In the High Arctic, the sea-ice cover changes and moves continuously because of the changes in temperature and wind. We use images from many satellites to tell where the sea ice is every day. We also use computer models to forecast where it will be in the next couple of days (like the weather forecast on Yr, but for sea ice). Because it is more difficult to predict sea ice than wind or rain, we started some specific research in the SIRANO project.
In SIRANO, we focus on the region around Svalbard and the Barents Sea. The project has three research tasks: 1) we want to make better use of the available satellites each on their own; 2) we want to use them better together; and 3) we want to better use the satellites together with the computer models (via data assimilation) to improve the sea-ice forecasts.
For the first task, SIRANO collaborates with the CIRFA SFI (led from University in Tromsø). CIRFA has developed automatic classification methods for Synthetic Aperture Radar (SAR) images. SAR instruments see many details in the sea-ice cover, but images are hard to fully decipher by a computer algorithm, and there are long gaps between the images. In SIRANO, we further evaluate the algorithm developed in CIRFA. We assess if slightly degrading the resolution of the SAR images improves the accuracy of the sea-ice classification. This evaluation work has started using Sentinel-1 SAR and optical images from Sentinel-2 and LandSAT-8. In addition, we worked with another type of satellite sensor: Passive Microwave Radiometers (PMR). PMRs do not see the small details like SAR do, but are more easily interpreted by computer algorithms and come with many images per day. In SIRANO, we developed a processing chain for satellite data from the Japanese AMSR2 mission. This prepares fields of sea-ice concentration at 2.5 km resolution and four times a day. The new data from SIRANO is now being assimilated in the 24/7 ocean-and-ice forecast model of MET Norway. The sea-ice concentration algorithm used for this task was adapted from one developed for the Climate Change Initiative (CCI) project of the European Space Agency (ESA).
The second task is to find methodologies for combining data from the SAR and the PMR satellites optimally. The work on this task has not started per se, but some of the preparation work was performed. Two methods for combining the SAR and PMR images have been identified. The first one is named pan-sharpening. It is a simple method that sharpens the gradients in images, and has already been used for satellite imagery although not for combining SAR and PMRs. The second method uses a satellite simulator. The satellite simulator is a piece of software that transforms an evenly spaced map of sea-ice concentration (as in a forecast model) to what the satellite actually sees while it orbits the Earth. So far in SIRANO we have implemented prototypes of the two methods.
Finally, the third task deals with improving the assimilation of sea-ice data into MET Norway's forecast models. Today, sea-ice satellite data are assimilated as daily averaged maps. The main objective for SIRANO is to test if to instead assimilate each satellite orbit separately improves the forecast. This work is well underway and will be published in 2023.
SIRANO has recruited one PhD student and one PostDoc researcher, in addition to supporting research scientists at MET Norway.
The Sea Ice Retrievals and data Assimilation in NOrway (SIRANO) project is a research collaboration between the Norwegian Meteorological Institute (MET) and UiT - the Arctic University of Norway.
In the Arctic, around Svalbard and in the Barents Sea, sea-ice moves every day, because the temperature and wind change. We use pictures from many satellites to tell where the sea-ice is every day. We also use computer models to tell where sea-ice will be in the next couple of days (like the weather forecast on Yr, but for sea-ice). But it is more difficult to predict sea-ice than wind and rain, and we need to do some research to make better sea-ice forecasts.
In our project, we will perform this new research. Three things that must be improved are: 1) to use satellites better individually, each on their own; 2) to use the satellites better all together; and 3) to use the satellites better together with the forecast models.
The main difficulty is to combine pictures from several very different satellites. For example, a type of satellite we want to use (Synthetic Apperture Radars) sees many details, but the pictures are hard to understand by a computer algorithm, and there are data gaps. Another type of satellites (MicroWave Radiometers) does not see the small details, but can easily be interpreted by computer algorithms and takes many images a day. How can we build a third satellite picture, that will combine the pictures from these two satellites well? This is one of the questions we must answer.
When we are done with our research, Norway will have new methods to use satellites and computer models to tell you where the sea-ice is, and where it is moving to. This is very useful for ships sailing or fishing around Svalbard, and for the Search and Rescue teams if there is an accident. It will also improve the weather forecasts we prepare for Yr.