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GOES‐16 10.3 μm brightness temperature on May 5, 2019 at 23:00 UTC across the U.S. Southern Plains (left) and the corresponding image of BT‐score (right). A scale of brightness temperature (BT) minus tropopause difference (BTTD) is also provided for reference.
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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...
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... Analyses presented here also include cloud top height (CTH) estimates from Advanced Baseline Imager channels on the National Oceanic and Atmospheric Administration GOES-16 satellite from 2018 to 2021 (Heidinger et al., 2020;Schmit et al., 2005). CTH from GOES-16 is reported at a 2-km spatial resolution every five minutes (Khlopenkov et al., 2021). Daily maximum CTH in each grid cell is conditionally sampled by ERA5 wind direction to evaluate whether there is evidence that the DFW conurbation induces higher topped clouds and by association enhancement/suppression of deep convection. ...
A range of multi‐year observational data sets are used to characterize the hydroclimate of the Dallas Fort‐Worth area (DFW) and to investigate the impact of urban land cover on daily accumulated precipitation, RADAR composite reflectivity (cREF), and cloud top height (CTH) during the warm season. Analyses of observational data indicate rainfall rates (RR) in a 45° annulus sector 50–100 km downwind of the city are enhanced relative to an upwind area of comparable size. Enhancement of mean precipitation intensity in this annulus sector is not observed on days with spatially averaged RR > 6 mm/day. Under some flow directions, the probability of cREF >30 dBZ, occurrence of hail, and the probability of CTH >10,000 geopotential meters are also enhanced up to 200 km downwind of DFW. Two deep convection events that passed over DFW are simulated with the Weather Research and Forecasting model using a range of microphysical schemes and evaluated using RADAR observations. Model configurations that exhibit the highest fidelity in these control simulations are used in a series of perturbation experiments where the areal extent of the city is varied between zero (replacement with grassland) and eight times its current size. These perturbation experiments indicate a non‐linear response of Mesoscale Convective System properties to the urban areal extent and a very strong sensitivity to the microphysical scheme used. The impact on precipitation from the urban area, even when it is expanded to eight‐times the current extent, is much less marked for deep convection with stronger synoptic forcing.
... One-minute Mesoscale Domain Sector (MDS) data from the Geostationary Operational Environmental Satellite (GOES)-16 (GOES-East during the study period) were used to identify the OTs in the SESA study domain. A detailed description of the OT detection method can be found in Khlopenkov et al. (2021). Generally, OTs were detected using visible reflectance imagery (Band 2), longwave infrared brightness temperature imagery (Band 13) (Schmit et al., 2017) and a blended reanalysis tropopause, with detection probabilities based on the tropopausenormalized temperature, the prominence of the OT, the anvil area and the uniformity of the temperature of the anvil . ...
... The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) was used to quantify the environment and the static stability parameters in the LS (Gelaro et al., 2017). MERRA-2 has been used widely in studies examining the LS and its associated characteristics Khlopenkov et al., 2021;Schmit et al., 2017;Wargan & Coy, 2016). All widely used reanalysis products (MERRA-2, ERA-Interim, JSA-55, CSFR) perform similarly with relatively small biases in the LS region, relative to the resolution of the models (Xian & Homeyer, 2019). ...
... In this study, all OTs with a detection probability greater than 0.8, as determined by the Khlopenkov et al. (2021) method, were analyzed. This threshold ensures high enough confidence in detections without removing too many OT candidate objects (Bedka & Khlopenkov, 2016;Grover, 2021) to balance OT detection while minimizing the false alarm ratio. ...
Overshooting tops (OTs) are manifestations of deep convective updrafts that extend above the tropopause into the stratosphere. They can induce dynamic perturbations and result in irreversible transport of aerosols, water vapor and other mass from the troposphere into the stratosphere, thereby impacting the chemical composition and radiative processes of the stratosphere. These and other effects of OTs depend on their characteristics such as depth and area, which are understood to connect to mid‐tropospheric updraft speed and width, respectively. Less understood is how static stability in the lower stratosphere (LS) potentially modulates these OT–updraft connections, thus motivating the current study. Here, LS static stability and observed OT characteristics are quantified and compared using a combination of reanalysis data, observed rawinsonde data and geostationary satellite data. A weak to moderate relationship between OT depth and LS lapse rate and Brunt‐Väisälä frequency (N²) (R = 0.38, −0.37, respectively) is found, implying that OT depth is reduced with an increasingly stable LS. In contrast, a weak relationship (R = −0.03, 0.03, respectively) is found between OT area and LS static stability, implying that OT area is controlled primarily by mid to upper tropospheric updraft area. OT duration has a weak relationship to LS lapse rate and N² (R = 0.02, −0.02, respectively). These relationships may be useful in interpreting mid‐ and low‐level storm dynamics from satellite‐observed characteristics of OTs in near real‐time.
