Figure 4. (a) Maximum CATS IWP near a cluster of ISS LIS flashes vs.
number of flashes in the cluster. The best-fit line is shown in red. (b)
Maximum CATS cloud optical depth near a cluster of ISS LIS flashes vs.
number of flashes in the cluster. The best-fit line is shown in red. (c)
Maximum CATS cloud-top height near a cluster of ISS LIS flashes vs.
number of flashes in the cluster. The best-fit line is shown in red.
Next, LIS-detected flashes were clustered following the methodology
described in Section 2.3 and compared to the maximum CATS-inferred cloud
properties (IWP, cloud-top height, and optical depth) within 50 km of
the cluster along the CATS ground track, and the results are shown in
Fig. 4. At first glance, the results are not as good as radar-lightning
analyses (e.g., Wiens et al., 2005), but in all subpanels the best-fit
lines have Spearman correlations ranging between +0.38-0.42, with
significance values > 99%. That is, the analysis found
that linear correlations between a proxy for lightning flash rate and
IWP (Fig. 4a), cloud optical depth (Fig. 4b), and cloud-top height (Fig.
4c) were all positive (which is expected and physically meaningful) and
highly statistically significant. These metrics demonstrate the utility
of comparing lightning measurements with lidar-inferred cloud
properties, satisfying the success criteria for this analysis.
Higher-power polynomial fitting (e.g., 5th power relationships like
Price and Rind, 1992) was not attempted due to the relatively small
number of samples available in this study. Instead, the focus was simply
to determine whether simple yet statistically significant relationships
could be found.
3.3 Using lidar to explore LIS false-alarm rate
Blakeslee et al. (2020) reported an ISS LIS false alarm rate (FAR) under
5%, based on comparisons with other reference lightning datasets. This
FAR likely results from unfiltered radiation-induced noise as well as a
combination of unfiltered cloud-based and surface-based solar glint. The
latter is nominally controlled by a surface glint filter already
contained within the ISS LIS processing code. This filter checks for the
continuous occurrence of transients over many successive frames (not
just intermittently like lightning) in a location that stays fixed
within the instrument’s frame of reference (similar to how solar glint
appears to an observer when viewing from, e.g., an airplane flying over
the sunlit ocean). It would be useful to use CATS “no cloud” null
cases (Fig. 3) to evaluate the performance of this surface glint filter.