Figure 6. (a) Raw, un-geolocated ISS LIS background image from approximately 0953 UTC on 9 August 2017. (b) The same ISS LIS background image but geolocated, showing ISS LIS events detected in the domain of the image during the overpass. The suspect LIS flash not seen near CATS-inferred cloud also is indicated, and LIS and CATS boresight ground tracks are shown.
Further analysis found only 6 instances where there were no apparent clouds in the LIS backgrounds. An example is shown in Fig. 6, which appears to show glint from a water surface (in this case, the Persian Gulf) made it through LIS data filters. Other cases (not shown) suggest that snow fields in mountainous regions also may have contributed to these false alarms. Overall, this analysis demonstrated that the surface glint-based FAR for ISS LIS is ~0.1% during daytime (6 confirmed false alarms out of 65 candidates, which themselves only represented ~1% of the daytime matchup dataset). Since these surface glint filters were inherited from the long-term TRMM LIS mission, this excellent performance is a highly encouraging sign of the quality of the long-term combined LIS dataset (1997-present).
4. Discussion and Conclusions
This was a pathfinder study that made use of a short (~8-month) overlapping dataset to explore the viability of combining spaceborne lidar and lightning observations to study thunderstorms. The results demonstrate that lidar observations can provide insight into the global thunderstorm climatology, are realistically correlated with lightning characteristics such as flash rate despite the lack of deep penetration into thunderclouds, and enable new methods for quality control and validation of spaceborne optical lightning observations.
Specifically, this study found that lidar-inferred cloud tops near lightning behave realistically given how the tropopause slopes downward from the tropics to the poles. In addition, these cloud-tops average approximately 2-km higher in altitude compared to radar-based climatologies using 20-dBZ echo tops. This demonstrated the advantage of using lidar as a more accurate measure of cloud-top height compared to less-sensitive Ku-band radar (e.g., TRMM, GPM), but at the same time also points toward a way to use lidar to help scale radar-based climatologies to more accurately convert echo-top heights to cloud-top heights.
Moreover, this study found that a proxy for lightning flash rate shows reasonable behavior relative to lidar-retrieved cloud properties, such as cloud optical depth, cloud-top height, and IWP. The results also demonstrated that CATS’ cloud-top phase retrieval worked with likely 90% or better accuracy.
Finally, this study determined that the false alarm rate for LIS-identified flashes associated with no nearby cloud (e.g., solar glint off water) is ~0.1%. This demonstrated that the surface glint filters in the ISS LIS processing code (inherited from the much longer TRMM dataset) are working extremely well and that surface glint likely is a nearly negligible contributor to LIS false alarms (which have been estimated at less than 5% of the LIS dataset; Blakeslee et al., 2020). This is in contrast to, e.g., GLM where surface glint has been a major contributor to false alarms, particularly before implementation of the blooming filter in GLM processing (Rudlosky et al., 2019).
Based on this pathfinder study, fruitful scientific insights are expected from larger combined lidar/lightning datasets. These could include analysis combining GLM with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP; Winker, 2022). In addition to building on the analyses performed in the present study, such an analysis could use CALIOP to help validate stereo retrievals of cloud-top height from dual GLM observations (Mach & Virts, 2021). Moreover, potential future datasets, including a lidar (or lidars) hosted on the AOS mission, are also exploitable using lessons learned from this study. These larger datasets potentially could mitigate some of the limitations of this study, specifically the short time period used here, which didn’t even cover one full annual cycle. Moreover, a larger dataset also would enable the use of tighter time/distance thresholds for defining instrument conjunctions, potentially leading to more accurate comparisons.
In addition, a larger number of samples may allow for detailed investigation of aerosol interactions with thunderstorms. Since lidar can determine aerosol type (e.g., Omar et al., 2009), lightning enhancement (or suppression) by smoke, dust, and other aerosol types could be studied.