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Mazzolini, Marco; Aalstad, Kristoffer; Treichler, Désirée & Alonso-Gonzalez, Esteban
(2024).
Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter.
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Mazzolini, Marco; Aalstad, Kristoffer; Treichler, Désirée & Alonso-Gonzalez, Esteban
(2023).
Satellite Altimetry for Data Assimilation.
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Mazzolini, Marco; Aalstad, Kristoffer; Treichler, Désirée & Alonso-Gonzalez, Esteban
(2023).
Spatio-temporal snow data assimilation with laser altimetry.
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Mazzolini, Marco; Treichler, Désirée; Aalstad, Kristoffer & Alonso-Gonzalez, Esteban
(2023).
Satellite Altimetry as a New Data Source for Snow Depth Data Assimilation.
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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.
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Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke
[Show all 8 contributors for this article]
(2022).
Evaluating modeled snow cover dynamics over Fennoscandia using Earth observations and reanalyses
.
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Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke
[Show all 8 contributors for this article]
(2022).
Evaluating modeled snow cover dynamics over Fennoscandia using Earth observations.
Show summary
The snow cover is an essential part of the climate system in cold regions through its effects on the terrestrial water, energy, and carbon balance. Due to the high spatiotemporal variability of snow, it is challenging to resolve snow cover dynamics in models. Thus, our ability to improve the representation of these dynamics in Earth System Models (ESMs) leans heavily on the accuracy and representativeness of the observational data sets used for model evaluation.
The big picture provided by the long-term climate data record from satellites helps us to monitor changes in land cover over large areas. At the same time, rapidly developing drone and terrestrial imaging technology provides higher resolution information over specific areas. This complimentary information from spaceborne, airborne, and terrestrial Earth observations is invaluable for better understanding the complex processes in the climate system. In our work, we are currently exploiting estimates of snow-covered area from different optical sensors onboard polar orbiting satellites that are imaging the Nordic region. Drone and terrestrial images are being explored as a source of validation and calibration data for the satellite products.
Having representative snow cover maps enables us to better evaluate the terrestrial component of the Norwegian Earth System Model (NorESM), namely the Community Land Model (CLM5). With a focus on snow processes, we are conducting an analysis using satellite-based estimates of snow-covered area (MODIS, Sentinel-2, and Landsat 8), snow simulations from CLM5, snow variables from several climate reanalyses (ERA5, ERA5-Land, and MERRA-2), and in-situ data from eddy covariance stations (LATICE flux sites). Two offline CLM5 simulations are conducted with different atmospheric forcing, namely the default data set (GSWP3) and ERA5. We are investigating trends in the snow cover phenology, which we characterize using snow cover duration, first and last days of the snow cover, and consecutive snow cover days for each snow season over the last two decades. This work illuminates a path to integrate Earth observations with Earth system modeling in cold environments to both identify and constrain sources of uncertainty.
Acknowledgement: This ongoing study is supported by the LATICE (Land-ATmosphere Interactions in Cold Environments) strategic research initiative funded by the University of Oslo, and the project EMERALD (294948) funded by the Research Council of Norway.
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Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Stordal, Frode & Tallaksen, Lena M.
(2021).
Fennoscandian snow cover dynamics in the MODIS era.
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Salzano, Roberto; Aalstad, Kristoffer; Boldrini, Enrico; Gallet, Jean-Charles; Kepski, Daniel & Luks, Bartlomiej
[Show all 9 contributors for this article]
(2021).
Terrestrial photography applications on snow cover in Svalbard (PASSES),
The State of Environmental Science in Svalbard.
Svalbard Integrated Arctic Earth Observing System.
ISSN 978-82-93871-00-2.
p. 236–251.
doi:
10.5281/zenodo.4294084.
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Salzano, Roberto; Aalstad, Kristoffer; Boldrini, Enrico; Gallet, Jean-Charles; Kȩpski, Daniel & Luks, Bartlomiej
[Show all 9 contributors for this article]
(2021).
Terrestrial photography applications on snow cover in Svalbard (PASSES),
The State of Environmental Science in Svalbard.
