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The rainy season is of high importance for livelihoods in the Southern Peruvian Andes (SPA), especially for agriculture, which is mainly rain fed and one of the main income sources in the region. Therefore, knowledge and predictions of the rainy season such as its onset and ending are crucial for planning purposes. However, such information is currently not readily available for the local population. Moreover, an evaluation of existing rainy season indices shows that they are not optimally suited for the SPA and may not be directly applicable in a forecasting context. Therefore, we develop a new index, named Climandes index, which is tailored to the SPA and designed to be of use for operational monitoring and forecasting purposes. Using this index, we analyse the climatology and trends of the rainy season in the SPA. We find that the rainy season starts roughly between September and January with durations between 3 and 8 months. Both onset and duration show a pronounced northeast‐southwest gradient, regions closer to the Amazon Basin have a considerably longer rainy season. The inter‐annual variability of the onset is very high, that is, 2–5 months depending on the station, while the end of the rainy season shows a much lower variability (i.e., 1.5–3 months). The spatial patterns of total precipitation amount and dry spells within the rainy season are only weakly related to its timing. Trends in rainy season characteristics since 1965 are mostly weak and not significant, but generally indicate a tendency towards a shortening of the rainy season in the whole study area due to a later onset and an increase in precipitation sums during the rainy season in the northwestern study area. [ABSTRACT FROM AUTHOR]

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]

Statistical postprocessing is applied in operational forecasting to correct systematic errors of numerical weather prediction models (NWP) and to automatically produce calibrated local forecasts for end-users. Postprocessing is particularly relevant in complex terrain, where even state-of-the-art high-resolution NWP systems cannot resolve many of the small-scale processes shaping local weather conditions. In addition, statistical postprocessing can also be used to combine forecasts from multiple NWP systems. Here we assess an ensemble model output statistics (EMOS) approach to produce seamless temperature forecasts based on a combination of short-term ensemble forecasts from a convection-permitting limited-area ensemble and a medium-range global ensemble forecasting model. We quantify the benefit of this approach compared to only postprocessing the high-resolution NWP. The multimodel EMOS approach (“mixed EMOS”) is able to improve forecasts by 30% with respect to direct model output from the high-resolution NWP. A detailed evaluation of mixed EMOS reveals that it outperforms either one of the single-model EMOS versions by 8%–12%. Temperature forecasts at valley locations profit in particular from the model combination. All forecast variants perform worst in winter (DJF); however, calibration and model combination improves forecast quality substantially. In addition to increasing skill as compared to single-model postprocessing, it also enables us to seamlessly combine multiple forecast sources with different time horizons (and horizontal resolutions) and thereby consolidates short-term to medium-range forecasting time horizons in one product without any user-relevant discontinuity. [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]

Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km 2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions. [ABSTRACT FROM AUTHOR]

Traditional Ensemble Streamflow Prediction systems (ESP) are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Prior of using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to account to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modeling steps remains open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions with areas between 80 and 1700 km². Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP based approach can provide superior prediction than the ESP approach, especially at shorter lead times. In snow- dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow related processes for subseasonal streamflow predictions in mountainous regions. [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]

Abstract: A novel quantum cascade laser absorption spectrometer (QCL-AS) was tested to monitor N2O exchange fluxes over an intensively managed grassland using the eddy covariance approach. The instrument employs a continuous wave quantum cascade laser to scan over the absorption features of N2O, CH4 and water vapor at 7.8μm. The precision of the N2O flux measurements was determined to be 0.2nmolm−2 s−1 but the accuracy can easily be affected by water vapor interferences twice as large. These water vapor interferences are not only due to the respective gas dilution effect but also due to an additional cross-sensitivity of the N2O analyzer to water vapor (0.3ppb N2O/% H2O). Both effects cause a negative bias of similar magnitude (0.3nmolm−2 s−1 N2O flux/mmolm−2 s−1 H2O flux) in the flux measurements. While the dilution (or density) correction is a well known and routinely applied procedure, the magnitude of the analyzer cross-talk may depend on the specific instrumental setup and should be empirically determined. The comparison with static chamber measurements shows the necessity of the cross-talk correction; otherwise the QCL-AS based eddy covariance system would yield unrealistically large uptake of N2O. [Copyright &y& Elsevier]