Web Story: Historic Greenland ice sheet rainfall unravelled

ESA Web Story – 25/05/2022

For the first time ever recorded, in the late summer of 2021, rain fell on the high central region of the Greenland ice sheet. This extraordinary event was followed by the surface snow and ice melting rapidly. Researchers now understand exactly what went on in those fateful summer days and what we can learn from it.

The never-before-seen rainfall, on 14 August 2021, made headlines around the world. The upper-most parts of Greenland’s enormous ice cap used to be too cold for anything other than snow to fall, but not anymore.

What caused this extreme rainfall and how did it affect the ice?

Researchers from the Department of Glaciology and Climate at the Geological Survey of Denmark and Greenland (GEUS) in collaboration with colleagues from France and Switzerland have scrutinised these questions and come up with the answers.

It didn’t only rain at Summit Camp – rain was measured by new automatic weather stations placed across the ice sheet by GEUS’ ice-sheet monitoring projects PROMICE and GC-Net.

Studying detailed data from these stations alongside measurements of surface reflectivity, or albedo, from the Copernicus Sentinel-3 satellite mission and information on atmospheric circulation patterns, the researchers discovered that the rain had been preceded by a heatwave at a time of year when seasonal melting is usually slowing down.

red more at the ESA Web Story

Web Story: Snow grain size – it matters

link to 29 MAY 2019 Web Story

Snow grain size – it matters

Most of us probably wouldn’t think of describing snow in terms of its grain size. However, grain size is fundamental to the amount of sunlight that snow reflects back into space – its albedo. With both snow and albedo part of the climate system, scientists are applying a novel analytical theory to Copernicus Sentinel-3 data and shedding new light on Greenland’s changing albedo.
The amount of sunlight absorbed or reflected by Earth’s surface drives our climate and weather.

About one-third of the sunlight that hits Earth is reflected back into space and the other two-thirds is absorbed by the land, oceans and atmosphere. This ratio is governed by the reflectivity, or albedo, of the surface that the sunlight hits.

Surfaces with lighter colours reflect more sunlight than darker surfaces. An everyday example of this is the difference we feel on a hot sunny day when wearing black clothes compared to wearing white. Earth is affected in the same way.

So hypothetically, if the planet were completely covered in ice, it would reflect over 80% of incident sunlight back into space. On the other hand, if it were covered by dark green forest, it would only reflect about 10%.

The albedo of Earth’s surface varies naturally according to the changing colours of the season, but long-term trends in changing snow and ice cover, as well as changing vegetation cover and air pollution, are having an impact on the overall balance of Earth’s albedo – and, hence, on how much heat it absorbs.

Grainy nature of snow
Grainy nature of snow

The Global Climate Observing System lists both albedo and snow as essential climate variables, which when measured and studied over time are used to understand, monitor and predict climate change.

Ice and snow are often cited as the first causalities of climate change, and are measured and monitored from space in a variety of ways. However, while ice and snow may be present, the melting process affects its albedo.

Snow grain size is a fundamental property of snow and is directly proportional to its surface area. Fresh dry snow tends to have a small grain size (under 0.5 mm in diameter), but as it melts the grain size grows and the larger grains reflect less sunlight.

Thanks to Alex Kokhanovsky from Vitrociset who, along with several authors, published an elegant analytical theory, scientists have a fast new way of retrieving snow grain size from satellite images.

Scientists from the Geological Survey of Denmark and Greenland (GEUS) in Copenhagen are coupling this theory with data from the Copernicus Sentinel-3 satellites’ Ocean Land and Colour Instruments – as the animation above shows.

Jason Box, from GEUS, explains, “One way of measuring the albedo of snow is to monitor how the surface colour changes because of pollution such as from wildfire soot. But this doesn’t give us the whole story. Remarkably, this exciting new theory allows us to retrieve snow grain size from satellite optical images.

Polluted snow and ice on Greenland
Polluted snow and ice on Greenland

“Through ESA’s Earth Observation Science for Society programme, we have been able to demonstrate this over Greenland. We have found that pulses of warm air cause dark blemishes far inland on the ice sheet, contributing to increased climate sensitivity.”

