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Global snow depths from spaceborne remote sensing for permafrost, high-elevation precipitation, and climate reanalyses (SNOWDEPTH)

Project details

Website: https://www.mn.uio.no/geo/english/research/projects/snowdepth/index.html

Status: Ongoing

Project period: 2021–2026

Principal: Research Council of Norway (RCN) (325519)

Coordinating institution: University of Oslo

The SNOWDEPTH project will, as the first in the world, directly measure snow depths globally at high spatial resolution from freely available ICESat-2 NASA spaceborne laser altimetry data available since autumn 2018.

To generate global monthly snow depth maps, including for mountainous and forested areas, we will combine the ICESat-2-derived snow depths with Sentinel snow cover/depth data in an ensemble-based data assimilation (DA) framework.

This global snow depth data will fill a large data and knowledge gap within hydrology and cryosphere/climate sciences and is directly relevant for the three application cases within the project: permafrost, high-elevation precipitation and climate reanalysis. The project has two parts and is supported by field activities for ground reference.

In phase 1, we will develop algorithms to derive snow depths at two complementary scales:

  • local snow depths from ICESat-2 profiles that capture the high spatial variability in areas with small-scale topography, and
  • global snow depth maps with monthly temporal resolution, using DA methods.

In phase 2, we will use the derived snow depths within three application fields where they directly benefit to advance the state of the art:

  • Permafrost: include snow depths in an existing model framework to greatly improve modelling of the ground thermal regime, both locally at targeted field sites and at global scale. The current lack of snow depth data is a key bottleneck for permafrost modelling.
  • High-elevation precipitation: analyse how snow depths vary across orographic barriers to increase understanding of high-altitude precipitation processes. These are currently largely unconstrained due to lack of measurements.
  • Climate reanalysis: verify and improve operational and climate reanalysis products through cross-comparison and improved process understanding. In data-sparse areas, reanalysis products are less accurate and largely model-driven given the lack of observations.

Updated: 04.07.2023

Global snow depths from spaceborne remote sensing for permafrost, high-elevation precipitation, and climate reanalyses has 1 publication at NILU:

Impacts of snow assimilation on seasonal snow and meteorological forecasts for the Tibetan Plateau

Li, Wei; Chen, Jie; Li, Lu; Orsolini, Yvan J.; Xiang, Yiheng; Senan, Retish; De Rosnay, Patricia