Lidar ratio (S), linear depolarization ratio (δ), and single-scattering albedo (ω) are central observables for dust typing and property retrieval in lidar remote sensing. We investigate their dependence on size parameter (x) and refractive index for absorbing mineral dust using TAMUdust2020 and triaxial-ellipsoid calculations. Results show a consistent asymptotic structure that is weakly sensitive to particle shape in both limits of scattering theory. In the Rayleigh limit (x ≪ 1), S ∝ x −3 and ω ∝ x 3 , while δ remains small. In the geometrical-optics limit (x ≫ 1), δ decreases toward low values and ω → 1/2, whereas S increases because backscatter is reduced relative to extinction by strong absorption and diffraction-dominated scattering. These asymptotic constraints provide a unified physical interpretation of multiwavelength dust-lidar behavior and help explain observed spectral variability of dust depolarization and lidar ratio. A key implication is that large, strongly absorbing dust can produce optical signatures that overlap with weakly depolarizing aerosol classes, which may bias standard classification and inversion schemes toward underestimation of coarse and super-coarse dust contributions.

The 2nd generation Ice, Cloud, and land Elevation Satellite (ICESat-2) is an altimetry mission designed primarily for measuring ice sheet elevation and sea ice thickness, provides atmospheric profiles of clouds and aerosols at 532 nm using a photo counting detection approach. While highly sensitive for the detection of tenuous aerosol and cloud features, during the day signal-to-noise-ratio (SNR) photon counting detectors are adversely impacted by solar contributions to the total signal. Averaging the data to coarser horizontal resolutions has been the standard way to increase SNR and thus allow clouds and aerosols to be more easily detectable. Recent work has demonstrated success in boosting SNR without decreasing resolution using advanced filtering techniques [Yorks et al., 2021], however, rapid advancements in Deep Learning based image denoising algorithms can further improve the SNR. Here, we present results using a state-of-the-art Deep Learning autoencoder applied to noisy daytime ICESat-2 data to improve SNR and discuss implications for atmospheric feature detection, classification, and optical property retrievals.

The Goddard Space Flight Center's Lidar Observation and Validation Experiment (GLOVE) was a field campaign conducted from 27 January to 28 February 2025, based out of NASA Armstrong Flight Research Center at Edwards Air Force Base in California. Its main goals were to validate atmospheric data products from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite missions. The campaign utilized NASA's high-altitude Earth Resources-2 (ER-2) aircraft, equipped with four remote sensing instruments - including two lidars, a radar, and a spectrometer. GLOVE carried out eight flights totaling 40 flight hours and successfully captured seven ICESat-2 and six EarthCARE underflight segments of varying atmospheric conditions (i.e., aerosols, cirrus, and stratocumulus clouds) and surface types. The data collected during ICESat-2 underflights, especially of cirrus clouds and aerosols, offer valuable opportunities to assess the performance of both the operational and newer research-grade atmospheric ICESat-2 data products during daytime. Notably, the data from the Cloud Radar System (CRS), especially from snow-producing clouds, will play an important role in understanding the potential errors and uncertainties in EarthCARE Cloud Profiling Radar (CPR) Doppler data, the first-ever radar Doppler velocity measurements from space. All GLOVE data products are publicly accessible through a NASA Distributed Active Archive Center (DAAC) or other free, open-access repositories (please see Sect. 4, "Data availability", for the DOIs and full data set citations). GLOVE serves as an example for conducting cost-effective and efficient airborne satellite validation campaigns.

Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime spaceborne lidar signals are degraded by solar background. To overcome this issue, data is averaged during science processing at the expense of spatial resolution. New machine learning tools for denoising daytime spaceborne lidar data enable improvements in signal-to-noise ratio and data products at finer resolutions. Here we use airborne data and spaceborne simulations of backscatter lidar systems to quantify the performance of cloud detection frequencies and cloud top heights using spatial averaging and DDUNet autoencoder denoising.