Svalbard Integrated Arctic Earth Observing System.
ISSN 978-82-93871-00-2.
p. 236–251.
doi:
10.5281/zenodo.4294084.
Show summary
Ground-based observations are critical requirements for many disciplines that are trying to monitor climate change in a remote environment such as the Svalbard archipelago. This overview of cameras operating in Svalbard has been compiled by searching for specific applications that monitor the snow cover and by collecting information about images that can be accessed on the internet, including those not solely dedicated to cryospheric research. The survey identified 43 cameras operating in the region that are managed by research institutions and private companies. These cameras include facilities operated by different nationalities. The datasets vary, but the feasibility of using them to determine fractional snow cover is generally limited. Identifying the key metadata necessary to survey the available devices revealed problems and knowledge gaps that prevent using the full potential of terrestrial photography networks in Svalbard.
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Aalstad, Kristoffer; Westermann, Sebastian; Pirk, Norbert; Fiddes, Joel & Bertino, Laurent
(2021).
Retrieving fractional snow-covered area from optical satellites using data assimilation.
Show summary
Mapping from noisy observations to the latent states that may have generated them falls under the umbrella of inverse problems. These problems are abundant in Earth system science since our uncertain mechanistic models need to be fully specified while the system is only partially and imperfectly observed. Combined with a steadily growing observing system, this abundance has fueled the development of probabilistic Data Assimilation (DA) schemes that use Bayesian inference to fuse uncertain information from models and observations. Widely used applications of DA include the production of global atmospheric reanalyses and initializing numerical weather predictions. At the same time, the added value that DA can bring to remote sensing as a generalized framework for building retrieval algorithms remains largely untapped.
In our contribution, we demonstrate the potential of data assimilation in the task of retrieving fractional snow-covered area (fSCA) from multispectral satellite imagery from moderate (MODIS) and higher (Sentinel-2 MSI, Landsat 8 OLI) optical sensors. In this analysis, we build on our previous work by focusing on the Bayelva catchment near Ny-Ålesund we have access to independent high-quality validation data obtained from terrestrial photography. We show how the general problem of linear spectral unmixing that is widely used for land cover classification can be recast as a Bayesian inverse problem. This can then be readily solved using ensemble-based data assimilation schemes, where we test both vanilla and sophisticated flavors of the particle filter and the ensemble Kalman filter, as well as Markov chain Monte Carlo benchmarks. By solving the problem in a transformed parameter space, the physical constraints of spectral unmixing are satisfied while reducing the need for ad hocery.
The Bayesian data assimilation fSCA retrieval approach lets us deal with ill-posedness, incorporate physical knowledge, and account for uncertainty in the observed reflectances. It performs favorably compared to widely used techniques for fSCA retrieval such as thresholding of the NDSI, regression on the NDSI, and classical (non-negative least squares) spectral unmixing. This method is also much more scalable than classical unmixing since iterations are pre-determined and can fully exploit vectorization. Furthermore, it does not require any tuning on in-situ observations and it can also be used to solve the endmember selection problem using the concept of model evidence. Crucially, the retrieved fSCA includes dynamic uncertainty estimates that are required for satellite retrievals to be of any use in dynamic data assimilation frameworks. We envisage further validation by leveraging the network of terrestrial cameras operated by our partners in the PASSES consortium (Salzano et al., 2021; SESS Report 2021, Ch. 10). Our aim is to exploit these satellite retrievals in our ongoing efforts to produce tailored high resolution permafrost and snow reanalyses in cold regions, including Svalbard. At the same time, the approach outlined here could also be modified to retrieve surface albedo and (sub-pixel) land cover globally with even broader implications to Earth system science.
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Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Bazilova, Varvara; Bertino, Laurent; Fiddes, Joel & Pirk, Norbert
[Show all 7 contributors for this article]
(2021).
Leveraging emerging Earth observations using data assimilation.
