Lightning endangers people and infrastructure and is a major source of wildfire ignitions in the western United States. This study builds upon a satellite-based machine learning model, LightningCast, by incorporating a radar-based predictor of near-term lightning probability. While numerous studies have merged satellite and radar predictors for lightning prediction, few have investigated the impact of using both predictors in tandem from these two remote sensing sources. Here, we focus on reflectivity observations at 2108C (Ref10), which have been widely used by forecasters to nowcast lightning occurrence. Using ablation experiments, we investigated the change in performance when using Ref10 1) in regions with radar coverage, 2) in regions without radar coverage, and 3) for first-flash events. Satellite predictors were provided by the GOES-16 Advanced Baseline Imager (ABI), while the Ref10 was computed from the Multi-Radar Multi-Sensor (MRMS) system. The target was created from the Geostationary Lightning Mapper data aboard GOES-16. Overall, we found that including the Ref10 predictor improves predictions significantly where radar coverage is valid while not signifi-cantly reducing performance in regions outside of radar coverage. Furthermore, the lead time to first-flash events is slightly improved overall but significantly improved in certain situations. From the ablation experiments, we determined that ABI predictors improve lead time to first-flash events on the order of 5-10 min, compared to a Ref10-only experiment. The combined ABI1Ref10 experiment achieved average lead times of 20-30 min for first-flash events at the most-skillful forecast probability thresholds of 25%-40%, the highest of all tested experiments. These results indicate that it is possible for models to leverage the strengths of multimodal observations for short-term lightning prediction.

To evaluate characteristics of output values from machine learning models in bulk, an object-based technique is developed and applied to a deep learning model called the Thunderstorm Nowcasting Tool (ThunderCast). ThunderCast predicts the occurrence of midlatitude lightning-producing convection (thunderstorms) in the next 0-60 min from satellite observations. This paper uses the Tracking and Object-Based Analysis of Clouds (tobac) tool to identify tracks of objects comprised of ThunderCast predictions from seven randomly selected days per month from April to September 2022. For each track, the maximum Multi-Radar Multi-Sensor (MRMS) system radar reflectivity at-10 degrees C (ThunderCast's target) is used to categorize the track as true (>= 30 dBZ at-10 degrees C) or false (<30 dBZ at-10 degrees C) positive. The tracks are further classified by the presence or absence of lightning from the GOES-16 Geostationary Lightning Mapper (GLM-16) or Earth Networks Total Lightning Network (ENTLN). Of the 17 054 tracks identified, 69.4% were true positives, but 62.5% of those true positives lacked associated GLM-16 or ENTLN lightning. A clear radar reflectivity threshold separating lightning-associated tracks from nonlightning tracks was not found. This demonstrates a limitation of using ground-based radar thresholds, indicative of micro-physical characteristics associated with thunderstorm electrification, as the target dataset in thunderstorm nowcasting models. Additionally, cloud-top properties from the 10.3-and 1.6-m Advanced Baseline Imager (ABI) spectral bands are consistent with cloud-top glaciation. This suggests a need for additional predictors to reduce both false positives and true positives without associated lightning in ThunderCast's predictions. The object-based analysis technique presented here can be adapted for evaluating output from other machine learning models. SIGNIFICANCE STATEMENT: Some of the leading thunderstorm nowcasting models use machine learning with a combination of satellite imagery and radar to make short-term predictions. Although built with the leading scientific understanding in mind, the models' definitions of thunderstorms are imperfect due to thunderstorms' physical complexity. This paper demonstrates how an object-based evaluation of the outputs of a deep learning model for thunderstorm nowcasting can be used to understand how the target dataset and predictors selected for a deep learning methodology impact the reliability and applicability of the model from a forecasting perspective. The results can inform improved selections of the model's target and predictors and improve the scientific understanding of thunderstorms from a radar and satellite perspective. The application of object identification and tracking techniques to a deep learning model is novel and can be used to understand and improve other machine learning models.