Ice crystal chain aggregates-linear structures of conjoined monomer crystals-have previously been observed in strongly electrified deep convection, likely forming via electric-field-enhanced aggregation. The NASA Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms field campaign conducted aircraft-based sampling of cold-season storms using cloud probes. These measurements reveal chain aggregates in weakly electrified winter storms. Chain aggregates were identified in 28 of 34 research flights from 2020 to 2023 (similar to 10% of total P-3 flight time), spanning -38 degrees C to +2.5 degrees C and 1.5-9.7 km. High-resolution Cloud Particle Imager and Particle Habit Imaging and Polar Scattering imagery revealed sublimation signatures on chain aggregates, consistent across instruments. The widespread occurrence of chain aggregates in weakly electrified clouds suggests alternative formation pathways than electric-field-enhanced aggregation.

Ice production and growth in mixed-phase clouds give a complicated picture of the radiative and mass characteristics due to the heterogeneity of particle shapes and sizes. Collocated airborne lidar and microphysics datasets from a recent winter storm field study are used to evaluate the effect of ice and liquid hydrometeors, observed in situ, on the multiple-wavelength backscatter coefficient values measured by the NASA Cloud Physics Lidar over the course of three winter deployments in the Midwest and Northeast United States.

The shape of crystals in ice clouds influences many aspects of the cloud lifecycle and radiative impact, yet they are extremely variable and hard to categorize. In this paper, we apply a recent crystal shape classification methodology to 33 months of spaceborne lidar measurements. We take advantage of their non-sun-synchronous nature to document the diurnal variability of the repartition of shapes inside clouds. We find that in mid-level clouds the repartition of shapes is dominated by bullets (in particular at higher altitudes) and horizontally-oriented columns, in agreement with previous results. Shape dependence on latitude is generally symmetric around the equator. We document the repartition of shapes with temperature, and show that the proportion of simple shapes (2D plates and columns) decreases at colder temperatures, while the proportion of complex shapes (Droxtals and Voronois) increases, becoming dominant below -60 degrees C. Finally, we document the diurnal cycle of the repartition of shapes according to temperature and latitude. We find there are more 2D plates and columns in the daytime repartition, while more complex shapes are more likely in the nighttime repartition. 3D bullets are frequent in the shape repartition and follow a unique behavior: at cold temperatures they are more frequent in the daytime repartition but at warmer temperatures more frequent in the nighttime repartition. The amplitude of diurnal cycles generally strengthens at colder temperatures. These results provide new constraints for the representation of ice cloud microphysics in atmospheric and climate models.

Abstract Spaceborne lidar provides unique capabilities for quantifying cloud and aerosol properties crucial for aerosol transport and cloud modeling. However, daytime lidar measurements suffer from significant random errors due to solar background radiation, limiting their utility. This study evaluates a novel deep learning dense dense U-Net (DDUNet) denoising approach that substantially reduces random errors in daytime spaceborne lidar measurements. Applied to simulated and real Cloud–Aerosol Transport System (CATS) data, the DDUNet model successfully reduces noise in daytime photon counts to levels approaching nighttime observations. Results demonstrate significant improvements in attenuated total backscatter measurements across various atmospheric features, with correlation coefficients increasing from 0.36 to 0.76 for ice clouds and relative root-mean-square error (RMSE) reductions of 66%–89% across cloud and aerosol types. These improvements propagate to higher-order retrievals, with optical depth correlations improving from 0.86 to 0.91 for liquid water clouds. The DDUNet denoising proves particularly effective for optically thin features, reducing optical depth RMSE by 89% for dust layers. Although the model introduces a small systematic bias following an exponential decay pattern, this bias can be characterized and accounted for in uncertainty quantification. This systematic bias has a minimal impact on the optical depth retrievals, especially for clouds and/or when lidar ratio errors are dominant. This approach significantly enhances the scientific value of daytime lidar observations by enabling near-nighttime quality retrievals of cloud and aerosol properties during daylight hours, addressing a critical limitation in current spaceborne atmospheric remote sensing capabilities.

