Snow Depth Trends of European Ski Resorts

Snow significantly affects the economy of many European countries, especially in the sector of winter tourism. It affects the operation of ski resorts, the mountain real estate, and can cause disruptions in transportations. The objective of this study is to analyze trends in snow depth at ski resorts in Europe based on the CERRA-land reanalysis data. Time series are computed between 1985–2020 for 4507 European ski resorts. Results show that the majority of ski resorts are severely affected by decreasing trends in snow depth, especially in winter and spring. Spatial patterns based on the elevation of ski resorts are also discussed

A Machine Learning Approach for Rainfall Nowcasting Using Numerical Model and Observational Data

The application of machine learning (ML) algorithms in large datasets in the field of meteorology is at the forefront of research. In this context, the use of satellite data to estimate the amount of rainfall is an important field of research, with operational applications. It is important to accurately predict the amount of rainfall (or rain rate) in a particular area for the proper taking of life and property protection measures. The present work intends to deepen the analysis of meteorological data with ML techniques to improve our capacity in short-range forecasting of rainfall. To this end, relationships between thermodynamic parameters derived by satellite measurements and recorded rainfall by in situ gauges, along with outputs from a numerical atmospheric model are analyzed. The main purpose of the work is to find the best relationships between the atmospheric conditions and the formation of clouds that lead to production of rainfall and build a ML model for nowcasting of rainfall. Several ML methods are used, i.e., Auto Regression, Ensemble Machine Learning, and Deep Learning, and their results are compared in order to find the best fit model

Pancreatic surgery outcomes: multicentre prospective snapshot study in 67 countries

Background: Pancreatic surgery remains associated with high morbidity rates. Although postoperative mortality appears to have improved with specialization, the outcomes reported in the literature reflect the activity of highly specialized centres. The aim of this study was to evaluate the outcomes following pancreatic surgery worldwide.Methods: This was an international, prospective, multicentre, cross-sectional snapshot study of consecutive patients undergoing pancreatic operations worldwide in a 3-month interval in 2021. The primary outcome was postoperative mortality within 90 days of surgery. Multivariable logistic regression was used to explore relationships with Human Development Index (HDI) and other parameters.Results: A total of 4223 patients from 67 countries were analysed. A complication of any severity was detected in 68.7 percent of patients (2901 of 4223). Major complication rates (Clavien-Dindo grade at least IIIa) were 24, 18, and 27 percent, and mortality rates were 10, 5, and 5 per cent in low-to-middle-, high-, and very high-HDI countries respectively. The 90-day postoperative mortality rate was 5.4 per cent (229 of 4223) overall, but was significantly higher in the low-to-middle-HDI group (adjusted OR 2.88, 95 per cent c.i. 1.80 to 4.48). The overall failure-to-rescue rate was 21 percent; however, it was 41 per cent in low-to-middle-compared with 19 per cent in very high-HDI countries.Conclusion: Excess mortality in low-to-middle-HDI countries could be attributable to failure to rescue of patients from severe complications. The authors call for a collaborative response from international and regional associations of pancreatic surgeons to address management related to death from postoperative complications to tackle the global disparities in the outcomes of pancreatic surgery (NCT04652271; ISRCTN95140761)