East and Southern Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2)

From October 3-11, an eight-day training of trainers (ToT) targeting East and Southern Africa (ESA) was implemented in Zanzibar, Tanzania by the International Research Institute for Climate and Society (IRI) of the Columbia Climate School, in close collaboration with the IGAD Climate Prediction and...

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Autores principales: Grossi, Amanda, Robertson, Andrew, Muñoz, Angel, Singh, Bohar, Dinku, Tufa, Demissie, Teferi Dejene, Solomon, Dawit
Formato: Informe técnico
Lenguaje:Inglés
Publicado: Accelerating Impacts of CGIAR Climate Research for Africa 2022
Materias:
Acceso en línea:https://hdl.handle.net/10568/126758
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author Grossi, Amanda
Robertson, Andrew
Muñoz, Angel
Singh, Bohar
Dinku, Tufa
Demissie, Teferi Dejene
Solomon, Dawit
author_browse Demissie, Teferi Dejene
Dinku, Tufa
Grossi, Amanda
Muñoz, Angel
Robertson, Andrew
Singh, Bohar
Solomon, Dawit
author_facet Grossi, Amanda
Robertson, Andrew
Muñoz, Angel
Singh, Bohar
Dinku, Tufa
Demissie, Teferi Dejene
Solomon, Dawit
author_sort Grossi, Amanda
collection Repository of Agricultural Research Outputs (CGSpace)
description From October 3-11, an eight-day training of trainers (ToT) targeting East and Southern Africa (ESA) was implemented in Zanzibar, Tanzania by the International Research Institute for Climate and Society (IRI) of the Columbia Climate School, in close collaboration with the IGAD Climate Prediction and Applications Centre (ICPAC), the International Livestock Research Institute (ILRI), and the Tanzania Meteorological Authority (TMA). The workshop, which was organized as part of the World Bank’s Accelerating the Impact of CGIAR Climate Research for Africa (AICCRA) project, brought together 10 national meteorological services from the ESA region, as well as its regional climate center— IGAD Climate Prediction and Applications Centre (ICPAC)—to improve seasonal forecasting capacities using the “NextGen” approach and its concomitant PyCPT version 2 interface (PyCPT2). In particular, the major objectives of the training were to strengthen the knowledge and understanding of regional and national meteorological services of seasonal forecasting tools and approaches, introduce the new advances and functionalities of the Python (PyCPT2) interface for the NextGen forecasting approach, configure and run PyCPT version 2 to make the best-available forecasts in participants’ home countries, including forecast verification, and provide foundational training on best practices for forecast communication including the Flexible Forecast format.
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institution CGIAR Consortium
language Inglés
publishDate 2022
publishDateRange 2022
publishDateSort 2022
publisher Accelerating Impacts of CGIAR Climate Research for Africa
publisherStr Accelerating Impacts of CGIAR Climate Research for Africa
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spelling CGSpace1267582025-11-11T16:33:53Z East and Southern Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2) Grossi, Amanda Robertson, Andrew Muñoz, Angel Singh, Bohar Dinku, Tufa Demissie, Teferi Dejene Solomon, Dawit agriculture next generation sequencing climate-smart agriculture climate change From October 3-11, an eight-day training of trainers (ToT) targeting East and Southern Africa (ESA) was implemented in Zanzibar, Tanzania by the International Research Institute for Climate and Society (IRI) of the Columbia Climate School, in close collaboration with the IGAD Climate Prediction and Applications Centre (ICPAC), the International Livestock Research Institute (ILRI), and the Tanzania Meteorological Authority (TMA). The workshop, which was organized as part of the World Bank’s Accelerating the Impact of CGIAR Climate Research for Africa (AICCRA) project, brought together 10 national meteorological services from the ESA region, as well as its regional climate center— IGAD Climate Prediction and Applications Centre (ICPAC)—to improve seasonal forecasting capacities using the “NextGen” approach and its concomitant PyCPT version 2 interface (PyCPT2). In particular, the major objectives of the training were to strengthen the knowledge and understanding of regional and national meteorological services of seasonal forecasting tools and approaches, introduce the new advances and functionalities of the Python (PyCPT2) interface for the NextGen forecasting approach, configure and run PyCPT version 2 to make the best-available forecasts in participants’ home countries, including forecast verification, and provide foundational training on best practices for forecast communication including the Flexible Forecast format. 2022-10 2023-01-10T16:45:40Z 2023-01-10T16:45:40Z Report https://hdl.handle.net/10568/126758 en Open Access application/pdf Accelerating Impacts of CGIAR Climate Research for Africa Grossi A, Robertson A, Muñoz A, Singh B, Dinku T, Demissie T, Solomon D. 2022. East and Southern Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2). AICCRA Workshop Report. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA).
spellingShingle agriculture
next generation sequencing
climate-smart agriculture
climate change
Grossi, Amanda
Robertson, Andrew
Muñoz, Angel
Singh, Bohar
Dinku, Tufa
Demissie, Teferi Dejene
Solomon, Dawit
East and Southern Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2)
title East and Southern Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2)
title_full East and Southern Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2)
title_fullStr East and Southern Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2)
title_full_unstemmed East and Southern Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2)
title_short East and Southern Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2)
title_sort east and southern africa regional training on the improved nextgen seasonal forecasting approach pycpt 2
topic agriculture
next generation sequencing
climate-smart agriculture
climate change
url https://hdl.handle.net/10568/126758
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