Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event

Elsberry, Russell L.; Feldmeier, Joel W.; Chen, Hway-Jen; Peng, Melinda S.; Velden, Christopher S.; Wang, Qing

doi:10.3390/atmos11111200

Cited by 4 publications

(9 citation statements)

References 14 publications

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“…Elsberry et al (2020a) [7] describe in detail the development of the FCDI technique that is capable of assimilating high-spatial-and high-temporal-resolution GOES-16 atmospheric motion vector (AMV) datasets. In addition to the routine GOES-16 full-disk multispectral image scan every 10 min and the Continental U.S. scanning every 5 min, the GOES-16 mesoscale scan mode allows one-minute imaging that can be targeted to follow a TC center within a 10 • latitude by 10 • longitude domain.…”

Section: Background On Newly Available Satellite-derived Amv Datasets...mentioning

confidence: 99%

“…In addition to describing the fundamentals of the FCDI technique, that allow it to be a time-efficient dynamic initialization capable of incorporating more than 10,000 AMVs every 15 min, Elsberry et al (Section 2.1.3 of [7] and their Appendix) introduced a nearsurface wind field adjustment that acts as a low-level constraint on the distribution of deep convection relative to the translating center. In that Hurricane Irma data assimilation application presented by Elsberry et al [7], the surface wind adjustment domain was translated every 15 min along a target pathway when a new AMV dataset became available. Each 6 h target pathway is simply a straight line from the t = 0 h of the assimilation cycle to the known 6 h ending position.…”

Section: Background On Newly Available Satellite-derived Amv Datasets...mentioning

confidence: 99%

“…In addition to the documentation of the new FCDI technique, Elsberry et al [7] analyzed at 15 min intervals and predicted an RI event in Hurricane Irma (2017). Cangialosi et al (2018) [13] presented the official National Hurricane Center (NHC) report on Hurricane Irma, which was one of the strongest and costliest hurricanes on record in the Atlantic basin.…”

Section: Additional Documentations Of the Enhanced Amv Datasets And F...mentioning

confidence: 99%

“…Only the COAMPS-TC forecast, initiated from an FCDI analysis that included the 15 min GOES-16 AMV dataset, accurately predicted the timing and persistence of the constant intensity period, and then resumed the deepening of Irma at the correct time and with an accurate rate of deepening. Elsberry et al [7] demonstrated the FCDI analysis that was used, as initial conditions had a stronger outflow toward the northeast that had pushed a northerly environmental flow back to the east. The COAMPS 24 h forecast then had a stronger outflow in all quadrants that established what we refer to as direct connections with the adjacent synoptic circulations to the northeast and to the northwest that are considered favorable for sustained RI events.…”

Section: Additional Documentations Of the Enhanced Amv Datasets And F...mentioning

confidence: 99%

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High Temporal Resolution Analyses with GOES-16 Atmospheric Motion Vectors of the Non-Rapid Intensification of Atlantic Pre-Bonnie (2022)

Elsberry,

Feldmeier,

Chen

et al. 2024

Atmosphere

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Four-dimensional COAMPS Dynamic Initialization (FCDI) analyses that include high-temporal- and high-spatial-resolution GOES-16 Atmospheric Motion Vector (AMV) datasets are utilized to understand and predict why pre-Bonnie (2022), designated as a Potential Tropical Cyclone (PTC 2), did not undergo rapid intensification (RI) while passing along the coast of Venezuela during late June 2022. A tropical cyclone lifecycle-prediction model based on the ECMWF ensemble indicated that no RI should be expected for the trifurcation southern cluster of tracks along the coast, similar to PTC 2, but would likely occur for two other track clusters farther offshore. Displaying the GOES-16 mesodomain AMVs in 50 mb layers illustrates the outflow burst domes associated with the PTC 2 circulation well. The FCDI analyses forced by thousands of AMVs every 15 min document the 13,910 m wind-mass field responses and the subsequent 540 m wind field adjustments in the PTC 2 circulation. The long-lasting outflow burst domes on both 28 June and 29 June were mainly to the north of PTC 2, and the 13,910 m FCDI analyses document conditions over the PTC 2 which were not favorable for an RI event. The 540 m FCDI analyses demonstrated that the intensity was likely less than 35 kt because of the PTC 2 interactions with land. The FCDI analyses and two model forecasts initialized from the FCDI analyses document how the PTC 2 moved offshore to become Tropical Storm Bonnie; however, they reveal another cyclonic circulation farther west along the Venezuelan coast that has some of the characteristics of a Caribbean False Alarm event.