Show summary
The task of mapping from noisy observations to the states that (given a forward model) may have generated them falls under the umbrella of inverse problems. These problems are abundant in Earth system science since our uncertain mechanistic models need to be fully specified while the system is only partially and imperfectly observed. This abundance has, combined with a steadily growing observing system, fueled the development of probabilistic Data Assimilation (DA) schemes that use Bayesian inference to fuse uncertain information from models and observations. Notable operational applications of DA include the production of global atmospheric reanalyses and initializing numerical weather predictions. Perhaps less appreciated is the added value that DA can bring to Earth Observations (EO) as a generalized framework for building retrieval algorithms. In our work we present two completely different inverse problems that show how DA can help us to make the most out of emerging EO.
The first problem is snowpack reconstruction where we constrain snow models using highly informative observations of the dynamics of snow cover depletion retrieved using satellite remote sensing. We provide examples assimilating retrievals from moderate (MODIS) and higher resolution (Landsat, Sentinel-2, PlanetScope cubesats) optical sensors as well as from all-weather radar data (Sentinel-1) in the Californian Sierra Nevada, Svalbard, Finse, the Pyrenees, the Swiss Alps, and Lebanon. This method is being developed to build tailored snow and permafrost reanalysis frameworks that lead to improved global cryospheric mapping capabilities and provide new benchmarks for Earth system models.
The second problem is flux inversion where we seek to infer surface fluxes of carbon, water, and heat using a drone swarm that provides distributed measurements of temperature, gas concentrations, and wind in the atmospheric boundary layer. To achieve this, we assimilate drone data into various boundary layer models, building up complexity from analytical flux-profile relationships based on the widely used Monin-Obukhov Similarity Theory to turbulence resolving large eddy simulations. Through flux inversion with the latter the hope is that we will be able to map fluxes in highly heterogeneous landscapes at the scale of Earth system models (10 km). This is not possible with existing methods like eddy covariance and can thereby shed new light on the role of flux heterogeneities in land-atmosphere coupling.
When solving these problems we test various probabilistic DA schemes including variants of the ensemble Kalman filter, the particle filter, and Markov chain Monte Carlo. These schemes have been adapted to our problems by casting them as smoothers that condition the model on future observations, rather than as sequential filters, which crucially allows information to propagate backwards in time. We also emphasize how we can use DA to move beyond the usual first level of inference where we “fit” our model to data, up to the second level of inference where we can compare different competing model structures and parametrizations using the model evidence framework.
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Aalstad, Kristoffer; Fiddes, Joel; Martin, Leo Celestin Paul; Alonso-Gonzalez, Esteban; Yilmaz, Yeliz A. & Pirk, Norbert
[Show all 7 contributors for this article]
(2021).
Workshop on downscaling with TopoSCALE for cryospheric applications.
Show summary
Meteorological forcing data is a crucial ingredient when modeling the past, present, and future state of the cryosphere. At the same time, cold regions (i.e. the high elevations and/or latitudes of our planet) where most of the cryosphere is found are typically remote with a very low density of in-situ observations. The few meteorological stations that do exist are typically quite unrepresentative due to the often extreme surface heterogeneity and complex terrain in these regions. This makes empirical-statistical and geostatistical downscaling techniques that rely heavily on station data somewhat impractical for cryospheric applications. On the other side of the scale, we have dynamical downscaling techniques that rely on using sophisticated regional climate models to downscale historical global reanalysis data or projections from global climate models. Although these are not as dependent on local observations in that they are able to mechanistically model the state of the atmosphere, they are prohibitively expensive to run at the decadal timescales and hillslope (1 km - 100 m) spatial scale that is often sought in cryospheric applications.
