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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−1in 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 × 109kg s−1), rapidly expanding umbrella cloud, and entrainment of abundant seawater vaporized from magma‐water interaction at the submarine vent. The eruption of Tonga's underwater Hunga Volcano culminated on 15 January 2022 with a giant volcanic plume that rose out of the ocean and into the mesosphere. This plume created record‐breaking amounts of volcanic lightning observed both from space and by radio antennas on the ground thousands of kilometers away. We show that the eruption created more lightning than any storm yet documented on Earth, including supercells and tropical cyclones. The volcanic plume rose to its maximum height and expanded outward as an umbrella cloud, creating fast‐moving concentric ripples known as gravity waves, analogous to a rock dropped in a pond. Point locations of lightning flashes also expanded outward in a pattern of donut‐shaped rings, following the movement of these ripples. Optically bright lightning was detected at unusually high altitudes, in regions of the volcanic cloud 20–30 km above sea level. Our findings show that a sufficiently powerful volcanic plume can create its own weather system, sustaining the conditions for electrical activity at heights and rates not previously observed. Overall, remote detection of lightning contributed to a detailed timeline of this historic eruption and, more broadly, provides a valuable tool for monitoring and nowcasting hazards of explosive volcanism worldwide. This eruption produced the most intense lightning rates ever documented in Earth's atmosphereLightning rings expand with enormous gravity waves in the umbrella cloud, caused by buoyant oscillation of the overshooting plume topVolcanic lightning and satellite analysis reveal at least four phases of eruptive activity from 02:57–15:12 UTC on 15 January 2022 This eruption produced the most intense lightning rates ever documented in Earth's atmosphere Lightning rings expand with enormous gravity waves in the umbrella cloud, caused by buoyant oscillation of the overshooting plume top Volcanic lightning and satellite analysis reveal at least four phases of eruptive activity from 02:57–15:12 UTC on 15 January 2022

The 15 January 2022 eruption of the Hunga volcano (Tonga) generated a rich spectrum of waves, some of which achieved global propagation. Among numerous platforms monitoring the event, two stratospheric balloons flying over the tropical Pacific provided unique observations of infrasonic wave arrivals, detecting five complete revolutions. Combined with ground measurements from the infrasound network of the International Monitoring System, balloon‐borne observations may provide additional constraint on the scenario of the eruption, as suggested by the correlation between bursts of acoustic wave emission and peaks of maximum volcanic plume top height. Balloon records also highlight previously unobserved long‐range propagation of infrasound modes and their dispersion patterns. A comparison between ground‐ and balloon‐based measurements emphasizes superior signal‐to‐noise ratios onboard the balloons and further demonstrates their potential for infrasound studies. The eruption of the Hunga volcano on 15 January 2022 was one of the most powerful blasts of the last century. This fast and strong perturbation of the atmosphere triggered atmospheric waves which were followed around the world multiple times. Here, we use records of sound waves emitted by the eruption from two balloons flying at about 20 km altitude over the Pacific combined with ground stations around the volcano to help characterize the event and its scenario. Due to weak relative wind and turbulence, the sounds on the balloon are generally clearer than on the ground, demonstrating the potential of high‐altitude measurements for extreme events. Comparison between balloon‐borne and ground‐based observations of infrasound waves triggered by the January 2022 Hunga eruptionEruption sequence from infrasound in broad agreement with plume top height evolutionBenchmark for long‐range monitoring of infrasound from large explosive sources using stratospheric balloon observations Comparison between balloon‐borne and ground‐based observations of infrasound waves triggered by the January 2022 Hunga eruption Eruption sequence from infrasound in broad agreement with plume top height evolution Benchmark for long‐range monitoring of infrasound from large explosive sources using stratospheric balloon observations

Abstract. Snow and ice are important hydrological resources. Their minimum spatial extent over land, here referred to as annual minimum snow/ice (MSI) cover, plays a very important role as an indicator of long-term changes and baseline capacity for surface water storage. Data from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite for the period of 2000–2014 were utilized in this study. The level-2 MODIS swath imagery for bands B1 to B7 was employed and the 500-m bands B3–B7 were spatially downscaled to a 250-m swath grid. The imagery is available daily with multiple overpasses. This allows for more accurate identification of annual minimum in comparison to high-resolution imagery (e.g., Landsat, ASTER, etc.) available at much coarser temporal rates. Atmospherically corrected 10-day clear-sky composites converted into normalized surface reflectance over the warm season (April 1 to September 20) were employed to identify persistent snow and ice presence. Results were compared with our previous results derived from the MODIS Circumpolar Arctic clear-sky composites, generated for the end of melting season, and showed smaller MSI extent by 24%, on average. Produced MSI distributions were also compared with the permanent snow and ice maps available from 6 global land cover datasets: (i) Global Land Cover GLC-2000, (ii & iii) European Space Agency's (ESA) Globcover circa 2005 and 2009, (iv–vi) land cover maps derived under the ESA Climate Change Initiative (CCI) for 2000, 2005, and 2010. Significant biases were discovered between various land cover datasets and our results. For example, GLC-2000 overestimated snow/ice extent by 194% (325,400 km2) for the Canadian Arctic. The biases over the entire landmass (excluding Greenland) are 135% (3.7 × 105km2), 113% (3.0 × 105km2), 89% (2.2 × 105km2), and 28% (0.8 × 105km2) between our results and GLC-2000, ESA Globcover 2005, ESA Globcover 2009, and ESA CCI datasets, correspondingly. The derived MSI extent was compared with Randolph Glacier Inventory (RGI) 4.0 and showed much better consistency (ranging from 1% to 15%).

Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies Rasim Latifovic, Alexander P. Trishchenko, Ji Chen, William B. Park, Konstantin V. Khlopenkov, Richard Fernandes, Darren Pouliot, Calin Ungureanu, Yi Luo, Shusen Wang, Andrew Davidson, and Josef Cihlar Pages 324-346 Abstract.Satellite data are an important component of the global climate observing system (GCOS). To serve the purpose of climate change monitoring, these data should satisfy certain criteria in terms of the length of observations and the continuity and consistency between different missions and instruments. Despite the great potential and obvious advantages of satellite observations, such as frequent repeat cycles and global coverage, their use in climate studies is hindered by substantial difficulties arising from large data volumes, complicated processing, and significant computer resources required for archiving and analysis. Successful examples of satellite earth observation (EO) data in climate studies include, among others, analyses of the earth's radiation budget (Earth Radiation Budget Experiment (ERBE), Scanner for Radiation Budget (ScaRaB), and Cloud and the Earth's Radiant Energy System (CERES)), cloudiness (International Satellite Cloud Climatology Project (ISCCP)), vegetation research (Global Inventory Modeling and Mapping Studies (GIMMS)), and the National Oceanic and Atmospheric Administration – National Aeronautics and Space Administration (NOAA–NASA) Pathfinder Program. Despite several attempts, the great potential of the advanced very high resolution radiometer (AVHRR) 1 km satellite data for climate research remains substantially underutilized. To address this issue, the generation of a comprehensive satellite data archive of AVHRR data and products at 1 km spatial resolution over Canada for 1981–2004 (24 years) has been initiated, and a new system for processing at level 1B has been developed. This processing system was employed to generate baseline 1 day and 10 day year-round clear-sky composites for a 5700 km × 4800 km area of North America. This region is centred over Canada but also includes the northern United States, Alaska, Greenland, and surrounding ocean regions. The baseline products include top-of-atmosphere (TOA) visible and near-infrared reflectance, TOA band 4 and band 5 brightness temperature, a cloud – clear – shadow – snow and ice mask, and viewing geometry. Details of the data processing system are presented in the paper. An evaluation of the system characteristics and comparison with previous results demonstrate important improvements in the quality and efficiency of the data processing. The system can process data in a highly automated manner, both for snow-covered and snow-free scenes, and for daytime and nighttime orbits, with high georeferencing accuracy and good radiometric consistency for all sensors from AVHRR NOAA-6 to AVHRR NOAA-17. Other processing improvements include the implementation of advanced algorithms for clear sky – cloud – shadow – snow and ice scene identification, as well as atmospheric correction and compositing. At the time of writing, the assembled dataset is the most comprehensive AVHRR archive at 1 km spatial resolution over Canada that includes all available observations from AVHRR between 1981 and 2004. The archive and the processing system are valuable assets for studying different aspects of land, oceans, and atmosphere related to climate variability and climate change.

Overshooting tops (OTs) are a well‐known indicator of updrafts capable of transporting air from the troposphere to the stratosphere and generating hazardous weather conditions. Satellites and radars have long been used to identify OTs, but the results have not been entirely consistent due to differences in sensor and measurement characteristics. OT detection approaches based on satellite infrared (IR) imagery have often been validated using human‐expert OT identifications, but such datasets are time‐consuming to compile over broad geographic regions. Despite radar limitations to detect the true physical cloud top, OTs identified within multi‐radar composites can serve as a stable reference for comprehensive satellite OT analysis and detection validation. This study analyzes a large OT data set compiled from Geostationary Operational Environmental Satellites (GOES)‐13/16 geostationary IR data and gridded volumetric Next‐Generation Radar (NEXRAD) reflectivity to better understand radar and IR observations of OTs, quantify agreement between satellite and radar OT detections, and demonstrate how an increased spatial sampling from GOES‐13 to GOES‐16 impacts OT appearance and detection performance. For nearly time‐matched scenes and moderate OT probability, the GOES‐13 detection rate (∼60%) is ∼15% lower than GOES‐16 (∼75%), which is mostly attributed to coarser spatial resolution. NEXRAD column‐maximum reflectivity and tropopause‐relative echo‐top height as a function of GOES OT probability were quite consistent between the two satellites however, indicating that efforts to account for differing resolution were largely successful. GOES false detections are unavoidable because outflow from nearby or recently decayed OTs can be substantially colder than the tropopause and look like an OT to an automated algorithm. Overshooting Top (OT) detections from weather radar are a stable reference for satellite OT detection analysis and validationCoarser Geostationary Operational Environmental Satellites (GOES)‐13 resolution reduces Overshooting Top (OT) detection rate compared to GOES‐16, but efforts to account for lower resolution were mostly successfulGeostationary Operational Environmental Satellites (GOES) Overshooting Top (OT) detections were often correlated with high tropopause‐relative echo tops, however GOES false detections are unavoidable Overshooting Top (OT) detections from weather radar are a stable reference for satellite OT detection analysis and validation Coarser Geostationary Operational Environmental Satellites (GOES)‐13 resolution reduces Overshooting Top (OT) detection rate compared to GOES‐16, but efforts to account for lower resolution were mostly successful Geostationary Operational Environmental Satellites (GOES) Overshooting Top (OT) detections were often correlated with high tropopause‐relative echo tops, however GOES false detections are unavoidable

