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{"metadata":{"id":"00c53429244f2e8c260faccdddb3092d","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/df9f40ca-23c9-4aff-8f71-30a884759207/retrieve"},"pageCount":9,"title":"Python Climate Predictability Tool (PyCPT) Training for improved Seasonal Climate Prediction over Ethiopia","keywords":[],"chapters":[{"head":"Acknowledgment","index":1,"paragraphs":[{"index":1,"size":94,"text":"The Accelerating Impact of CGIAR Climate Research for Africa (AICCRA) project is supported by a grant from the International Development Association (IDA) of the World Bank. IDA helps the world's poorest countries by providing grants and low to zero-interest loans for projects and programs that boost economic growth, reduce poverty, and improve poor people's lives. IDA is one of the largest sources of assistance for the world's 76 poorest countries, 39 of which are in Africa. Annual IDA commitments have averaged about $21 billion over circa 2017-2020, with approximately 61 percent going to Africa. "}]},{"head":"About the authors","index":2,"paragraphs":[]},{"head":"Background","index":3,"paragraphs":[{"index":1,"size":43,"text":"Training on weather forecasting tools and techniques is a fundamental requirement for meteorological services to improve the accuracy and reliability of weather and climate forecasts. These tools greatly support the generation and packaging of forecasts that are destined for private and public consumption."},{"index":2,"size":82,"text":"Ethiopia's National Meteorological Agency (NMA), under the support of the International Research Institute for Climate and Society (IRI), through the project Adapting Agriculture to Climate Today, for Tomorrow (ACToday), is working together with the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) -East Africa (EA) to address the needs and demands of different stakeholders including governmental, non-governmental organizations and other non-state actors by conducting staff training to improve the generation of reliable, timely and accurate weather and seasonal forecasts."},{"index":3,"size":48,"text":"With the support of the IRI and CCAFS -EA, training on the Next Generation (NextGen) seasonal forecasting was given from January 11-15, 2021, to 26 participants from the National Metrological Agency of Ethiopia (NMA). Participants were selected from NMA's Regional Meteorological Service Centers (RMSC's) and NMA head office."},{"index":4,"size":86,"text":"The Next Generation (NextGen) multi-model approach is a general systematic approach for designing, implementing, producing, and verifying objective climate forecasts. It involves identifying decision-relevant variables by stakeholders and analyzing the physical mechanisms, sources of predictability, and suitable candidate predictors (in models and observations) for key relevant variables. When prediction skill is high enough, NextGen helps select the best dynamic models for the region of interest through a process-based evaluation and automizes the generation and verification of tailored multi-model, statistically calibrated predictions at seasonal and sub-seasonal timescales."}]},{"head":"Training Objectives","index":4,"paragraphs":[{"index":1,"size":27,"text":"The main objective of the training was strengthening the capacity of NMA's staff in the application and use of PyCPT to generate approved and accurate seasonal forecasts."},{"index":2,"size":4,"text":"The specific objectives include:"},{"index":3,"size":14,"text":"• strengthening the capacity of meteorologists at both regional and head offices of NMA;"},{"index":4,"size":41,"text":"• enhanced packaging of weather forecasts using flexible information by improving the packaging of seasonal forecasts using flexible format information; and • enabling NMA staff to access the predictability skill of the North American Multi-Model Ensemble over Ethiopia in different seasons."}]},{"head":"Training Tools and Modules","index":5,"paragraphs":[{"index":1,"size":13,"text":"• Processing of dynamical forecasts using the Python Climate Predictability Tool (PyCPT) package. "}]}],"figures":[{"text":"oo Introduction to CPT, the software operation and the purpose of calibration; o Downscaling of model outputs using Canonical Correlation Analysis (CCA); • Tailored forecasting for climate services; o Skill assessment of each real-time North American Multi-Model Ensemble (NMME) model, which includes (CMC1-CanCM3, CMC2-CanCM4, NCEP-CFSv2, COLA-RSMAS-CCSM4, GFDL-CM2p1-aer04, GFDL-CM2p5-FLOR-A06, GFDL-CM2p5-FLOR-B01, NASA-GEOSS2S); o Compare Principal Component Regression (PCR) and CCA with respect to non-calibrated model; Flexible representation of forecast; o Real-time forecast script; and o Use PyCPT for all the above. • Data formatting and analysis packages like grads and climate data operator tool /CDO/ Proceedings during the PyCPT training workshop Training Outcomes At the end of the workshop, participants had underpinned understanding of the principles of generating tailored forecasts for climate services and the development of skills to independently install and operate PyCPT to calibrate CHRIPS forecasts and apply seasonal forecasting procedures and techniques by using the PyCPT tool. In general, the participants were able to: • Independently, Install and operate PyCPT to calibrate CHRIPS forecasts • Understand the principles of generating tailored forecasts for climate services • Understand seasonal forecasting procedures and techniques by using the PyCPT tool. • Understand the whole process of the PyCPT scripts • Experience sharing of within their staff members regarding PyCPT tool The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) brings together some of the world's best researchers in agricultural science, development research, climate science and Earth system science, to identify and address the most important interactions, synergies and tradeoffs between climate change, agriculture and food security. For more information, visit us at https://ccafs.cgiar.org/. Titles in this series aim to disseminate interim climate change, agriculture and food security research and practices and stimulate feedback from the scientific community. AICCRA is led by: AICCRA is supported by the International Development Association of the World Bank: "},{"text":" "},{"text":" Jemal Seid is a Python Climate Predictability Tool (PyCPT) coordinator at the Ethiopian Institute of Agricultural Research.Asaminew Teshome is a Senior Meteorologist at the National Meteorological Agency in Ethiopia.Teferi Demissie is a Scientist on Climate Information and Agro-Advisory at the CGIAR Research Program on Climate Change, Agriculture, and Food Security East Africa. "}],"sieverID":"41814b5d-8745-41d3-89e3-5e3b22196c9b","abstract":"Titles in this series aim to disseminate interim climate change, agriculture, and food security research and practices and stimulate feedback from the scientific community."} |