In fact, the Copernicus Sentinel-3 satellite constellation can now take the relay in maintaining the climate record on snow albedo, which was first provided by the Advanced Very High Resolution Radiometer instruments on the US NOAA and Europe’s MetOp satellites, and then the Moderate Resolution Imaging Spectroradiometer on the US Terra and Aqua satellites.

In the future, the method will be extended and applied to areas with more complex terrain than Greenland. Furthermore, grain size data is now on the horizon for being used operationally to improve weather, hydrological and hazards forecasts, in service to society.


Web Story: Sentinel-3 validation team forge ahead with satellite data

link to 16 MARCH 2017 Web Story

Sentinel-3 Validation Team forge ahead with satellite data

Cryosphere Excerpt copied form above link:

Sentinel-3 is providing key observations addressing cryosphere’s evolution given that decreasing snow cover is one of the clearest indicators of a warming climate. Snow has a major influence on Earth’s radiation balance and it is also a very sensitive indicator of high latitude climate change, being closely related to shifts in temperature and precipitation regimes.

Owing to its importance, snow albedo or reflectivity has been designated as an ECV and a Target Requirement for climate monitoring.

A team lead by Professor Jason Box at the Geological Survey Denmark and Greenland, (GEUS) approaches the snow problem in two ways. Firstly, by studying the feasibility and benefit of snow model data assimilation of Sentinel-3’s optical data.

Within this work frame, Marie Dumont, Head of the snowpack observation and modelling team from Centre d’Etudes de la Neige (CNRM/Meteo France-CNRS UMR3589) in Grenoble, will push data assimilation capabilities to the near-real time frame, thus serving operational models to improve flood and avalanche hazard forecasting.

Greenland drone

Another aspect of work related to snow involves field campaigns throughout 2017 and 2018 in Greenland, the French Alps and Antarctica.

In Greenland, validation involves fixed-wing drone technology from Aberystwyth University, Wales, supported by the Glaciology Professor at GEUS, Jason Box, who said, “We’re planning to under-fly Sentinel-3 in June 2017 with a 2 m fixed-wing drone, carrying four bands matching Sentinel-3 and with significant overlap with NASA’s MODIS.

“The drone flights, combined with ground measurements of snow crystal morphology and impurity content, give us closure on bridging Sentinel-3 with MODIS to build a Climate Data Record of snow properties, identified as an Essential Climate Variable.”


snow albedo validation publications

Box, J.E., D. van As, K. Steffen, 2017. Greenland, Canadian and Icelandic land ice albedo grids (2000-2016), Geological Survey of Denmark and Greenland Bulletin, 38, 53-56. http://www.geus.dk/DK/publications/geol-survey-dk-gl-bull/38/Documents/nr38_p53-56.pdf

Hall, D. K., G. A. Riggs, and V.V. Salomonson (1995). Development of Methods for Mapping Global Snow Cover Using Moderate Resolution Imaging Spectroradiometer Data. Remote Sensing of Environment, 54(2), 127-140.

Hall, D. K., Riggs, G. A., and Salomonson, V. V.: MODIS/Terra Snow Cover Daily L3 Global 500m Grid V004, January to March 2003, Digital media, updated daily. National Snow and Ice Data Center, Boulder, CO, USA, 2011.

Hall, D. K. and G. A. Riggs. 2016. MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 6. Greenland coverage. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/MODIS/MOD10A1.006. Accessed December, 2016.

Lyapustin, A., et al. (2014), Science impact of MODIS C5 calibration degradation and C6+ improvements, Atmos. Meas. Tech. Discuss., 7, 7281–7319.

Riggs, G.A. and D.K. Hall. 2015. MODIS Snow Products Collection 6 User Guide, 11 December 2015 https://nsidc.org/sites/nsidc.org/files/files/MODIS-snow-user-guide-C6.pdf

Stroeve, J.: Assessment of Greenland albedo variability from the advanced very high resolution radiometer Polar Pathfinder data set, J. Geophys. Res.-Atmos., 106, 33989–34006, doi:10.1029/2001jd900072, 2001.

