Stochastic weather generators are commonly used to simulate time series of daily weather, especially minimum (Tmin) and maximum (Tmax) temperature and amount of precipitation. Recently, generalized linear models (GLM) have been proposed as a convenient approach to fitting weather generators. One limitation of weather generators is a marked tendency to underestimate the observed interannual variance in monthly, seasonal, or annual total precipitation and mean temperature, termed the 'overdispersion' phenomenon. In this study, aggregated statistics, consisting of seasonal total precipitation and mean Tmin and Tmax, are introduced as additional covariates into the GLM weather generator. With an appropriate degree of smoothing of these aggregated statistics, this approach is shown to virtually eliminate overdispersion when applied to 2 sites, Pergamino and Pilar, in the Argentine Pampas. The addition of these covariates does not distort the performance of the weather generator in other respects, such as annual cycles in the probability of precipitation and in the mean Tmin and Tmax. For seasonal total precipitation, the reduction in overdispersion is partially attributable to a corresponding reduction in the overdispersion of the frequency of precipitation occurrence, as well as to apparent temporal trends or 'regime' shifts. For seasonal mean Tmin and Tmax, the reduction in overdispersion is largely due to temporal trends on an interannual time scale.

Among people infected with SARS-CoV-2, the determination of clinical features associated with poor outcome is essential to identify those at high risk of deterioration. Here, we aimed to investigate clinical phenotypes of patients hospitalized due to COVID-19 and to examine the predictive value of the neutrophil-to-lymphocyte ratio (NLR) in a representative patient collective of the Swiss population. We conducted a retrospective monocentriccohort study with patients hospitalized due to COVID-19 between 27 February and 31 December 2020. Data were analyzed descriptively, using the binary logistic regression model, proportional odds logistic regression model, competing risk analysis, and summary measure analysis. A total of 454 patients were included in our study. Dyspnea, elevated respiratory rate, low oxygen saturation at baseline, age, and presence of multiple comorbidities were associated with a more severe course of the disease. A high NLR at baseline was significantly associated with disease severity, unfavorable outcome, and mortality. In non-survivors, NLR further increased during hospital stay, whereas in survivors, NLR decreased. In conclusion, our data emphasize the importance of accurate history taking and clinical examination upon admission and confirm the role of baseline NLR as a surrogate marker for increased disease severity, unfavorable outcome, and mortality in patients hospitalized due to infection with SARS-CoV-2.