... 4, while discussions and The present study considers IR imagery from geostationary MSG Spinning Enhanced Visible and InfraRed Imager (SEVIRI) (Schmetz et al., 2002) between 2016 to 2020 at a continuous temporal resolution of 15 minutes over south-central Europe. 155 Only OTs detected with the Khlopenkov et al. (2021) algorithm having a probability >50% are considered, similar to Punge et al. (2023). This statistical constraint was derived by the comparison of OT detections with radar echo tops (Cooney et al., 2021) that demonstrated enhanced reliability being indicative of colder and more prominent anvil-relative tops. ...
... 155 Only OTs detected with the Khlopenkov et al. (2021) algorithm having a probability >50% are considered, similar to Punge et al. (2023). This statistical constraint was derived by the comparison of OT detections with radar echo tops (Cooney et al., 2021) that demonstrated enhanced reliability being indicative of colder and more prominent anvil-relative tops. The spatial distribution of the 991,042 OTs detected over 872 days is shown on a 10-km regular grid in Fig. 2b. ...
... Recent findings suggest links between convective storm severity and specific characteristics of the OT detections, such as their spatial extension (Marion et al., 2019) or the temperature gradient between the OT and the tropopause (Khlopenkov et al., 2021). However, some OTs with intense updrafts reaching the tropopause and penetrating the lower stratosphere may be associated with convective environments not necessarily supportive of severe weather phenomena such as hail. ...
The challenges associated with reliably observing and simulating hazardous hailstorms call for new approaches that combine information from different available sources, such as remote sensing instruments, observations, or numerical modeling, to improve understanding of where and when severe hail most often occurs. In this work, a proxy for hail frequency is developed by combining overshooting cloud top (OT) detections from the Meteosat Second Generation (MSG) weather satellite with convection-permitting SPHERA reanalysis predictors describing hail-favorable environmental conditions. Atmospheric properties associated with ground-based reports from the European Severe Weather Database (ESWD) are considered to define specific criteria for data filtering. Five convection-related parameters from reanalysis data quantifying key ingredients for hailstorm occurrence enter the filter, namely: most unstable convective available potential energy (CAPE), K index, surface lifted index, deep-layer shear, and freezing level height. A hail frequency estimate over the extended summer season (April–October) in south-central Europe is presented for a test period of 5 years (2016–2020). OT-derived hail frequency peaks at around 15 UTC in June–July over the pre-Alpine regions and the northern Adriatic sea. The hail proxy statistically matches with ∼62 % of confirmed ESWD reports, which is roughly 22 % more than the previous estimate over Europe coupling deterministic satellite detections with coarser global reanalysis ambient conditions. The separation of hail events according to their severity highlights enhanced appropriateness of the method for large-hail-producing hailstorms (with hailstones diameters ≥ 3 cm). Further, signatures for small-hail missed occurrences are identified, which are characterized by lower instability and organization, and warmer cloud-top temperatures.
... In addition to this, satellite sensors have potential advantages for detecting strong convective updrafts [19][20][21][22][23][24][25][26][27][28][29][30]. Hail clouds are produced in deep convective storms that are characterized by strong updrafts; large, supercooled liquid water content; and high cloud tops [26]. ...
... Hail clouds are produced in deep convective storms that are characterized by strong updrafts; large, supercooled liquid water content; and high cloud tops [26]. Thus, the overshooting cloud top (OT) may represent a convective updraft suitable for generating hail [27,28]. Consequently, satellites can detect OT by following strong convective updrafts to identify potential hazardous hailstorms across a wide area [29,30]. ...
Severe hailstorms frequently occurred in Beijing between May and August 2021, leading to extensive hail damage. These hailstorms were observed by radar and satellite data, and reported by surface observers. In this study, the spectral and cloud microphysical characteristics of typical Beijing events in 2021 were analyzed using Himawari-8 satellite products and ground-based S-band weather radar data obtained from the Beijing Meteorological Bureau. The relationship between Himawari-8 brightness temperature differences (BTD) and radar reflectivity was also investigated. The results revealed that the significant spectral depression of brightness temperatures (BTs) in hail clouds was observed by a satellite. Furthermore, the stronger the radar reflectivity was, the more rapidly BTD decreased, with a nonlinear relationship between them. The results of cloud physical characteristics show that, for cloud-top heights above 10 km, the cloud effective radius was about 25 μm, with a cloud-top temperature of 225 K during these hail events. By means of Gaussian fitting, the BT threshold value (11.2 μm) was determined by satellite at 230 K, with a BTD focused on 1.9 K when hailstorms occurred. These results will help us better understand the characteristics of hailstorms, while also providing information for future hail suppression in Beijing.
... Bedka and Khlopenkov (2016) introduced an IR-based method for OT detection that improved upon a previous method described in Bedka et al. (2010). This method has been further developed and is described in Khlopenkov et al. (2021), which serves as a companion to this paper. The algorithm attempts to quantify how much a cluster of pixels resemble an OT on the basis of spatial T b patterns in GOES ∼11 μm IR images and their relationship with a tropopause temperature forecast or reanalysis. ...
