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Service Description: Date of Images:
Post-Event: 10/10/2024
Summary:
Severe weather clouds exhibit distinct patterns in satellite imagery. These patterns, commonly referred to as overshooting cloud tops (OTs) and above anvil cirrus plumes (AACPs), are routinely observed by the human-eye in satellite imagery atop the most intense storms on Earth. With the latest geostationary high spatio-temporal resolution satellite imagers such as, GOES-16 and GOES-17, these severe weather patterns are being observed better than ever before. Advances in deep learning methods allow us to not only detect these signatures but also automate the detection process for real-time applications. Our software code was developed in Python and contains up to 3 model types, U-Net, MultiResU-Net, and AttentionU-Net with varying geostationary satellite channels used as model inputs. The MultiresU-Net model has been found to identify the severe storm signatures better than the other 2 model types and is the default provided in the software release. The models' were trained using a large database of OT and AACP patterns identified from human analysts based on 1-minute GOES-16 ABI imagery. In testing, the models have been shown to successfully detect OTs and AACPs with high precision. The software has also successfully been run for real-time and archived applications. The goal of this software is for users to accurately detect these severe storm signatures for a variety of forecast and research applications.
Version 2 released on 6 March 2024. LARGE model performance enhancement, particularly with AACP detection. Added A LOT of new model input combinations as possibilities, including TROPDIFF (10.3 μm - GFS Tropopause Temperature), DIRTYIRDIFF (12.3 μm - 10.3 μm), SNOWICE (1.6 μm), and CIRRUS (1.37 μm). Added new and improved checkpoint files associated with those model runs. Optimal models and thresholds have been updated since the Version 1 release. Changed how we interpolate the GFS data onto the GOES grid. We now follow the methods outlined in Khlopenkov et al. (2021). New version changes how we decide between daytime and nighttime model runs. Previously we used the maximum solar zenith angle within the domain and checked if that exceeded 85°, however, there was an issue of only nighttime models being used for CONUS domains due to how the satellite views the Earth and the data are gridded. Thus, now the software checks to see if more than 5% of the pixels in the domain have solar zenith angles that exceed 85°. If they do, then the software acts as if the domain is nighttime and if not the software acts as if it is daytime within the domain. Fixed major issue with looping over post-processing dates and the month or year changed during a real-time or archived model run. New version speeds up post-processing for OTs. Set variables to None after using them in order to clear cached memory. Catch and hide RunTime warnings that the User does not need to worry about. Software Users prior to 5 March 2024 will need to pull the latest python files from GitHub. This should be very fast. Users MUST also download the new checkpoint files from 'https://science-data.larc.nasa.gov/LaRC-SD-Publications/2023-05-05-001-JWC/data/ML_data.zip'. Once downloaded and unzipped, replace the old model_checkpoints subdirectory with the latest one that was just downloaded. This directory includes all of the improved model detection files as well as checkpoint files for the new input combination. If you have issues downloading and unzipping the file from the browser, run the run_download_model_chkpoint_files.py script included with the software. Users have experienced issues downloading the checkpoint files from the browser and this script has been successful.
Suggested Use:
Minimum Tropopause-Relative IR Temperature: Minimum difference between the GFS Tropopause Temperature and the Cloud Top Infrared Brightness Temperature for each Grid Box During the Study Period (Kelvin). Calculated by GFS Tropopause Temperature - Cloud Top Infrared Brightness Temperature and then finding the minimum throughout the study period at each grid box. Infrared brightness temperatures nearer to or colder than the tropopause can indicate locations of more severe convection.
Cold Duration: Number of Minutes in which the Cloud Top Infrared Brightness Temperature is Colder than the GFS Tropopause Temperature During the Study Period. Longer time periods can indicate locations of prolonged severe convective activity.
Overshooting Cloud Top Tropopause-Relative IR Temperature: Minimum difference between GFS Tropopause Temperature and the IR Brightness Temperature for Overshooting Top Detections During the Study Period. Locations where algorithm detected the Overshooting Top severe storm signature and its associated GFS Tropopause Temperature - IR Brightness Temperature (Kelvin). Infrared brightness temperatures nearer to or colder than the tropopause can be another indication of a location experiencing severe convection.
Overshooting Cloud Top Storm Updraft Detections: Earliest Overshooting Top Detection Date and Time During the Study Period. Locations where algorithm detected Overshooting Top severe storm signature and the earliest time the model detected it. This variable can indicate approximately when and where severe weather occurred.
