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Medium to subseasonal hydrological forecasts contain more information relevant to water and environmental management tasks than climatological forecasts. However, extracting this information at the most appropriate level of accuracy and spatiotemporal resolution remains a difficulty. Many studies show that the skill of the extended range forecasts with daily resolution tends toward zero after 7-14 days for small mountainous catchments. Beyond that forecast horizon the application of highly sophisticated pre- and postprocessing methods generally produce limited gains. Consequently, current forecasting techniques cannot effectively represent forecast extremes at extended ranges such as anomalously high and low runoff or soil moisture. To tackle these deficiencies, this study analyzes the value of tercile forecasts for weekly aggregates of runoff and soil moisture forecasts available at a daily resolution for Switzerland. The forecasts are classified into three categories: below, above, and normal conditions, which are derived from long-term simulations and correspond approximately to climatological conditions. To achieve improved reliability and skill of the predicted tercile probabilities, a nonparametric probabilistic classification method has been tested. It is based on Gaussian process (GP), which is attractive in machine learning (ML) applications because of its ability to estimate the predictive uncertainty. The outcome of these postprocessed forecasts was compared to preprocessing methods where the meteorological predictions are statistically corrected before passing to the hydrological model. Our results indicate that tercile forecasts of weekly aggregates produce a suitable skill up to 3 weeks lead time using the preprocessed input and up to 4 weeks lead time using the GP method. [ABSTRACT FROM AUTHOR]

Floods in the Alpine region can be destructive and cause large economic losses. Many rivers and lakes in Switzerland are regulated and flood damage can be mitigated through an optimal management of lake levels and runoff. This requires high-quality forecasts of atmospheric flood precursors extending beyond short-range (forecast days 1-5) predictions. In several places around the world atmospheric rivers or extreme integrated vapor transport (IVT) are causally related to flood events. Also in Switzerland, extreme IVT oriented perpendicular to the main orography heralds extreme flood events. This relationship is exploited in an operational flood warning system on the medium-range (here forecast days 6-10) time scale based on probabilistic medium-range forecasts of IVT and precipitation over Switzerland provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). This entails first a comprehensive probabilistic verification of the direction and magnitude of (extreme) IVT and second the development of compact visualizations for the operational use by hydrologists. Based on 20 years of probabilistic reforecasts, we show that both regular and extreme IVT has a better predictability than precipitation and IVT is predictable out to day 8. As the direction of IVT is of central importance for flood risk in Switzerland, we develop a visualization that summarizes probabilistic information on both the direction and magnitude of the IVT together with users of the product. The result is an operational flood warning system based solely on atmospheric flood precursors to extend flood warning information beyond the range of high-resolution deterministic weather forecasts. [ABSTRACT FROM AUTHOR]

Global warming will lead to earlier beginnings and prolongation of growing seasons in temperate regions and will have pronounced effects on phenology and life-history adaptation in many species. These changes were not easy to simulate for actual phenologies because of the rudimentary temporal (season) and spatial (regional) resolution of climate model projections. We investigate the effect of climate change on the regional incidence of a pest insect with nearly worldwide distribution and very high potential for adaptation to season length and temperature - the Codling Moth, Cydia pomonella. Seasonal and regional climate change signals were downscaled to the hourly temporal scale of a pest phenology model and the spatial scale of pest habitats using a stochastic weather generator operating at daily scale in combination with a resampling approach for simulation of hourly weather data. Under future conditions of increased temperatures (2045-2074), the present risk of below 20% for a pronounced second generation (peak larval emergence) in Switzerland will increase to 70-100%. The risk of an additional third generation will increase from presently 0-2% to 100%. We identified a significant two-week shift to earlier dates in phenological stages, such as overwintering adult flight. The relative extent (magnitude) of first generation pupae and all later stages will significantly increase. The presence of first generation pupae and later stages will be prolonged. A significant decrease in the length of overlap of first and second generation larval emergence was identified. Such shifts in phenology may induce changes in life-history traits regulating the life cycle. An accordingly lifehistory adaptation in photoperiodic diapause induction to shorter day-length is expected and would thereby even more increase the risk of an additional generation. With respect to Codling Moth management, the shifts in phenology and voltinism projected here will require adaptations of plant protection strategies to maintain their sustainability. [ABSTRACT FROM AUTHOR]

We present a user-friendly tool for footprint calculations of flux measurements in the surface layer. The calculations are based on the analytical footprint model by Kormann, R. and Meixner, F.X. [2001. An analytical footprint model for Non-neutral Stratification. Boundary-Layer Meteorology 99, 207–224]. The footprint density function of a flux sensor is determined using readily available data from standard eddy covariance measurements. This footprint density function is integrated over defined surface areas given as quadrangular polygons representing e.g. agricultural fields. We illustrate the use and performance of the tool by applying it to CO2 flux measurements with three eddy covariance system at the Swiss CarboEurope grassland site. Two flux towers were positioned in the centre of two neighbouring fields, respectively, that showed a very different CO2 flux during the study period. The third tower was located near the border of the two fields and was frequently influenced by both fields to a similar degree. The calculated footprint fractions were used to simulate the latter flux from the other two systems. The measured and simulated fluxes showed a good agreement and thus support the reliability of the footprint calculation. The presented simple footprint tool can be used as a routine quality check for flux monitoring stations influenced by surface areas with varying vegetation covers and/or land-use. [Copyright &y& Elsevier]

Weather forecasts for arbitrary locations in space are becoming increasingly important. Likewise, the need for an adequate quantification of the forecast uncertainty makes ensemble approaches indispensable. Even though numerical weather prediction models (NWPs) are run at increasingly high resolutions, raw ensemble forecasts still tend to be biased and underdispersed. Hence, statistical postprocessing is expected to improve forecast skill and may help to condense the forecast information. At MeteoSwiss a project on postprocessing of the most relevant weather variables for public weather forecasts, i.e. near surface temperature, precipitation, 10 meters wind speed, and cloud cover, has recently been launched. The project's goal is the introduction of an operational suite that providesproviding spatial fields of postprocessed predictions based on most of the observation data available and on raw ensemble forecasts provided by COSMO-E and the IFS-ENS of ECMWF. While the project does not focus on the development of novel methods, the optimal use of existing statistical postprocessing approaches is at the core of the project. This presentation will first summarize the project and then focus on the example of cloud cover. As for the other variables, we are developing a method routine to obtain postprocessed predictions of cloud cover for arbitrary locations in Switzerland. As ground based cloud cover observations are rather scarce, the training of the statistical model is based on EUMETSAT CM SAF satellite data, which will potentially be augmented by ground based observations. Methodologically, we start from ensemble model output statistics like and models that are suitable for the bounded variable of cloud cover. In a first step spatial consistency is ensured by ensemble copula coupling, but other approaches are to be tested as well. [ABSTRACT FROM AUTHOR]