World Meteorological Organization launches Year of Polar Prediction

The World Meteorological Organization (WMO) and the Helmholtz Centre for Polar and Marine Research (AWI) ON 15 May 2017 officially announced the start of the Year of Polar Prediction (YOPP). The Year of Polar Prediction (YOPP) is one of the key elements of the Polar Prediction Project. The YOPP’s mission is to enable a significant improvement in environmental prediction capabilities for the polar regions and beyond. It will be done by coordinating a period of intensive observing, modelling, verification, user-engagement and education activities.

To improve the polar observing system to provide good coverage of high-quality observations in a cost effective manner. To gather additional observations through field programmes aimed at improving understanding of polar key processes. To develop improved representation of polar key processes in uncoupled and coupled models used for prediction, including those which are a particular hindrance to high-quality prediction for the polar regions, such as stable boundary layer representation, surface exchange, and steep orography.

To develop improved data assimilation systems that account for challenges in the polar regions such as sparseness of observational data, steep orography, model error and the importance of coupled processes. To explore the predictability of sea ice on time scales from days to a season. To improve understanding of linkages between polar regions and lower latitudes and assess skill of models representing these.

To improve verification of polar weather and environmental predictions to obtain quantitative knowledge on model performance, and on the skill of operational forecasting systems for user-relevant parameters; and efficiently monitor progress by World Meteorological Organization. To improve understanding of the benefits of using existing prediction information and services in the polar regions, differentiated across the spectrum of user types and benefit areas. To provide training opportunities to generate a sound knowledge base on polar prediction related issues.

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