Kari Luojus

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.

Investigating the feasibility of the globsnow snow water equivalent data for climate research purposes

This paper presents the efforts for creating two global scale snow dataset covering 15 and 30 years of satellite-based observations, one describing the extent of snow cover (SE) the other describing the snow water equivalent (SWE) characteristics. The main emphasis of the paper is describing the validation work carried out for the SWE product that will cover the non-mountainous regions of Northern Hemisphere on a daily basis starting from 1979. The work has been carried out within the ESA Globsnow project.

From EO data to snow covered area (SCA) end products using automated processing system

The operative fractional snow mapping system over Finland and cross-border watersheds run by Finnish Environment Institute (SYKE) is presented. The method to estimate the regional fraction of snow covered area (SCA) is applicable to various optical sensors and can be implemented to cover large regions in boreal zone. Since 2003, data provided by SCAmod have been successfully assimilated to the operational hydrological model improving the performance of run-off and river discharge forecasts provided by the model. In addition of using EO data based SCA as input for hydrological modelling, SCA information is also distributed through internet as thematic maps for other end users, such as hydropower industry and citizens. SYKEs snow mapping activities will be complemented with SAR-based SCA-procedure in

A new global Snow Extent product based on ATSR-2 and AATSR

The ESA project GlobSnow develops products and services for snow extent and snow water equivalent. The time series of Snow Extent (SE) products will cover the whole seasonally snow-covered Earth for the years 1995–2010 based on the optical sensors ERS-2 ATSR-2 and Envisat AATSR data. A laboratory processing chain has been developed for testing and improving algorithms in an iterative process. The final version of the laboratory processing chain will function as a reference system for the implementation of an operational system for production of the full time series of products as well as near-real-time products produced on a daily basis. The first version of the SE product set spanning 15 years of the Northern Hemisphere is expected to be ready by the end of 2010 and will be made freely available.

New Snow Water Equivalent Processing System With Improved Resolution Over Europe and its Applications in Hydrology

The presence and amount of snow, given in terms of snow water equivalent (SWE), is an essential physical characteristic influencing climate and hydrological processes. For the recent decades, remote sensing has proven to be a valuable tool for deriving regional and global scale information on SWE. However, determining SWE reliably from remote sensing data for many local-scale applications remains a challenge. Microwave radiometers are currently the best option to determine SWE since they respond to snow depth and density. Further, weather phenomena and solar illumination are not of concern. However, for some purposes the typical spatial resolution of space-borne radiometers (in the order of tens of kilometers) is not sufficient. In this study, the spatial resolution of existing operational SWE products (GlobSnow and H-SAF product portfolios) is improved by performing assimilation of ground truth observations of snow depth and space borne derived SWE estimates in a resolution grid of 0.05° × 0.05° (approximately 5 km × 5 km). Some modifications to the SWE algorithm and the applied auxiliary data (such as an improved forest stem volume map) are introduced. We will present how the improved resolution enhances spatial details in the retrieved SWE, while the validation results show that in terms of accuracy, the new product is on similar level than the existing operational products. Finally, the gained new SWE estimates are ingested into the HOPS hydrological model in the Ounasjoki river basin. The results indicate that simulation of snow melt driven river discharge can be improved by ingesting the retrieved SWE data into a hydrological model.

Investigating hemispherical trends in snow accumulation using GlobSnow snow water equivalent data

This paper presents the evaluation of the 30-years GlobSnow SWE data record, spanning Northern Hemisphere, for climate research purposes. It includes a brief validation of the SWE data record with ground-based reference data and evaluation of the hemispherical scale SWE trends.

Implementing hemispherical snow water equivalent product assimilating weather station observations and spaceborne microwave data

Snow water equivalent (SWE) is one of the key parameters describing seasonal snow cover. Traditional methods such as interpolating ground-based measurements or estimating SWE from spaceborne measurements have their shortcomings. In this paper an assimilation approach has been used to estimate a time series of SWE in hemispherical scale for 30 years. The behaviour of the algorithm is analyzed and scatterplot of validation results is presented. Results show an improvement over using traditional algorithms.

Fusion of low resolution optical and high resolution SAR data for land cover classification

A set of ERS SAR and optical MODIS-images were classified to land cover and tree species classes. Different methods for pixel and decision based data fusion were tested. Classifications of featuresets were carried out using Bayes rule for minimum error. The results were not very successful, the classification accuracies of land cover classes varied from 43% to 75%, depending on the used features and classes. The decision based data fusion method, where the a posteriori probabilities representing the proportions of different land cover classes of low resolution classification are used as a priori probabilities in high resolution classification looks promising. Using this method, the increase of overall and classwise accuracies can be more than 10 and 25 %-units, respectively.

New approach for the global mapping of fractional snow coverage in boreal forest and tundra belt applicable to various sensors

A feasible method for estimating the areal fraction of snow cover for boreal forest and tundra belt from optical data is presented. The method SCAmod by the Finnish Environment Institute is based on a semi-empirical model where fractional snow cover is expressed as a function of at-satellite observed reflectance. The apparent forest transmissivity and reflectance of three major contributors (wet snow, forest canopy and snow-free ground) serve as model parameters. The forest transmissivity describes the visibility of the ground through forest canopy from above, and was previously determined from MODIS reflectance data with a great effort. Here we present a new method for transmissivity generation using global land cover map. Validation of gained FSC estimates as well as of NASA MOD10_L2 fractional snow product against Finnish ground truth data is presented.

Mapping of Snow Water Equivalent and Snow Coverage from Combined EO and in situ Data for Climatic Studies and Hydrological Forecasting Models

Information on physical snow cover characteristics, such as snow water equivalent (SWE) and the areal coverage fraction of snow covered area (SCA), can be obtained from space-borne remote sensing data. The feasible instruments include optical spectrometers and microwave radars (SCA mapping), and microwave radiometers (SWE mapping). As data assimilation techniques are applied, the EO data-derived information can improve the performance of river discharge forecasting models and the knowledge on snow climatology. The results discussed here indicate that the assimilation of EO data-based SCA estimates to hydrological modeling significantly improves the accuracy of operational river discharge forecasts. The results also indicate that the employment of space-borne microwave radiometer data using the data assimilation technique improves the SWE or snow depth mapping accuracy when compared with the use of values interpolated from synoptic observations.