The January 2022 eruption of the Hunga Tonga-Hunga Ha'apai volcano in the South Pacific emitted significant sulfur dioxide SO2 $\left({\mathrm{S}\mathrm{O}}_{2}\right)$ into the atmosphere, forming a large stratospheric cloud. This study employs the HYSPLIT model, a Lagrangian atmospheric transport and dispersion model, along with satellite retrievals of SO2 ${\text{SO}}_{2}$ cloud properties to model the long range transport of the cloud. To reduce the uncertainty and complexity of modeling the near-source behavior of the umbrella cloud, we utilize a data insertion technique that initializes the model at a downwind plume location. Satellite retrievals provide estimates of column mass loading and plume top height, though the plume top height may be uncertain above the tropopause. Additionally, the vertical mass distribution must be estimated by making assumptions about the cloud thickness. We use a back trajectory analysis to provide better estimations of plume top height and thickness. Our findings reveal that trajectory-derived cloud top heights substantially exceeded satellite estimates, with 60% ranging between 20 and 40 km, compared to most satellite-derived estimates being around 15 km. Long range 5-day forecasts produced with data insertion using the revised cloud top heights and estimated thickness are compared with forecasts using retrieved cloud top heights and an assumed simple thickness of 1 km, and a control run initiated from the vent at the eruption start time. A qualitative comparison with satellite retrievals and data from ground based lidar stationed at R & eacute;union Island shows the use of the back trajectory analysis significantly improves the forecast.

National Oceanic and Atmospheric Administration (NOAA)/Cooperative Institute for Meteorological Satellite Studies (CIMSS) ProbSevere, or the "probability of severe" models, provide next-hour probabilistic guidance on severe weather in the United States (large hail, strong wind gusts, and tornadoes) using machine learning and meteorological data from remotely sensed platforms and short-term numerical weather models. ProbSevere has been widely used by NOAA's National Weather Service (NWS) since 2016. ProbSevere version 3 employs multiplatform, multiscale storm object identification and tracking to collect storm features and then uses gradient boosting decision trees to make predictions of convective hazards from the extracted predictors. ProbSevere v3 was trained, validated, and tested on data from 2018 to 2023. Results demonstrate improved performance over ProbSevere v2 for the majority of the United States across all seasons, spanning a wide range of meteorological regimes. The greatest improvements are for the hail, wind, and any severe models, whereas the tornado model demonstrated modest improvement. ProbSevere v3 models were evaluated by NWS forecasters in simulated real-time experiments from 2021 to 2023. The majority of forecasters preferred ProbSevere v3 over v2 due to improved confidence in predictions and greater lead times for the issuance of severe weather warnings. An analysis of predictor importance revealed several radar, lightning, satellite, and numerical model fi elds that strongly contribute to the model predictions, which is consistent with past research and knowledge of severe thunderstorm forecasting. This reinforces the importance of environmental data fusion for nowcasting severe weather associated with midlatitude convection. SIGNIFICANCE STATEMENT: This study details the updates made to a U.S.-wide, real-time, probabilistic severe- weather guidance system aimed to improve forecaster confidence and the accuracy of the U.S. National Weather Service severe weather warnings. An evaluation of the system demonstrated improved performance over the operational predecessor, which was driven by radar, ground-based lightning, satellite, and numerical model data sources.

Abstract On 15 January 2022, Hunga Volcano in Tonga produced the most violent eruption in the modern satellite era, sending a water‐rich plume at least 58 km high. Using a combination of satellite‐ and ground‐based sensors, we investigate the astonishing rate of volcanic lightning (>2,600 flashes min−1) and what it reveals about the dynamics of the submarine eruption. In map view, lightning locations form radially expanding rings. We show that the initial lightning ring is co‐located with an internal gravity wave traveling >80 m s−1 in the stratospheric umbrella cloud. Buoyant oscillations of the plume's overshooting top generated the gravity waves, which enhanced turbulent particle interactions and triggered high‐current electrical discharges at unusually high altitudes. Our analysis attributes the intense lightning activity to an exceptional mass eruption rate (>5 × 109 kg s−1), rapidly expanding umbrella cloud, and entrainment of abundant seawater vaporized from magma‐water interaction at the submarine vent.