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Section: Background On Newly Available Satellite-derived Amv Datasets...mentioning

confidence: 99%

Section: Background On Newly Available Satellite-derived Amv Datasets...mentioning

confidence: 99%

Section: Additional Documentations Of the Enhanced Amv Datasets And F...mentioning

confidence: 99%

Section: Additional Documentations Of the Enhanced Amv Datasets And F...mentioning

confidence: 99%

See 2 more Smart Citations

High Temporal Resolution Analyses with GOES-16 Atmospheric Motion Vectors of the Non-Rapid Intensification of Atlantic Pre-Bonnie (2022)

Elsberry,

Feldmeier,

Chen

et al. 2024

Atmosphere

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“…Given the increasing availability of high-density observational data -e.g. hurricane hunter flights 27,31 , dropsondes 32 , satellites 33,34 , and radars 28,35 -we wish to develop a new framework to more accurately and efficiently assimilate observational data into the vortex reconstruction.…”

Section: Main Textmentioning

confidence: 99%

Physics-Informed Neural Networks for Hurricane Reconstruction and Data Assimilation

Eusebi

Vecchi

Lai

et al. 2023

Preprint

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Tropical cyclones (TCs) are responsible for large-scale loss of life and property, and billions of dollars worth of damage annually. Forecasting is typically accomplished through the use of dynamical models which directly integrate the governing equations of motion. These forecasts, and especially intensity forecasts, rely on accurate initial conditions obtained by assimilating real-time observational data. However, current methods used to generate these initial conditions are computationally expensive and limited, and TC intensity forecasts have much room for improvement. Here, we show that a Physics-Informed Neural Network can provide a promising alternative for data assimilation for TC initial conditions. Using synthetic training data sparsely sampled from hurricanes simulated in a forecast model, a PINN is able to accurately reconstruct full 2- and 3-dimensional wind and pressure fields of a TC. We also demonstrate how a set of sparse, real-time observations, can be used to accurately reconstruct Hurricane Ida. Our results demonstrate how recent advances in computational capabilities and deep learning can be applied to TC data assimilation, offering an accurate and efficient complement to existing data assimilation methods.

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Realistic tropical cyclone wind and pressure fields can be reconstructed from sparse data using deep learning

Eusebi,

Vecchi,

Lai

et al. 2024

Commun Earth Environ

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Tropical cyclones are responsible for large-scale loss of life and property1–4, motivating accurate risk assessment and forecasting. These objectives require accurate reconstructions of storms’ wind and pressure fields which assimilate real-time observations5–9, but current methods used for these reconstructions remain computationally expensive and limited10. Here, we show that a physics-informed neural network11,12 can be a promising and computationally efficient algorithm for tropical cyclone data assimilation. Using synthetic training data sparsely sampled from hurricanes simulated in a forecast model, a physics-informed neural network is able to reconstruct full realistic 2- and 3-dimensional wind and pressure fields which capture key features of the cyclone. We also demonstrate how a set of sparse, real-time observations, can be used to accurately reconstruct Hurricane Ida. Our results highlight how recent advances in deep learning can augment data assimilation schemes. The methods are also general and can be applied to other flow problems.

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Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event

Cited by 4 publications

References 14 publications

High Temporal Resolution Analyses with GOES-16 Atmospheric Motion Vectors of the Non-Rapid Intensification of Atlantic Pre-Bonnie (2022)

High Temporal Resolution Analyses with GOES-16 Atmospheric Motion Vectors of the Non-Rapid Intensification of Atlantic Pre-Bonnie (2022)

Physics-Informed Neural Networks for Hurricane Reconstruction and Data Assimilation

Realistic tropical cyclone wind and pressure fields can be reconstructed from sparse data using deep learning

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