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  • 1
    Online Resource
    Online Resource
    Random Forest Model to Assess Predictor Importance and Nowcast Severe Storms using High-Resolution Radar–GOES Satellite–Lightning Observations
    American Meteorological Society ; 2021
    In:  Monthly Weather Review ( 2021-03-08)
    Details
    In: Monthly Weather Review, American Meteorological Society, ( 2021-03-08)
    Abstract: Few studies have assessed combined satellite, lightning, and radar databases to diagnose severe storm potential. The research goal here is to evaluate next-generation, 60-second update frequency geostationary satellite and lightning information with ground-based radar to isolate which variables, when used in concert, provide skillful discriminatory information for identifying severe (hail ≥2.5 cm in diameter, winds ≥25 m s –1 , tornadoes) versus non-severe storms. The focus of this study is predicting severe thunderstorm and tornado warnings. A total of 2,004 storms in 2014–2015 were objectively tracked with 49 potential predictor fields related to May, daytime Great Plains convective storms. All storms occurred when 1-min Geostationary Operational Environmental Satellite (GOES)–14 “super rapid scan” data were available. The study used three importance methods to assess predictor importance related to severe warnings, and random forests to provide a model and skill evaluation measuring the ability to predict severe storms. Three predictor importance methods show that GOES mesoscale atmospheric motion vector derived cloud-top divergence and above anvil cirrus plume presence provide the most satellite-based discriminatory power for diagnosing severe warnings. Other important fields include Earth Networks Total Lightning flash density, GOES estimated cloud-top vorticity, and overshooting-top presence. Severe warning predictions are significantly improved at the 95% confidence level when a few important satellite and lightning fields are combined with radar fields, versus when only radar data are used in the random forests model. This study provides a basis for including satellite and lightning fields within machine-learning models to help forecast severe weather.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-19-0274.1
    RVK:
    RA 1000
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 2
    Online Resource
    Online Resource
    Evaluating the Ability of Remote Sensing Observations to Identify Significantly Severe and Potentially Tornadic Storms
    American Meteorological Society ; 2019
    In:  Journal of Applied Meteorology and Climatology Vol. 58, No. 12 ( 2019-12), p. 2569-2590
    Details
    In: Journal of Applied Meteorology and Climatology, American Meteorological Society, Vol. 58, No. 12 ( 2019-12), p. 2569-2590
    Abstract: Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 m s −1 ) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur.
    Type of Medium: Online Resource
    ISSN: 1558-8424 , 1558-8432
    URL: Article
    DOI: 10.1175/JAMC-D-18-0241.1
    RVK:
    UA 4547
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2019
    detail.hit.zdb_id: 2227779-1
    detail.hit.zdb_id: 2227759-6
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  • 3
    Online Resource
    Online Resource
    Analysis of Cumulus Cloud Updrafts as Observed with 1-Min Resolution Super Rapid Scan GOES Imagery
    American Meteorological Society ; 2016
    In:  Monthly Weather Review Vol. 144, No. 2 ( 2016-02-01), p. 811-830
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 144, No. 2 ( 2016-02-01), p. 811-830
    Abstract: A study was undertaken to examine growing cumulus clouds using 1-min time resolution Super Rapid Scan Operations for Geostationary Operational Environmental Satellite-R (GOES-R) (SRSOR) imagery to diagnose in-cloud processes from cloud-top information. SRSOR data were collected using GOES-14 for events in 2012–14. Use of 1-min resolution SRSOR observations of rapidly changing scenes provides far more insights into cloud processes as compared to when present-day 5–15-min time resolution GOES data are used. For midday times on five days, cloud-top temperatures were cataloged for 71 cumulus clouds as they grew to possess anvils and often overshooting cloud tops, which occurred over 33–152-min time periods. Characteristics of the SRSOR-observed updrafts were examined individually, on a per day basis, and collectively, to reveal unique aspects of updraft behavior, strength, and acceleration as related to the ambient stability profile and cloud-top glaciation. A conclusion is that the 1-min observations capture two specific cumulus cloud growth periods, less rapid cloud growth between the level of free convection and the 0°C isotherm level, followed by more rapid growth shortly after the time of cloud-top glaciation. High correlation is found between estimated vertical motion (w) and the amount of convective available potential energy (CAPE) realized to the cloud-top level as clouds grew, which suggests that updrafts were responding to the local buoyancy quite strongly. Influences of the environmental buoyancy profile shape and evidence of entrainment on cloud growth are also found through these SRSOR data analyses.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-14-00399.1