In this workshop, we will provide an overview of various downscaling techniques and introduce the topography-based downscaling routine TopoSCALE as well as its many applications and downstream methods listed below. TopoSCALE was developed to provide a computationally feasible technique for generating hourly hillslope scale atmospheric forcing for cryospheric modeling from global reanalysis data or other atmospheric model outputs, without the need for in-situ data. It relies heavily on topographic parameters derived from digital elevation models to be able to scale the input atmospheric forcing to the local topography. It has been tested quite extensively in a variety of environments, including: the Swiss Alps, Svalbard, the California Sierra Nevada, and High Mountain Asia. TopoSCALE is also being used as part of ongoing permafrost reanalysis efforts in the ESA Permafrost_CCI project. The scheme can be coupled to a clustering framework (TopoSUB) which can speed up distributed simulations by orders of magnitude compared to the more traditional gridded approach to land surface modeling. TopoSCALE can also be used together with bias-correction techniques to help downscale future regional climate projections to the hillslope scale with applications to cryospheric climate impact studies. Recent and ongoing applications of TopoSCALE together with snow data assimilation have been particularly fruitful in being able to handle biases in solid precipitation, which has been one of the most challenging problems for downscaling in cryospheric applications. Importantly, TopoSCALE lets modellers shift limited computational resources away from the downscaling exercise to probing uncertainties through ensemble simulations and constraining these with Earth observations.
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Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Stordal, Frode & Tallaksen, Lena M.
(2021).
Benchmarking CLM5 snow cover dynamics with MODIS and reanalyses over Fennoscandia
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Aalstad, Kristoffer & Westermann, Sebastian
(2020).
ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway).
Show summary
The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestiral automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway.
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Aalstad, Kristoffer; Westermann, Sebastian; Fiddes, Joel; McCreight, James & Bertino, Laurent
(2020).
Retrieving, validating, and assimilating fractional snow-covered area from emerging optical satellites for snow reanalysis.
Show summary
Accurately estimating the snow water equivalent (SWE) that is stored in the worlds mountains remains a challenging and important unsolved problem. The SWE reconstruction approach, where the remotely sensed seasonal depletion of fractional snow-covered area (fSCA) is used with a snow model to build up the snowpack in reverse, has been used for decades to help tackle this problem retrospectively. Despite some success, this deterministic approach ignores uncertainties in the snow model, the meteorological forcing, and the remotely sensed fSCA. A trade-off has also existed between the desired temporal and spatial resolution of the satellite-retrieved fSCA depletion. Recently, ensemble-based data assimilation techniques that can account for the uncertainties inherent in the reconstruction exercise have allowed for probabilistic snow reanalyses. In addition, new higher resolution optical satellite constellations such as Sentinel-2 and the PlanetScope cubesats have been launched into polar orbit, potentially eliminating the aforementioned trade-off.
We combine these two developments, namely ensemble-based data assimilation and the emerging remotely sensed data streams, to see if snow reanalyses can be improved at the hillslope (100 m) scale in complex terrain. As a first step, we develop accurate high-resolution binary snow-cover maps using a terrestrial automatic camera system installed on a mountaintop near Ny-Ålesund (Svalbard, Norway). These maps are used to validate fSCA retrieved from various satellite sensors (MODIS, Sentinel-2 MSI, and Landsat 8 OLI) using algorithms ranging from simple thresholding of the normalized difference snow index to spectral unmixing. Through the validation, we demonstrate that the spectral unmixing technique can obtain unbiased fSCA retrievals at the hillslope scale. Next, we move to the Mammoth Lakes basin in the Californian Sierra Nevada, USA, where we have access to independent validation data retrieved from several Airborne Snow Observatory (ASO) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flights. Using these airborne retrievals as a reference, we show that fSCA can be retrieved at the hillslope scale with reasonable accuracy at an unprecedented near daily revisit period using a combination of the Landsat, Sentinel-2 MSI, RapidEye, and PlanetScope satellite constellations. In a series of data assimilation experiments we show how the combination of these constellations can lead to significant improvements in hillslope scale snow reanalyses as gauged by various evaluation metrics. Furthermore, it is suggested that an iterative ensemble smoother data assimilation scheme can provide more robust SWE estimates than other smoothers that have previously been proposed for snow reanalysis. We briefly conclude with thoughts as to the current impediments to conducting a global hillslope scale snow reanalysis and propose avenues for further research, such as how snow reanalyses can help in the prediction exercise.
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Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon Vincent P; Stordal, Frode & Tallaksen, Lena M.
(2020).
The Representation of the Fennoscandian Snow Cover Phenology in Reanalyses and CLM5 during the MODIS-era.
