Weather forecasting is a complex process that involves numerous specialized fields of expertise. The output from computationally intensive numerical weather prediction (NWP) models forms the starting point of the forecasting process. Expert forecasters have both a general knowledge of large-scale weather systems and specific knowledge about the idiosyncratic behavior of local scale weather phen...

Burgstahler and Eames (2003) present evidence that analysts commonly anticipate earnings management to avoid small losses, but often incorrectly predict its occurrence. Here we consider whether the market’s behavior mimics that of analysts. Our results suggest that analysts exhibit more forecast optimism in their zero earnings forecasts than in their other small earnings forecast levels, and ma...

Highly resolved precipitation forecasts are necessary in many applications, especially in mountain meteorology and flash flood forecasts for smallto medium-sized alpine watersheds. Here we present precipitation forecasts simulated by the limited area model ALADIN applying different grid resolutions (1x = 10 km and 4 km). Target area of the investigations is the Alpine Ticino-Verzasca-Maggia wat...

Unfavorable weather currently ranks among the major challenges facing agricultural development in many African countries. Impact mitigation through access to reliable and timely weather forecasts and other adaptive mechanisms are foremost in Africa’s policy dialogues and socio-economic development agendas. This paper analyzed the factors influencing access to forecasts on incidence of pests/dis...

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble foreca...

The study of time-dependent univariate systems plays an important role in several physical and applied sciences. Time-series behaviour of such systems is mostly complex in nature and sophisticated mathematical modelling tools are needed for making accurate forecasts. These forecasts can be used for specific purposes in different domains, for example, to plan resources, develop market strategies...

This paper presents a new approach that aims to incorporate prior judgmental forecasts into a statistical forecasting model. The result is a set of forecasts that are consistent with both the judgment and latest observations. The approach is based on constructing a model with a combined dataset where the expert forecasts and the historical data are described by means of corresponding regression...

Many researchers use GARCH models to generate volatility forecasts. Using data on three major U.S. dollar exchange rates we show that such forecasts are too high in volatile periods. We argue that this is due to the high persistence of shocks in GARCH forecasts. To obtain more flexibility regarding volatility persistence, this paper generalizes the GARCH model by distinguishing two regimes with...

This paper introduces the concept of Temporal Hierarchies for time series forecasting. A temporal hierarchy can be constructed for any time series by means of non-overlapping temporal aggregation. Predictions constructed at all aggregation levels are combined with the proposed framework to result in temporally reconciled, accurate and robust forecasts. The implied combination mitigates modellin...

Water managers throughout the Western U.S. depend on seasonal forecasts to assist with operations and planning. In this study, we develop a seasonal forecasting model to aid water resources decision-making in the Truckee-Carson River System. We analyze large-scale climate information that has a direct impact on our basin of interest to develop predictors to spring runoff. The predictors are sno...