This paper presents the Thunderstorm Nowcasting Tool (ThunderCast), a 24-h, year-round model for predicting the location of convection that is likely to initiate or remain a thunderstorm in the next 0-60 min in the continental United States, adapted from existing deep learning convection applications. ThunderCast utilizes a U-Net convolutional neural network for semantic segmentation trained on 320 km x 320 km data patches with four inputs and one target data-set. The inputs are satellite bands from the Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) in the visible, shortwave infrared, and longwave infrared spectra, and the target is Multi-Radar Multi-Sensor (MRMS) radar reflectivity at the-10 degrees C isotherm in the atmosphere. On a pixel-by-pixel basis, ThunderCast has high accuracy, recall, and specificity but is subject to false-positive predictions resulting in low precision. However, the number of false positives decreases when buffering the target values with a 15 km x 15 km centered window, indicating ThunderCast's predictions are useful within a buffered area. To demonstrate the initial prediction capabilities of Thunder-Cast, three case studies are presented: a mesoscale convective vortex, sea-breeze convection, and monsoonal convection in the southwestern United States. The case studies illustrate that the ThunderCast model effectively nowcasts the location of newly initiated and ongoing active convection, within the next 60 min, under a variety of geographical and meteorological conditions.

This dataset contains lightning and volcanic plume data for the eruption of Hunga Volcano in Tonga from 13–15 January 2022. The dataset consists of two files. The first is a spreadsheet containing four tabs: (1) Ground-based flashes, which include lightning flashes from combined ground-based networks from 13–15 January 2022; (2) Ground-based rates, which include flash rates and pulse rates in one-minute bins from 13–15 January 2022 using the combined networks; (3) Optical GLM flashes & rates, which include GLM flashes and per-minute rates from 15 January 2022; and (4) Volcanic plume dimensions, which include maximum plume heights and umbrella radii through time on 15 January 2022. The second file is a Google Earth KMZ file of umbrella cloud areas outlined from stereoscopic cloud height retrievals from 04:17–07:07 UTC on 15 January 2022. Refer to journal article "Lightning rings and gravity waves: Insights into the giant eruption plume from Tonga’s Hunga Volcano on 15 January 2022" published in Geophysical Research Letters for further details about data processing.

A SmallSat mission concept is formulated here to carry out Time-varying Optical Measurements of Clouds and Aerosol Transport (TOMCAT) from space while embracing low-cost opportunities enabled by the revolution in Earth science observation technologies. TOMCAT's "around-the-clock" measurements will provide needed insights and strong synergy with existing Earth observation satellites to 1) statistically resolve diurnal and vertical variation of cirrus cloud properties (key to Earth's radiation budget), 2) determine the impacts of regional and seasonal planetary boundary layer (PBL) diurnal variation on surface air quality and low-level cloud distribu-tions, and 3) characterize smoke and dust emission processes impacting their long-range transport on the subseasonal to seasonal time scales. Clouds, aerosol particles, and the PBL play critical roles in Earth's climate system at multiple spatiotemporal scales. Yet their vertical variations as a function of local time are poorly measured from space. Active sensors for profiling the atmosphere typically utilize sun-synchronous low-Earth orbits (LEO) with rather limited temporal and spatial coverage, inhibiting the characterization of spatiotemporal variability. Pairing compact active lidar and passive multiangle remote sensing technologies from an inclined LEO platform enables measurements of the diurnal and vertical variability of aerosols, clouds, and aerosol-mixing-layer (or PBL) height in tropical-to-midlatitude regions where most of the world's population resides. TOMCAT is conceived to bring potential societal benefits by delivering its data products in near-real time and offering on-demand hazard-monitoring capabilities to profile fire injection of smoke particles, the frontal lofting of dust particles, and the eruptive rise of volcanic plumes.