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Description: Date of Images:Post-Event: 10/10/2024Summary:Severe weather clouds exhibit distinct patterns in satellite imagery. These patterns, commonly referred to as overshooting cloud tops (OTs) and above anvil cirrus plumes (AACPs), are routinely observed by the human-eye in satellite imagery atop the most intense storms on Earth. With the latest geostationary high spatio-temporal resolution satellite imagers such as, GOES-16 and GOES-17, these severe weather patterns are being observed better than ever before. Advances in deep learning methods allow us to not only detect these signatures but also automate the detection process for real-time applications. Our software code was developed in Python and contains up to 3 model types, U-Net, MultiResU-Net, and AttentionU-Net with varying geostationary satellite channels used as model inputs. The MultiresU-Net model has been found to identify the severe storm signatures better than the other 2 model types and is the default provided in the software release. The models' were trained using a large database of OT and AACP patterns identified from human analysts based on 1-minute GOES-16 ABI imagery. In testing, the models have been shown to successfully detect OTs and AACPs with high precision. The software has also successfully been run for real-time and archived applications. The goal of this software is for users to accurately detect these severe storm signatures for a variety of forecast and research applications.Version 2 released on 6 March 2024. LARGE model performance enhancement, particularly with AACP detection. Added A LOT of new model input combinations as possibilities, including TROPDIFF (10.3 μm - GFS Tropopause Temperature), DIRTYIRDIFF (12.3 μm - 10.3 μm), SNOWICE (1.6 μm), and CIRRUS (1.37 μm). Added new and improved checkpoint files associated with those model runs. Optimal models and thresholds have been updated since the Version 1 release. Changed how we interpolate the GFS data onto the GOES grid. We now follow the methods outlined in Khlopenkov et al. (2021). New version changes how we decide between daytime and nighttime model runs. Previously we used the maximum solar zenith angle within the domain and checked if that exceeded 85°, however, there was an issue of only nighttime models being used for CONUS domains due to how the satellite views the Earth and the data are gridded. Thus, now the software checks to see if more than 5% of the pixels in the domain have solar zenith angles that exceed 85°. If they do, then the software acts as if the domain is nighttime and if not the software acts as if it is daytime within the domain. Fixed major issue with looping over post-processing dates and the month or year changed during a real-time or archived model run. New version speeds up post-processing for OTs. Set variables to None after using them in order to clear cached memory. Catch and hide RunTime warnings that the User does not need to worry about. Software Users prior to 5 March 2024 will need to pull the latest python files from GitHub. This should be very fast. Users MUST also download the new checkpoint files from 'https://science-data.larc.nasa.gov/LaRC-SD-Publications/2023-05-05-001-JWC/data/ML_data.zip'. Once downloaded and unzipped, replace the old model_checkpoints subdirectory with the latest one that was just downloaded. This directory includes all of the improved model detection files as well as checkpoint files for the new input combination. If you have issues downloading and unzipping the file from the browser, run the run_download_model_chkpoint_files.py script included with the software. Users have experienced issues downloading the checkpoint files from the browser and this script has been successful.Suggested Use:Minimum Tropopause-Relative IR Temperature: Minimum difference between the GFS Tropopause Temperature and the Cloud Top Infrared Brightness Temperature for each Grid Box During the Study Period (Kelvin). Calculated by GFS Tropopause Temperature - Cloud Top Infrared Brightness Temperature and then finding the minimum throughout the study period at each grid box. Infrared brightness temperatures nearer to or colder than the tropopause can indicate locations of more severe convection.Cold Duration: Number of Minutes in which the Cloud Top Infrared Brightness Temperature is Colder than the GFS Tropopause Temperature During the Study Period. Longer time periods can indicate locations of prolonged severe convective activity.Overshooting Cloud Top Tropopause-Relative IR Temperature: Minimum difference between GFS Tropopause Temperature and the IR Brightness Temperature for Overshooting Top Detections During the Study Period. Locations where algorithm detected the Overshooting Top severe storm signature and its associated GFS Tropopause Temperature - IR Brightness Temperature (Kelvin). Infrared brightness temperatures nearer to or colder than the tropopause can be another indication of a location experiencing severe convection.Overshooting Cloud Top Storm Updraft Detections: Earliest Overshooting Top Detection Date and Time During the Study Period. Locations where algorithm detected Overshooting Top severe storm signature and the earliest time the model detected it. This variable can indicate approximately when and where severe weather occurred.
