A Neural Network Approach to Estimate Tropical Cyclone Heat Potential in the Indian Ocean

Ali, M. M.; Jagadeesh, P. S. V.; Lin, I. I.; Hsu, Je‐Yuan

doi:10.1109/lgrs.2012.2190491

Cited by 46 publications

(23 citation statements)

References 32 publications

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“…ANN is a massive parallel-distributed model consisting of simple processing units called artificial neurons that are the basic functioning units (more details are available in [18] and [19]). It is shown that ANN-based estimations of mixed layer depth [20] and sonic layer depth [21] are better than those estimated from the multiple regression method.…”

Section: Amf Developmentmentioning

confidence: 99%

An Artificial Neural Network Model Function (AMF) for SARAL-Altika Winds

Ali

Bhowmick

Sharma

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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High-quality winds over the ocean surface, at an enhanced spatio-temporal resolution are required for a better understanding of the dynamics of the ocean and atmosphere. Altimetry helps in increasing the frequency of satellite observations. Traditional algorithms for wind speed retrievals from altimeter consider only the backscatter (sigma-0) and possibly the significant wave height (SWH). In this study, we propose an artificial neural network (ANN) model function for AltiKa on board Satellite for ARgos and ALtiKa (SARAL) to relate wind speed to sigma-0, SWH, the width of the waveform leading edge, the two brightness temperatures (TBK and TBKa), and the amplitude of the 1-Hz echo. These parameters influence either the backscatter from the ocean or the propagation of the altimeter radar signal. The wind estimates have significantly improved by incorporating these parameters.Index Terms-AltiKa, artificial neural network (ANN), geophysical data records, wind speed.

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Section: Amf Developmentmentioning

confidence: 99%

An Artificial Neural Network Model Function (AMF) for SARAL-Altika Winds

Ali

Bhowmick

Sharma

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…A survey on rainfall prediction using ANN shows that Back Propagation Network, Radial Basis Function Networks, Super Vector Machine and Self Organizing Map are the most commonly for several forecasting technique [13]. In another literature discussed on several types of flood forecasting systems used Artificial Neural Network and Support Vector Machine in the model [3,11]. Suliman, et al [16] shows that ANN are well suited for problems which have enough data or observations to see the trend for predictions.…”

Section: Introductionmentioning

confidence: 99%

Monthly Weather Forecasting through ANN Model: A Case Study in Barisal, Bangladesh

Islam¹,

Saha²,

Evan³

et al. 2016

International Journal of Advanced Research in Computer and Comm

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Bangladesh demonstrates seasonal alterations with six seasons where natural calamities cause tragic death of lives and severe hazardous in Bangladesh regularly and frequently. It is difficult to predict the weather pattern because of non-linear characteristics of natural disaster and seasonal changes in the country. The conventional weather prediction models are conducting in a huge domain and high resolution in Bangladesh which is constraint to show reliable seasonal disaster predictions. The research uses a methodology that considers specific and significant weather parameters from meteorological data in MATLAB platform to train the ANN based model. This paper includes a case study on prediction of heavy rainfall in Barisal City of Bangladesh using the model of artificial neural network (ANN) for monthly basis reliable weather forecasting planning. The model has been tested with individual month's average rainfall percentage to obtain the confirmation of the model. The results based on mean square error function (MSE) confirm the reliability of model based on multilayer perceptions for weather forecasting with successful application. The results are compared with weather information of FORECA limited to validate the seasonal disaster prediction criteria. This paper is important for the preparedness of people about forthcoming disaster in Bangladesh.

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“…The ANN technique has proved its capability in the estimation of various oceanic parameters and subsurface information such as tropical cyclone heat potential (TCHP), mixed and sonic layer depths from the surface observations [19]- [22]. Ali et al [23] estimated the TCHP (the OHC from surface to the depth of 26 • C) from SST and SSHA through ANN approach. Chacko et al [24] used a similar approach to estimate OHC up to 700 m depth.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Heat Content and Mean Temperature of Different Ocean Layers

Jagadeesh

Kumar

Ali

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Oceans are reservoirs of heat energy represented by the heat content or the mean temperature, and are the source of energy for the atmospheric processes. Which process of the atmosphere interacts with the energy of which layer of the ocean is not clear, primarily, because of the nonavailability of oceanic heat energy of different layers on a required temporal and spatial scales. Realizing this requirement, we compute the ocean heat content (OHC) and the ocean mean temperature (OMT) from surface to 50, 100, 150, 200, 300, 500, 700 m and upto 26 • C isotherm depth. Thus, we computed altogether 16 variables from satellite observations of sea surface height anomaly (SSHA), sea surface temperature (SST), and the climatological values of the above 16 variables through an artificial neural network (ANN). The model is developed using 11 472 in situ and satellite collocated observations and is validated using 2479 independent values that are not used for developing the model. These estimations have a strong Pearson correlation coefficient, r, of more than 0.90 (at 99% confidence level) between the estimated and in situ values. These parameters are provided on near real time daily basis at a spatial resolution of 0.25 • at the Bhuvan website of National Remote Sensing Centre, Indian Space Research Organisation, which can be downloaded by a researcher for further ocean-atmosphere interaction investigations. Index Terms-Artificial neural network (ANN), ocean heat content (OHC), ocean mean temperature (OMT), sea surface height anomaly (SSHA), sea surface temperature (SST).

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A Neural Network Approach to Estimate Tropical Cyclone Heat Potential in the Indian Ocean

Cited by 46 publications

References 32 publications

An Artificial Neural Network Model Function (AMF) for SARAL-Altika Winds

An Artificial Neural Network Model Function (AMF) for SARAL-Altika Winds

Monthly Weather Forecasting through ANN Model: A Case Study in Barisal, Bangladesh

Estimation of Heat Content and Mean Temperature of Different Ocean Layers

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