Comparative analysis of subjective/advanced objective technique  of tropical cyclone intensity estimation

lkj & m".kdfVca/kh; pØokr dh rhozrk dk vkdyu djus ds fy, lewps fo’o esa O;kogkfjd :i ls mi;ksx dh tkus okyh M~oksjd rduhd esa mixzg ls izkIr fp=ksa dk mi;ksx fd;k tkrk gSA blesa O;ofLFkr laogu ds laca/k esa fo’ys"kd }kjk fd, x, foospu lfgr dqN izk;ksfxd ekunaMksa ds vk/kkj ij mixzg ls izkIr fp= ds iSVuZ dh igpku dh tkrh gSA fofHkUu fo’ys"k.k dsUnzksa }kjk fdlh ,d pØokr dk vkdyu djus esa gksus okyh fo"k;ijd foospu laca/kh folaxfr;k¡ daI;wVj ij vk/kkfjr ,yxksfjFe ds ek/;e ls de gqbZA bl la’kksf/kr rduhd dks fodflr fo"k;ijd M~oksjd rduhd ¼,- vks- Mh- Vh-½ dgk x;k vkSj ;g iw.kZ fodflr m".k dfVca/kh; pØokrksa ds fy, mi;qDr gSA bl 'kks/k&Ik= esa o"kZ 2004 esa vk, rhu m".kdfVca/kh; pØokrksa ds laca/k esa ,- vks- Mh- Vh- ds dk;Z & fu"iknu dk ewY;kdau fd;k x;k gSA buds rqYukRed fo’ys"k.k ls ;g irk pyk fd ,- vks- Mh- Vh- rduhd M~oksjd rduhd ds vk/kkj ij fd, x, pØokr dh rhozrk ds vkdyuksa] tks m".kdfVca/kh; fo’ys"k.k dsUnzksa ds mixzg ls izkIr fp=ksa ds fo’ys"kdksa }kjk O;kogkfjd :Ik ls rS;kj fd, x,] ds eqdkcys dh jghA Dvorak technique operationally used all over the world for estimating the tropical cyclone intensity is based on satellite observations. It involves image pattern recognition based on certain empirical rules along with the analyst interpretation of organized convection. The computer-based algorithm can minimize these subjective judgement discrepancies between different analysis centers estimating the same storm. This modified version is called Advanced Objective Dvorak Technique (AODT) and which is applicable for well-developed tropical cyclones. In this paper the performance of the AODT is evaluated on three cases of the year 2004 tropical cyclones. Comparative analysis indicates the technique to be competitive with, the Dvorak-based intensity estimates produced operationally by satellite analysts from tropical analysis centers.

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

Weather and Forecasting ◽

10.1175/waf-d-19-0007.1 ◽

2019 ◽

Vol 34 (4) ◽

pp. 905-922 ◽

Cited By ~ 14

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.

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Satellite-based monitoring and prediction of tropical cyclone intensity and movement

MAUSAM ◽

10.54302/mausam.v48i2.3965 ◽

2021 ◽

Vol 48 (2) ◽

pp. 157-168

Author(s):

R. R. KELKAR

Keyword(s):

Tropical Cyclone ◽

Tropical Cyclones ◽

Tropical Cyclone Intensity ◽

Intensity Estimation ◽

Cyclone Intensity ◽

Estimation Scheme ◽

Water Vapour Absorption ◽

Upper Level ◽

Absorption Channel ◽

The Impact

ABSTRACT. Capabilities of meteorological satellites have gone a long way in meeting requirements of synoptic analysis and forecasting of tropical cyclones. This paper shows the impact made by the satellite data in the intensity estimation and track prediction of tropical cyclones in the Indian Seas and also reviews the universally applied Dvorak algorithm for performing tropical cyclone intensity analysis. Extensive use of Dvorak's intensity estimation scheme has revealed many of its limitations and elements of subjectivity in the analysis of tropical cyclones over the Arabian Sea and the Bay of Bengal, which, like cyclones in other ocean basins, also exhibit wide structural variability as seen in the satellite imagery. Satellite-based cyclone tracking techniques include: (i) use of satellite-derived mean wind flow, (ii) animation of sequence of satellite images and extrapolation of the apparent motion of the cloud system and (iii) monitoring changes in the upper level moisture patterns in the water vapour absorption channel imagery. Satellite-based techniques on tropical cyclone intensity estimation and track prediction have led to very significant improvement in disaster warning and consequent saving of life and property.

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A Novel Deep Learning Framework for Tropical Cyclone Intensity Estimation Using FY-4 Satellite Imagery

Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence ◽

10.1145/3390557.3394298 ◽

2020 ◽

Author(s):

Xingxing Yu ◽

Zhao Chen ◽

He Zhang ◽

Yuxin Zheng

Keyword(s):

Deep Learning ◽

Tropical Cyclone ◽

Satellite Imagery ◽

Tropical Cyclone Intensity ◽

Intensity Estimation ◽

Cyclone Intensity ◽

Learning Framework

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A Multiple Linear Regression Model for Tropical Cyclone Intensity Estimation from Satellite Infrared Images

Atmosphere ◽

10.3390/atmos7030040 ◽

2016 ◽

Vol 7 (3) ◽

pp. 40 ◽

Cited By ~ 8

Author(s):

Yong Zhao ◽

Chaofang Zhao ◽

Ruyao Sun ◽

Zhixiong Wang

Keyword(s):

Tropical Cyclone ◽

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Multiple Linear Regression Model ◽

Tropical Cyclone Intensity ◽

Infrared Images ◽

Intensity Estimation ◽

Cyclone Intensity

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

Weather and Forecasting ◽

10.1175/waf-d-15-0100.1 ◽

2016 ◽

Vol 31 (5) ◽

pp. 1643-1654 ◽

Cited By ~ 5

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.

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