Show summary
Snow plays an important role in cold regions through its effect on the terrestrial exchange of energy, water, and carbon. Accurately simulating snow processes is therefore important in capturing various climate feedbacks in Earth system models. The representation of the subgrid heterogeneity of snow properties (e.g. coverage, depth, density, albedo) are, in addition to accumulation and snowmelt, major sources of uncertainty in the snow modules of land surface schemes. Using multiple data sources is essential to address these uncertainties and to evaluate overall model performance. Unlike in-situ observations, satellite remote sensing products provide unique representative information at the scale of Earth system models. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Aqua and Terra satellites provide a continuous long-term climate record for the last two decades.
In this study, two daily snow cover data sets from MODIS (MOD10A1 and MYD10A1) were used to retrieve fractional snow-covered area (fSCA) and several snow cover metrics (e.g. snow cover duration, first and last day of snow) over Fennoscandia for the 2001-2020 water years. We use these retrievals to evaluate the fSCA outputs from multiple reanalyses (ERA5-Land, ERA5, and MERRA-2) and the latest version of the Community Land Model (CLM5) which is the land component of the Community Earth System Model (CESM) and the Norwegian Earth System Model (NorESM). In order to test the accuracy of the MODIS data, we employed Sentinel-2 and Landsat 8 satellite retrievals as well as local‐scale measurements around the Finse Eco-Hydrological Observatory (Finse EcHO), a low-alpine site in central Norway. Lastly, we compared the trends in snow cover metrics with terrestrial water storage anomalies obtained from the Gravity Recovery and Climate Experiment (GRACE) to better understand the regional water cycle dynamics over this region. This study provides a useful starting point for integrating Earth observations into Earth system modeling in cold regions to help identify and constrain sources of uncertainty.
Acknowledgement : This study is conducted under the LATICE strategic research initiative funded by the Faculty of Mathematics and Natural Sciences at the University of Oslo, and the EMERALD (project #294948) funded by the Research Council of Norway.
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Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon Vincent P; Stordal, Frode & Tallaksen, Lena M.
(2020).
Fennoscandian snow cover phenology from MODIS, CLM5, and climate reanalyses.
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Aalstad, Kristoffer; Westermann, Sebastian; Fiddes, Joel & Bertino, Laurent
(2019).
Retrieving the depletion of snow-covered area from multiple optical satellite sensors with applications for SWE reanalysis.
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Aalstad, Kristoffer; Westermann, Sebastian; Karsten, Logan; Gutmann, Ethan; McCreight, James & Fiddes, Joel
[Show all 7 contributors for this article]
(2018).
Ensemble-based reanalysis of the seasonal montane snowpack: Lessons from the ASO.
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Aalstad, Kristoffer; Westermann, Sebastian; Karsten, Logan; Fiddes, Joel & Bertino, Laurent
(2018).
Snow history matching in mountainous terrain.
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Aalstad, Kristoffer; Westermann, Sebastian; Bertino, Laurent; Schuler, Thomas; Boike, Julia & Karsten, Logan
(2018).
Towards high-resolution Bayesian snow reconstruction in permafrost regions.
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Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent
(2017).
Ensemble-based subgrid snow data assimilation.
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Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent
(2017).
Towards High-Resolution SWE Mapping in Permafrost Regions.
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Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent
(2017).
Towards High-Resolution SWE Mapping in Permafrost Regions.
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Leclercq, Paul Willem; Aalstad, Kristoffer; Altena, Bas & Elvehøy, Hallgeir
(2017).
Modelling of glacier surface mass balance
with assimilation of glacier mass balance
and snow cover observations from remote
sensing.
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Christakos, Konstantinos; Varlas, George; Cheliotis, Ioannis; Aalstad, Kristoffer; Papadopoulos, Anastasios & Katsafados, Petros
[Show all 7 contributors for this article]
(2017).
Quantitative variability of renewable energy resources in Norway.
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Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent
(2016).
An ensemble-based subgrid snow data assimilation framework applied to the southern Swiss alps.
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Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent
(2016).
