8 resultados para real-time passenger information

em Plymouth Marine Science Electronic Archive (PlyMSEA)


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Using the NEODAAS-Dundee AVHRR receiving station (Scotland), NEODAAS-Plymouth can provide calibrated brightness temperature data to end users or interim users in near-real time. Between 2000 and 2009 these data were used to undertake volcano hot spot detection, reporting and time-average discharge rate dissemination during effusive crises at Mount Etna and Stromboli (Italy). Data were passed via FTP, within an hour of image generation, to the hot spot detection system maintained at Hawaii Institute of Geophysics and Planetology (HIGP, University of Hawaii at Manoa, Honolulu, USA). Final product generation and quality control were completed manually at HIGP once a day, so as to provide information to onsite monitoring agencies for their incorporation into daily reporting duties to Italian Civil Protection. We here describe the processing and dissemination chain, which was designed so as to provide timely, useable, quality-controlled and relevant information for ‘one voice’ reporting by the responsible monitoring agencies.

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Using the NEODAAS-Dundee AVHRR receiving station (Scotland), NEODAAS-Plymouth can provide calibrated brightness temperature data to end users or interim users in near-real time. Between 2000 and 2009 these data were used to undertake volcano hot spot detection, reporting and time-average discharge rate dissemination during effusive crises at Mount Etna and Stromboli (Italy). Data were passed via FTP, within an hour of image generation, to the hot spot detection system maintained at Hawaii Institute of Geophysics and Planetology (HIGP, University of Hawaii at Manoa, Honolulu, USA). Final product generation and quality control were completed manually at HIGP once a day, so as to provide information to onsite monitoring agencies for their incorporation into daily reporting duties to Italian Civil Protection. We here describe the processing and dissemination chain, which was designed so as to provide timely, useable, quality-controlled and relevant information for ‘one voice’ reporting by the responsible monitoring agencies.

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The AMSR-E satellite data and in-situ data were applied to retrieve sea surface air temperature (Ta) over the Southern Ocean. The in-situ data were obtained from the 24~(th) -26~(th) Chinese Antarctic Expeditions during 2008-2010. First, Ta was used to analyze the relativity with the bright temperature (Tb) from the twelve channels of AMSR-E, and no high relativity was found between Ta and Tb from any of the channels. The highest relativity was 0.38 (with 23.8 GHz). The dataset for the modeling was obtained by using in-situ data to match up with Tb, and two methods were applied to build the retrieval model. In multi-parameters regression method, the Tbs from 12 channels were used to the model and the region was divided into two parts according to the latitude of 50°S. The retrieval results were compared with the in-situ data. The Root Mean Square Error (RMS) and relativity of high latitude zone were 0.96℃and 0.93, respectively. And those of low latitude zone were 1.29 ℃ and 0.96, respectively. Artificial neural network (ANN) method was applied to retrieve Ta.The RMS and relativity were 1.26 ℃ and 0.98, respectively.