Abstract

AICCRA Ethiopia, along with the WaterPricing project, the Ethiopian Meteorological Institute (EMI), and the Awash Basin Development Office (AwBDO), collaborate to provide weather forecast services and real-time climate data to enhance the decision-making process and ultimately to save more water and increase productivity. The project can particularly benefit from EMI and AwBDO experts who have completed Python training. This training aimed to improve EMI and AwBDO experts in data collection, organization, manipulation, and analysis skills, making them more effective and efficient in forecasting and sharing information with endusers and the project office. The objective of the Python training for climate data analysis and visualization was to equip the participants with the necessary knowledge and skills required for processing, analyzing, and visualizing climate data using Python. Based on the training objectives mentioned in the ToR, the trainees were able to develop and improve their Python programming skills related to climate data analysis and visualization. They were able to comfortably use the Linux command-line interface to run Python scripts, which is an essential skill for working with large datasets. This allowed them to post-process model output datasets and make forecasts of needed parameters for the Water pricing project. The training has equipped EMI and AwBDO experts with the necessary skills to provide valuable services to the project. The following are the key benefits of Python training: Improved Forecasting Services: EMI and AwBDO experts can now deliver daily forecasts on the additional parameters requested by the project. The experts can provide daily forecasts on relative humidity and wind speed at specific locations. The training aimed to improve their skills in using the WRF model output effectively and delivering forecasts on other parameters like relative humidity and wind speed at 2 meters above the ground. During the training, they prepared Python scripts to manage, analyze, and share climate and weather data needed by the project.