Novel methods for spatial prediction of soil functions within landscapes (SP0531)

Previous studies showed that soil patterns could be predicted in agriculturally managed landscapes by modelling and extrapolating from extensive existing but related integrated datasets. Based on these results we proposed to develop and apply predictive models of the relationships between environmental data and known soil patterns to predict capacity for key soil functions within diverse landscapes for which there is little detailed underpinning soil information available. Objectives were: To develop a high-level framework in which the non-specialist user-community could explore questions. To generate digital soil maps for three selected catchments at a target resolution of 1:50000 to provide the base information for soil function prediction. To use a modelling approach to predict the performance of key soil functions in catchments undergoing change but where only sparse or low resolution soil survey data are available. To use a modelling approach to assess the impact of different management scenarios and/or environmental conditions on the delivery of multiple soil functions within a catchment. To create a detailed outline of the requirements for ground-truthing to test the predicted model outputs at a catchment scale. To contribute to the development of a high-level framework for decision makers

Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression

Non-homogeneous regression is a frequently used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different timeadaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on a specific change in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing. The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. While this may not be surprising under fully stable model conditions, it is shown that “remembering the past” from multiple years of training data is typically also superior to the classical sliding-window approach when the ensemble prediction system is affected by certain model changes. Thus, reducing the variance of the non-homogeneous regression estimates due to increased training data appears to be more important than reducing its bias by adapting rapidly to the most current training data only

NWP-based lightning prediction using flexible count data regression

A method to predict lightning by postprocessing numerical weather prediction (NWP) output is developed for the region of the European Eastern Alps. Cloud-to-ground (CG) flashes – detected by the ground-based Austrian Lightning Detection &amp; Information System (ALDIS) network – are counted on the 18×18&thinsp;km2 grid of the 51-member NWP ensemble of the European Centre for Medium-Range Weather Forecasts (ECMWF). These counts serve as the target quantity in count data regression models for the occurrence of lightning events and flash counts of CG. The probability of lightning occurrence is modelled by a Bernoulli distribution. The flash counts are modelled with a hurdle approach where the Bernoulli distribution is combined with a zero-truncated negative binomial. In the statistical models the parameters of the distributions are described by additive predictors, which are assembled using potentially nonlinear functions of NWP covariates. Measures of location and spread of 100 direct and derived NWP covariates provide a pool of candidates for the nonlinear terms. A combination of stability selection and gradient boosting identifies the nine (three) most influential terms for the parameters of the Bernoulli (zero-truncated negative binomial) distribution, most of which turn out to be associated with either convective available potential energy (CAPE) or convective precipitation. Markov chain Monte Carlo (MCMC) sampling estimates the final model to provide credible inference of effects, scores, and predictions. The selection of terms and MCMC sampling are applied for data of the year 2016, and out-of-sample performance is evaluated for 2017. The occurrence model outperforms a reference climatology – based on 7 years of data – up to a forecast horizon of 5 days. The flash count model is calibrated and also outperforms climatology for exceedance probabilities, quantiles, and full predictive distributions.</p

Upward Lightning at the Gaisberg Tower: Initiation Mechanism and Flash Type and the Atmospheric Influence

Upward lightning is much rarer than downward lightning and requires tall ($100+$~m) structures to initiate. It may be either triggered by other lightning discharges or completely self-initiated. While conventional lightning location systems reliably detect downward lightning, they miss a specific flash type of upward lightning that consists only of a continuous current. Globally, only few specially instrumented towers can detect this flash type. The proliferation of wind turbines in combination with large damage from upward lightning necessitates an improved understanding under which conditions the self-initiated and the undetected subtype of upward lightning occur. To find larger-scale meteorological conditions favorable for self-initiated and undetectable upward lightning, this study uses a random forest machine learning model. It combines direct measurements at the specially instrumented tower at Gaisberg mountain in Austria with explanatory variables from larger-scale atmospheric reanalysis data (ERA5). Atmospheric variables reliably explain whether upward lightning is self-initiated by the tower or triggered by other lightning discharges. The most important variable is the height of the $-10~^\circ$C isotherm above the tall structure: the closer it is the higher is the probability of self-initiated upward lightning. Two-meter temperature and the amount of CAPE are also important. For the occurrence of upward lightning undetectable by lightning location systems, this study finds a strong relationship to the absence of lightning in the vicinity

To Protect Fatty Livers from Ischemia Reperfusion Injury: Role of Ischemic Postconditioning

