Remotely Sensed Tropical Cyclone Structure/Intensity Changes

2008 ◽  
Author(s):  
Jeffrey D. Hawkins
Keyword(s):  
Tropical Cyclone ◽  
Remotely Sensed ◽  
Intensity Changes

scholarly journals Precipitation Properties Observed during Tropical Cyclone Intensity Change

2015 ◽  
Vol 143 (11) ◽  
pp. 4476-4492 ◽  
Author(s):  
George R. Alvey III ◽  
Jonathan Zawislak ◽  
Edward Zipser
Keyword(s):  
Tropical Cyclone ◽  
Passive Microwave ◽  
Intensity Change ◽  
Rapid Intensification ◽  
Relative Importance ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
East Pacific ◽  
Intensity Changes ◽  
Strong Convection

Abstract Using a 15-yr (1998–2012) multiplatform dataset of passive microwave satellite data [tropical cyclone–passive microwave (TC-PMW)] for Atlantic and east Pacific storms, this study examines the relative importance of various precipitation properties, specifically convective intensity, symmetry, and area, to the spectrum of intensity changes observed in tropical cyclones. Analyses are presented not only spatially in shear-relative quadrants around the center, but also every 6 h during a 42-h period encompassing 18 h prior to onset of intensification to 24 h after. Compared to those with slower intensification rates, storms with higher intensification rates (including rapid intensification) have more symmetric distributions of precipitation prior to onset of intensification, as well as a greater overall areal coverage of precipitation. The rate of symmetrization prior to, and during, intensification increases with increasing intensity change as rapidly intensifying storms are more symmetric than slowly intensifying storms. While results also clearly show important contributions from strong convection, it is concluded that intensification is more closely related to the evolution of the areal, radial, and symmetric distribution of precipitation that is not necessarily intense.

scholarly journals Tropical Cyclone Intensity Change Prediction Based on Surrounding Environmental Conditions with Deep Learning

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2685
Author(s):  
Xin Wang ◽  
Wenke Wang ◽  
Bing Yan
Keyword(s):  
Deep Learning ◽  
Tropical Cyclone ◽  
Environmental Conditions ◽  
Environmental Variables ◽  
Intensity Change ◽  
Prediction Algorithm ◽  
Hybrid Features ◽  
Distribution Features ◽  
Intensity Changes ◽  
Change Prediction

Tropical cyclone (TC) motion has an important impact on both human lives and infrastructure. Predicting TC intensity is crucial, especially within the 24 h warning time. TC intensity change prediction can be regarded as a problem of both regression and classification. Statistical forecasting methods based on empirical relationships and traditional numerical prediction methods based on dynamical equations still have difficulty in accurately predicting TC intensity. In this study, a prediction algorithm for TC intensity changes based on deep learning is proposed by exploring the joint spatial features of three-dimensional (3D) environmental conditions that contain the basic variables of the atmosphere and ocean. These features can also be interpreted as fused characteristics of the distributions and interactions of these 3D environmental variables. We adopt a 3D convolutional neural network (3D-CNN) for learning the implicit correlations between the spatial distribution features and TC intensity changes. Image processing technology is also used to enhance the data from a small number of TC samples to generate the training set. Considering the instantaneous 3D status of a TC, we extract deep hybrid features from TC image patterns to predict 24 h intensity changes. Compared to previous studies, the experimental results show that the mean absolute error (MAE) of TC intensity change predictions and the accuracy of the classification as either intensifying or weakening are both significantly improved. The results of combining features of high and low spatial layers confirm that considering the distributions and interactions of 3D environmental variables is conducive to predicting TC intensity changes, thus providing insight into the process of TC evolution.

The Relative Importance of Factors Influencing Tropical Cyclone Rapid Intensity Changes

2019 ◽  
Vol 46 (4) ◽  
pp. 2282-2292 ◽  
Author(s):  
Saiprasanth Bhalachandran ◽  
Ziad S. Haddad ◽  
Svetla M. Hristova‐Veleva ◽  
F. D. Marks Jr.
Keyword(s):  
Tropical Cyclone ◽  
Relative Importance ◽  
Factors Influencing ◽  
Intensity Changes

scholarly journals Performance of a New Convective Parameterization Scheme on Model Convergence in Simulations of a Tropical Cyclone at Grey-Zone Resolutions

2014 ◽  
Vol 71 (6) ◽  
pp. 2078-2088 ◽  
Author(s):  
Yuan Sun ◽  
Lan Yi ◽  
Zhong Zhong ◽  
Yao Ha
Keyword(s):  
Tropical Cyclone ◽  
Radial Pressure ◽  
Cumulus Parameterization ◽  
Parameterization Scheme ◽  
Convective Heating ◽  
Finite Limit ◽  
Grey Zone ◽  
Gray Zone ◽  
Grid Spacing ◽  
Intensity Changes

Abstract The latest version of the Weather Research and Forecasting model (WRFV3.5) is used to evaluate the performance of the Grell and Freitas (GF13) cumulus parameterization scheme on the model convergence in simulations of a tropical cyclone (TC) at gray-zone resolutions. The simulated TC intensity converges to a finite limit as the grid spacing varies from 7.5 to 1 km. The reasons for the model convergence are investigated from perspectives of subgrid-scale processes and thermodynamic and dynamic structures. It is found that the impacts of above factors are notably different with varying model resolutions. The convective heating and drying increase as the grid spacing decreases, which inhibits the explicit microphysical parameterization preventing the simulated TC from overly intensifying. As the grid spacing decreases from 7.5 to 5 km, the TC intensity increases because of a stronger secondary circulation, a larger magnitude and proportion of strong eyewall updraft, and a greater amount of latent heating in the eyewall. As the grid spacing decreases from 5 to 3 km, the radius of maximum wind (RMW) decreases and the radial pressure gradient increases leading to an increase in TC intensity. The simulated TC intensity changes slightly as the grid spacing decreases from 3 to 1 km since the RMW and the storm structure both change little. The slight changes in the simulated TC intensity at such high resolutions indicate a great model convergence. Therefore, the GF13 presents an appropriate option that increases the model convergence in the TC intensity simulation at gray-zone resolution.

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