Training for improved seasonal climate prediction over Ethiopia
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 public consumption.
The Next Generation (NextGen) multi-model approach is a systematic general approach for designing, implementing, producing and verifying objective climate forecasts. It involves the identification of decision-relevant variables by stakeholders, and analysis of the physical mechanisms, sources of predictability and suitable candidate predictors (in models and observations) for key relevant variables. In those cases, 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.
Building the capacity of Ethiopia’s National Meteorological Agency
Ethiopia’s National Meteorology 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) in East Africa 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.
This interactive training took place from January 11-15, 2021 in Addis Ababa, Ethiopia, with the participation of 26 NMA staff members selected from Regional Meteorological Service Centers and from the NMA Head Office, using local capacity and technical support from IRI and CCAFS East Africa.
Participants during climate prediction and seasonal forecast training in Ethiopia. Photo: A. Teshome (NMA)
The main objective of the training was to strengthen the capacity of NMA’s staff in the application and use of the Python Climate Predictability Tool (PyCPT) to generate accurate seasonal forecasts. The specific training objectives were to:
- Strengthen the capacity of meteorologists at both regional and head offices of NMA
- Improve accuracy of seasonal forecasts
- Enhance packaging of weather forecasts using flexible information by improving the packaging of seasonal forecasts using flexible format information
- Access the predictability skill of the North American Multi-Model Ensemble (NMME) over Ethiopia in different seasons
The specific tools and areas of training included:
- Processing of dynamical forecasts using the PyCPT package, including software operation, the purpose of calibration and downscaling of model outputs
- Tailored forecasting for climate services, including a skills assessment of NMME models, comparison of non-calibrated models, flexible representation of forecasts, generation of real time forecasts, and data formatting and analysis packages
Following the workshop, participants communicated a deepened understanding of the principles of generating tailored forecasts for climate services, as well as the development of skills to independently install and operate PyCPT to calibrate forecasts, apply seasonal forecasting procedures and techniques by using the PyCPT tool, and share the acquired knowledge amongst their colleagues, institutions and networks.
Jemal Seid is a Postdoctoral Researcher at the Ethiopian Institute of Agricultural Research (EIAR) and the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) in East Africa. Asaminew Teshome is a Meteorologist at the National Meteorology Agency (NMA) of Ethiopia. Teferi Demissie is a Climate Scientist at CCAFS in East Africa.