Zambia Report on the Africa Wide Training on the Improved NextGen Approach (PyCPT2.5) for Seasonal and Subseasonal Forecasting
This training focused on generation of high-resolution sub-seasonal rainfall forecast. The objective of the training was to strengthen knowledge of seasonal and sub-seasonal forecasting tools: Global Climate Models (GCMs) and observed data and statistical methods; introduce PyCPT 2.5: structure, inp...
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| Format: | Informe técnico |
| Language: | Inglés |
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Accelerating Impacts of CGIAR Climate Research for Africa
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/137643 |
| _version_ | 1855527965328146432 |
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| author | Sinachikupo, Kenneth |
| author_browse | Sinachikupo, Kenneth |
| author_facet | Sinachikupo, Kenneth |
| author_sort | Sinachikupo, Kenneth |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This training focused on generation of high-resolution sub-seasonal rainfall forecast. The objective of the training was to strengthen knowledge of seasonal and sub-seasonal forecasting tools: Global Climate Models (GCMs) and observed data and statistical methods; introduce PyCPT 2.5: structure, inputs, outputs, workflow, examples, and automation; configure and run PyCPT 2.5 to make the best seasonal and subseasonal forecasts of precipitation and related quantities in participants’ home countries, including forecast verification; and capitalize on GCM forecast products from WMO Global Producing Centres and U.S. research centers, accessible through the IRI Data Library (NMME, C3S, S2S, and SubX databases), as well as online global precipitation datasets and offline prediction data files. |
| format | Informe técnico |
| id | CGSpace137643 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Accelerating Impacts of CGIAR Climate Research for Africa |
| publisherStr | Accelerating Impacts of CGIAR Climate Research for Africa |
| record_format | dspace |
| spelling | CGSpace1376432025-11-11T17:02:17Z Zambia Report on the Africa Wide Training on the Improved NextGen Approach (PyCPT2.5) for Seasonal and Subseasonal Forecasting Sinachikupo, Kenneth forecasting agriculture climate change climate-smart agriculture This training focused on generation of high-resolution sub-seasonal rainfall forecast. The objective of the training was to strengthen knowledge of seasonal and sub-seasonal forecasting tools: Global Climate Models (GCMs) and observed data and statistical methods; introduce PyCPT 2.5: structure, inputs, outputs, workflow, examples, and automation; configure and run PyCPT 2.5 to make the best seasonal and subseasonal forecasts of precipitation and related quantities in participants’ home countries, including forecast verification; and capitalize on GCM forecast products from WMO Global Producing Centres and U.S. research centers, accessible through the IRI Data Library (NMME, C3S, S2S, and SubX databases), as well as online global precipitation datasets and offline prediction data files. 2023-09 2024-01-12T14:31:06Z 2024-01-12T14:31:06Z Report https://hdl.handle.net/10568/137643 en Open Access application/pdf Accelerating Impacts of CGIAR Climate Research for Africa Sinachikupo K. 2023 . Zambia Report on the Africa Wide Training on the Improved NextGen Approach (PyCPT2.5) for Seasonal and Subseasonal Forecasting . AICCRA Report. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA). |
| spellingShingle | forecasting agriculture climate change climate-smart agriculture Sinachikupo, Kenneth Zambia Report on the Africa Wide Training on the Improved NextGen Approach (PyCPT2.5) for Seasonal and Subseasonal Forecasting |
| title | Zambia Report on the Africa Wide Training on the Improved NextGen Approach (PyCPT2.5) for Seasonal and Subseasonal Forecasting |
| title_full | Zambia Report on the Africa Wide Training on the Improved NextGen Approach (PyCPT2.5) for Seasonal and Subseasonal Forecasting |
| title_fullStr | Zambia Report on the Africa Wide Training on the Improved NextGen Approach (PyCPT2.5) for Seasonal and Subseasonal Forecasting |
| title_full_unstemmed | Zambia Report on the Africa Wide Training on the Improved NextGen Approach (PyCPT2.5) for Seasonal and Subseasonal Forecasting |
| title_short | Zambia Report on the Africa Wide Training on the Improved NextGen Approach (PyCPT2.5) for Seasonal and Subseasonal Forecasting |
| title_sort | zambia report on the africa wide training on the improved nextgen approach pycpt2 5 for seasonal and subseasonal forecasting |
| topic | forecasting agriculture climate change climate-smart agriculture |
| url | https://hdl.handle.net/10568/137643 |
| work_keys_str_mv | AT sinachikupokenneth zambiareportontheafricawidetrainingontheimprovednextgenapproachpycpt25forseasonalandsubseasonalforecasting |