SFE: Seasonal Forecasting Engine
The aim of SFE is to develop a state-of-the-art operational seasonal climate prediction system for Northern Europe and the Arctic.
Objectives
Our vision is to meet a growing demand in the private and public sectors for advanced, relevant and applicable seasonal climate predictions to support risk management and operational planning in Northern Europe and the Arctic.
We plan to realise this vision and effectively kick-start operational seasonal forecasting in Norway by pursuing the following objectives:
- Facilitate co-production of knowledge through two-way dialogue with our users, using best practices from the emerging field of climate service, to ensure that their specific needs are met.
- Tailor predictions for our target region to user requirements.
- Develop statistical algorithms that combine dynamical and empirical predictions to produce unified probabilistic forecasts.
- Create flexible digital interfaces that enable users to access the forecasts and to integrate these in their own digital tools.
- Forge strong relationships and work in complementary fashion with international research groups.
Project Summary
The aim of SFE is to develop a state-of-the-art operational seasonal climate prediction system for Northern Europe and the Arctic. 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. 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. 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. 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.