NASA has established the Earth System Observatory (ESO) to fulfill the science needs presented in the 2017 Earth Science Decadal Survey, and the Atmosphere Observing System (AOS) is a key component of ESO. The AOS mission will make measurements of aerosols, clouds, convection, and precipitation, which represents two of the designated observables in the decadal survey, to advance our understanding of the processes that drive cloud and precipitation properties (for low, high, convective, and frozen/mixed-phase clouds), convective vertical motion, air quality, aerosol redistribution, radiative transfer, and the relationships between these processes. AOS employs a two-orbit architecture (a polar orbit and an inclined orbit), thereby allowing AOS to cover a wide range of temporal and spatial scales and transforming our understanding of this critical part of the Earth's system. The inclined-orbit sensor suite (AOS-I, 55-degree inclination, 407 km altitude, notional launch in July 2028) includes a backscatter lidar called the Atmospheric Lidar Instrument for Clouds and Aerosol Transport (ALICAT), a Ku-band Doppler radar, and two microwave radiometers that provide short-time-differenced measurements. The goal of AOS-I is to determine the varying-time-of-day processes that control aerosol-convection-precipitation interactions, as well as the diurnal variability of convection, precipitation, high clouds, aerosol emissions, and air quality. This extended abstract provides (1) an overview of the AOS-I mission, (2) a description of the AOS-I science objectives, and (3) a preview of the critical role ALICAT plays in AOS-I science, applications, and synergy.

Lightning strikes pose a hazard to human life and property, and can be difficult to forecast in a timely manner. In this study, a satellite-based machine learning model was developed to provide objective, short-term, location-specific probabilistic guidance for next-hour lightning activity. Using a convolutional neural network architecture designed for semantic segmentation, the model was trained using GOES-16 visible, shortwave infrared, and longwave infrared bands from the Advanced Baseline Imager (ABI). Next-hour GOES-16 Geostationary Lightning Mapper data were used as the truth or target data. The model, known as LightningCast, was trained over the GOES-16 ABI contiguous United States (CONUS) domain. However, the model is shown to generalize to GOES-16 full disk regions that are outside of the CONUS. LightningCast provides predictions for developing and advecting storms, regardless of solar illumination and meteorological conditions. LightningCast, which frequently provides 20 min or more of lead time to new lightning activity, learned salient features consistent with the scientific understanding of the relationships between lightning and satellite imagery interpretation. We also demonstrate that despite being trained on data from a single geostationary satellite domain (GOES-East), the model can be applied to other satellites (e.g., GOES-West) with comparable specifications and without substantial degradation in performance. LightningCast objectively transforms large volumes of satellite imagery into objective, actionable information. Potential application areas are also highlighted. Significance StatementThe outcome of this research is a model that spatially forecasts lightning occurrence in a 0-60-min time window, using only images of clouds from the GOES-R Advanced Baseline Imager. This model has the potential to provide early alerts for developing and approaching hazardous conditions.

Satellite retrievals of column mass loading of volcanic ash are incorporated into the HYSPLIT transport and dispersion modeling system for source determination, bias correction, and forecast verification of probabilistic ash forecasts of a short eruption of Bezymianny in Kamchatka. The probabilistic forecasts are generated with a dispersion model ensemble created by driving HYSPLIT with 31 members of the NOAA global ensemble forecast system (GEFS). An inversion algorithm is used for source determination. A bias correction procedure called cumulative distribution function (CDF) matching is used to very effectively reduce bias. Evaluation is performed with rank histograms, reliability diagrams, fractions skill score, and precision recall curves. Particular attention is paid to forecasting the end of life of the ash cloud when only small areas are still detectable in satellite imagery. We find indications that the simulated dispersion of the ash cloud does not represent the observed dispersion well, resulting in difficulty simulating the observed evolution of the ash cloud area. This can be ameliorated with the bias correction procedure. Individual model runs struggle to capture the exact placement and shape of the small areas of ash left near the end of the clouds lifetime. The ensemble tends to be overconfident but does capture the range of possibilities of ash cloud placement. Probabilistic forecasts such as ensemble-relative frequency of exceedance and agreement in percentile levels are suited to strategies in which areas with certain concentrations or column mass loadings of ash need to be avoided with a chosen amount of confidence.

