Although physical snow cover simulations are increasingly used in regional avalanche forecasting, the process of forecasting avalanches still relies on expert-based decisionmaking. Recently quantitative tools emerged to assist forecasters objectively in their assessment process. This thesis deals with the aspect of two of these assisting tools. First, a cluster algorithm based approach on identifying regions with similar precipitation patterns was developed with the ultimate goal to divide the forecasting domain of the avalanche warning service Tyrol into small micro-regions with similar snow climatology. The results of the cluster analysis re-confirmed the pre-existing expert-based splitting of micro-regions in Tyrol. The presented approach helped to refine some micro-regions as introducing various general flow directions into the precipitation patterns revealed finer splitting patterns. These were even in line with experts based suggestions for improvements. Second, more and more forecasters are using avalanche problems as a process-based starting point to assess regional avalanche danger in recent years. To support this workflow, we used and adjusted an algorithm for classifying snow avalanche climatology based on snow cover models and simulated instabilities to ultimately facilitate the assessment of the prevailing avalanche problem. In the first step, we adapted the existing algorithm to provide an objective first guess of prevailing avalanche problems based on snow-cover simulations using the 1-D physics-based model SNOWPACK. The algorithm is based on the snow cover model output and, thus, is compatible with various model chains. We focused on two setups: (I) a SNOWPACK model chain driven by automated weather stations (AWS) to obtain a nowcast of avalanche problems. (II) Furthermore, we established a model chain where the SNOWPACK simulations are initialized by observed snow profiles and driven by numerical weather predictions (NWP). To meet the needs of operational avalanche forecasting we have refined the assessment of avalanche problems by including slope simulations for different aspects. The model chain is applied to multiple AWS locations within a forecasting region and enriched with profile-based simulations to generate a frequency distribution of avalanche problems. Initial evaluations of a case study of the avalanche warning service Tyrol for the season 2021-2022 show that results are helpful in supporting the forecasters’ workflow. In fact, avalanche problems based on models show fairly similar behavior as the subjectively assessed problems assigned by forecasters. When assigning the Wet Snow Problem and the Persistent Weak Layer Problem, the algorithm selects critical aspects in a more sensitive manner. The algorithm can add substantial value in the decision-making process when and where avalanche problems develop and for how long they are critical. It can support warning services with an objective first guess on the prevailing avalanche problem and its location (esp. aspects) and timing. The enhancements we present streamline traditional forecasting with the modeling world and pave the way for consistent integration of automated avalanche problem identification into operational snow cover simulations.