An ensemble-based subgrid snow data assimilation framework.
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Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent
(2016).
An ensemble-based snow data assimilation framework.
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Aalstad, Kristoffer; Westermann, Sebastian; Boike, Julia; Bertino, Laurent & Aas, Kjetil Schanke
(2016).
An ensemble-based snow data assimilation framework with applications to permafrost modeling.
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Aalstad, Kristoffer; Bertino, Laurent & Westermann, Sebastian
(2016).
An ensemble-based subgrid snow data assimilation framework.
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Leclercq, Paul Willem; Aalstad, Kristoffer; Elvehøy, Hallgeir & Altena, Bas
(2016).
Assimilation of glacier mass balance and snow cover fraction observations in a glacier surface mass balance model.
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Leclercq, Paul Willem; Aalstad, Kristoffer; Elvehøy, Hallgeir & Altena, Bas
(2016).
Assimilation of glacier mass balance and snow cover fraction observations in a glacier surface mass balance model.
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Westermann, Sebastian; Langer, Moritz; Østby, Torbjørn Ims; Peter, Maria; Boike, Julia & Gisnås, Kjersti
[Show all 12 contributors for this article]
(2016).
Mapping the thermal state of permafrost through modeling and remote sensing .
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Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent
(2019).
Ensemble-based retrospective analysis of the seasonal snowpack.
University of Oslo.
ISSN 1501-7710.
Show summary
This thesis presents a satellite-based modeling framework that can estimate how much snow was stored in the terrain. These estimates can help guide climate analysis and prediction. Accurate quantification of Earth’s snow mass is a long-standing problem to which a solution is direly needed with ongoing climate change. Snow plays an essential role in the climate system and snowmelt is a vital source of freshwater for a quarter of the world’s population.
The framework combines satellite imagery and historic weather data to remotely estimate snow mass by leveraging enhanced ensemble-based data assimilation algorithms. The result is a retrospective analysis (reanalysis) of the snow mass that can be obtained for any location on Earth. So far, this framework has been successfully implemented in three different environments: Svalbard, the Californian Sierra Nevada, and the Swiss Alps. In the future, snow reanalyses could be used to train algorithms to predict snow mass in near real time. They may also help validate and subsequently improve climate models. Ultimately this would allow us to make even more informed future projections of the possible fate of the environment that sustains us.
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Aalstad, Kristoffer & Berntsen, Terje Koren
(2015).
Applying the Eddy Covariance Method Under
Difficult Conditions.
Universitetet i Oslo.
Show summary
We assess how reliable the Eddy-Covariance (EC) method is in estimating surface fluxes under the difficult conditions that occur in the high Arctic. Emphasis is placed on stable stratification and the breakdown of EC assumptions that may occur in such a regime.
To investigate these difficulties we developed an EC processing module from scratch, providing an extensive and transparent overview of the EC method. Raw data was obtained from an open path EC system located in the Bayelva catchment near Ny Ålesund (79 ◦ N), Svalbard, Norway.
Our flux estimates are in reasonable agreement with those found from the standardized EC package TK2. Strong relative non-stationarity represents the greatest hindrance to data quality at Bayelva, occurring for 11% of the data period. Overall, average relative flux uncertainties were found to be 20% for both the sensible (SH) and latent heat (LH) flux. Under stable stratification these uncertainties were considerably higher, 27% on average. Through Ogive classification we found that the traditional 30 minute SH and LH fluxes converged (resolved the turbulent cospectrum) 70% of the time. Here too the stable regime stands out, with low convergence fractions of 41% and 48% for LH and SH, respectively. To our knowledge it is the first time such an analysis has been carried out in the Arctic.
Concluding, while usually successful for neutral and unstable conditions, the traditional 30 minute flux averaging period is, more often than not, poorly suited for the stable regime. We attribute this to the observed and predicted shift in cospectral peaks towards lower periods under stable stratification, along with an erosion of the cospectral gap. An apparently simple fix of reducing the averaging period is not generally a valid solution. The required reduction could introduce unacceptable levels of flux uncertainty.