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2016
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 4
    Online Resource
    Online Resource
    Relationships between Deep Convection Updraft Characteristics and Satellite-Based Super Rapid Scan Mesoscale Atmospheric Motion Vector–Derived Flow
    American Meteorological Society ; 2018
    In:  Monthly Weather Review Vol. 146, No. 10 ( 2018-10-01), p. 3461-3480
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 146, No. 10 ( 2018-10-01), p. 3461-3480
    Abstract: Rapid acceleration of cloud-top outflow near vigorous storm updrafts can be readily observed in Geostationary Operational Environmental Satellite-14 (GOES-14) super rapid scan (SRS; 60 s) mode data. Conventional wisdom implies that this outflow is related to the intensity of updrafts and the formation of severe weather. However, from an SRS satellite perspective, the pairing of observed expansion and updraft intensity has not been objectively derived and documented. The goal of this study is to relate GOES-14 SRS-derived cloud-top horizontal divergence (CTD) over deep convection to internal updraft characteristics, and document evolution for severe and nonsevere thunderstorms. A new SRS flow derivation system is presented here to estimate storm-scale ( & lt;20 km) CTD. This CTD field is coupled with other proxies for storm updraft location and intensity such as overshooting tops (OTs), total lightning flash rates, and three-dimensional flow fields derived from dual-Doppler radar data. Objectively identified OTs with (without) matching CTD maxima were more (less) likely to be associated with radar-observed deep convection and severe weather reports at the ground, suggesting that some OTs were incorrectly identified. The correlation between CTD magnitude, maximum updraft speed, and total lightning was strongly positive for a nonsupercell pulse storm, and weakly positive for a supercell with multiple updraft pulses present. The relationship for the supercell was nonlinear, though larger flash rates are found during periods of larger CTD. Analysis here suggests that combining CTD with OTs and total lightning could have severe weather nowcasting value.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-18-0119.1
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2018
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 5
    Online Resource
    Online Resource
    On the Origin of Rotation Derived from Super Rapid Scan Satellite Imagery at the Cloud-Tops of Severe Deep Convection
    American Meteorological Society ; 2021
    In:  Monthly Weather Review ( 2021-03-30)
    Details
    In: Monthly Weather Review, American Meteorological Society, ( 2021-03-30)
    Abstract: Severe thunderstorms routinely exhibit adjacent maxima and minima in cloud-top vertical vorticity (CTV) downstream of overshooting tops within flow fields retrieved using sequences of fine-temporal resolution (1-min) geostationary operational environmental satellite (GOES)-R series imagery. Little is known about the origin of this so-called “CTV couplet” signature, and whether the signature is the result of flow field derivational artifacts. Thus, the CTV signature’s relevance to research and operations is currently ambiguous. Within this study, we explore the origin of near-cloud-top rotation using an idealized supercell numerical model simulation. Employing an advanced dense optical flow algorithm, image stereoscopy, and numerical model background wind approximations, the artifacts common with cloud-top flow field derivation are removed from two supercell case studies sampled by GOES-R imagers. It is demonstrated that the CTV couplet originates from tilted and converged horizontal vorticity that is baroclinically generated in the upper levels (above 10 km) immediately downstream of the overshooting top. This baroclinic generation would not be possible without a strong and sustained updraft, implying an indirect relationship to rotationally-maintained supercells. Furthermore, it is demonstrated that CTV couplets derived with optical flow algorithms originate from actual rotation within the storm anvils in the case studies explored here, though supercells with opaque above anvil cirrus plumes and strong anvil-level negative vertical wind shear may produce rotation signals as an artifact without quality control. Artifact identification and quality control is discussed further here for future research and operations use.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-20-0209.1
