An original fuzzy team control model is presented in this article. The model is based on a non-traditional combination of classical and contemporary achievements of management and mathematical theories of fuzzy logic and fuzzy sets. In methodological terms, the article also offers a set of tools for measuring and evaluating both team performance and the effectiveness of the team control system in the organization. Fuzzy tools and techniques for decision-making, studying of hidden effects and joint influences, and quantification of evaluations are employed in this set of tools. The suggested fuzzy model contributes to overcoming theoretical deficits on the issues of team control, and the methodology of team control fills a gap in the toolkit of team management. The results from verification of the fuzzy team control model at a small-sized Bulgarian enterprise are also discussed in this article. They indicate that it is possible to develop a fuzzy model for team control, increasing the effectiveness of the team control system in the enterprise.
This paper constitutes the sensitivity study of application the Polar WRF model to the Svalbard area with testing selected parameterizations, including planetary boundary layer, radiation and microphysics schemes. The model was configured, using three one-way nested domains with 27 km, 9 km and 3 km grid cell resolutions. Results from the innermost domain were presented and compared against measured wind speed and air temperature at 10 meteorological stations. The study period covers two months: June 2008 and January 2009. Significant differences between simulations results occurred for planetary boundary layer (PBL) schemes in January 2009. The Mellor-Yamada-Janjic (MYJ) planetary boundary layer (PBL) scheme resulted in the lowest errors for air temperature, according to mean error (ME), mean absolute error (MAE) and correlation coefficient values, where for wind speed this scheme was the worst from all the PBL schemes tested. In the case of June 2008, shortwave and longwave radiation schemes influenced the results the most. Generally, higher correlations were obtained for January, both for air temperature and wind speed. However, the model performs better for June in terms of ME and MAE error statistics. The results were also analyzed spatially, to summarize the uncertainty of the model results related to the analyzed parameterization schemes groups. Significant variability among simulations was calculated for January 2009 over the northern part of Spitsbergen and fjords for the PBL schemes. Standard deviations for monthly average simulated values were up to 3.5°C for air temperature and around 1 m s-1 for wind speed.