{"title":"低地球轨道卫星(L3S-LEO) NOAA全球网格超整理海温数据的算法改进与一致性检验","authors":"O. Jonasson, I. Gladkova, A. Ignatov, Y. Kihai","doi":"10.1117/12.2585819","DOIUrl":null,"url":null,"abstract":"NOAA provides satellite sea surface temperature (SST) products using the Advanced Clear-Sky Processor for Oceans (ACSPO) system. With the large number of earth-viewing sensors in orbit, data volume has become difficult to manage. In response, NOAA has developed gridded super-collated (L3S-LEO) SST products which collate L3U data from multiple Low-Earth-Orbiting satellites into a multi-sensor product. In this work we describe recent L3S algorithm improvements, aimed at improving spatial continuity of SST imagery and reducing impact of cloud leakages from individual sensor L3U data. We also present results of long-term validation of L3S-LEO products versus in-situ data in NOAA SQUAM system.","PeriodicalId":184582,"journal":{"name":"Ocean Sensing and Monitoring XIII","volume":"776 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Algorithmic improvements and consistency checks of the NOAA global gridded super-collated SSTs from low Earth orbiting satellites (L3S-LEO)\",\"authors\":\"O. Jonasson, I. Gladkova, A. Ignatov, Y. Kihai\",\"doi\":\"10.1117/12.2585819\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"NOAA provides satellite sea surface temperature (SST) products using the Advanced Clear-Sky Processor for Oceans (ACSPO) system. With the large number of earth-viewing sensors in orbit, data volume has become difficult to manage. In response, NOAA has developed gridded super-collated (L3S-LEO) SST products which collate L3U data from multiple Low-Earth-Orbiting satellites into a multi-sensor product. In this work we describe recent L3S algorithm improvements, aimed at improving spatial continuity of SST imagery and reducing impact of cloud leakages from individual sensor L3U data. We also present results of long-term validation of L3S-LEO products versus in-situ data in NOAA SQUAM system.\",\"PeriodicalId\":184582,\"journal\":{\"name\":\"Ocean Sensing and Monitoring XIII\",\"volume\":\"776 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ocean Sensing and Monitoring XIII\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2585819\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ocean Sensing and Monitoring XIII","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2585819","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Algorithmic improvements and consistency checks of the NOAA global gridded super-collated SSTs from low Earth orbiting satellites (L3S-LEO)
NOAA provides satellite sea surface temperature (SST) products using the Advanced Clear-Sky Processor for Oceans (ACSPO) system. With the large number of earth-viewing sensors in orbit, data volume has become difficult to manage. In response, NOAA has developed gridded super-collated (L3S-LEO) SST products which collate L3U data from multiple Low-Earth-Orbiting satellites into a multi-sensor product. In this work we describe recent L3S algorithm improvements, aimed at improving spatial continuity of SST imagery and reducing impact of cloud leakages from individual sensor L3U data. We also present results of long-term validation of L3S-LEO products versus in-situ data in NOAA SQUAM system.
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