Spaceborne lidar offers unique advantages for improving global estimates of fine particulate matter (), traditionally limited by critical data gaps in the vertical dimension. Here, we present a new method to retrieve relying on ensembles on aerosol extinction available within the GEOS Aerosol Data Assimilation. This study uses 1064‐nm backscatter lidar data from the NASA Cloud‐Aerosol Transport System (CATS) and model priors from the GEOS model. First, we developed a 1‐D ensemble‐based variational technique (1‐D EnsVar) to perform vertically resolved retrievals of speciated aerosol extinction and surface . Next, we evaluated the performance of 1‐D EnsVar retrievals of and extinction through an independent validation using measurements from spaceborne, airborne, and ground‐based platforms. This approach overcomes traditional limitations by leveraging the strengths of complementary vertical aerosol information from CATS and GEOS to better resolve speciated aerosol optical properties and mass. Assimilating CATS lidar data with the GEOS model reduced bias in surface prediction by 1.1 over the CONUS in 2016, potentially reducing model errors by up to 20%. Given the unique capability of CATS to process vertical profile data in near real‐time, this work demonstrates the powerful utility of spaceborne lidar for improving air quality forecasting. While this pilot study is not yet performed within a cycling data assimilation system, the developed algorithm can easily be integrated in such systems. These results have broader implications for validating aerosol transport models, refining passive satellite retrievals of , and developing data assimilation techniques for future lidar platforms.

Smoke particles from biomass burning events are typically assumed to be spherical despite previous observations of non-spherical smoke. As such, large uncertainties exist in some physical and optical parameters used in lidar aerosol retrievals, including depolarization and lidar ratio of non-spherical smoke aerosols. In this analysis, using NASA’s Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data during the biomass burning season over Africa from 2015 to 2017, we studied the frequency and distribution of non-spherical smoke particles to compare with findings of smoke particle non-sphericity from the Cloud-Aerosol Transport System (CATS) lidar. A supplemental smoke aerosol typing algorithm was developed to identify aerosol layers containing non-spherical smoke particles, which might otherwise be misclassified as desert dust, polluted dust, or dusty marine by the CALIOP standard aerosol typing algorithm. Then, the relationships between smoke particle sphericity, lidar ratio, and relative humidity are analyzed for CATS and CALIOP observations over Africa. Approximately 18% of smoke layers observed by CALIOP over Africa are non-spherical (depolarization ratio > 0.075) and agree with spatial distributions of non-spherical smoke found in CATS observations. A dependance of lidar ratio on relative humidity was found for layers of spherical smoke over Africa in both CATS and CALIOP data; however, no such dependance was evident for the depolarization ratio and layer relative humidity. While the supplemental smoke aerosol typing algorithm presented in this analysis was targeted only for specific biomass burning regions during biomass burning seasons and is not meant for global operational use, it presents one potential method for improved backscatter lidar aerosol typing. These results suggest that a dynamic lidar ratio, based on layer-relative humidity for spherical smoke, could be used to reduce uncertainties in smoke aerosol extinction retrievals for future backscatter lidars.

Microphysical measurements within winter storms are commonly analyzed using two-dimensional radar cross sections from airborne vertically pointing radars or ground-based scanning radars. While these radars offer valuable insights, they provide limited insights into the storm's microphysical characteristics within the context of the storm's threedimensional structure. To address this limitation, this analysis uses conically scanning X-band radar data to investigate the three-dimensional structure of a shallow generating cell (GC) driven snowstorm (,6 km deep) sampled over central Illinois and Indiana on 25 February 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. The observed microphysical properties and reflectivity structures along the nadir-pointing radar cross section represent the superposition of 3D trajectories of fall streaks originating in GCs upwind of the aircraft's flight track. GCs formed in a potentially unstable layer near cloud top based on HRRR analysis, where supercooled water formed and created a droplet-rich environment for ice crystal nucleation, growth, and fallout. In situ micro-physics measurements beneath cloud top allowed for the assessment of particle aspect ratios within and outside of GC fall streaks. When sampled 2-3 km below cloud top, fall streaks typically contained larger ice crystals and aggregates with higher aspect ratios compared to the surrounding cloud, and increased reflectivity in nadir and plan-view scans. The southern end of the storm lacked GCs, was supercooled, and contained smaller, low-aspect-ratio ice crystals in high concentrations.