Service Item Id: 1ff9c6fd6cc94366b1964576e6d3b9b9
Copyright Text: NASA; John Cooney, Kris Bedka
Spatial Reference:
4326
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Single Fused Map Cache: false
Initial Extent:
XMin: -85.26408259439704
YMin: 23.691013160669325
XMax: -74.23540453797564
YMax: 31.391654006232276
Spatial Reference: 4326
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Full Extent:
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YMin: 18.814642180799105
XMax: -73.77708648748418
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Spatial Reference: 4326
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Time Info:
Time Extent:
[2024/10/10 01:30:40 UTC, 2024/10/10 01:30:40 UTC]
Time Reference:
N/A
Units: esriDecimalDegrees
Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP
Document Info:
Title: Severe Weather Detection Product for Hurricane Milton
Author:
Comments: Date of Images:Post-Event: 10/10/2024Summary:Severe weather clouds exhibit distinct patterns in satellite imagery. These patterns, commonly referred to as overshooting cloud tops (OTs) and above anvil cirrus plumes (AACPs), are routinely observed by the human-eye in satellite imagery atop the most intense storms on Earth. With the latest geostationary high spatio-temporal resolution satellite imagers such as, GOES-16 and GOES-17, these severe weather patterns are being observed better than ever before. Advances in deep learning methods allow us to not only detect these signatures but also automate the detection process for real-time applications. Our software code was developed in Python and contains up to 3 model types, U-Net, MultiResU-Net, and AttentionU-Net with varying geostationary satellite channels used as model inputs. The MultiresU-Net model has been found to identify the severe storm signatures better than the other 2 model types and is the default provided in the software release. The models' were trained using a large database of OT and AACP patterns identified from human analysts based on 1-minute GOES-16 ABI imagery. In testing, the models have been shown to successfully detect OTs and AACPs with high precision. The software has also successfully been run for real-time and archived applications. The goal of this software is for users to accurately detect these severe storm signatures for a variety of forecast and research applications.Version 2 released on 6 March 2024. LARGE model performance enhancement, particularly with AACP detection. Added A LOT of new model input combinations as possibilities, including TROPDIFF (10.3 μm - GFS Tropopause Temperature), DIRTYIRDIFF (12.3 μm - 10.3 μm), SNOWICE (1.6 μm), and CIRRUS (1.37 μm). Added new and improved checkpoint files associated with those model runs. Optimal models and thresholds have been updated since the Version 1 release. Changed how we interpolate the GFS data onto the GOES grid. We now follow the methods outlined in Khlopenkov et al. (2021). New version changes how we decide between daytime and nighttime model runs. Previously we used the maximum solar zenith angle within the domain and checked if that exceeded 85°, however, there was an issue of only nighttime models being used for CONUS domains due to how the satellite views the Earth and the data are gridded. Thus, now the software checks to see if more than 5% of the pixels in the domain have solar zenith angles that exceed 85°. If they do, then the software acts as if the domain is nighttime and if not the software acts as if it is daytime within the domain. Fixed major issue with looping over post-processing dates and the month or year changed during a real-time or archived model run. New version speeds up post-processing for OTs. Set variables to None after using them in order to clear cached memory. Catch and hide RunTime warnings that the User does not need to worry about. Software Users prior to 5 March 2024 will need to pull the latest python files from GitHub. This should be very fast. Users MUST also download the new checkpoint files from 'https://science-data.larc.nasa.gov/LaRC-SD-Publications/2023-05-05-001-JWC/data/ML_data.zip'. Once downloaded and unzipped, replace the old model_checkpoints subdirectory with the latest one that was just downloaded. This directory includes all of the improved model detection files as well as checkpoint files for the new input combination. If you have issues downloading and unzipping the file from the browser, run the run_download_model_chkpoint_files.py script included with the software. Users have experienced issues downloading the checkpoint files from the browser and this script has been successful.Suggested Use:Minimum Tropopause-Relative IR Temperature (top left figure): Minimum difference between the GFS Tropopause Temperature and the Cloud Top Infrared Brightness Temperature for each Grid Box During the Study Period (Kelvin). Calculated by GFS Tropopause Temperature - Cloud Top Infrared Brightness Temperature and then finding the minimum throughout the study period at each grid box. Infrared brightness temperatures nearer to or colder than the tropopause can indicate locations of more severe convection.Cold Duration (top right figure): Number of Minutes in which the Cloud Top Infrared Brightness Temperature is Colder than the GFS Tropopause Temperature During the Study Period. Longer time periods can indicate locations of prolonged severe convective activity.Overshooting Cloud Top Tropopause-Relative IR Temperature (bottom left figure): Minimum difference between GFS Tropopause Temperature and the IR Brightness Temperature for Overshooting Top Detections During the Study Period. Locations where algorithm detected the Overshooting Top severe storm signature and its associated GFS Tropopause Temperature - IR Brightness Temperature (Kelvin). Infrared brightness temperatures nearer to or colder than the tropopause can be another indication of a location experiencing severe convection.Overshooting Cloud Top Storm Updraft Detections (ot_utc; bottom right figure): Earliest Overshooting Top Detection Date and Time During the Study Period. Locations where algorithm detected Overshooting Top severe storm signature and the earliest time the model detected it. This variable can indicate approximately when and where severe weather occurred.
Subject: Severe Weather Detection Product for Hurricane Milton October 2024, Storm, Updraft
Category:
Keywords: Severe weather,troposphere,NASA,NASA Disasters,Hurricane,Hurricane Milton,storm,updraft
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