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    PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data

    Source: Bulletin of the American Meteorological Society:;2018:;volume 099:;issue 007::page 1329
    Author:
    Raspaud, Martin
    ,
    Hoese, David
    ,
    Dybbroe, Adam
    ,
    Lahtinen, Panu
    ,
    Devasthale, Abhay
    ,
    Itkin, Mikhail
    ,
    Hamann, Ulrich
    ,
    Rasmussen, Lars Ørum
    ,
    Nielsen, Esben Stigård
    ,
    Leppelt, Thomas
    ,
    Maul, Alexander
    ,
    Kliche, Christian
    ,
    Thorsteinsson, Hrobjartur
    DOI: 10.1175/BAMS-D-17-0277.1
    Publisher: American Meteorological Society
    Abstract: AbstractPyTroll (http://pytroll.org) is a suite of open-source easy-to-use Python packages to facilitate processing and efficient sharing of Earth Observation (EO) satellite data. The PyTroll software is intended for both 24/7 real-time operations as well as research and development. PyTroll grew out of the need to provide a resilient and agile platform that can respond quickly to new user needs and new data sources. PyTroll, being open source, stimulates international collaboration, which is vital with the rapid increase of satellite information availability. The PyTroll software development is strongly user driven and has grown over the past eight years from a collaborative effort between the Danish and Swedish national meteorological services to encompass a worldwide community with active contributors. PyTroll is being used at least operationally in the national meteorological services of Denmark, Norway, Sweden, Finland, Germany, Switzerland, Italy, Estonia, and Latvia. However, given its simplicity, minimal demand on user resources, and community-driven approach, it also encourages and facilitates usage of EO data for individual applications. While PyTroll was originally developed to cater to the needs of the atmospheric remote sensing community, it could be equally useful for land and ocean applications and within hydrology. This article provides an overview of PyTroll, with examples showing the capability of some of the core packages.
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      PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4261304
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    • Bulletin of the American Meteorological Society

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    contributor authorRaspaud, Martin
    contributor authorHoese, David
    contributor authorDybbroe, Adam
    contributor authorLahtinen, Panu
    contributor authorDevasthale, Abhay
    contributor authorItkin, Mikhail
    contributor authorHamann, Ulrich
    contributor authorRasmussen, Lars Ørum
    contributor authorNielsen, Esben Stigård
    contributor authorLeppelt, Thomas
    contributor authorMaul, Alexander
    contributor authorKliche, Christian
    contributor authorThorsteinsson, Hrobjartur
    date accessioned2019-09-19T10:04:51Z
    date available2019-09-19T10:04:51Z
    date copyright5/9/2018 12:00:00 AM
    date issued2018
    identifier otherbams-d-17-0277.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261304
    description abstractAbstractPyTroll (http://pytroll.org) is a suite of open-source easy-to-use Python packages to facilitate processing and efficient sharing of Earth Observation (EO) satellite data. The PyTroll software is intended for both 24/7 real-time operations as well as research and development. PyTroll grew out of the need to provide a resilient and agile platform that can respond quickly to new user needs and new data sources. PyTroll, being open source, stimulates international collaboration, which is vital with the rapid increase of satellite information availability. The PyTroll software development is strongly user driven and has grown over the past eight years from a collaborative effort between the Danish and Swedish national meteorological services to encompass a worldwide community with active contributors. PyTroll is being used at least operationally in the national meteorological services of Denmark, Norway, Sweden, Finland, Germany, Switzerland, Italy, Estonia, and Latvia. However, given its simplicity, minimal demand on user resources, and community-driven approach, it also encourages and facilitates usage of EO data for individual applications. While PyTroll was originally developed to cater to the needs of the atmospheric remote sensing community, it could be equally useful for land and ocean applications and within hydrology. This article provides an overview of PyTroll, with examples showing the capability of some of the core packages.
    publisherAmerican Meteorological Society
    titlePyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data
    typeJournal Paper
    journal volume99
    journal issue7
    journal titleBulletin of the American Meteorological Society
    identifier doi10.1175/BAMS-D-17-0277.1
    journal fristpage1329
    journal lastpage1336
    treeBulletin of the American Meteorological Society:;2018:;volume 099:;issue 007
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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