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<br /><table class="table itemDisplayTable"><tbody><tr><td class="metadataFieldValue"><strong>Retrieval of water vapour profiles from radio occultation refractivity using artificial neural network</strong></td></tr></tbody></table>

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Title Statement <br /><table class="table itemDisplayTable"><tbody><tr><td class="metadataFieldValue"><strong>Retrieval of water vapour profiles from radio occultation refractivity using artificial neural network</strong></td></tr></tbody></table>
 
Added Entry - Uncontrolled Name Shyam, Abhineet ; Space Applications Centre (SAC), Indian Space Research Organisation
Gohil, B S ; Space Applications Centre (SAC), ISRO
Basu, Sujit ; Space Applications Centre (SAC), ISRO
 
Uncontrolled Index Term Artificial Neural Network, Radio Occultation refractivity, Water vapor profile
 
Summary, etc. <br /><table class="table itemDisplayTable"><tbody><tr><td class="metadataFieldValue">Artificial neural network (ANN) technique has been used to derive water vapour pressure profiles in the troposphere from radio occultation data over India and adjoining region. A fully connected three-layer network, with one hidden layer, has been constructed and standard back<span lang="EN-GB"> propagation algorithm has been used to train the network. While month, latitude and vertical profile of refractivity<span lang="EN-GB">/bending angle constitute the input vector, the water v<span lang="EN-GB">apour <span lang="EN-GB">partial <span lang="EN-GB">pressure profile forms the output vector. Only the moisture-laden summer monsoon months of June, July, August and September of 2010 have been considered for developing the retrieval algorithm. There are 2120 input and output pairs, out of which 1696 pairs form the training set while the remaining pairs constitute the validation set. The retrieved profiles of water vapour pressures in the validation set have been compared with the corresponding COSMIC operational products of water vapour pressure profiles. The effectiveness of the algorithm is apparent from this comparison and also from the vertical profiles of bias and root mean square error (RMSE). <span lang="EN-GB">The statistics show better performance of the algorithm with refractivity as one of the inputs than with bending angle. The RMSE in water vapour retrieval from refractivity is within 1.5-2 hPa compared to markedly higher values of 6 hPa when derived from bending angle. Additionally, the algorithm has <span lang="EN-GB">also <span lang="EN-GB">been tested in an independent year 2009 and the performance of the refractivity based retrieval has been found to be <span lang="EN-GB">highly consistent in the year 2010, with RMSE within 1.5 hPa<span lang="EN-GB">.</span></span></span></span></span></span></span></span></span></span></td></tr></tbody></table>
 
Publication, Distribution, Etc. Indian Journal of Radio & Space Physics (IJRSP)
2016-12-29 14:08:01
 
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http://op.niscair.res.in/index.php/IJRSP/article/view/985
 
Data Source Entry Indian Journal of Radio & Space Physics (IJRSP); ##issue.vol## 42, ##issue.no## 6 (2013): Indian Journal of Radio & Space Physics
 
Language Note en
 
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