The eruption of Calbuco volcano on April 22-23, 2015 is the first volcanic eruption detected by a weather radar in South America. The detection was performed by the first domestically produced Argentinean weather radar, called RMA0 and located at Bariloche International Airport. It is a C-band Doppler dualpolarization system, manufactured by INVAP S.E. as a part of the new radar network of Argentina. The aim of this study is to present analysis of the time evolution of the structure of the volcanic plume using polarimetric observables. In order to explore the potential of this new data set for the analysis of the Calbuco volcano eruption column and dispersed ash cloud, synthetic backscattering signatures at C-band have been simulated and used to set up a threshold-based algorithm for tephra-type classification. An evaluation of lightning activity and its relationships with the volcanic particle spatial distribution and attendant polarimetric radar signatures are also discussed.
This work examines a severe weather event that took place over central Argentina on 11 December 2018. The evolution of the storm from its initiation, rapid organization into a supercell, and eventual decay was analyzed with high-temporal resolution observations. This work provides insight into the spatio-temporal co-evolution of storm kinematics (updraft area and lifespan), cloud-top cooling rates, and lightning production that led to severe weather. The analyzed storm presented two convective periods with associated severe weather. An overall decrease in cloud-top local minima IR brightness temperature (MinIR) and lightning jump (LJ) preceded both periods. LJs provided the highest lead time to the occurrence of severe weather, with the ground-based lightning networks providing the maximum warning time of around 30 min. Lightning flash counts from the Geostationary Lightning Mapper (GLM) were underestimated when compared to detections from ground-based lightning networks. Among the possible reasons for GLM's lower detection efficiency were an optically dense medium located above lightning sources and the occurrence of flashes smaller than GLM's footprint. The minimum MinIR provided the shorter warning time to severe weather occurrence. However, the secondary minima in MinIR that preceded the absolute minima improved this warning time by more than 10 min. Trends in MinIR for time scales shorter than 6 min revealed shorter cycles of fast cooling and warming, which provided information about the lifecycle of updrafts within the storm. The advantages of using observations with high-temporal resolution to analyze the evolution and intensity of convective storms are discussed. Plain Language Summary Severe thunderstorms can have distinctive signals in satellite observations. Cloud-top temperature minima are one of the most studied metrics as a severe weather indicator. The newest geostationary weather satellites (GOES-16/17) offer a unique opportunity to study storms through their rapid-scan mode and lightning detector. In this study, we analyzed high-temporal (1-min) observations of cloud-top temperature and lightning detected from space and the surface to study the evolution of a severe thunderstorm that took place over central Argentina on 11 December 2018. Overall, cloud-top temperature minimum and fast increases in lightning activity preceded the occurrence of severe weather. The signature present in lightning observations provided the highest severe weather lead time, with ground-based sensors providing the maximum warning time (~30 min). A cloud-top temperature absolute minimum provided the shortest warning time, whereas secondary minima, that preceded the absolute minima, improved the warning time by more than 10 min. This improvement in lead time can result in better societal preparedness for imminent hazardous weather where no ground-based lightning observations are available. Observations with high-temporal resolution also show cycles of fast cloud-top cooling and warming that can provide important insight o...
Storms are one of nature's most dangerous phenomena; therefore, knowing their spatial distribution and evolution over time is of great interest for the protection of society, as well as for climate change adaptation strategies. The measurement of Thunderstorm days (Td) was one of the first tools used to monitor storms. The advent of automatic detection networks on the surface has allowed us to advance in the understanding and characterization of the electrical activity in the atmosphere, locating in real-time electrical discharges and providing information over previously unrecorded regions. This work focuses on the integration of human observations at conventional meteorological stations and the data provided by the WWLLN surface discharge detection network in Argentina. The calibration methodology applied determined a mean human thunderstorm detection radius of 21 km which allowed the elaboration of isokeraunic maps for the period 2008-2017 for the region of interest. The spatial distribution of storms yielded the highest values of Td in the Argentine Northwest region with values above 100 TdÁyear −1 followed by a relative maximum in the Argentine Northeast with 80 TdÁyear −1 and the Sierras de C ordoba with 50 TdÁyear −1 .
Thunderstorms in southeastern South America (SESA) stand out in satellite observations as being among the strongest on Earth in terms of satellite-based convective proxies, such as lightning flash rate per storm, the prevalence for extremely tall, wide convective cores and broad stratiform regions. Accurately quantifying when and where strong convection is initiated presents great interest in operational forecasting and convective system process studies due to the relationship between convective storms and severe weather phenomena. This paper generates a novel methodology to determine convective initiation (CI) signatures associated with extreme convective systems, including extreme events. Based on the well-established area-overlapping technique, an adaptive brightness temperature threshold for identification and backward tracking with infrared data is introduced in order to better identify areas of deep convection associated with and embedded within larger cloud clusters. This is particularly important over SESA because ground-based weather radar observations are currently limited to particular areas. Extreme rain precipitation features (ERPFs) from Tropical Rainfall Measurement Mission are examined to quantify the full satellite-observed life cycle of extreme convective events, although this technique allows examination of other intense convection proxies such as the identification of overshooting tops. CI annual and diurnal cycles are analyzed and distinctive behaviors are observed for different regions over SESA. It is found that near principal mountain barriers, a bimodal diurnal CI distribution is observed denoting the existence of multiple CI triggers, while convective initiation over flat terrain has a maximum frequency in the afternoon.
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