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

Snow in the mountains is a source for drinking water, hydropower, irrigation, but can also cause floods and geohazards. There are currently no efficient methods to measure depth of snow in mountains and remote areas. The first aim of this project is to combine snow depth measurements from satellite data with elevation data, climate data and statistical methods to get currently lacking global snow depth maps. The second aim is to use the novel maps to improve global climate reanalyses and our knowledge on high-mountain precipitation and permafrost.

Knowledge about snow depth in mountains is limited. The laser satellite ICESat-2 records the snow surface elevation along six profiles. In this research project we will combine different types of satellite data, ground data, and observations in the field to determine snow mass in different remote areas. Illustration: Désirée Treichler/UiO

About the project

This research effort is, as the first in the world, to directly measure snow depths globally at high spatial resolution from open 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 data from the ESA's Copernicus Sentinel satellite snow cover/depth data in an ensemble-based data assimilation (DA) framework.

During the first part of the project, we aim to develop methods to retrieve global snow depths by means of ensemble-based data assimilation, similar to the methods used within climate reanalyses. In the second part of the project includes three application areas where our global snow depths have especially great potential to improve our knowledge, also in the light of climate change, and is permafrost, climate reanalyses, and high-elevation precipitation.

The new global snow depth data map 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.

Objectives

The research effort with the combination of data is carried out in two phases and is along the way supported by field activities for ground references. In phase 1, we will develop algorithms to derive snow depths at two complementary scales: A) local snow depths from ICESat-2 profiles that capture the high spatial variability in areas with small-scale topography, and B) global snow depth maps with monthly temporal resolution, using DA methods.

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

i) 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.
ii) 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.
iii) 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.

Financing

This research project is funded by the “ROMFORSK-Program for romforskning” of The Research Council of Norway and is given as a Researcher Project for Young Talents to project leader Désirée Treichler. The project number at NFR is 325519.

The project period for SNOWDEPTH is from 2021 to 2026.

Cooperation

The SNOWDEPTH project is a collaboration with researchers from Norway and Svitzerland from these departments or research institutions:

Department of Geosciences, University of Oslo (Norway)
NILU – Norwegian Institute for Air Research (Norway)
University of Fribourg (Switzerland)
WSL Institute for Snow and Avalanche Research, SLF (Switzerland)

