Tropical Cyclone Movement Forecasts Based on Observations from Satellites

Fett, Robert W.; Brand, Samson

doi:10.1175/1520-0450(1975)014<0452:tcmfbo>2.0.co;2

Cited by 13 publications

(15 citation statements)

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“…No matter which model, it relies on the use of meteorological data that may be obtained from satellites, land-based radar, and reconnaissance aircrafts [2][3]. For more specific, the parameters to position the typhoon center may include surface pressure, wind circulation center, and the cloud system center.…”

Section: Introductionsupporting

confidence: 71%

Nonlinear Autoregressive Network with the Use of a Moving Average Method for Forecasting Typhoon Tracks

Kerh¹,

Wu²

2017

IJAIA

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Section: Introductionsupporting

confidence: 71%

Nonlinear Autoregressive Network with the Use of a Moving Average Method for Forecasting Typhoon Tracks

Kerh¹,

Wu²

2017

IJAIA

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“…Further, TCs do not continue towards cumulonimbus free sectors in the outer circulation. Fett and Brand (1975) also noted that rotation of gross cloud features, such as an elliptical cloud mass or a major outer band; provide a very good indication of cyclone direction changes during the next 24 hours. The above results indicate that a cyclone's track is indicated by the shape and relative position of the surrounding cumulonimbus clouds.…”

Section: A New Methods For Automated Cyclone Track Forecastingmentioning

confidence: 99%

An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting

Roy

2016

Linköping Studies in Science and Technology. Dissertations

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Introduction: Tropical Cyclones (TCs) inflict considerable damage to life and property every year. A major problem is that residents often hesitate to follow evacuation orders when the early warning messages are perceived as inaccurate or uninformative. The root problem is that providing accurate early forecasts can be difficult, especially in countries with less economic and technical means. Aim:The aim of the thesis is to investigate how cyclone early warning systems can be technically improved. This means, first, identifying problems associated with the current cyclone early warning systems, and second, investigating if biologically based Artificial Neural Networks (ANNs) are feasible to solve some of the identified problems.Method: First, for evaluating the efficiency of cyclone early warning systems, Bangladesh was selected as study area, where a questionnaire survey and an in-depth interview were administered. Second, a review of currently operational TC track forecasting techniques was conducted to gain a better understanding of various techniques' prediction performance, data requirements, and computational resource requirements. Third, a technique using biologically based ANNs was developed to produce TC track and intensity forecasts. Systematic testing was used to find optimal values for simulation parameters, such as feature-detector receptive field size, the mixture of unsupervised and supervised learning, and learning rate schedule. Five types of 2D data were used for training. The networks were tested on two types of novel data, to assess their generalization performance. Results:A major problem that is identified in the thesis is that the meteorologists at the Bangladesh Meteorological Department are currently not capable of providing accurate TC forecasts. This is an important contributing factor to residents' reluctance to evacuate. To address this issue, an ANN-based TC track and intensity forecasting technique was developed that can produce early and accurate forecasts, uses freely available satellite images, and does not require extensive computational resources to run. Bidirectional connections, combined supervised and unsupervised learning, and a deep hierarchical structure assists the parallel extraction of useful features from five types of 2D data. The trained networks were tested on two types of novel data: First, tests were performed with novel data covering the end of the lifecycle of trained cyclones; for these test data, the forecasts produced by the networks were correct in 91-100% of the cases. Second, the networks were tested with data of a novel TC; in this case, the networks performed with between 30% and 45% accuracy (for intensity forecasts). Conclusions:The ANN technique developed in this thesis could, with further extensions and up-scaling, using additional types of input images of a greater number of TCs, improve the efficiency of cyclone early warning systems in countries with less economic and technical means. The thesis work also creates opportunities for furthe...

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“…Changes in the orientation of these cloud bands indicate that a similar change in cyclone movement direction may occur in the next 12-24 h. Further, tropical cyclones do not continue towards cumulonimbus-free sectors in the outer circulation. Fett and Brand (1975) also noted that rotation of gross cloud features, such as an elliptical cloud mass or a major outer band, provide a very good indication of cyclone direction changes during the next 24 h.…”

Section: A New Methods For Automated Cyclone Track Forecastingmentioning

confidence: 99%

Cyclone track forecasting based on satellite images using artificial neural networks

Kovordanyi

Roy

2009

ISPRS Journal of Photogrammetry and Remote Sensing

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Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone events. To mitigate this damage, improved forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAA-AVHRR satellite images. A multi-layer neural network, resembling the human visual system, was trained to forecast the movement of cyclones based on satellite images. The trained network produced correct directional forecast for 98% of test images, thus showing a good generalization capability. The results indicate that multi-layer neural networks could be further developed into an effective tool for cyclone track forecasting using various types of remote sensing data. Future work includes extension of the present network to handle a wide range of cyclones and to take into account supplementary information, such as wind speeds, water temperature, humidity, and air pressure

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Tropical Cyclone Movement Forecasts Based on Observations from Satellites

Cited by 13 publications

References 0 publications

Nonlinear Autoregressive Network with the Use of a Moving Average Method for Forecasting Typhoon Tracks

Nonlinear Autoregressive Network with the Use of a Moving Average Method for Forecasting Typhoon Tracks

An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting

Cyclone track forecasting based on satellite images using artificial neural networks

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