Jarkko Koskinen

Chemical composition of fine particles in fresh smoke plumes from boreal wild-land fires in Europe.

A series of smoke plumes was detected in Helsinki, Finland, during a one-month-lasting period in August 2006. The smoke plumes originated from wildfires close to Finland, and they were short-term and had a high particulate matter (PM) concentration. Physical and chemical properties of fine particles in those smokes were characterised by a wide range of real-time measurements that enabled the examination of individual plume events. Concurrently PM(1) filter samples were collected and analysed off-line. Satellite observations employing MODIS sensor on board of NASA EOS Terra satellite with the dispersion model SILAM and the Fire Assimilation System were used for evaluation of the emission fluxes from wildfires. The model predicted well the timing of the plumes but the predicted PM concentrations differed from the observed. The measurements showed that the major growth in PM concentration was caused by submicrometer particles consisting mainly of particulate organic matter (POM). POM had not totally oxidised during the transport based on the low WSOC-to-OC ratio. The fresh plumes were compared to another major smoke episode that was observed in Helsinki during April-May 2006. The duration and the source areas of the two episode periods differed. The episode in April-May was a period of nearly constantly upraised level of long-range transported PM and it was composed of aged particles when arriving in Helsinki. The two episodes had differences also in the chemical composition of PM. The mass concentrations of biomass burning tracers (levoglucosan, potassium, and oxalate) increased during both the episodes but different concentration levels of elemental carbon and potassium indicated that the episodes differed in the form of burning as well as in the burning material. In spring dry crop residue and hay from the previous season were burnt whereas in August smokes from smouldering and incomplete burning of fresh vegetation were detected.

Compensation of forest canopy effects in the estimation of snow covered area from SAR data

As spaceborne SAR is used for monitoring the snow cover during the spring melt period, temporal changes in backscattering properties of forest cover disturb the quantitative estimation of snow covered area (SCA). This paper introduces an algorithm that eliminates the related disturbances in SCA estimation. The algorithm is developed based on the Helsinki University of Technology (HUT) forest backscattering model. An extensive multitemporal ERS-2 C-band SAR data set from Northern Finland is used for validating the backscattering model and for testing the SCA estimation algorithm. Comparison of SCA estimation results with available ground truth data for SCA indicates a good performance and a significant improvement when compared with the estimates produced by commonly used linear interpolation algorithms.

Transmissivity of boreal forest canopies for microwave radiometry of snow

The HUTRAD airborne microwave radiometer (Helsinki University of Technology RADiometer) was used to collect data on microwave emission from snow-covered terrain, including a variety of forest types and land-cover categories in two test sites: the Tuusula test site in southern Finland, and the Syote test site in northern Finland. HUTRAD operates at 6.8, 10.65, 18.7, 23.8, 36.5, and 94 GHz, vertical and horizontal polarization, using a look angle of 50/spl deg/ off nadir. Forest information was used to determine the effect of forest canopies to the brightness temperature of snow-covered terrain. The results are expressed as the forest transmissivity vs. stem volume and observation frequency.

Urban morphology retrieval bymeansofremote sensing for the modelling of atmospheric dispersion and micro-meteorology

This paper describes a prototype of an urban morphological database for micro-meteorological and dispersion modelling. The database relies on digital maps and satellite observations including optical images, SAR interferometry and digital maps. The structure of the database is presented and methods to produce thematic layers in an affordable way are shown. A fine resolution model was compiled regarding urban morphological features that cover a rectangular area of 6}3 km2 in central Paris. The model contains selected thematic layer types on water, parks, trees, streets, buildings, average height of blocks, digital elevation model of the terrain, coherence and the urban heat fluxes. This study is part of an EU-funded project ”Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation” -MEGAPOLI.

Estimation of snow covered area by applying apparent regional transmissivity

The detection of snow from optical instruments is often hampered by forest canopy. In this paper, an empirical reflectance model for estimating regional values for snow covered area (SCA) from optical data is presented. In the model, SCA is expressed as a function of apparent vegetation transmissivity. The estimation of SCA has been tested for NOAA/AVHRR data with drainage basins as calculation units. The same areas are used in an operative hydrological model. Comparison of estimated SCA with reference data indicates good correlation.

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.

Merging flat/forest and mountainous snow products for extended European area

In the frame of EUMETSAT Satellite Application Faculty on Hydrology and Water Management (H-SAF) project, two different approaches have been developed for snow products. One is focused on flat/forested areas and has been developed by Finnish Meteorological Institute (FMI) (originally for EUMETSAT Land-SAF), and the other one by Turkish State Meteorological Service (TSMS) for mountainous areas. Snow cover over mountainous areas and over flat/forest areas show completely different physical properties, thus usage of two separate algorithms makes it possible to get better results. On the other hand, for users it would be easier to have only one unified product. This paper presents the method used in the merging of the two Snow Recognition products.

Monitoring of snow cover properties during the spring melting period in forested areas

As spaceborne C-band synthetic aperture radar (SAR) observations are used for monitoring the snow cover during the spring melt period, temporal changes in backscatter from forest cover disturb the mapping of snow cover. This paper presents an analysis of snow backscattering properties in eight test areas situated around weather stations. Test areas represent open and forested landscapes in Northern Finland. Analyses are carried out using an extensive multitemporal ERS-2 C-band SAR data set from the snow melt period. We validate the following topics: 1) forest backscattering model for forest compensation; 2) Helsinki University of Technology (TKK) fractional snow-covered area (SCA) method with in situ observations; and 3) inversion of a combined forest/snow/ground backscattering model in an application to yield estimates of the relative changes of snow wetness during full snow cover conditions. The results show that the semiempirical TKK backscattering model describes the average C-band backscattering properties of all test regions well as a function of forest stem volume. Comparison of SCA estimation results with available ground-truth data also shows a good performance. The retrieved relative snow wetness values agree well with temperature observations.

Operational snow monitoring using satellite observations

Finnish meteorological institute (FMI) has initiated together with Finnish Environment Institute (SYKE) and Helsinki University of Technology (TKK) the development of operational European wide snow monitoring system that will employ satellite data, models and in situ observations. This will be developed in framework of two international projects: (1) HydroSAF supported by Eumetsat and (2) Polarview GMES service element sponsored by ESA. The goal is to provide following snow services operationally: (1) Snow recognition (SR), (2) Fractional snow covered area (SCA), (3) Snow cover status (ST) and (4) Snow water equivalence (SWE). The first two products are designed to cover Europe and the last two will cover also the Northern Eurasia.

Estimation of snow pack characteristics and snow covered area in boreal forests from ERS-2 SAR and Envisat ASAR data

Estimation of snow moisture (total liquid water content) and the fraction of snow covered area (SCA) are investigated by applying multi-year ERS-2 SAR and Envisat ASAR data sets. An inversion approach for the moisture retrieval is introduced. The results suggest that C-band radar is operationally feasible for both applications