The combination of simultaneous, collocated aircraft in situ measurements and remote sensing data at multiple wavelengths is of tremendous value in physical process studies but is hard to obtain in practice. Appropriate multiaircraft and multisensor resources for a given project must be coupled with agile mission support (people and tools) and close coordination with the Federal Aviation Administration to implement successfully. Obtaining closely coordinated in situ and remote sensing measurements was key to meeting the science objectives for the NASA Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS), and it required a team effort. IMPACTS flew a complementary suite of remote sensing and in situ instruments in three 6-week deployments on the NASA ER-2 and P-3 aircraft to provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution. The collocated IMPACTS data subset encompassed 106 flight legs during 22 storms, and it included over 21 h where the ER-2 and P-3 were only up to 5 min and 4 km apart. This unique dataset on winter storm conditions in the Northeast and Midwest United States provides a wealth of information, which will have lasting value for the community. This paper explains how the science team, engineers, aircrews, and NASA mission support accomplished the measurement goals and key aspects of the IMPACTS coordinated dataset. Future field campaigns with similar science applications can maximize their flight hours by leveraging the lessons learned from IMPACTS coordination.

Ice- and mixed-phase clouds play an important role in the global radiative budget and hydrological cycle, yet the complexity of ice crystal shapes and the presence of supercooled liquid water (SCLW) present challenges for retrieving cloud properties from airborne and spaceborne remote sensing instruments. Airborne lidar measurements of the backscatter coefficient (beta), color ratio (chi), and volume depolarization ratio (delta) provide additional spatial context for the cloud phase and to some extent the particle shapes both vertically and horizontally through clouds. Coordination between a NASA P-3 and ER-2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms field campaign provided 3.3 hr of Cloud Physics Lidar observations from 16 events to be spatially related to the particle size and morphological properties from the Cloud Particle Imager, in addition to temperature and SCLW measurements obtained by the P-3 aircraft. After the lidar data were matched to the P-3 location, in situ microphysics data were mapped in beta-delta and triple-wavelength frameworks involving chi. Compared to SCLW regions, regions dominated by ice typically exhibited lower beta (<10(-2) km(-1) sr(-1)), lower 532/355-nm chi (<0.3), and greater delta (>0.2) and were associated with particle sizes that were on average 105% larger and area ratios that were 40% lower. The relationships between cloud properties and lidar measurements established in this study have implications for future cloud phase and particle habit algorithms using airborne and spaceborne lidar data.

Since its launch in 2018, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has provided atmospheric products, including calibrated backscatter profiles and cloud and aerosol layer detection. While not the primary focus of the mission, these products garnered more interest after the end of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data collection in 2023. In comparing the cloud and aerosol detection frequencies from CALIOP and ICESat-2, we find general agreement in the global patterns. The global cloud detection frequencies were similar in June, July, and August of 2019 (64.7% for ICESat-2 and 59.8% for CALIOP), as were the location and altitude of the tropical maximum; however, low daytime signal-to-noise ratios (SNRs) reduced ICESat-2’s detection frequencies compared to those of CALIOP. The ICESat-2 global aerosol detection frequencies were likewise lower. ICESat-2 generally retrieved a higher average global aerosol optical depth compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) over the ocean, but the two were in closer agreement over regions with higher aerosol concentrations such as the Eastern Atlantic Ocean and the Northern Indian Ocean. The ICESat-2 and CALIOP orbital coincidences reveal highly correlated backscatter profiles as well as similar cloud and aerosol layer top altitudes. Future work with machine learning denoising techniques may allow for improved feature detection, especially during daytime.

The NASA Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign provides high-quality, high-altitude aircraft lidar (532 nm), radar (W band), and in-cloud microphysical aircraft data taken during wintertime storm events impacting the United States. This study evaluates two mass-dimensional relationships [Brown and Francis (BF95); Heymsfield (H14)] and two lidar-radar microphysical retrieval algorithms [CloudSat and CALIPSO Ice Cloud Property Product (2C-ICE); VarPy (a variational method derived from the satellite lidar-radar data community)] to estimate aircraft-retrieved volume extinction coefficient (a-), ice water content (IWC), and effective radius (re) during the 2020 IMPACTS deployment. BF95 and H14 have a close 1:1 correlation (R2 = 0.98) with in situ observations of a-. However, only BF95 displays a linear, consistent, and almost temperatureindependent low bias for IWC and re, which likely arises from the environmental conditions used to determine each. Unlike the field-campaign-derived BF95 and H14 relationships, VarPy and 2C-ICE directly ingest the aircraft-based lidar and radar data to simulate a-, IWC, and re. For all three microphysical parameters, VarPy and 2C-ICE retrieval errors became notably more pronounced around the dendritic growth zone (from -15 degrees to -10 degrees C) and near freezing (>=-5 degrees C), which suggests that both algorithms experience difficulty addressing riming and aggregation processes and with larger particles (dendrites and plates) due in part to their simplified ice particle assumptions. However, the mean-melt diameter ice-particle assumption did yield more accurate IWC estimates, which led to slightly better overall results for VarPy.

