In the harsh Arctic environment, accurate, reliable and timely environmental information is key.

We combine satellite remote sensing and numerical modelling to improve our understanding, monitoring capacities. and forecasting skills of important geophysical processes at the ocean surface. This includes sea ice, icebergs, currents, waves, and wind over oceans, and marine pollution.

We focus especially on synthetic aperture radar (SAR) but also use other sensors. In addition, we develop new algorithms that may become part of already existing ocean and weather models.

conceptual image integrated RS
We combine satellite remote sensing and numerical modelling to improve our understanding, monitoring capacities and forecasting skills of important geophysical processes at the ocean surface. This concept is called integrated remote sensing and forecasting.

Satellites

Image credit: ESA.

An increasing number of satellites map sea ice, wind, ocean currents, and other properties of the entire Arctic Ocean within short time. They carry instruments that scan the ocean surface despite clouds or darkness, and send the data to a global network of ground stations.

Research airplanes

Image credit: SIOS / Derek McKay.

Research airplanes perform observations from intermediate elevations through carrying the same cameras or sensors as satellites do. Data from research airplanes resolve more details than satellite-derived data.

Drones

Image credit: William Copeland. Drone from Maritime Robotics.

Drones equipped with sensors and cameras may access areas that are large, impossible, or dangeous for humans to get to. This can be, for example, thin sea ice or snow avalance areas. Drones may also scout for obstacles or sea ice conditions ahead of a vessel, and aid finding a suitable travel route.

Expeditions

Image credit: Sebastian Gerland.

Expeditions help to study what cannot be observed from afar. During fieldwork, satellite acquisitions are often timed with manual observations of sea ice or snow. In addition, expeditions are an important real-life testing ground for algorithms, models and forecasts.

Programming

Image credit: Barents-2.5-model-domain (MET).

CIRFA researchers and partners use programming and algorithms that analyse large datasets. Combined with direct observations and models, the results improve forecasts of various ocean surface conditions, as well as uncertainty estimates.