Seasonal Forecasting Engine

What we do

To our users, the SFE will be accessible through a flexible interface which can be queried to obtain predictions of relevant climate indices and variables. Under the hood, our "engine" consists of statistical algorithms that merge vast amounts of data into unified forecasts. Our team consists of experts in handling big data, statisticians, climatologists, climate modellers, and climate service practitioners. Working in complementary fashion with the international research community, and guided by an international peer advisory committee, we will both improve our own models and, taking existing seasonal forecast ensembles, employ innovative empirical-statistical approaches to make the forecasts better and more relevant for our users.

Why is this important?

Tailored seasonal predictions can be helpful tools for risk mitigation, and they can guide more efficient use of resources in many sectors of society, including agriculture, energy, water, transportation, and insurance. As we increasingly understand the mechanisms that drive the enormously complex climate system, both dynamical and empirical models are steadily improving. At the same time, increased computational power, enhanced observations and remote sensing, and advanced statistical methods to blend models and observations, are driving a big data-fuelled revolution in climate prediction. What is urgently needed now is careful, but speedy, transformation of research into innovative practical applications and services.

Goals

The aim of SFE is to develop a state-of-the-art operational seasonal climate prediction system for Northern Europe and the Arctic.