Fuel conditions and fire behavior characteristics of managed Picea abies and Pinus sylvestris forests in Finland

The objectives of this study were to analyze the impact of structural stand characteristics on ignition potential, surface fuel moisture, and fire behavior in Pinus sylvestris L. and Picea abies (L.) Karst stands in Finland and to explain stand-specific fire danger using the Canadian Fire Weather Index System and the Finnish Fire Risk Index. Additionally, the study analyzes the relationship between observed fire activity and fire weather indices at different stages of growing season. Field experiments were carried out in Pinus sylvestris or Picea abies dominated stands during fire seasons 2001 and 2002. Observations on ignition potential, fuel moisture, and fire behavior were analyzed in relation to stand structure and the outputs of the Finnish and Canadian fire weather indices. Seasonal patterns of fire activity were examined based on national fire statistics 1996 2003, effective temperature sum, and the fire weather indices. Point fire ignition potential was highest in Pinus clear-cuts and lowest in closed Picea stands. Moss-dominated surface fuels were driest in clear-cut and sapling stage stands and presented the highest moisture content under closed Picea canopy. Pinus sylvestris stands carried fire under a wide range of fire weather conditions under which Picea abies stands failed to sustain fire. In the national fire records, the daily number of reported ignitions presented its highest value during late fire season whereas the daily area burned peaked most substantially during early season. The fire weather indices correlated significantly with ignition potential and fuel moisture but were unable to explain fire behavior in the experimental fires. During the initial and final stages of the growing season, fire activity was disconnected from weather-based fire danger ratings. Information on stand structure and season stage would benefit the assessment of fire danger in Finnish forest landscape for fire suppression and controlled burning purposes.

Categorical Meteorological Products: Evaluation and Analysis

In meteorology, observations and forecasts of a wide range of phenomena for example, snow, clouds, hail, fog, and tornados can be categorical, that is, they can only have discrete values (e.g., "snow" and "no snow"). Concentrating on satellite-based snow and cloud analyses, this thesis explores methods that have been developed for evaluation of categorical products and analyses. Different algorithms for satellite products generate different results; sometimes the differences are subtle, sometimes all too visible. In addition to differences between algorithms, the satellite products are influenced by physical processes and conditions, such as diurnal and seasonal variation in solar radiation, topography, and land use. The analysis of satellite-based snow cover analyses from NOAA, NASA, and EUMETSAT, and snow analyses for numerical weather prediction models from FMI and ECMWF was complicated by the fact that we did not have the true knowledge of snow extent, and we were forced simply to measure the agreement between different products. The Sammon mapping, a multidimensional scaling method, was then used to visualize the differences between different products. The trustworthiness of the results for cloud analyses [EUMETSAT Meteorological Products Extraction Facility cloud mask (MPEF), together with the Nowcasting Satellite Application Facility (SAFNWC) cloud masks provided by Météo-France (SAFNWC/MSG) and the Swedish Meteorological and Hydrological Institute (SAFNWC/PPS)] compared with ceilometers of the Helsinki Testbed was estimated by constructing confidence intervals (CIs). Bootstrapping, a statistical resampling method, was used to construct CIs, especially in the presence of spatial and temporal correlation. The reference data for validation are constantly in short supply. In general, the needs of a particular project drive the requirements for evaluation, for example, for the accuracy and the timeliness of the particular data and methods. In this vein, we discuss tentatively how data provided by general public, e.g., photos shared on the Internet photo-sharing service Flickr, can be used as a new source for validation. Results show that they are of reasonable quality and their use for case studies can be warmly recommended. Last, the use of cluster analysis on meteorological in-situ measurements was explored. The Autoclass algorithm was used to construct compact representations of synoptic conditions of fog at Finnish airports.