<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bobylev, Leonid</style></author><author><style face="normal" font="default" size="100%">Zabolotskikh, Elizaveta</style></author><author><style face="normal" font="default" size="100%">Mitnik, L. M.</style></author><author><style face="normal" font="default" size="100%">Mitnik, M. L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Atmospheric Water Vapor and Cloud Liquid Water Retrieval Over the Arctic Ocean Using Satellite Passive Microwave Sensing</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Geoscience and Remote Sensing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Microwave radiometry</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Water</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5272345</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">http://www.nersc.no/sites/www.nersc.no/files/manuscript.pdf</style></url></related-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">IEEE Geoscience and Remote Sensing Society </style></publisher><volume><style face="normal" font="default" size="100%">48</style></volume><abstract><style face="normal" font="default" size="100%">New algorithms for total atmospheric water vapor content (Q) and total cloud liquid water content (W) retrieval from satellite microwave radiometer data, based on Neural Networks (NNs) and applicable for high latitude open water areas, were developed. For algorithm development, a radiative transfer equation numerical integration was carried out for Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR-E) channel characteristics for non-precipitating conditions over the open ocean. Sets of sea surface temperatures less than 15°C, surface winds and radiosonde reports collected by Russian research vessels served as input data for integration. It was shown that NNs perform better than the conventional regression techniques. Q retrieval algorithms were validated both for SSM/I and AMSR-E instrument using satellite radiometric measurements collocated in space and time with polar station radiosonde data. The resulting SSM/I and AMSR-E retrieval errors proved to be 1.09 kg/m2 and 0.90 kg/m2 correspondingly. For SSM/I Q retrievals the algorithms were compared with Wentz global operational algorithm. This comparison demonstrated the advantages of NNs-based polar regional algorithm in comparison with Wentz global one. The retrieval errors proved to be 1.34 kg/m2 and 1.90 kg/m2 (~40% worse) for NNs and Wentz algorithms correspondingly.</style></abstract><notes><style face="normal" font="default" size="100%">ISI:000272998200024</style></notes><section><style face="normal" font="default" size="100%">283 - 294 </style></section><auth-address><style face="normal" font="default" size="100%">NERSC</style></auth-address></record></records></xml>