Stroeve, J.C., Box, J.E., Haran, T., 2006: Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet, Remote Sensing of Environment, 105(2), 155-171 doi:10.1016/j.rse.2006.06.009

Stroeve, J.C., J.E. Box, Z. Wang, C. Schaaf, A. Barrett. 2013. Re-evaluation of MODIS MCD43 Greenland albedo accuracy and trends, Remote Sensing of Environment, 138, 199–214. doi:10.1016/j.rse.2013.07.023

workshop including Model uses of Optical Remotely Sensed Data


see session 4 in the following that is designed to resonate with this ESA contract…
Workshop: Modeling Meltwater in Snow and Firn: Processes, Validation, Intercomparison and Model uses of Optical Remotely Sensed Data
20-22 September 2017, Copenhagen.
  • The workshop starts Wednesday Sep 20 at 12.00, and ends Friday Sep 22 at 12.00.
  • A PROMICE 10-year jubilee and reception is planned for the afternoon of Sep 22.


  • to present and discuss results on modeling of meltwater retention processes in snow and firn on ice sheets and glaciers;
  • to plan and coordinate meltwater retention model development;
  • to emphasize optical remote sensing snow parameter data comparison and data assimilation;
  • to formulate a protocol for a meltwater retention model intercomparison project (RetMIP)

This is workshop two as part of Danish Council for Independent Research (DFF) Natural Sciences program (FNU) project 4002-00234: Understanding and predicting non-linear change in the permeability of Greenland firn and has a special session co-sponsored by the ESA Scientific Exploitation of Operational Missions (SEOM) Sentinel-3 for Science, Land Study 1: SNOW.



  1. Snow model development
  2. Meltwater retention model validation
  3. Meltwater retention model intercomparison project (RetMIP)
  4. Optical remote sensing to improve snow models

Session 1 welcomes, for example, model considerations of water availability vs. percolation rate vs. refreezing rate to explore the importance of heterogeneous percolation modelling in polar firn; fine-and-local scale/detailed modeling with possible suggestions to including bulk effects in larger-scale models; inclusion of piping (for instance, by “skipping layers” during percolation) in distributed or single-column models; inclusion of horizontal water motion in snow and firn, i.e. between grid cells.

Session 2 will focus on discussions of useful model validation metrics and aim to compile observational datasets that may be used to validate the above processes.

Session 3 will discuss and formulate a protocol for a meltwater retention model intercomparison project.
Session 4 serves an ESA Sentinel-3 for Science Land Study: Snow “S-3-Snow” that includes an element to gather and prioritize snow modeller interest and requirements about optical remotely sensed snow parameters (snow extent, albedo, grain size, impurity content, etc.). The ESA study is to engage users of Sentinel-3 snow optical retrievals in 1.) model comparison and/or 2.) data assimilation. Theworkshop session is to:
  • survey and prioritize remotely sensed snow parameter data users’ requirements for global and regional snow information
  • discuss how to enhance methods for estimating snow parameters, either from remote sensing or from modeling, and to evaluate advantages and disadvantages of the different approaches
  • consult users about the utility/interest in other snow products suitable for studying climate-related issues.
Workshop expected outcomes
Geological Survey of Denmark and Greenland (GEUS), Østervoldgade 10, DK-1350 Copenhagen K
Contributions are welcomed in the form of oral presentations and/or posters. Presentation duration will be 15-20 minutes, including discussion. One or more longer invited keynote presentations are planned.
Abstracts and registration
Those intending to attend the workshop should submit abstracts (maximum length 200 words) by email to Peter Langen (pla@dmi.dk) no later than the registration deadline Friday 18 August 2017.  Abstracts should indicate whether an oral or poster presentation is preferred.
Financial support
We are working to raise some financial support for early career scientists to participate in the workshop. Please indicate at registration whether you will request support.
A range of hotels and hostels exist near the workshop location. Public transit is very efficient, including bike rental. Booking these early is recommended due to high demand that may occur.
Hope to see you in Copenhagen, Peter Langen (DMI) and Jason Box (GEUS)

Collocated Sentinel-3 OLCI and SLSTR reflectance imagery

The S3 4Sci Snow research project is gaining momentum, now with scripts that enable batch processing of two important steps: 1.) collocating OLCI and SLSTR imagery, that is, aligning pixels to a common geographic grid and 2.) converting the imagery to top of the atmosphere reflectance.


(above, 22 August 2016 southern Greenland Sentinel-3 scene of TOA reflectance from OLCI band 21, 1020 nm). In this part of the optical spectrum we find strong reflectance differences between fresh snow cover, melting snow and bare ice (dark areas).