Cloud-top phase (CTP) impacts cloud albedo and pathways for ice particle nucleation, growth, and fallout within extratropical cyclones. This study uses airborne lidar, radar, and Rapid Refresh analysis data to characterize CTP within extratropical cyclones as a function of cloud-top temperature (CTT). During the 2020, 2022, and 2023 Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign deployments, the Earth Resources 2 (ER-2) aircraft flew 26 research flights over the northeast and midwest United States to sample the cloud tops of a variety of extratropical cyclones. A training dataset was developed to create probabilistic phase classifications based on Cloud Physics Lidar measurements of known ice and liquid clouds. These classifications were then used to quantify dominant CTP in the top 150 m of clouds sampled by the Cloud Physics Lidar in storms during IMPACTS. Case studies are presented illustrating examples of supercooled liquid water at cloud top at different CTT ranges (-3 degrees < CTTs < -35 degrees C) within extratropical cyclones. During IMPACTS, 19.2% of clouds had supercooled liquid water present at cloud top. Supercooled liquid was the dominant phase in extratropical cyclone cloud tops when CTTs were >-20 degrees C. Liquid-bearing cloud tops were found at CTTs as cold as -37 degrees C.

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model’s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~7.3×10−4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6×10−4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products.

The Planetary Boundary Layer Height (PBLH) significantly impacts weather, climate, and air quality. Understanding the global diurnal variation of the PBLH is particularly challenging due to the necessity of extensive observations and suitable retrieval algorithms that can adapt to diverse thermodynamic and dynamic conditions. This study utilized data from the Cloud-Aerosol Transport System (CATS) to analyze the diurnal variation of PBLH in both continental and marine regions. By leveraging CATS data and a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm, along with machine learning denoising, the study determined the diurnal variation of the PBLH in continental mid-latitude and marine regions. The CATS DTDS-PBLH closely matches ground-based lidar and radiosonde measurements at the continental sites, with correlation coefficients above 0.6 and well-aligned diurnal variability, although slightly overestimated at nighttime. In contrast, PBLH at the marine site was consistently overestimated due to the viewing geometry of CATS and complex cloud structures. The study emphasizes the importance of integrating meteorological data with lidar signals for accurate and robust PBLH estimations, which are essential for effective boundary layer assessment from satellite observations.

A limitation of traditional airborne and spaceborne lidar instruments is the inability to provide data products in real time. This challenge is compounded by typical research-driven desires to build ever more complicated lidar sensors, which overlooks the need to provide simple, but timely, data products to operational forecast models. Machine learning techniques using convolution neural networks (CNNs) have been developed and applied to single wavelength (e.g., 1064 nm) data from the airborne Cloud Physics Lidar (CPL) and have shown encouraging results for feature detection at finer resolutions compared to traditional methods, notably during noisy daytime conditions. Current technologies and properly scoped measurement goals, not intended as be-all/end-all research tools, permit designs for miniaturized lidar sensors that can be placed on drones and, ultimately, in constellations of minisats. Use of advanced machine learning techniques for data processing permits generation of real time data products that can be quickly assimilated into predictive models (for air quality and human health) and for generating real-time data products for decision making (such as hazardous plume detection and monitoring).

Aerosols and clouds play critical roles in the Earth's weather, air quality, and climate system at multiple spatiotemporal scales. To achieve better characterization of spatiotemporal variability of aerosols and clouds, we need new sensors and architectures that creatively utilize advanced SmallSat technologies. A compact backscatter lidar has been designed to improve spatiotemporal sampling and fit the current mass, volume, and power limits of a SmallSat. A version of this lidar concept, called the Atmospheric Lidar Instrument for Clouds and Aerosol Transport (ALICAT), is being considered for the NASA Atmosphere Observing System (AOS) mission. ALICAT adds capabilities and improves performance compared to previous space-based lidars. ALICAT is mature, as it draws heritage from the Cloud-Aerosol Transport System (CATS) and maturation/testing through Earth Science Technology Office (ESTO) investments.