Multi-parameter retrieval

Maps of retrieved parameters and their retrieved uncertainties for January 1, 2023.

The multi-parameter retrieval uses measured satellite microwave brightness temperatures to simultaneously and self-consistently retrieve several geophysical parameters and their uncertainties.

It is based on the inversion of a forward model using optimal estimation (includes a priori information) to obtain the most likely set of nine geophysical atmospheric and surface parameters and their uncertainties, namely integrated water vapor (also called precipitable water), liquid water path, sea ice concentration, multi-year ice fraction, snow depth, snow-ice interface temperature and snow-air interface temperature as well as sea-surface temperature and wind speed (over open ocean).

Over open ocean, the provided snow depth, snow-ice interface temperature and snow-air interface temperature are given by the a priori data and contain no additional information from the satellite measurements, likewise, over sea ice, sea-surface temperature and wind speed are given by the a priori data.

 

The retrieval is build upon works of Melsheimer et al. (2008), Scarlat et al. (2017) and Scarlat (2018) and is described in Rückert et al. (2023). An evaluation against in-situ measurements from the MOSAiC campaign (www.mosaic-expedition.org) is described therein.

 

Attention: The multi-parameter retrieval product is a research product providing several parameters and the retrieval uncertainty given by the inversion method. There are still unknown uncertainties in addition to the ones provided. We are happy to receive feedback on the data product’s performance from you!

Also please note: the satellite brightness temperature data stems from two different sensors (AMSR-E, 2002-2011) and AMSR2 (2012 until today) and is NOT intercalibrated. Time series spanning the two sensors need to be handled with care.

 

 

Data Availability and User Guide

Data Availability

The product is available for freezing conditions, that is, from October until May and for the Arctic only. Data is available as daily, gridded data product (on the EASE grid with 25 km resolution), but swath data is available upon request.

In addition, overview plots of all parameters are available per day (see example image above) for a quick look.

Data is retrieved from the satellite sensor AMSR-E (Advanced Microwave Scanning Radiometer for EOS) on the NASA satellite Aqua from 2002 to 2011 and from its successor AMSR2 on JAXA's satellite GCOM-W1 starting in 2012.

User Guide

For detailed information on the data product, please refer to our user guide.

Data Archive

All data can be found in the data archive, which is accessible via FTP and HTTP.

FTP

For FTP access, connect to "data.seaice.uni-bremen.de" (use a ftp client, browser will not work). The products are available in the "MultiParameter" directory and then in the respective directories directories, e.g., "AMSR2" for the AMSR2 data. For further information about the data structure, please check the user guide.

HTTP

For HTTP access, visit https://data.seaice.uni-bremen.de/, the structure is the same as for FTP.

 

 

Data Citation and Contact

How to cite

Please help maintaining this service by properly citing and acknowledging if you use the data for publications:

Rückert, J. E., Huntemann, M., Tonboe, R. T., & Spreen, G. (2023). Modeling snow and ice microwave emissions in the Arctic for a multi-parameter retrieval of surface and atmospheric variables from microwave radiometer satellite data. Earth and Space Science, 10, e2023EA003177. https://doi.org/10.1029/2023EA003177

 

Contact

For questions regarding the data please contact Janna Rückert

Institute of Environmental Physics, University of Bremen, Germany.

References

  • Melsheimer, C., G. Heygster, and L. T. Pedersen (2008). Integrated retrieval of surface and atmospheric parameters over the Arctic from AMSR-E satellite microwave radiometer data using inverse methods. In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, volume 4, pp. IV – 986–IV – 989. doi:10.1109/IGARSS.2008.4779890.
  • Rückert, J. E., M. Huntemann, R. T. Tonboe, and G. Spreen (2023a). Modeling snow and ice microwave emissions in the Arctic for a multi-parameter retrieval of surface and atmospheric variables from microwave radiometer satellite data. Earth and Space Science, 10(10): p. e2023EA003177. doi:10.1029/2023EA003177.
  • Scarlat, R. C., G. Heygster, and L. T. Pedersen (2017). Experiences with an optimal estimation algorithm for surface and atmospheric parameter retrieval from passive microwave data in the Arctic. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9): pp. 3934–3947. doi:10.1109/JSTARS.2017.2739858.
  • Scarlat, R. (2018). Improving an optimal estimation algorithm for surface and atmospheric parameter retrieval using passive microwave data in the Arctic. Ph.D. thesis, University of Bremen, Bremen.