Plain Language Summary
Data from a laser-based instrument called a lidar (which can measure clouds and aerosols) and an optically based lightning detection instrument, both hosted on the International Space Station during March-October 2017, were used to study global thunderstorms from space. Because these types of instruments have only been rarely combined in the past, this study focused on analyzing a small dataset in order to determine how useful the instrument combination can be. The results found that thunderstorm cloud-top heights slope downward from the Equator toward the poles, similar to how the tropopause height also slopes downward. Lidar measurements of cloud properties, like cloud-top height and the amount of ice in the cloud, were quantitatively related to lightning observations like flash rate. The lidar also was helpful in finding instances where the lightning instrument accidentally detected glint from the sun on water, snow, etc. instead of lightning, because there were no lidar-detected clouds nearby. However, this rarely occurred due to how the lightning instrument’s data are processed. Additional fruitful scientific insights can be expected from other, larger combined lidar/lightning datasets.
1 Introduction
1.1 Background
Lidar is a common tool for measuring clouds, aerosols, and atmospheric state, and has been used for many decades from ground-based (e.g., Sassen, 1977), airborne (e.g., McGill et al., 2007), and spaceborne platforms (e.g., Winker et al., 2006). A fundamental aspect of lidar is its difficulty in penetrating deeply into optically thick clouds, such as cumulonimbus (i.e., thunderstorms). Nevertheless, some studies have successfully used lidar to study characteristics of deep convection, thunderstorms, and even lightning. For example, Sassen (1977) took advantage of relatively high-altitude cloud bases in Wyoming to use a ground-based lidar to study the optical scattering characteristics of melting precipitation in summertime thunderstorms. Airborne lidars have been used to document many aspects of thunderstorms as well as their surrounding environments. For example, Sassen et al. (2000) and Campbell et al. (2005) used lidar to study the microphysical properties of deep convective cloud tops and thunderstorm anvils. These studies often have made use of polarization-diversity lidar measurements to infer cloud-particle phase, habit, and orientation within these anvils, qualitatively similar to how polarimetric microwave radar has been used to study precipitation characteristics deep within thunderstorms (e.g., Kumjian & Ryzhkov, 2008). Outside of precipitation, airborne lidar has been used to quantify wind profiles near deep convection (e.g., Cui et al., 2020). Meanwhile, ground-based ozone lidars have been used to document lightning-produced nitrogen oxides (NOx) in the vicinity of thunderstorms (Wang et al., 2015). Lidar has even been proposed as a method to remotely sense electromagnetic fields in order to estimate the possibility of lightning strikes outside of clouds (Shiina et al., 2006).
Combined observations of deep convection from radar, microwave radiometers, infrared spectrometers, and lidar have also been made (Heymsfield & Fulton, 1988; McGill et al., 2004; van Diedenhoven et al., 2016). Indeed, these combined measurements with lidar and other instruments form the scientific basis for the CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) missions (Mace et al., 2009), as well as the overall A-Train satellite constellation (Delanoe & Hogan, 2010) and the future Atmosphere Observing System (AOS, 2022). Such combined measurements can enable more accurate retrievals of microphysical processes near cloud top.
However, detailed comparisons between lightning and lidar observations are rare in the literature. One notable recent study was Allen et al. (2021), which made use of airborne observations from the Fly’s Eye Geostationary Lightning Mapper Simulator (FEGS; Quick et al., 2021), the Cloud Physics Lidar (CPL; McGill et al., 2002), and an ultraviolet (UV) visible spectrometer to estimate NOx production by lightning. The role played by CPL was to measure cloud-top pressure, which played an important role in the NOx calculations, while FEGS was used to document the detection efficiencies of ground-based and spaceborne lightning observations for the storms that were studied. However, the airborne lidar and lightning observations were not directly compared to better resolve thunderstorm structure or processes. Moreover, coupled spaceborne lidar/lightning studies do not appear to exist in the literature. Potential advantages and challenges of doing more direct lidar/lightning comparisons are discussed below, particularly from the perspective of spaceborne platforms.
