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Publications (19)
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Monitoring and predicting highly localized weather events over a very short-term period, typically ranging from minutes to a few hours, are very important for decision makers and public action. Nowcasting these events usually relies on radar observations through monitoring and extrapolation. With advanced high-resolution imaging and sounding observ...
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 neur...
Pulse severe storms are single-cell thunderstorms that produce severe wind and/or severe hail for a brief period of time. These storms pose a major warm season forecasting problem since forecasters presently do not have sufficient guidance to know which, if any, of the cells that are observed will become severe. The empirical Probability of Severe...
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...
Intense thunderstorms threaten life and property, impact aviation, and are a challenging forecast problem, particularly without precipitation-sensing radar data. Trained forecasters often look for features in geostationary satellite images such as rapid cloud growth, strong and persistent overshooting tops, U- or V-shaped patterns in storm-top temp...
Severe convective storms are hazardous to both life and property and thus their accurate and timely prediction is imperative. In response to this critical need to help fulfill the mission of the National Oceanic and Atmospheric Administration (NOAA), NOAA and the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University o...
Ash rich clouds, produced by explosive volcanic eruptions, are a major hazard to aviation. Unfortunately, explosive volcanic eruptions are not always detected in a timely manner in satellite data. The large optical depth of emergent volcanic clouds greatly limits the effectiveness of multispectral infrared-based techniques for distinguishing betwee...
Providing advance warning for impending severe convective weather events (i.e., tornadoes, hail, wind) fundamentally requires an ability to predict and/or detect these hazards and subsequently communicate their potential threat in real time. The National Weather Service (NWS) provides advance warning for severe convective weather through the issuan...
The empirical Probability of Severe (ProbSevere) model, developed by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS), automatically extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical foreca...
A new approach for quantitatively detecting volcanic ash and dust from satellite has been developed. The Spectrally Enhanced Cloud Objects (SECO) algorithm utilizes a combination of radiative transfer theory, a statistical model, and image processing techniques to identify volcanic ash and dust clouds in satellite imagery with a very low false alar...
While satellites are a proven resource for detecting and tracking volcanic ash and dust clouds, existing algorithms for automatically detecting volcanic ash and dust either exhibit poor overall skill or can only be applied to a limited number of sensors and/or geographic regions. As such, existing techniques are not optimized for use in real-time a...
The formation and maintenance of thunderstorms that produce large hail, strong winds, and tornadoes are often difficult to forecast due to their rapid evolution and complex interactions with environmental features that are challenging to observe. Given inherent uncertainties in storm development, it is intuitive to predict severe storms in a probab...
Geostationary satellites [e.g., the Geostationary Operational Environmental Satellite (GOES)] provide high temporal resolution of cloud development and motion, which is essential to the study of many mesoscale phenomena, including thunderstorms. Initial research on thunderstorm growth with geostationary imagery focused on the mature stages of storm...
Geostationary Operational Environmental Satellite (GOES)-14 imager was operated by National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode that emulates the high-temporal resolution sampling of the Advanced Baseline Imager (ABI) on the next generation GOES-R series. Imagery with a refresh rate of 1 min of man...
The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center's (NOAA/NCDC) Storm Data publication, whi...
The Multi-Year Reanalysis Of Remotely-Sensed Storms (MYRORSS) is a cooperative endeavor between the National Oceanic and Atmospheric Administration's (NOAA) National Severe Storms Laboratory (NSSL) and the National Climatic Data Center (NCDC) to reconstruct and evaluate numerical model output and radar products derived from 15 years of WSR-88D data...
This paper describes the use of severe weather products derived from the coterminous United States (CONUS) radar network, the lightning detection network, GOES satellites, and model analysis fields to determine the confidence-level of National Weather Service-issued tornado warnings. Severe weather attributes such as low-level shear, reflectivity a...
A probabilistic cloud-‐to-‐ground lightning algorithm was created by training a neural network on storm characteristics. The input dataset consisted of all storm cells over the entire coterminous United States on 12 days in 2008-‐2009 (one day per month). The input characteristics include radar and near-‐storm environmental parameters and the n...
