West Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2)
From October 10-19, a nine-day training targeting West Africa (WA) was implemented in Lomé, Togo by the International Research Institute for Climate and Society (IRI) of the Columbia Climate School, in close collaboration with the AICCRA-West Africa team, the Regional Center for Training and App...
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
Accelerating Impacts of CGIAR Climate Research for Africa
2022
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/126770 |
| _version_ | 1855520705168277504 |
|---|---|
| author | Grossi, Amanda Robertson, Andrew Trzaska, Sylwia Dinku, Tufa Zougmoré, Robert B. Minoungou, Bernard Mohamed, Hamatan |
| author_browse | Dinku, Tufa Grossi, Amanda Minoungou, Bernard Mohamed, Hamatan Robertson, Andrew Trzaska, Sylwia Zougmoré, Robert B. |
| author_facet | Grossi, Amanda Robertson, Andrew Trzaska, Sylwia Dinku, Tufa Zougmoré, Robert B. Minoungou, Bernard Mohamed, Hamatan |
| author_sort | Grossi, Amanda |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | From October 10-19, a nine-day training targeting West Africa (WA) was implemented in Lomé, Togo by
the International Research Institute for Climate and Society (IRI) of the Columbia Climate School, in
close collaboration with the AICCRA-West Africa team, the Regional Center for Training and Application
in Agrometeorology and Operational Hydrology (AGRHYMET) and Meteo Togo. 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 7 national meteorological services from the WA region, as well as its
regional climate center (AGRHYMET) 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 national meteorological services of
seasonal forecasting tools, 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. |
| format | Informe técnico |
| id | CGSpace126770 |
| 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 |
| record_format | dspace |
| spelling | CGSpace1267702025-11-11T16:33:55Z West Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2) Grossi, Amanda Robertson, Andrew Trzaska, Sylwia Dinku, Tufa Zougmoré, Robert B. Minoungou, Bernard Mohamed, Hamatan agriculture climate-smart agriculture climate change forecasting capacity development climate variability From October 10-19, a nine-day training targeting West Africa (WA) was implemented in Lomé, Togo by the International Research Institute for Climate and Society (IRI) of the Columbia Climate School, in close collaboration with the AICCRA-West Africa team, the Regional Center for Training and Application in Agrometeorology and Operational Hydrology (AGRHYMET) and Meteo Togo. 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 7 national meteorological services from the WA region, as well as its regional climate center (AGRHYMET) 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 national meteorological services of seasonal forecasting tools, 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 2023-01-10T19:35:26Z 2023-01-10T19:35:26Z Report https://hdl.handle.net/10568/126770 en Open Access application/pdf Accelerating Impacts of CGIAR Climate Research for Africa Grossi A, Robertson A, Trzaska S, Dinku T, Zougmoré R, Minoungou B, Mohamed H. 2022. West Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2). AICCRA Workshop. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA). |
| spellingShingle | agriculture climate-smart agriculture climate change forecasting capacity development climate variability Grossi, Amanda Robertson, Andrew Trzaska, Sylwia Dinku, Tufa Zougmoré, Robert B. Minoungou, Bernard Mohamed, Hamatan West Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2) |
| title | West Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2) |
| title_full | West Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2) |
| title_fullStr | West Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2) |
| title_full_unstemmed | West Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2) |
| title_short | West Africa Regional Training On the Improved NextGen Seasonal Forecasting Approach (PyCPT 2) |
| title_sort | west africa regional training on the improved nextgen seasonal forecasting approach pycpt 2 |
| topic | agriculture climate-smart agriculture climate change forecasting capacity development climate variability |
| url | https://hdl.handle.net/10568/126770 |
| work_keys_str_mv | AT grossiamanda westafricaregionaltrainingontheimprovednextgenseasonalforecastingapproachpycpt2 AT robertsonandrew westafricaregionaltrainingontheimprovednextgenseasonalforecastingapproachpycpt2 AT trzaskasylwia westafricaregionaltrainingontheimprovednextgenseasonalforecastingapproachpycpt2 AT dinkutufa westafricaregionaltrainingontheimprovednextgenseasonalforecastingapproachpycpt2 AT zougmorerobertb westafricaregionaltrainingontheimprovednextgenseasonalforecastingapproachpycpt2 AT minoungoubernard westafricaregionaltrainingontheimprovednextgenseasonalforecastingapproachpycpt2 AT mohamedhamatan westafricaregionaltrainingontheimprovednextgenseasonalforecastingapproachpycpt2 |