Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event
This study utilizes an extremely high spatial resolution GOES-16 atmospheric motion vector (AMV) dataset processed at 15 min intervals in a modified version of our original dynamic initialization technique to analyze and forecast a rapid intensification (RI) event in Hurricane Irma (2017). The most...
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| Format: | Article |
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MDPI AG
2020-11-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/11/11/1200 |
| _version_ | 1797548709358075904 |
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| author | Russell L. Elsberry Joel W. Feldmeier Hway-Jen Chen Melinda Peng Christopher S. Velden Qing Wang |
| author_facet | Russell L. Elsberry Joel W. Feldmeier Hway-Jen Chen Melinda Peng Christopher S. Velden Qing Wang |
| author_sort | Russell L. Elsberry |
| collection | DOAJ |
| description | This study utilizes an extremely high spatial resolution GOES-16 atmospheric motion vector (AMV) dataset processed at 15 min intervals in a modified version of our original dynamic initialization technique to analyze and forecast a rapid intensification (RI) event in Hurricane Irma (2017). The most important modifications are a more time-efficient dynamic initialization technique and adding a near-surface wind field adjustment as a low-level constraint on the distribution of deep convection relative to the translating center. With the new technique, the Coupled Ocean/Atmospheric Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model initial wind field at 12.86 km elevation quickly adjusts to the cirrus-level GOES-16 AMVs to better detect the Irma outflow magnitude and areal extent every 15 min, and predicts direct connections to adjacent synoptic circulations much better than a dynamic initialization with only lower-resolution hourly GOES-13 AMVs and also better than a cold-start COAMPS-TC initialization with a bogus vortex. Furthermore, only with the GOES-16 AMVs does the COAMPS-TC model accurately predict the timing of an intermediate 12 h constant-intensity period between two segments of the Irma RI. By comparison, HWRF model study of the Irma case that utilized the same GOES-16 AMV dataset predicted a continuous RI without the intermediate constant-intensity period, and predicted more limited outflow areal extents without strong direct connections with adjacent synoptic circulations. |
| first_indexed | 2024-03-10T15:03:29Z |
| format | Article |
| id | doaj.art-d7531fd3178e43698647e1b97bbc5862 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T15:03:29Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-d7531fd3178e43698647e1b97bbc58622023-11-20T19:59:51ZengMDPI AGAtmosphere2073-44332020-11-011111120010.3390/atmos11111200Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification EventRussell L. Elsberry0Joel W. Feldmeier1Hway-Jen Chen2Melinda Peng3Christopher S. Velden4Qing Wang5Trauma, Health and Hazards Center, University Colorado-Colorado Springs, Colorado Springs, CO 80918, USADepartment of Meteorology, Naval Postgraduate School, Monterey, CA 93943, USADepartment of Meteorology, Naval Postgraduate School, Monterey, CA 93943, USATrauma, Health and Hazards Center, University Colorado-Colorado Springs, Colorado Springs, CO 80918, USACooperative Institute Meteorological Satellite Studies, Madison, WI 53706, USADepartment of Meteorology, Naval Postgraduate School, Monterey, CA 93943, USAThis study utilizes an extremely high spatial resolution GOES-16 atmospheric motion vector (AMV) dataset processed at 15 min intervals in a modified version of our original dynamic initialization technique to analyze and forecast a rapid intensification (RI) event in Hurricane Irma (2017). The most important modifications are a more time-efficient dynamic initialization technique and adding a near-surface wind field adjustment as a low-level constraint on the distribution of deep convection relative to the translating center. With the new technique, the Coupled Ocean/Atmospheric Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model initial wind field at 12.86 km elevation quickly adjusts to the cirrus-level GOES-16 AMVs to better detect the Irma outflow magnitude and areal extent every 15 min, and predicts direct connections to adjacent synoptic circulations much better than a dynamic initialization with only lower-resolution hourly GOES-13 AMVs and also better than a cold-start COAMPS-TC initialization with a bogus vortex. Furthermore, only with the GOES-16 AMVs does the COAMPS-TC model accurately predict the timing of an intermediate 12 h constant-intensity period between two segments of the Irma RI. By comparison, HWRF model study of the Irma case that utilized the same GOES-16 AMV dataset predicted a continuous RI without the intermediate constant-intensity period, and predicted more limited outflow areal extents without strong direct connections with adjacent synoptic circulations.https://www.mdpi.com/2073-4433/11/11/1200tropical cyclone rapid intensificationgeostationary meteorological satellite datasetsassimilation of atmospheric motion vectors |
| spellingShingle | Russell L. Elsberry Joel W. Feldmeier Hway-Jen Chen Melinda Peng Christopher S. Velden Qing Wang Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event Atmosphere tropical cyclone rapid intensification geostationary meteorological satellite datasets assimilation of atmospheric motion vectors |
| title | Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event |
| title_full | Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event |
| title_fullStr | Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event |
| title_full_unstemmed | Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event |
| title_short | Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event |
| title_sort | challenges and opportunities with new generation geostationary meteorological satellite datasets for analyses and initial conditions for forecasting hurricane irma 2017 rapid intensification event |
| topic | tropical cyclone rapid intensification geostationary meteorological satellite datasets assimilation of atmospheric motion vectors |
| url | https://www.mdpi.com/2073-4433/11/11/1200 |
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