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

Martin Raspaud Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

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David Hoese Space Science and Engineering Center (SSEC), University of Wisconsin—Madison, Madison, Wisconsin

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Adam Dybbroe Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

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Panu Lahtinen Finnish Meteorological Institute (FMI), Helsinki, Finland

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Abhay Devasthale Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

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Mikhail Itkin Norwegian Polar Institute (NPI), Tromsø, Norway

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Ulrich Hamann Federal Office of Meteorology and Climatology (MeteoSwiss), Locarno-Monti, Switzerland

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Lars Ørum Rasmussen Danish Meteorological Institute (DMI), Copenhagen, Denmark

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Esben Stigård Nielsen Think Big Analytics, Copenhagen, Denmark

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Thomas Leppelt Deutscher Wetterdienst (DWD), Offenbach am Main, Germany

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Alexander Maul Deutscher Wetterdienst (DWD), Offenbach am Main, Germany

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Christian Kliche EBP Deutschland GmbH, Berlin, Germany

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Hrobjartur Thorsteinsson Videntifier Technologies ehf, Reykjavik, Iceland

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Abstract

PyTroll (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.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Martin Raspaud, martin.raspaud@smhi.se

A supplement to this article is available online (10.1175/BAMS-D-17-0277.2)

Abstract

PyTroll (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.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Martin Raspaud, martin.raspaud@smhi.se

A supplement to this article is available online (10.1175/BAMS-D-17-0277.2)

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