The processing chain the project follows is illustrated below. The collocation happens as OLCI and SLSTR radiances are combined.


2-year post-doctoral position in snow optical remote sensing at LGGE

This project is about monitoring snow properties evolution on the long-term using Sentinel satellite series at global scale. The successful candidates will evaluate and develop retrieval algorithms. The project will more specifically focus on two primary sites, one in Canada and the other one in the French Alps. Secondary sites including Greenland and Antarctica will be exploited for the validation.

Research project

Optical sensors on Sentinel-2 (MSI) and Sentinel-3 (OLCI and SLSTR) provide a new and unique opportunity to investigate the temporal and spatial variability of the snow cover properties at high spatial resolution and on the long-term. The objective of this post-doctoral position is to 1) evaluate global moderate-resolution algorithms for snow cover properties retrieval (snow cover fraction, spectral and broadband albedo, snow specific surface area SSA and light absorbing impurity content) from S-3 images developed by other parties and 2) develop and evaluate high- resolution algorithms adapted to complex terrains from S-2. The second objective is interesting by itself and will also provide validation data for the first objective on specific sites.

The general evaluation will use data from many sites around the world where in-situ observations are available (Alps, Arctic, EGRIP in Greenland, Dome C in Antarctica, …). The second objective will be especially focused at 2 locations :

    • Umijaq area in Canadian Arctic where the interest is on monitoring snow-vegetation interactions in relationship with climate change, greening and permafrost evolution.
    • Col du Lautaret in the French Alps which features complex terrain and potential high human impact. The interest is in following the evolution of snow impurities and induced changes in snow physics at the site which is largely impacted by Saharan dust deposition events as well as providing data for snow-vegetation interaction.

These two sites have been equipped with specific instrumentation dedicated to snow monitoring (including in situ spectral reflectance measurements, SSA and light absorbing impurities measurements).

The work plan includes :

    • Adapting atmospheric and topographic corrections existing algorithms to S-2 and S-3 images (MODINLAB by Sirguey et al., 2009 ; Dumont et al., 2012)
    • Developing snow cover fraction, snow SSA, light absorbing impurities content, snow spectral and broadband albedo based on methodology for S2 proposed in Dumont et al., 2012 ; Kokhanovsky and Zege, 2004, Malinka, 2014 and Picard et al., 2016.
    • Participating in field measurements campaigns at Col du Lautaret, Umijaq and possibly EGRIP.
    • Evaluate the accuracy of the proposed algorithms at the two sites
    • Evaluate the accuracy of available moderate-resolution algorithms at the two sites and at other sites where in-situ data are available.
    • Investigate the temporal and spatial variability of the snow cover properties for theses locations in relationship to the specific scientific objectives.

Within this project, special attention will be drawn to the effect of terrain complexity on the accuracy of the surface reflectance and the effect of snow surface roughness on retrieved surface reflectance and snow properties.

Progresses and results shall be documented in project reports. As part of the scientific dissemination it is also expected that the postdoc present results in scientific journals and at international conferences.

This work will contribute to two projects; one funded by the European Space Agency (PI Jason Box, GEUS, Danemark) and the other one by the BNP Paribas fundation (PI Florent Dominé, Takuvik, U. Laval, Canada). The position is based in Grenoble, a place known for its excellence in snow science, but also implies strong international collaborations with colleagues in Canada, Germany and Denmark.

Position summary

Full-time temporary employment. The position is funded for 24 months from January 2017 to December 2018.


To qualify as a candidate for the postdoc position you must have a Ph.D. degree with strength in remote sensing, snow optics and physics or related disciplines and you must have a solid theoretical background in mathematics, physics, statistical analysis and programming. A genuine interest and curiosity in the field measurements campaigns and excellent programming skills (e.g. python) are needed. Experience in processing and analysis of data from satellites, in situ measurements or snow models is regarded as an advantage for the position. The position requires good verbal and written communication skills in English, as most of the work will take place in an international environment. An initial scientific publication record is expected.


Ghislain Picard (ghislain.picard@univ-grenoble-alpes.fr)

Marie Dumont (marie.dumont@meteo.fr)