Numerical simulation of tropical cyclones by an axisymmetric nonhydrostatic model

1996 ◽  
Vol 60 (4) ◽  
pp. 207-224
Author(s):  
X. Zeng
Keyword(s):  
Numerical Simulation ◽  
Tropical Cyclones ◽  
Nonhydrostatic Model

Warm Cores, Eyewall Slopes, and Intensities of Tropical Cyclones Simulated by a 7-km-Mesh Global Nonhydrostatic Model

2016 ◽  
Vol 73 (11) ◽  
pp. 4289-4309 ◽  
Author(s):  
Tomoki Ohno ◽  
Masaki Satoh ◽  
Yohei Yamada
Keyword(s):  
Tropical Cyclones ◽  
Inner Core ◽  
Grid Spacing ◽  
Satellite Measurements ◽  
Warm Core ◽  
Nonhydrostatic Model ◽  
Proposed Model ◽  
Tangential Wind ◽  
Balanced Model ◽  
Horizontal Grid

Abstract Based on the data of a 1-yr simulation by a global nonhydrostatic model with 7-km horizontal grid spacing, the relationships among warm-core structures, eyewall slopes, and the intensities of tropical cyclones (TCs) were investigated. The results showed that stronger TCs generally have warm-core maxima at higher levels as their intensities increase. It was also found that the height of a warm-core maximum ascends (descends) as the TC intensifies (decays). To clarify how the height and amplitude of warm-core maxima are related to TC intensity, the vortex structures of TCs were investigated. By gradually introducing simplifications of the thermal wind balance, it was established that warm-core structures can be reconstructed using only the tangential wind field within the inner-core region and the ambient temperature profile. A relationship between TC intensity and eyewall slope was investigated by introducing a parameter that characterizes the shape of eyewalls and can be evaluated from satellite measurements. The authors found that the eyewall slope becomes steeper (shallower) as the TC intensity increases (decreases). Based on a balanced model, the authors proposed a relationship between TC intensity and eyewall slope. The result of the proposed model is consistent with that of the analysis using the simulation data. Furthermore, for sufficiently strong TCs, the authors found that the height of the warm-core maximum increases as the slope becomes steeper, which is consistent with previous observational studies. These results suggest that eyewall slopes can be used to diagnose the intensities and structures of TCs.

Impact of period and timescale of FDDA analysis nudging on the numerical simulation of tropical cyclones in the Bay of Bengal

2014 ◽  
Vol 74 (3) ◽  
pp. 2109-2128 ◽  
Author(s):  
V. Yesubabu ◽  
C. V. Srinivas ◽  
S. S. V. S. Ramakrishna ◽  
K. B. R. R. Hari Prasad
Keyword(s):  
Numerical Simulation ◽  
Tropical Cyclones ◽  
Bay Of Bengal

A Study on the Impact of Observation Assimilation on the Numerical Simulation of Tropical Cyclones JAL and THANE Using 3DVAR

2013 ◽  
Vol 171 (8) ◽  
pp. 2023-2042 ◽  
Author(s):  
V. Yesubabu ◽  
C. V. Srinivas ◽  
K. B. R. R. Hariprasad ◽  
R. Baskaran
Keyword(s):  
Numerical Simulation ◽  
Tropical Cyclones ◽  
The Impact

Detecting Tropical Cyclones using Deep Learning Techniques

2020 ◽  
Author(s):  
Daniel Galea ◽  
Bryan Lawrence ◽  
Julian Kunkel
Keyword(s):  
Numerical Simulation ◽  
Deep Learning ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Learning Model ◽  
Training Data ◽  
Detection Methods ◽  
Western Atlantic ◽  
Original Dataset ◽  
Deep Learning Model

<p>Finding and identifying important phenomena in large volumes of simulation data consumes time and resources. Deep Learning offers a route to improve speeds and costs. In this work we demonstrate the application of Deep Learning in identifying data which contains various classes of tropical cyclone. Our initial application is in re-analysis data, but the eventual goal is to use this system during numerical simulation to identify data of interest before writing it out.</p><p>A Deep Learning model has been developed to help identify data containing varying intensities of tropical cyclones. The model uses some convolutional layers to build up a pattern to look for, and a fully-connected classifier to predict whether a tropical cyclone is present in the input. Other techniques such as batch normalization and dropout were tested. The model was trained on a subset of the ERA-Interim dataset from the 1st of January 1979 until the 31st of July 2017, with the relevant labels obtained from the IBTrACS dataset. The model obtained an accuracy of 99.08% on a test set, which was a 20% subset of the original dataset. </p><p>An advantage of this model is that it does not rely on thresholds set a priori, such as a minimum of sea level pressure, a maximum of vorticity or a measure of the depth and strength of deep convection, making it more objective than previous detection methods. Also, given that current methods follow non-trivial algorithms, the Deep Learning model is expected to have the advantage of being able to get the required prediction much quicker, making it viable to be implemented into an existing numerical simulation.</p><p>Most current methods also apply different thresholds for different basins (planetary regions). In principle, the globally trained model should avoid the necessity for such differences, however, it was found that while differing thresholds were not required, training data for specific regions was required to get similar accuracy when only individual basins were examined.</p><p>The existing version, with greater than 99% accuracy globally and around 91% when trained only on cases from the Western Pacific and Western Atlantic basins, has been trained on ERA-Interim data. The next steps with this work will involve assessing the suitability of the pre-trained model for different data, and deploying it within a running numerical simulation.</p>

scholarly journals Future Changes in Structures of Extremely Intense Tropical Cyclones Using a 2-km Mesh Nonhydrostatic Model

2013 ◽  
Vol 26 (24) ◽  
pp. 9986-10005 ◽  
Author(s):  
Sachie Kanada ◽  
Akiyoshi Wada ◽  
Masato Sugi
Keyword(s):  
Global Warming ◽  
Tropical Cyclones ◽  
General Circulation ◽  
Potential Temperature ◽  
Circulation Model ◽  
Future Climate ◽  
Central Pressure ◽  
Near Surface ◽  
Nonhydrostatic Model ◽  
The Future

Abstract Recent studies have projected that global warming may lead to an increase in the number of extremely intense tropical cyclones. However, how global warming affects the structure of extremely intense tropical cyclones has not been thoroughly examined. This study defines extremely intense tropical cyclones as having a minimum central pressure below 900 hPa and investigates structural changes in the inner core and thereby changes in the intensity in the future climate. A 2-km mesh nonhydrostatic model (NHM2) is used to downscale the 20-km mesh atmospheric general circulation model projection forced with a control scenario and a scenario of twenty-first-century climate change. The eyewall region of extremely intense tropical cyclones simulated by NHM2 becomes relatively smaller and taller in the future climate. The intense near-surface inflow intrudes more inward toward the eye. The heights and the radii of the maximum wind speed significantly decrease and an intense updraft area extends from the lower level around the leading edge of thinner near-surface inflows, where the equivalent potential temperature substantially increases in the future climate. Emanuel’s potential intensity theory suggests that about half of the intensification (increase in central pressure fall) is explained by the changes in the atmospheric environments and sea surface temperature, while the remaining half needs to be explained by other processes. It is suggested that the structural change projected by NHM2, which is significant within a radius of 50 km, is playing an important role in the intensification of extremely intense tropical cyclones in simulations of the future climate.

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