Advances in global lightning detection have provided novel ways to characterize explosive volcanism. However, researchers are still at the early stages of understanding how volcanic plumes become electrified on different spatial and temporal scales. We deconstructed the phreatomagmatic eruption of Taal volcano (Philippines) on 12 January 2020 to investigate the origin of its powerful volcanic thunderstorm. Satellite analysis indicated that the waterrich plume rose >10 km high before creating lightning detected by Vaisala's global lightning data set (GLD360). Flash rates increased with plume heights and cloud expansion over time, producing >70 flashes min(-1). Photographs revealed a highly electrified region at the base of the umbrella cloud, where we infer strong convective updrafts and icy collisions enhanced the electrical activity. These findings inform a conceptual model with overlapping regimes of charge generation in wet eruptions-initially due to ash particle collisions near the vent, followed by thunderstorm-like electrification in icy regions of the upper plume. Despite the wide reach of Taal's ash cloud, most of the lightning occurred within 20-30 km of the volcano, producing thousands of hazardous cloud-to-ground flashes over a densely populated area. The eruption demonstrates that volcanic lightning can pose a hazard in its own right, embedded within the broader hazards of explosive volcanism in an urban setting.

First posted December 19, 2022 For additional information, contact: Director, Volcano Science CenterU.S. Geological Survey1300 SE Cardinal CourtVancouver, WA 38683 A significant number of the world's approximately 1,400 subaerial volcanoes with Holocene eruptions are unmonitored by ground-based sensors yet constitute a potential hazard to nearby residents and infrastructure, as well as air travel and global commerce. Data from an international constellation of more than 60 current satellite instruments provide a cost-effective means of tracking activity and potentially forecasting hazards at volcanoes around the world. These data span the electromagnetic spectrum: ultraviolet, optical, infrared, and microwave (synthetic aperture radar). They can measure volcanic thermal and gas emissions, ground displacement, and surface and topographic change, providing information that addresses one of the grand challenges in volcanology—to overcome our incomplete understanding of the relation between volcanic unrest and eruption, which is currently based on only a few well-studied volcanoes.Although the potential of volcano remote sensing has been recognized for decades, there are many hurdles to clear before remote sensing data can be used fully by all volcano observatories. These include: (1) the limited temporal and spatial coverage of active volcanoes by satellites and the delayed distribution of those data; (2) the lack of background data acquired at all volcanoes; and (3) limited access to, and utilization of, remote sensing data in some areas owing to a lack of expertise, licensing, user-friendly formats, data access portals, or computational infrastructure.While remote sensing data will never replace ground-based monitoring, a joint observation strategy provides a powerful means of assessing volcanic activity before, during, and after hazardous eruptions, especially given the unique spatial, temporal, and spectral perspective provided by remote measurements. A coordinated international remote sensing observation strategy for volcanoes—similar to one used by the cryosphere community—along with a volcano space task group to maximize the utility of satellite data for volcano monitoring would be highly beneficial. Such a vision could facilitate (1) global coordination of satellite observations (as done for polar regions) for background monitoring and eruption response, (2) open data that can be rapidly distributed during crises, (3) communication tools and forums for discussion of satellite data, (4) integrated ground and satellite databases of unrest, and (5) global capacity building.

Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to estimate the SO2 source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional SO2 datasets from ultraviolet (UV) sensors lack altitude information, which must be assumed, to correctly calibrate the SO2 data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded SO2 data. We then fit a solution to the general time-averaged dispersion of SO2 from a point source. We demonstrate these techniques using SO2 data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System's Suomi-NPP and NOAA-20 satellites. This SO2 data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR SO2 data can fill an important data gap in the UV-based record: estimating SO2 emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used.

Volcanic ash clouds often become multilayered and thin with distance from the vent. We explore one mechanism for development of this layered structure. We review data on the characteristics of turbulence layering in the free atmosphere, as well as examples of observations of layered clouds both near-vent and distally. We then explore dispersion models that explicitly use the observed layered structure of atmospheric turbulence. The results suggest that the alternation of turbulent and quiescent atmospheric layers provides one mechanism for development of multilayered ash clouds by modulating vertical particle motion. The largest particles, generally > 100 μm, are little affected by turbulence. For particles in which both settling and turbulent diffusion are important to vertical motion, mostly in the range of 10-100 μm, the greater turbulence intensity and more rapid turbulent diffusion in some layers causes these particles to spend greater time in the more turbulent layers, leading to a layering of concentration. For smaller particles, mostly in the submicron range, the more rapid diffusion in the turbulent layers causes these particles to “wash out” quickly.

