Software for acquiring, post-processing and visualizing C3S forecasts.

Seasonal climate forecasts are commonly based on model runs from fully coupled forecasting

systems that use Earth system models to represent interactions between the atmosphere, ocean,

land, and other Earth-system components. This deliverable provides various tools and techniques to

download, process, bias-correct and visualize seasonal forecasts and observational data products

relevant for the Greater Horn of Africa (GHA) region, with a particular focus on precipitation.

Tools developed in the programming language R provide functionality for processing and evaluating

seasonal weather forecasts, with an emphasis on tercile forecasts. These tools follow the guidelines

by the World Meteorological Organization (WMO) regarding evaluation of seasonal forecasts. Tools

developed in Python provide functionality for statistical downscaling and bias-correction of daily

precipitation amounts on the seasonal scale, probabilistic predictions of rainy season onset dates and

probabilistic predictions of seasonal precipitation amounts using machine learning techniques.