kristaal

• Fiddes, Joel; Aalstad, Kristoffer & Westermann, Sebastian (2019). Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering. Hydrology and Earth System Sciences.  ISSN 1027-5606.  23, s 4717- 4736 . doi: 10.5194/hess-23-4717-2019 Vis sammendrag

  Spatial variability in high-relief landscapes is immense, and grid-based models cannot be run at spatial resolutions to explicitly represent important physical processes. This hampers the assessment of the current and future evolution of important issues such as water availability or mass movement hazards. Here, we present a new processing chain that couples an efficient sub-grid method with a downscaling tool and a data assimilation method with the purpose of improving numerical simulation of surface processes at multiple spatial and temporal scales in ungauged basins. The novelty of the approach is that while we add 1–2 orders of magnitude of computational cost due to ensemble simulations, we save 4–5 orders of magnitude over explicitly simulating a high-resolution grid. This approach makes data assimilation at large spatio-temporal scales feasible. In addition, this approach utilizes only freely available global datasets and is therefore able to run globally. We demonstrate marked improvements in estimating snow height and snow water equivalent at various scales using this approach that assimilates retrievals from a MODIS snow cover product. We propose that this as a suitable method for a wide variety of operational and research applications where surface models need to be run at large scales with sparse to non-existent ground observations and with the flexibility to assimilate diverse variables retrieved by Earth observation missions.

• Yilmaz, Yeliz; Aalstad, Kristoffer & Sen, Omer L. (2019). Multiple remotely sensed lines of evidence for a depleting seasonal snowpack in the near east. Remote Sensing.  ISSN 2072-4292.  11(5) . doi: 10.3390/rs11050483
• Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent (2018). Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere.  ISSN 1994-0416.  12(1), s 247- 270 . doi: 10.5194/tc-12-247-2018 Vis sammendrag

  With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain en- vironments. However, estimating the snow water equiva- lent (SWE) is challenging in remote-sensing applications al- ready at medium spatial resolutions of 1km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1km scale by assimilating fractional snow-covered area (fSCA) satel- lite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be log- normally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previ- ous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimi- lation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Reso- lution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79◦ N, Svalbard, Norway) where field measure- ments of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20%, re- spectively, when compared to the prior, yielding RMSEs of 0.01, 0.09m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

Se alle arbeider i Cristin

• Christakos, Konstantinos; Varlas, George; Cheliotis, Ioannis; Aalstad, Kristoffer; Papadopoulos, Anastasios; Katsafados, Petros & Steeneveld, Gert Jan (2017). Quantitative variability of renewable energy resources in Norway.
• 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.
• 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.
• Aalstad, Kristoffer; Bertino, Laurent & Westermann, Sebastian (2016). An ensemble-based subgrid snow data assimilation framework.
• 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.
• Schuler, Thomas; Aalstad, Kristoffer; Aas, Kjetil Schanke; Burkhart, John; Dunse, Thorben; Filhol, Simon; Hulth, John; Østby, Torbjørn Ims & Westermann, Sebastian (2016). Towards real-time snow products for Svalbard.
• Westermann, Sebastian; Langer, Moritz; Østby, Torbjørn Ims; Peter, Maria; Boike, Julia; Gisnås, Kjersti; Aalstad, Kristoffer; Schuler, Thomas; Etzelmüller, Bernd; Jaroslav, Obu; Georg, Schwamborn & Lutz, Schirrmeister (2016). Mapping the thermal state of permafrost through modeling and remote sensing.

Se alle arbeider i Cristin