Alonso-Gonzalez, Esteban; Aalstad, Kristoffer; Pirk, Norbert; Mazzolini, Marco; Treichler, Désirée & Leclercq, Paul [Show all 9 contributors for this article] (2023). Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 27(24), p. 4637–4659. doi: 10.5194/hess-27-4637-2023. Full text in Research Archive Show summary
Data assimilation techniques that integrate available observations with snow models have been proposed as a viable option to simultaneously help constrain model uncertainty and add value to observations by improving estimates of the snowpack state. However, the propagation of information from spatially sparse observations in high-resolution simulations remains an under-explored topic. To remedy this, the development of data assimilation techniques that can spread information in space is a crucial step. Herein, we examine the potential of spatio-temporal data assimilation for integrating sparse snow depth observations with hyper-resolution (5 m) snow simulations in the Izas central Pyrenean experimental catchment (Spain). Our experiments were developed using the Multiple Snow Data Assimilation System (MuSA) with new improvements to tackle the spatio-temporal data assimilation. Therein, we used a deterministic ensemble smoother with multiple data assimilation (DES-MDA) with domain localization. Three different experiments were performed to showcase the capabilities of spatio-temporal information transfer in hyper-resolution snow simulations. Experiment I employed the conventional geographical Euclidean distance to map the similarity between cells. Experiment II utilized the Mahalanobis distance in a multi-dimensional topographic space using terrain parameters extracted from a digital elevation model. Experiment III utilized a more direct mapping of snowpack similarity from a single complete snow depth map together with the easting and northing coordinates. Although all experiments showed a noticeable improvement in the snow patterns in the catchment compared with the deterministic open loop in terms of correlation (r=0.13) and root mean square error (RMSE = 1.11 m), the use of topographical dimensions (Experiment II, r=0.63 and RMSE = 0.89 m) and observations (Experiments III, r=0.92 and RMSE = 0.44 m) largely outperform the simulated patterns in Experiment I (r=0.38 and RMSE = 1.16 m). At the same time, Experiments II and III are considerably more challenging to set up. The results of these experiments can help pave the way for the creation of snow reanalysis and forecasting tools that can seamlessly integrate sparse information from national monitoring networks and high-resolution satellite information.
Berthier, Etienne; Floriciou, Dana; Gardner, Alex S.; Gourmelen, Noel; Jakob, Livia & Paul, Frank [Show all 16 contributors for this article] (2023). Measuring glacier mass changes from space-a review. Reports on progress in physics (Print). ISSN 0034-4885. 86(3). doi: 10.1088/1361-6633/acaf8e. Full text in Research Archive
Li, Wei; Chen, Jie; Li, Lu; Orsolini, Yvan J.; Xiang, Yiheng & Senan, Retish [Show all 7 contributors for this article] (2022). Impacts of snow assimilation on seasonal snow and meteorological forecasts for the Tibetan Plateau. The Cryosphere. ISSN 1994-0416. 16(12), p. 4985–5000. doi: 10.5194/tc-16-4985-2022. Full text in Research Archive Show summary
The Tibetan Plateau (TP) contains the largest amount of snow outside the polar regions and is the source of many major rivers in Asia. An accurate long-range (i.e. seasonal) meteorological forecast is of great importance for this region. The fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5) provides global long-range meteorological forecasts including over the TP. However, SEAS5 uses land initial conditions produced by assimilating Interactive Multisensor Snow and Ice Mapping System (IMS) snow data only below 1500 m altitude, which may affect the forecast skill of SEAS5 over mountainous regions like the TP. To investigate the impacts of snow assimilation on the forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer 2018. Significant changes occur in the springtime. Without snow assimilation, the reforecasts overestimate snow cover and snow depth while underestimating daily temperature over the TP. Compared to satellite-based estimates, precipitation reforecasts perform better in the west TP (WTP) than in the east TP (ETP). With snow assimilation, the reforecasts of snow cover, snow depth and temperature are consistently improved in the TP in the spring. However, the positive bias between the precipitation reforecasts and satellite observations worsens in the ETP. Compared to the experiment with no snow assimilation, the snow assimilation experiment significantly increases temperature and precipitation for the ETP and around the longitude 95∘ E. The higher temperature after snow assimilation, in particular the cold bias reduction after initialization, can be attributed to the effects of a more realistic, decreased snowpack, providing favourable conditions for generating more precipitation. Overall, snow assimilation can improve seasonal forecasts through the interaction between land and atmosphere.

View all works in Cristin

Mazzolini, Marco; Aalstad, Kristoffer; Treichler, Désirée & Alonso-Gonzalez, Esteban (2024). Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter.
Treichler, Désirée; Mazzolini, Marco; Piermattei, Livia; Webster, Clare; Girod, Luc & Aalstad, Kristoffer [Show all 7 contributors for this article] (2023). SNOWDEPTH: Spaceborne snow depth measurements from ICESat-2 laser altimetry and data assimilation.
Liu, Zhihao; Treichler, Désirée & Mazzolini, Marco (2023). Snow depth retrieval using satellite altimetry, climate reanalysis data and machine learning: A case study in mainland Norway.
Treichler, Désirée; Mazzolini, Marco; Liu, Zhihao & Guidicelli, Matteo (2023). SNOWDEPTH: Global snow depths from spaceborne remote sensing for permafrost, high-elevation precipitation, and climate reanalyses.
Treichler, Désirée; Mazzolini, Marco; Piermattei, Livia; Webster, Clare; Girod, Luc & Bühler, Yves (2023). Spaceborne snow depth measurements from ICESat-2 laser altimetry.
Mazzolini, Marco; Treichler, Désirée; Aalstad, Kristoffer & Alonso-Gonzalez, Esteban (2023). Satellite Altimetry as a New Data Source for Snow Depth Data Assimilation.
Mazzolini, Marco; Aalstad, Kristoffer; Treichler, Désirée & Alonso-Gonzalez, Esteban (2023). Spatio-temporal snow data assimilation with laser altimetry.
Mazzolini, Marco; Aalstad, Kristoffer; Treichler, Désirée & Alonso-Gonzalez, Esteban (2023). Satellite Altimetry for Data Assimilation.
Li, Lu; Li, Wei; Chen, Jie & Orsolini, Yvan Joseph Georges Emile G. (2023). Impacts of Snow Assimilation and Dynamic Downscaling on Seasonal Meteorological Forecasts over the Third Pole Region.
Treichler, Désirée; Mazzolini, Marco; Piermattei, Livia; Webster, Clare & Girod, Luc (2022). Spaceborne snow depth measurements from ICESat-2 laser altimetry.