This paper describes an updated method for automated detection of overshooting cloud tops (OT) using a combination of spatial infrared (IR) brightness temperature patterns and modeled tropopause temperature. IR temperatures are normalized to the tropopause, which serves as a stable reference that modulates how cold a convective cloud should become within a given region. Anvil clouds are identified using histogram analysis and cold spots embedded within anvils serve as OT candidate regions. OT candidates are then assigned an OT probability, which can be interpreted as a metric of storm intensity and an estimate of confidence in a detection for a particular pixel. It is produced using an original mathematical composition of four factors: Tropopause‐normalized temperature, prominence relative to the surrounding anvil, surrounding anvil area, and spatial uniformity of anvil temperature, which are calculated from empirically derived sensitivity curves. The shape of the curves is supported by independent analysis of a large sample of matched IR and radar‐derived OT regions. An optimal sensitivity for each factor was determined by maximizing correlation between the OT probability and a set of human‐identified OT regions. Coarser spatial resolution of GOES‐13 data cause OTs to be less prominent compared to GOES‐16, necessitating different sensitivities for each satellite. Detection performance is quantified for each satellite based on human OT identifications and as a function of how prominent the OT appeared in visible and IR imagery. Based on analyses of human‐identified OTs, OT detection accuracy, defined by the area under a receiver operating characteristic curve, is determined to be 0.94 for GOES‐16 and 0.78 for GOES‐13. A new OT detection method combines tropopause‐relative infrared (IR) temperature, anvil‐relative prominence, anvil area and its spatial uniformityOvershooting cloud tops (OT) probability derived from spatial cloud analyses is validated with human‐identified OTs, revealing improvements over previous methodsDiffering imagery spatial resolution necessitates optimization of sensitivity curves to account for warmer observed OTs from coarser imagery A new OT detection method combines tropopause‐relative infrared (IR) temperature, anvil‐relative prominence, anvil area and its spatial uniformity Overshooting cloud tops (OT) probability derived from spatial cloud analyses is validated with human‐identified OTs, revealing improvements over previous methods Differing imagery spatial resolution necessitates optimization of sensitivity curves to account for warmer observed OTs from coarser imagery

The Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory provides 10 narrowband spectral images of the sunlit side of the Earth. The blue (443 nm), green (551 nm), and red (680 nm) channels are used to derive EPIC broadband radiances based upon narrowband‐to‐broadband regressions developed using collocated MODIS equivalent channels and Clouds and the Earth's Radiant Energy System (CERES) broadband measurements. The pixel‐level EPIC broadband radiances are averaged to provide global daytime means at all applicable EPIC times. They are converted to global daytime mean shortwave (SW) fluxes by accounting for the anisotropy characteristics using a cloud property composite based on lower Earth orbiting satellite imager retrievals and the CERES angular distribution models (ADMs). Global daytime mean SW fluxes show strong diurnal variations with daily maximum‐minimum differences as great as 20 W/m2depending on the conditions of the sunlit portion of the Earth. The EPIC SW fluxes are compared against the CERES SYN1deg hourly SW fluxes. The global monthly mean differences (EPIC‐SYN) between them range from 0.1 W/m2in July to −4.1 W/m2in January, and the RMS errors range from 3.2 to 5.2 W/m2. Daily mean EPIC and SYN fluxes calculated using concurrent hours agree with each other to within 2% and both show a strong annual cycle. The SW flux agreement is within the calibration and algorithm uncertainties, which indicates that the method developed to calculate the global anisotropic factors from the CERES ADMs is robust and that the CERES ADMs accurately account for the Earth's anisotropy in the near‐backscatter direction. Measurements from Earth Polychromatic Imaging Camera onboard Deep Space Climate Observatory were used to derive the global daytime mean shortwave fluxes. They agree with those derived from the Clouds and the Earth's Radiant Energy System to within 2%. Global daytime mean radiances from EPIC are converted to SW fluxes by accounting for the anisotropy using CERES angular distribution modelsThe global monthly mean daytime SW fluxes from EPIC agree with those from CERES to within 2%The CERES angular distribution models accurately account for the Earth's anisotropy in the near‐backscatter direction