Volcanic ash clouds often become multilayered and thin with distance from the vent. We explore one mechanism for development of this layered structure. We review data on the characteristics of turbulence layering in the free atmosphere, as well as examples of observations of layered clouds both near-vent and distally. We then explore and contrast the output of volcanic ash transport and dispersal models with models that explicitly use the observed layered structure of atmospheric turbulence. The results suggest that the alternation of turbulent and quiescent atmospheric layers provides one mechanism for development of multilayered ash clouds by modulating the manner in which settling occurs.

The objective of this work is to evaluate GOES-R (Geostationary Operational Environmental Satellites-R series) data-based fog conditions which occurred during the C-FOG (Toward Improving Coastal Fog Prediction) field campaign. The C-FOG campaign was designed to advance understanding of fog formation, development, and dissipation over coastal environments to improve predictability. The project took place along coastlines and open water environments of eastern Canada (Nova Scotia, and the Island of Newfoundland) during August−October of 2018 where environmental conditions play an important role for late season fog formation. During the C-FOG field campaign, coastal instruments were mainly located at the Ferryland supersite, Newfoundland, with two main sites, and five satellite sites, as well as on the Research Vessel Hugh R. Sharp. Key in-situ measurement instruments included microphysical, meteorological, radiation, and aerosol sensors. A fog spectral probe was used for measuring droplet spectra from 1–50 µm at the Ferryland supersite. A laser precipitation monitor with 100 µm to 10 mm size range and an optical particle counter with 0.3–17 µm at 16 spectral channels provided information for fog and drizzle discrimination. Remote sensing platforms, e.g. profiling microwave radiometer, ceilometer, microwave rain radar, lidar, meteorological towers, tethered balloons, and GOES-R products for fog coverage, and droplet size and liquid water path) were used to evaluate fog over horizontal and vertical dimensions. Results suggest that effective radius, phase, liquid water path, and liquid water content values obtained from GOES-R and the profiling microwave radiometer are comparable to ground-based in-situ observations. It is concluded that integration of observations and nowcasting products may help improve short-term local fog predictions.

Abstract. During most volcanic eruptions and many periods of volcanic unrest, detectable quantities of sulfur dioxide (SO2) are injected into the atmosphere at a wide range of altitudes, from ground level to the lower stratosphere. Because the fine ash fraction of a volcanic plume is, at times, collocated with SO2 emissions, global tracking of volcanic SO2 is useful in tracking the hazard long after ash detection becomes dominated by noise. Typically, retrievals of SO2 loading have relied heavily on hyperspectral ultraviolet measurements. More recently, infrared sounders have provided additional loading measurements and estimates of the SO2 layer altitude, adding significant value to real-time monitoring of volcanic emissions as well as climatological analyses. These methods leverage the relative simplicity of infrared radiative transfer calculations, providing fast and accurate physics-based retrievals of loading and altitude. In this study, we detail a probabilistic enhancement of an infrared SO2 retrieval method, based on a modified trace-gas retrieval, to estimate SO2 loading and altitude probabilistically using the Cross-track Infrared Sounder (CrIS) on the Joint Polar Satellite System (JPSS) series of satellites. The methodology requires the characterization of real SO2-free spectra aggregated seasonally and spatially. The probabilistic approach replaces loading and altitude estimates with non-parametric probability density functions, fully quantifying the retrieval uncertainty. This framework adds significant value over basic loading and altitude retrieval because it can be readily incorporated into Monte Carlo forecasting of volcanic emission transport. We highlight results including successes and challenges from analysis of several recent significant eruptions including the 22 June 2019 eruption of Raikoke volcano, Kuril Islands; the mid-December 2016 eruption of Bogoslof volcano; and the 26 June 2018 eruption of Sierra Negra volcano, Galapagos Islands. This retrieval method is currently being implemented in the VOLcanic Cloud Analysis Toolkit (VOLCAT), where it will be used to generate additional cloud object properties for real-time detection, characterization, and tracking of volcanic clouds in support of aviation safety.