Climatological Tools for Low Visibility Forecasting

Forecasters need climatological forecasting tools because of limitations of numerical weather prediction models. In this article, using Finnish SYNOP observations and ERA-40 model reanalysis data, low visibility cases are studied using subjective and objective analysis techniques. For the objective analysis, we used an AutoClass clustering algorithm, concentrating on three Finnish airports, namely, the Rovaniemi in northern Finland, Kauhava in western Finland, and Maarianhamina in southwest Finland. These airports represent different climatological conditions. Results suggested that combining of subjective analysis with an objective analysis, e.g., clustering algorithms such as the AutoClass method, can be used to construct climatological guides for forecasters. Some higher level subjective “meta-clustering” was used to make the results physically more reasonable and easier to interpret by the forecasters.     

Mineralogical investigations in the interpretation of heavy-mineral geochemical results from till

Heavy-mineral geochemistry and its use in prospecting are based on the fact that some ore minerals (e.g. cassiterite, scheelite, wolframite, chromite, pyrochlore and columbite-tantalite) have a higher specific gravity than the common rock-forming silicates and are highly resistant to weathering in the secondary environment. Till, the most common type of surficial deposit in Finland, is relatively homogeneous and thus constitutes an appropriate sampling material for heavy-mineral geochemical studies. This is especially true in reconnaissance prospecting.The present work concentrates on the interpretation of heavy-mineral anomalies by reference to mineralogical and mineral chemistry data. Tetrabromoethane and a Frantz Isodynamic Separator were used to divide the heavy-mineral samples into subfractions, after which the minerals present were identified by light microscopy, scanning electron microscopy combined with energy-dispersive X-ray spectrometry, electron microprobe techniques and X-ray diffraction.Applications of the methods at three tin anomaly sites in southern Finland are described. The results successfully predicted the presence of a greisen in one area and a pegmatite in another. Those for the third area were somewhat contradictory, the mineralogical composition of the samples suggesting a greisen source for the tin while the composition of the cassiterite pointed to a pegmatite or granite source.     

Classification of Soil Groups Using Weights-of-Evidence-Method and RBFLN-Neural Nets

Weights-of-Evidence (WofE) and Radial Basis Function Link Net (RBFLN) were applied to soil group mapping in eastern Finland. The data consisted of low altitude airborne geophysical measurements, Landsat 5 TM-satellite image, and digital elevation model (DEM) and slope information derived from it. Probability maps were constructed for each soil group one by one and combined into a prediction map of soil groups using maximum posterior probability (WofE) or pattern membership (RBFLN). Self-Organizing Map (SOM) and Sammon’s Mapping were applied for selecting the data sets for modeling and visualizing the data. The soil types belonging to each soil group used in the Arc-SDM modeling were defined by clusters revealed by the SOM and Sammon’s Mapping algorithms. The soil types with similar characters were collected in the same cluster. Numerical evaluation of the models’ performance was performed using the confusion matrix. The Ratio of Correct Classifications (RCC) for the best WofE model was 0.64 in the training area and 0.61 in the testing area. The RCC for the best RBFLN model was 0.62. Modeling of soil groups using Arc-SDM is time consuming because models need to be constructed for each soil group before combining them into a final prediction map. In this study a simple method was tested for combining the maps. In the future, more attention should be paid to combining the posterior probability models and also to selecting data sets used for modeling.     

The use of the log-normal distribution of individuals among species in monitoring zoobenthos in the northern Baltic archipelago

The log-normal distribution of individuals among species of shallow, brackish water benthos is compared to other, more commonly used methods. This method, the use of which was introduced in marine monitoring in 1979, has not previously been tested on macrobenthos of the northern Baltic Sea. The relevance of other indices are discussed in relation to the log-normal distribution. Based on samples from three localities (soft littoral benthos, soft bottom benthos and sand bottom benthos) it is shown that the log-normal distribution could well be used as an important tool in interpreting benthic data sets from low-saline waters with few macrozoobenthic species. It illustrates natural and anthropogenic changes on the zoobenthic communities more adequately than the other parameters calculated.     

Geochemistry of till and humus in the Kotkajärvi Cu---Co---Au prospect, Kalvola, southern Finland

A small Cu---Co---Au occurrence was found by means of till geochemistry. The metal anomalies in the till are highly local and the fineness of the gold grains and their association with goethite suggest that Au may first have been dissolved and then reprecipitated. The till very probably contains remnants of an older preglacial weathering crust. There are also high concentrations of Cu, Co, As, Mo and Zn in the humus horizon. Only one humus sample had a Au content above the detection limit, 0.02 ppm. The heavy-mineral samples from the near-surface part of the till proved useful in the regional prospecting phase.