Estimation of sonic layer depth from surface parameters

Jain, Sarika; Ali, M. M.; Sen, P. N.

doi:10.1029/2007gl030577

Cited by 20 publications

(14 citation statements)

References 16 publications

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“…NN is a massive parallel-distributed computer model consisting of simple processing units called artificial neurons that are the basic functioning units (more details are available in . It is proved that NN based estimations of mixed layer depth (Swain et al, 2006) and sonic layer depth (Jain et al, 2012) are better than those from the multiple regression method. ANN analysis requires three sets of data for (1) training, (2) verification and (3) validation.…”

Section: Methodsmentioning

confidence: 98%

Net surface radiation retrieval using Earth Observation Satellite data and machine learning algorithm

Mahalakshmi

Paul

Dutta

et al. 2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VIII, WG VIII/3 KEY WORDS: ANN, MODIS, Terra, Eddy flux tower, NSR, Bonnie camp. ABSTRACT:We present a method to estimate net surface radiation (NSR) from Terra MODIS data using Artificial Neural Network (ANN) technique. For this purpose, we trained the ANN model using MODIS atmospheric profile product of air temperature, dew point temperature, solar zenith angle and land surface temperature from Terra as independent parameters and the net surface radiation from eddy flux tower measurements at Bonnie camp location of Sundarban region as the dependent variable. The NSR is estimated with a root mean square accuracy of 64 w/m 2 and the square of the correlation coefficient (R 2 ) is 0.75 respectively. This technique is extended to estimate NSR over the entire Sundarban area and has a potential for climate and agricultural water management studies.

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Section: Methodsmentioning

confidence: 98%

Net surface radiation retrieval using Earth Observation Satellite data and machine learning algorithm

Mahalakshmi

Paul

Dutta

et al. 2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…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. The ANN analysis requires three sets of data for (1) training, (2) verification, and (3) validation.…”

Section: Amf Developmentmentioning

confidence: 98%

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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“…In addition to various methods of estimations, artificial neural network (ANN) is another possible method to derive OHC from satellite observations over larger temporal and spatial scales. 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.…”

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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Estimation of sonic layer depth from surface parameters

Cited by 20 publications

References 16 publications

Net surface radiation retrieval using Earth Observation Satellite data and machine learning algorithm

Net surface radiation retrieval using Earth Observation Satellite data and machine learning algorithm

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

Estimation of Heat Content and Mean Temperature of Different Ocean Layers

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