    RVK:
    RA 1000
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 6
    Online Resource
    Online Resource
    Environmental Analysis of GOES-R Proving Ground Convection-Initiation Forecasting Algorithms
    American Meteorological Society ; 2015
    In:  Journal of Applied Meteorology and Climatology Vol. 54, No. 7 ( 2015-07), p. 1637-1662
    Details
    In: Journal of Applied Meteorology and Climatology, American Meteorological Society, Vol. 54, No. 7 ( 2015-07), p. 1637-1662
    Abstract: Enhanced temporal and spatial resolution of the Geostationary Operational Environmental Satellite–R Series (GOES-R) will allow for the use of cloud-top-cooling-based convection-initiation (CI) forecasting algorithms. Two such algorithms have been created on the current generation of GOES: the University of Wisconsin cloud-top-cooling algorithm (UWCTC) and the University of Alabama in Huntsville’s satellite convection analysis and tracking algorithm (SATCAST). Preliminary analyses of algorithm products have led to speculation over preconvective environmental influences on algorithm performance. An objective validation approach is developed to separate algorithm products into positive and false indications. Seventeen preconvective environmental variables are examined for the positive and false indications to improve algorithm output. The total dataset consists of two time periods in the late convective season of 2012 and the early convective season of 2013. Data are examined for environmental relationships using principal component analysis (PCA) and quadratic discriminant analysis (QDA). Data fusion by QDA is tested for SATCAST and UWCTC on five separate case-study days to determine whether application of environmental variables improves satellite-based CI forecasting. PCA and significance testing revealed that positive indications favored environments with greater vertically integrated instability (CAPE), less stability (CIN), and more low-level convergence. QDA improved both algorithms on all five case studies using significantly different variables. This study provides an examination of environmental influences on the performance of GOES-R Proving Ground CI forecasting algorithms and shows that integration of QDA in the cloud-top-cooling-based algorithms using environmental variables will ultimately generate a more skillful product.
    Type of Medium: Online Resource
    ISSN: 1558-8424 , 1558-8432
    URL: Article
    DOI: 10.1175/JAMC-D-14-0190.1
    RVK:
    UA 4547
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2015
    detail.hit.zdb_id: 2227779-1
    detail.hit.zdb_id: 2227759-6
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  • 7
    Online Resource
    Online Resource
    Analysis of Mesoscale Atmospheric Flows above Mature Deep Convection Using Super Rapid Scan Geostationary Satellite Data
    American Meteorological Society ; 2016
    In:  Journal of Applied Meteorology and Climatology Vol. 55, No. 9 ( 2016-09), p. 1859-1887
    Details
    In: Journal of Applied Meteorology and Climatology, American Meteorological Society, Vol. 55, No. 9 ( 2016-09), p. 1859-1887
    Abstract: Super Rapid Scan Operations for the Geostationary Operational Environmental Satellite (GOES) R series (SRSOR) using GOES-14 have made experimentation with 1-min time-step data possible prior to the launch of the new satellite. A mesoscale atmospheric motion vector (mAMV) program is utilized in SRSOR with a Barnes analysis to produce objectively analyzed flow fields at the cloud tops of deep convection. Two nonsupercell and four supercell storm cases are analyzed. Data from the SRSOR mAMV analysis are compared with both multi-Doppler analyses when available and idealized convection cases within the Weather Research and Forecasting (WRF) Model framework. It is found that using SRSOR data provides several additional trackable targets to produce mAMVs in rapidly “bubbling” regions at the deep convective cloud-top level not previously available at lower temporal resolutions ( 〈 1 min). Results also show that supercell storm cases produce long-lived maxima in SRSOR cloud-top divergence (CTD) and “couplet” signatures in cloud-top vorticity (CTV), which when compared with idealized WRF Model simulations appear to form as a result of environmental horizontal vorticity tilting. Nonsupercell convection in contrast produced weaker, short-lived CTD signatures and no “CTV couplet” signatures. These case study results suggest that with SRSOR data it might be possible to uniquely identify supercells using only mAMV-derived flow fields.
