Improving our ability to forecast extreme weather events and predict the changing climate is critical to manage risks effectively, understand adaptation needs and plan accordingly with systematic and anticipatory action. Climate change and extreme weather events are now threatening lives and hampering global efforts to reduce poverty. Accurate weather forecasts and climate prediction is critical for all sectors and in particular for those that rely heavily on weather and climate, such as agriculture, transport, renewable energy and insurance.
Surface-based weather observations underpin weather forecasts, early warning systems, and climate information everywhere. Global Numerical Weather Prediction models are the backbones of all weather forecasts and climate prediction products. These systems require continued access to a wealth of real-time weather data from the entire globe. Surface-based observations are fundamental to the quality of the output of these models. These observations are essential to measure certain weather parameters that cannot yet be reliably measured from space and they play a vital role for calibration and validation of satellite weather data.
The current gaps in global surface-based data sharing significantly impact the quality of weather and climate information locally, regionally and globally. While some parts of the globe provide a reliable feed of these data, many others contribute only limited amounts and, in several instances the amount of data shared is even declining.
In Small Islands Developing States (SIDS) and Least Developed Countries (LDCs) the data gaps are striking. Despite substantial investments in observational infrastructure supported by development finance institutions in these countries, there has been limited lasting improvement in global data sharing. In fact, the European Centre for Medium-Range Weather Forecasts observed a dramatic decrease in the number of shared radiosonde data (the most important surface-based data for weather prediction models) of almost 50% in Africa from 2015 to 2020. This situation does not include the further decline in observations since January 2020 due to the impact of COVID-19.
The principal reason for the mismatch between investments and limited improvement in global data sharing in SIDS and LDCs is the fact that these countries have not been able to operate and maintain their observational infrastructure. Providing these countries with the means and the incentives to invest, operate and maintain weather observation systems will have a large payoff in terms of long-term weather data collection and sharing and, ultimately, improvements in national and global development outcomes.