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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.
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.
Abstract
Climate and climate change are among the scientific topics most widely recognized by the public. Thus, climatologists seek out effective ways of communicating results of their research to various constituencies—a task made difficult by the complexity of the concept of climate. The current standard for communicated variability of climate on the global scale is a map based on the Köppen-Geiger classification (KGC) of climates, and maps of change in average annual temperatures and total annual precipitation for communicating climate change. The ClimateEx (Climate Explorer) project (http://sil.uc.edu/webapps/climateex/) communicates spatial variability and temporal change of global climate in a novel way by using the data science concept of similarity-based query. ClimateEx is implemented as a web-based visual spatial search tool. Users select a location (query), and ClimatEx returns a similarity map that visually communicates locations of places in the world having climates similar to the climate at a query location. ClimateEx can also inform about magnitude of temporal climate change by calculating a global map of local magnitudes of climate change. It offers personalized means of communicating climate heterogeneity and conveying magnitude of climate change in a single map. It has the advantage of relating climate to a user’s own experience, and is well-suited for communicating character of global climate to specialists and nonspecialists alike.
Abstract
Climate and climate change are among the scientific topics most widely recognized by the public. Thus, climatologists seek out effective ways of communicating results of their research to various constituencies—a task made difficult by the complexity of the concept of climate. The current standard for communicated variability of climate on the global scale is a map based on the Köppen-Geiger classification (KGC) of climates, and maps of change in average annual temperatures and total annual precipitation for communicating climate change. The ClimateEx (Climate Explorer) project (http://sil.uc.edu/webapps/climateex/) communicates spatial variability and temporal change of global climate in a novel way by using the data science concept of similarity-based query. ClimateEx is implemented as a web-based visual spatial search tool. Users select a location (query), and ClimatEx returns a similarity map that visually communicates locations of places in the world having climates similar to the climate at a query location. ClimateEx can also inform about magnitude of temporal climate change by calculating a global map of local magnitudes of climate change. It offers personalized means of communicating climate heterogeneity and conveying magnitude of climate change in a single map. It has the advantage of relating climate to a user’s own experience, and is well-suited for communicating character of global climate to specialists and nonspecialists alike.
Abstract
Seasonal prediction provides critical information for the tropical Pacific region, where the economy and livelihood is highly dependent on climate variability. While the highest skills of dynamical prediction systems are usually found in the tropical Pacific, National Hydrological and Meteorological Services (NHMS) in the Pacific Islands Countries (PICs) do not take full advantage of such scientific achievements. The Republic of Korea-Pacific Islands Climate Prediction Services (ROK-PI CliPS) project aims to help PICs produce regionally tailored climate prediction information using a dynamical seasonal prediction system. The project is being jointly implemented by the APEC Climate Center (APCC) and the Secretariat of the Pacific Regional Environment Programme (SPREP), in close collaboration with NHMSs in PICs. The regionally tailored, dynamical-statistical hybrid climate prediction system uses predictors that were identified through communications with NHMSs. The predictors were selected based on the empirical physical relationship of the local climate fluctuations, indicated by multi-institutional and multimodel ensembles. This hybrid system makes full use of dynamical seasonal predictions, which have not been commonly utilized in current operation in PICs. In accordance with system development, additional efforts have been made for PIC NHMSs to build capacity by increasing their knowledge and skill needed to develop such methodologies and systems. Nonetheless, the successive and strategic efforts to sustain and further improve climate predictions in the Pacific Islands region are required.
Abstract
Seasonal prediction provides critical information for the tropical Pacific region, where the economy and livelihood is highly dependent on climate variability. While the highest skills of dynamical prediction systems are usually found in the tropical Pacific, National Hydrological and Meteorological Services (NHMS) in the Pacific Islands Countries (PICs) do not take full advantage of such scientific achievements. The Republic of Korea-Pacific Islands Climate Prediction Services (ROK-PI CliPS) project aims to help PICs produce regionally tailored climate prediction information using a dynamical seasonal prediction system. The project is being jointly implemented by the APEC Climate Center (APCC) and the Secretariat of the Pacific Regional Environment Programme (SPREP), in close collaboration with NHMSs in PICs. The regionally tailored, dynamical-statistical hybrid climate prediction system uses predictors that were identified through communications with NHMSs. The predictors were selected based on the empirical physical relationship of the local climate fluctuations, indicated by multi-institutional and multimodel ensembles. This hybrid system makes full use of dynamical seasonal predictions, which have not been commonly utilized in current operation in PICs. In accordance with system development, additional efforts have been made for PIC NHMSs to build capacity by increasing their knowledge and skill needed to develop such methodologies and systems. Nonetheless, the successive and strategic efforts to sustain and further improve climate predictions in the Pacific Islands region are required.