1.2 Potential advantages of using spaceborne lidar to study thunderstorms
Some potential advantages of spaceborne lidar include its ability to provide a more accurate measurement of cloud-top characteristics than, for example, spaceborne radar. Cloud-top height has previously been related to lightning flash rate (e.g., Price & Rind, 1992). In addition, lightning within the overshooting tops of thunderstorms has been shown to be of significance to thunderstorm electrification and charge structure (MacGorman et al., 2017). However, echo-top height as determined by radar strongly depends on the sensitivity of the radar itself. For example, the Ku-band radar used in the Tropical Rainfall Measuring Mission (TRMM), and the Ku- and Ka-band radars used in the Global Precipitation Measurement (GPM) mission, have minimum reflectivity sensitivities in excess of 10 dBZ (Kummerow et al., 1998; Hou et al., 2014). This means that these radars are not sensitive to the weaker echoes associated with true cloud tops (Hagihara et al., 2014). Lidar has been used to help diagnose cloud-top height underestimates in thermal imagery as well (e.g., Sherwood et al., 2004).
In addition, lidars are able to infer the dominant phase of hydrometeors near cloud top (Yoshida et al., 2010), as well as other microphysically related attributes like cloud optical depth and ice water content (IWC) and path (IWP; e.g., Avery et al., 2012). That being said, optical measurements do not penetrate deeply into thick clouds, particularly deep convection. However, studies like Rutledge et al. (2020) have demonstrated that near-cloud-top measurements of thunderstorms are useful as their optical properties have (among other things) implications for the detection efficiency of optically based lightning mappers like the Lightning Imaging Sensor (LIS; Kummerow et al., 1998; Blakeslee et al., 2020). In addition, lidars are capable of providing vertical structure information within the portions of clouds they do penetrate.
Lidars have been in space for longer than a decade (Winker, 2022), providing climate-quality records of cloud properties. This time period overlaps both LIS and Geostationary Lightning Mapper (GLM; Rudlosky et al. 2019) observations for many years. This means that there may be enough conjunctions between these two observations that useful analysis of thunderstorm properties could be performed. Moreover, lidars are planned to be part of the forthcoming AOS (2020), while GLMs and other spaceborne observations of lightning are also planned into the near future (e.g., Holmlund et al., 2021).
Though these data were not examined in this study, lidars are capable of detecting and categorizing aerosol properties (Ceolato & Berg, 2021). Since aerosols have been shown to be important for the strength and properties of convection, including thunderstorms (Khain et al., 2005), combining lidar observations of both cloud and aerosol properties with lightning observations could provide potentially useful information about thunderstorm-aerosol interactions. Moreover, it would be of interest to perform composition studies like Allen et al. (2021) using spaceborne platforms to provide a more global perspective on NOx production by lightning.
1.3 Potential challenges of using spaceborne lidar to study thunderstorms
All the above being said, there are significant challenges to using lidar to study thunderstorm properties. In addition to lidar’s well-known inability to penetrate thick clouds, the sampling characteristics of spaceborne lidar and lightning observations are very different. For example, lightning observations from LIS and GLM are distributed horizontally, and include information about flash two-dimensional (2D) location (Rudlosky et al., 2019; Blakeslee et al., 2020). But they do not measure the vertical structure of lightning. On the other hand, lidars typically measure along a nadir curtain (Yorks et al., 2016; Winker, 2022), which means they provide vertically distributed observations that lack horizontal context. Thus, care needs to be taken when comparing spaceborne lidar and lightning datasets, but no well-tread analysis pathways exist with comparing these two datasets unlike, e.g., radar and lightning datasets (Rust & Doviak, 1982; Lopez & Aubagnac, 1997; Wiens et al., 2005; Carey et al., 2019). There is utility in exploring a small overlapping dataset of spaceborne lidar observations of thunderstorms, to better understand the advantages and challenges of doing this combined analysis, without committing significant resources on a “wild goose chase” if the analysis does not yield much scientific value.
1.4 Goals of this study
As it turns out, such a small overlapping dataset exists. For just short of 8 months in 2017, the Cloud-Aerosol Transport System (CATS) lidar (Yorks et al., 2016) overlapped with a LIS instrument on the International Space Station (ISS). As will be shown in this study, these co-located instruments provided a useful dataset for demonstrating the value of using lidar to study thunderstorm characteristics. This paper will discuss the advantages and challenges associated with this combined analysis, and will provide a path forward for more detailed analysis using larger overlapping datasets.
