Browsing by Subject "Snow"

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  • Dragosics, Monika; Meinander, Outi; Jonsdottir, Tinna; Durig, Tobias; De Leeuw, Gerrit; Palsson, Finnur; Dagsson-Waldhauserova, Pavla; Thorsteinsson, Throstur (2016)
    In the Arctic region, Iceland is an important source of dust due to ash production from volcanic eruptions. In addition, dust is resuspended from the surface into the atmosphere as several dust storms occur each year. During volcanic eruptions and dust storms, material is deposited on the glaciers where it influences their energy balance. The effects of deposited volcanic ash on ice and snow melt were examined using laboratory and outdoor experiments. These experiments were made during the snow melt period using two different ash grain sizes (1 phi and 3.5 phi) from the Eyjafjallajokull 2010 eruption, collected on the glacier. Different amounts of ash were deposited on snow or ice, after which the snow properties and melt were measured. The results show that a thin ash layer increases the snow and ice melt but an ash layer exceeding a certain critical thickness caused insulation. Ash with 1 phi in grain size insulated the ice below at a thickness of 9-15 mm. For the 3.5 phi grain size, the insulation thickness is 13 mm. The maximum melt occurred at a thickness of 1 mm for the 1 phi and only 1-2 mm for 3.5 phi ash. A map of dust concentrations on Vatnajokull that represents the dust deposition during the summer of 2013 is presented with concentrations ranging from 0.2 up to 16.6 g m(-2).
  • Leppänen, Leena (2019)
    Finnish Meteorological Institute Contributions 158
    Information on snow water equivalent (SWE) of seasonal snow is used for various purposes, including longterm climate monitoring and river discharge forecasting. Global monitoring of SWE is made feasible through remote sensing. Currently, passive microwave observations are utilized for SWE retrievals. The main challenges in the interpretation of microwave observations include the spatial variability of snow characteristics and the inaccurate characterization of snow microstructure in retrieval algorithms. Even a minor variability in snow microstructure has a notable impact to microwave emission from the snowpack. This thesis work aims to improve snow microstructure modelling and measurement methods, and understanding the influence of snow microstructure to passive microwave observations, in order to enable a more accurate SWE estimation from remote sensing observations. The thesis work applies two types of models: physical snow models and radiative transfer models that simulate microwave emission. The physical snow models use meteorological driving data to simulate physical snow characteristics, such as SWE and snow microstructure. Models are used for different purposes such as hydrological simulations and avalanche forecasting. On the other hand, microwave emission models use physical snow characteristics for predicting microwave emission from a snowpack. Microwave emission models are applied for the interpretation of spaceborne passive microwave remote sensing observations, for example. In this study, physical snow model simulations and microwave emission model simulations are compared with field observations to investigate problems in characterizing snow for microwave emission models. An extensive set of manual field measurements of snow characteristics is used for the comparisons. The measurements are collected from taiga snow in Sodankylä, northern Finland. The representativeness of the measurements is defined by investigating the spatial and temporal variability of snow characteristics. The work includes studies on microwave emission modelling from natural snowpacks and from excavated snow slabs. Radiometric observations of microwave emission from natural snowpacks are compared with simulations from three microwave emission models coupled with three physical snow models. Additionally, homogenous snow samples are excavated from the natural snowpack during the Arctic Snow Microstructure Experiment, and the incident snow characteristics and microwave emission characteristics are measured with an experimental set-up developed for this study. Predictions of two microwave emission models are compared with the radiometric observations of collected snow samples. The results indicate that none of the model configurations can accurately simulate the microwave emission from natural snowpack or snow samples. The results also suggest that the characterization of microstructure in the applied microwave emission models is not adequate.
  • Maanpää, Jyri; Taher, Josef; Manninen, Petri; Pakola, Leo; Melekhov, Iaroslav; Hyyppä, Juha (IEEE, 2021)
    Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition, Milan, 10 – 15 January 2021
    Autonomous driving is challenging in adverse road and weather conditions in which there might not be lane lines, the road might be covered in snow and the visibility might be poor. We extend the previous work on end-to-end learning for autonomous steering to operate in these adverse real-life conditions with multimodal data. We collected 28 hours of driving data in several road and weather conditions and trained convolutional neural networks to predict the car steering wheel angle from front-facing color camera images and lidar range and reflectance data. We compared the CNN model performances based on the different modalities and our results show that the lidar modality improves the performances of different multimodal sensor-fusion models. We also performed on-road tests with different models and they support this observation.