... IR T b colder than the tropopause temperature likely represent an intrusion into the lower stratosphere. Changes in spatial resolution throughout the GOES data record affect how prominently OTs appear and how much colder they are relative to the tropopause (Khlopenkov et al., 2021). Satellite-based OT detection approaches have often been validated using manual OT identifications from human experts because the algorithms are designed to detect OT-like patterns in the imagery itself, independent of whether or not they are detected with weather radars or other methods (Bedka & Khlopenkov, 2016;Khlopenkov et al., 2021;Kim et al., 2017). ...
... Changes in spatial resolution throughout the GOES data record affect how prominently OTs appear and how much colder they are relative to the tropopause (Khlopenkov et al., 2021). Satellite-based OT detection approaches have often been validated using manual OT identifications from human experts because the algorithms are designed to detect OT-like patterns in the imagery itself, independent of whether or not they are detected with weather radars or other methods (Bedka & Khlopenkov, 2016;Khlopenkov et al., 2021;Kim et al., 2017). These manual identifications are able to provide a glimpse into the accuracy of satellite-based detections, however, they are time-consuming, so previous studies only analyze detections from a few OT storm events. ...
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 tops (OTs), prominent signatures within deep convective storms, are produced by intense updrafts and are closely linked to heavy rainfall, strong winds, and other severe weather conditions. Using an OT dataset derived from multiyear observations of precipitation radar on board the Global Precipitation Measurement core observatory as a reference, the performances of two commonly used OT detection algorithms are evaluated for the Himawari-8 and Fengyun-4A satellites. The results indicate that the infrared contour-based algorithm based on Himawari-8 is the most effective for objective OT detection in eastern China. It exhibits a probability of detection (POD) of 62.1% and a false-alarm ratio (FAR) of 36.6%, outperforming others by achieving a greater POD and a lower FAR. Furthermore, based on the severe weather records from surface meteorological stations and nearby OT detections, a strong relationship is revealed between GEO-detected OTs and the occurrence of short-term heavy rainfall (e.g., ≥20 mm h⁻¹) and extreme wind speed (e.g., ≥17.2 m s⁻¹) events. The OT matched percentages for these events are 61.8% and 54.0%, respectively. This suggests that GEO satellite-based OT data can serve as an important objective product for forecasters to increase their understanding of severe convective storms.
The presence of deep convective clouds is directly related to potential convective hazards, such as lightning strikes, hail, severe storms, flash floods, and tornadoes. On the other hand, Mexico has a limited and heterogeneous network of instruments that allow for efficient and reliable monitoring and forecasting of such events. In this study, a quasi-real-time framework for deep convective cloud identification and modeling based on machine learning (ML) models was developed. Eight different ML models and model assembly approaches were fed with Interest Fields estimated from Advanced Baseline Imager (ABI) sensor data on the Geostationary Operational Environmental Satellite-R Series (GOES-R) for one region in central Mexico and another in northeastern Mexico, both selected for their intense convective activity and high levels of vulnerability to severe weather. The results indicate that a simple approach such as Logistic Regression (LR) or Random Forest (RF) can be a good alternative for the identification and simulation of deep convective clouds in both study areas, with a probability of detection of (POD) ≈ 0.84 for Los Mochis and POD of ≈ 0.72 for Mexico City. Similarly, the false alarm ratio (FAR) ≈ 0.2 and FAR ≈ 0.4 values were obtained for Los Mochis and Mexico City, respectively. Finally, a post-processing filter based on lightning incidence (Lightning Filter) was applied with data from the Geostationary Lightning Mapper (GLM) of the GOES-16 satellite, showed great potential to improve the probability of detection (POD) of the ML models. This work sets a precedent for the implementation of an early-warning system for hazards associated with intense convective activity in Mexico
Global Navigation Satellite Systems (GNSS) are not only a state‐of‐the‐art sensor for positioning and navigation applications but also a valuable tool for remote sensing. Through the usage of L‐band carrier frequencies, GNSS acts as an all‐weather‐operation system, offering substantial benefits compared to optical systems. Nevertheless, severe weather can still have an impact on the strength of signals received at a ground station, as we show in this study. We investigate GNSS Signal‐to‐Noise Ratio (SNR) observations during two severe convective storm events over the city of Zurich, Switzerland. We make use of a GNSS‐SNR‐based algorithm originally developed for the detection of hail particles from volcanic eruptions. Results indicate that, although GNSS observations are considered to be fairly insensitive to the presence of hydrometeors, convective storm events are visible in SNR observations. SNR levels of affected satellites show a significant drop during event periods, which are determined by weather radar observations.
This study focuses on integrating dual‐frequency precipitation radar (DPR) observations on‐board the Global Precipitation Mission with the storm index from INSAT‐3D for the development of reconstructed radar reflectivity. Performance of the derived product was tested against the independent DPR observations. A correlation coefficient of 0.81 is reported between the reconstructed radar reflectivity and the DPR observations. Derived reconstructed reflectivity is shown to be an excellent product for the weather monitoring over the regions without radar coverage.
Geostationary satellite imagers provide historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are made where satellites observe cold clouds, indicative of convection, located in favorable storm environments. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record.
Statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, vertical wind shear, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a likelihood estimate with a critical success index of 0.511 and Heidke skill score of 0.407, which is exceptional among analogous hail studies. Furthermore, applications of the DNN to case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied.