2 Data and Methodology
2.1 ISS LIS
The International Space Station Lightning Imaging Sensor (ISS LIS) is a high-speed camera (500 frames per second) affixed to a telescope that detects lightning via monitoring transients at 777.4 nm. LIS is a modified flight spare of the TRMM LIS (1997-2015) instrument, hosted within the 5th Space Test Program – Houston (STP-H5) payload, launched in 2017.
ISS LIS extends TRMM LIS time series observations, expands latitudinal coverage, provides near-realtime data to operational users, and enables cross-sensor calibrations (e.g., with GLM). A thorough review of the ISS LIS sensor is provided in Blakeslee et al. (2020). The instrument’s flash detection efficiency is approximately 60%, with sub-pixel (< 4 km) location accuracy and sub-frame (< 2 ms) timing accuracy. Quality-controlled, flash-level data and science backgrounds from ISS LIS (Blakeslee 2000a, b) were used in this study. These data are available starting 1 March 2017.
2.2 CATS
The CATS lidar made range-resolved measurements of clouds and aerosols at 1064 and 532 nm during 2015-2017 (Yorks et al., 2016). CATS had a vertical resolution of 60 m and a horizontal resolution of 5 km. The Level 2 products used in this study included vertical feature mask (e.g., liquid vs. frozen cloud, or aerosol type), as well as profiles of cloud properties (e.g., IWC, cloud optical depth).
CATS overlapped on the ISS with LIS during 1 March through 29 October 2017. After this period the CATS instrument’s mission concluded. Coincidentally, the original CATS ray-tracing code was adapted and used within ISS LIS geolocation routines (Lang, 2019), which have been demonstrated to provide the aforementioned sub-pixel (< 4-km) location accuracy for LIS-detected lightning (Blakeslee et al., 2020). The previous TRMM-based LIS geolocation code was found to be not easily translated to the ISS.
2.3 Combining LIS with CATS
Combining the 2D horizontally distributed LIS dataset is not straightforward to do with the vertically profiling CATS lidar measurements. Since CATS provided a nadir-focused curtain, the first step to combining the datasets was to threshold on LIS flash centroid distance from the CATS ground track. During the nearly 8-month overlap period, this study determined that 8246 ISS LIS flashes had centroids within 25 km of the CATS ground track. The 25-km threshold was chosen to balance obtaining a co-location dataset large enough to enable useful statistical analysis, while still only comparing lightning that was close enough to the ground track to be potentially physically related to CATS-measured cloud properties. Sensitivity experiments were also performed with 50- and 10-km ground-track distance thresholds. These results (not shown), were found to be qualitatively similar to the 25-km results discussed in this paper.
An example of a typical LIS/CATS matchup is shown in Fig. 1. Near 0903 UTC on 15 March 2017, CATS shows high-level cloud (close to 17 km MSL maximum height), a few km below which the signal is attenuated (Fig. 1a). The cloud itself is identified as primarily ice by the CATS feature mask (Fig. 1b). This is suggestive of the anvil region above and around deep convection. Meanwhile, more than a dozen ISS LIS flashes are identified whose centroids are within 25 km of the CATS ground track around this same time. This figure (and hundreds like it, not shown) demonstrate that these very different datasets can be combined to provide meaningful qualitative information about thunderstorms.
In order to explore the ability to retrieve more quantitative conclusions about thunderstorms using the combined dataset, an automated statistical analysis of CATS-retrieved cloud properties near ISS LIS measured lightning was performed. When lightning was observed, the maximum values of cloud parameters (e.g., IWP, cloud-top height, optical depth) within 50 km along the CATS track were determined. In addition, clusters of lightning flashes were identified (e.g., like in Fig. 1) using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm (Ester et al., 1996) with a minimum of 1 flash to create a cluster, and a maximum distance of 50 km between flashes in a cluster. The total number of flashes in each of these clusters was similarly compared to maximum values of CATS cloud properties within 50 km along the ground track. The use of the 50-km distance threshold along the track was to allow for the possibility that CATS did not gain a well-placed overpass of the relevant thunderstorm’s core.