Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network

2018 ◽  
Vol 27 (2) ◽  
pp. 692-702 ◽  
Author(s):  
Ritesh Pradhan ◽  
Ramazan S. Aygun ◽  
Manil Maskey ◽  
Rahul Ramachandran ◽  
Daniel J. Cecil
Keyword(s):  
Neural Network ◽  
Tropical Cyclone ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Tropical Cyclone Intensity ◽  
Intensity Estimation ◽  
Cyclone Intensity

scholarly journals Tropical Cyclone Intensity Estimation Using RVM and DADI Based on Infrared Brightness Temperature

2016 ◽  
Vol 31 (5) ◽  
pp. 1643-1654 ◽  
Author(s):  
Chang-Jiang Zhang ◽  
Jin-Fang Qian ◽  
Lei-Ming Ma ◽  
Xiao-Qin Lu
Keyword(s):  
Tropical Cyclone ◽  
Linear Regression ◽  
Absolute Error ◽  
Regression Method ◽  
Northwest Pacific ◽  
Linear Regression Method ◽  
Tropical Cyclone Intensity ◽  
Statistical Parameters ◽  
Intensity Estimation ◽  
Cyclone Intensity

Abstract An objective technique is presented to estimate tropical cyclone intensity using the relevance vector machine (RVM) and deviation angle distribution inhomogeneity (DADI) based on infrared satellite images of the northwest Pacific Ocean basin from China’s FY-2C geostationary satellite. Using this technique, structures of a deviation-angle gradient co-occurrence matrix, which include 15 statistical parameters nonlinearly related to tropical cyclone intensity, were derived from infrared satellite imagery. RVM was then used to relate these statistical parameters to tropical cyclone intensity. Twenty-two tropical cyclones occurred in the northwest Pacific during 2005–09 and were selected to verify the performance of the proposed technique. The results show that, in comparison with the traditional linear regression method, the proposed technique can significantly improve the accuracy of tropical cyclone intensity estimation. The average absolute error of intensity estimation using the linear regression method is between 15 and 29 m s−1. Compared to the linear regression method, the average absolute error of the intensity estimation using RVM is between 3 and 10 m s−1.

scholarly journals The Advanced Dvorak Technique (ADT) for Estimating Tropical Cyclone Intensity: Update and New Capabilities

2019 ◽  
Vol 34 (4) ◽  
pp. 905-922 ◽  
Author(s):  
Timothy L. Olander ◽  
Christopher S. Velden
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Surface Wind ◽  
Performance Characteristics ◽  
Tropical Cyclone Intensity ◽  
Algorithm Development ◽  
Intensity Estimation ◽  
Development Team ◽  
Cyclone Intensity ◽  
Estimation Scheme

Abstract The advanced Dvorak technique (ADT) is used operationally by tropical cyclone forecast centers worldwide to help estimate the intensity of tropical cyclones (TCs) from operational geostationary meteorological satellites. New enhancements to the objective ADT have been implemented by the algorithm development team to further expand its capabilities and precision. The advancements include the following: 1) finer tuning to aircraft-based TC intensity estimates in an expanded development sample, 2) the incorporation of satellite-based microwave information into the intensity estimation scheme, 3) more sophisticated automated TC center-fixing routines, 4) adjustments to the intensity estimates for subtropical systems and TCs undergoing extratropical transition, and 5) addition of a surface wind radii estimation routine. The goals of these upgrades and others are to provide TC analysts/forecasters with an expanded objective guidance tool to more accurately estimate the intensity of TCs and those storms forming from, or converting into, hybrid/nontropical systems. The 2018 TC season is used to illustrate the performance characteristics of the upgraded ADT.

The tropical cyclone intensity estimation based on MODIS data: a case study

2011 ◽  
Author(s):  
Zengzhou Hao ◽  
Fang Gong ◽  
Qianguang Tu ◽  
Delu Pan ◽  
Difeng Wang
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclone Intensity ◽  
Intensity Estimation ◽  
Cyclone Intensity ◽  
Modis Data
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