-dBZ echo-top height vs. latitude for TRMM RPFs with at least
one LIS-detected flash during March-October 1998-2914. (c)
Two-dimensional histogram maximum 20-dBZ echo-top height vs. latitude
for GPM RPFs with at least one WWLLN-detected flash during March-October
2014-2020. Frequency values below 0.0005 are not shown in any of these
subplots.
3.2 Comparison of thunderstorm characteristics
It is also of interest to compare LIS-detected lightning to
CATS-inferred thunderstorm properties, such as IWP, dominant hydrometeor
type, optical depth, and other characteristics. For this analysis, it is
understood that a lidar cannot penetrate deeply into optically thick
thunderstorms, and that statistical correlations thus will be
significantly reduced compared to standard radar-lightning comparisons
(Wiens et al., 2005; Carey et al., 2019). However, Rutledge et al.
(2020) demonstrated that passive infrared measurements of cloud
properties (again, largely limited to near-cloud-top characteristics)
can still provide useful physical insights into thunderstorm properties.
Thus, the success criteria for this lidar-based analysis were modest,
and the primary goals were to demonstrate weak yet statistically
significant (and physically meaningful) correlations between lightning
and cloud properties.
First, the CATS hydrometeor feature mask was analyzed when lightning was
detected by LIS. For this, the analysis took advantage of the fact that
the CATS hydrometeor mask uses an increasing index scale ranging from 0
(no cloud), to 1 (liquid cloud), to 2 (undetermined cloud phase), to 3
(ice cloud), and determined the frequency of the highest value index
within 50 km of lightning along the CATS ground track. The results are
shown in Fig. 3. Over 90% of profiles with lightning were associated
with ice-phase or undetermined (likely mixed-phase) cloud. Only
~7% of profiles were associated with liquid cloud.
Assuming that CATS is not providing evidence of solely warm-phase clouds
producing lightning, this suggests that CATS’ hydrometeor mask can
identify ice-containing cloud with better than 90% accuracy (notable
given validation of radar-based hydrometeor identification is difficult;
e.g., Ryzhkov et al., 2005), and thus demonstrates the value of using
lightning observations to validate remote sensing of hydrometeor type.
Moreover, given that lidar cannot penetrate deeply into optically thick
clouds, one cannot rule out the presence of ice below the
cloud-top-biased lidar observations, nor can one rule out the
fundamental differences in LIS and CATS sampling geometry contributing
to any discrepancies. The very small number of “no cloud” observations
are also notable and will be analyzed in detail later in the next
subsection.