    Type of Medium: Online Resource
    ISSN: 1558-8424 , 1558-8432
    URL: Article
    DOI: 10.1175/JAMC-D-15-0253.1
    RVK:
    UA 4547
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2016
    detail.hit.zdb_id: 2227779-1
    detail.hit.zdb_id: 2227759-6
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  • 8
    Online Resource
    Online Resource
    Comparison of Optical Flow Derivation Techniques for Retrieving Tropospheric Winds from Satellite Image Sequences
    American Meteorological Society ; 2022
    In:  Journal of Atmospheric and Oceanic Technology Vol. 39, No. 12 ( 2022-12), p. 2005-2021
    Details
    In: Journal of Atmospheric and Oceanic Technology, American Meteorological Society, Vol. 39, No. 12 ( 2022-12), p. 2005-2021
    Abstract: This study introduces a validation technique for quantitative comparison of algorithms that retrieve winds from passive detection of cloud- and water vapor–drift motions, also known as atmospheric motion vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh-rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds, which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages mesoscale, multilayer, multiband wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or “optical flow,” retrieval algorithms used within AMVs, 1) patch matching (PM; used within operational AMVs) and 2) an advanced variational optical flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multilayer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g., gravity waves), where the image temporal noise exceeds the natural variability, and when the height assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh-rate imagery, such as 1- versus 10-min data.
    Type of Medium: Online Resource
    ISSN: 0739-0572 , 1520-0426
    URL: Article
    DOI: 10.1175/JTECH-D-22-0057.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2022
    detail.hit.zdb_id: 2021720-1
    detail.hit.zdb_id: 48441-6
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  • 9
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    Online Resource
    Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery
    Copernicus GmbH ; 2020
    In:  Atmospheric Measurement Techniques Vol. 13, No. 3 ( 2020-04-02), p. 1593-1608
    Details
    In: Atmospheric Measurement Techniques, Copernicus GmbH, Vol. 13, No. 3 ( 2020-04-02), p. 1593-1608
    Abstract: Abstract. Sudden wind direction and speed shifts from outflow boundaries (OFBs) associated with deep convection significantly affect weather in the lower troposphere. Specific OFB impacts include rapid variation in wildfire spread rate and direction, the formation of convection, aviation hazards, and degradation of visibility and air quality due to mineral dust aerosol lofting. Despite their recognized importance to operational weather forecasters, OFB characterization (location, timing, intensity, etc.) in numerical models remains challenging. Thus, there remains a need for objective OFB identification algorithms to assist decision support services. With two operational next-generation geostationary satellites now providing coverage over North America, high-temporal- and high-spatial-resolution satellite imagery provides a unique resource for OFB identification. A system is conceptualized here designed around the new capabilities to objectively derive dense mesoscale motion flow fields in the Geostationary Operational Environmental Satellite 16 (GOES-16) imagery via optical flow. OFBs are identified here by isolating linear features in satellite imagery and backtracking them using optical flow to determine if they originated from a deep convection source. This “objective OFB identification” is tested with a case study of an OFB-triggered dust storm over southern Arizona. The results highlight the importance of motion discontinuity preservation, revealing that standard optical flow algorithms used with previous studies underestimate wind speeds when background pixels are included in the computation with cloud targets. The primary source of false alarms is the incorrect identification of line-like features in the initial satellite imagery. Future improvements to this process are described to ultimately provide a fully automated OFB identification algorithm.
    Type of Medium: Online Resource
    ISSN: 1867-8548
    URL: Article
    URL: Article
    DOI: 10.5194/amt-13-1593-2020
    DOI: 10.5194/amt-13-1593-2020-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2505596-3
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