BACKGROUND The benefit of ischemic postconditioning (IPostC) might be the throttled inflow following cold ischemia. The current study investigated advantage and mechanisms of IPostC in healthy and fatty rat livers. METHODS Male SD rats received a high-fat diet to induce fatty livers. Isolated liver perfusion was performed after 24 h ischemia at 4°C as well as in vivo experiments after 90 min warm ischemia. The so-called follow-up perfusions served to investigate the hypothesis that medium from IPostC experiments is less harmful. Lactate dehydrogenase (LDH), transaminases, different cytokines, and gene expressions, respectively, were measured. RESULTS Fatty livers showed histologically mild inflammation and moderate to severe fat storage. IPostC reduced LDH and TXB2 in healthy and fatty livers and increased bile flow. LDH, TNF-\textgreeka, and IL-6 levels in serum decreased after warm ischemia + IPostC. The gene expressions of Tnf, IL-6, Ccl2, and Ripk3 were downregulated in vivo after IPostC. CONCLUSIONS IPostC showed protective effects after ischemia in situ and in vivo in healthy and fatty livers. Restricted cyclic inflow was an important mechanism and further suggested involvement of necroptosis. IPostC represents a promising and easy intervention to improve outcomes after transplantation

Predicting power ramps from joint distributions of future wind speeds

Power ramps are sudden changes in turbine power and must be accurately predicted to minimize costly imbalances in the electrical grid. Doing so requires reliable wind speed forecasts, which can be obtained from ensembles of physical numerical weather prediction (NWP) models through statistical postprocessing. Since the probability of a ramp event depends jointly on the wind speed distributions forecasted at multiple future times, these postprocessing methods must not only correct each individual forecast but also estimate the temporal dependencies among them. Typically though, crucial dependencies are adopted directly from the raw ensemble, and the postprocessed forecast is limited to the tens of members computationally feasible for an NWP model. We extend statistical postprocessing to include temporal dependencies using novel multivariate Gaussian regression models that forecast 24-dimensional distributions of next-day hourly wind speeds at three offshore wind farms. The continuous joint distribution forecast is postprocessed from an NWP ensemble using flexible generalized additive models for the components of its mean vector μ and for parameters defining the forecast error covariance matrix Σ. Modeling these parameters on predictors which characterize the empirical joint distribution of the NWP ensemble allows forecasts for each hour and their temporal dependencies to be adjusted in one step. Wind speed ensembles of any size can be simulated from the postprocessed joint distribution and transformed into power for computing high-resolution ramp predictions that outperform state-of-the-art reference methods.</p

Ischemic Postconditioning (IPostC) Protects Fibrotic and Cirrhotic Rat Livers after Warm Ischemia

Background. Decreased organ function following liver resection is a major clinical issue. The practical method of ischemic postconditioning (IPostC) has been studied in heart diseases, but no data exist regarding fibrotic livers. Aims. We aimed to determine whether IPostC could protect healthy, fibrotic, and cirrhotic livers from ischemia reperfusion injury (IRI). Methods. Fibrosis was induced in male SD rats using bile duct ligation (BDL, 4 weeks), and cirrhosis was induced using thioacetamide (TAA, 18 weeks). Fibrosis and cirrhosis were histologically confirmed using HE and EvG staining. For healthy, fibrotic, and cirrhotic livers, isolated liver perfusion with 90 min of warm ischemia was performed in three groups (each with n=8): control, IPostC 8x20 sec, and IPostC 4x60 sec. additionally, healthy livers were investigated during a follow-up study. Lactate dehydrogenase (LDH) and thromboxane B-2 (TXB2) in the perfusate, as well as bile flow (healthy/TAA) and portal perfusion pressure, were measured. Results. LDH and TXB2 were reduced, and bile flow was increased by IPostC, mainly in total and in the late phase of reperfusion. The follow-up study showed that the perfusate derived from a postconditioned group had much less damaging potential than perfusate derived from the nonpostconditioned group. Conclusion. IPostC following warm ischemia protects healthy, fibrotic, and cirrhotic livers against IRI. Reduced efflux of TXB2 is one possible mechanism for this effect of IPostC and increases sinusoidal microcirculation. These findings may help to improve organ function and recovery of patients after liver resection

Thunderstorm environments in Europe

Meteorological environments favorable for thunderstorms are studied across Europe, including rare thunderstorm conditions from seasons with climatologically few thunderstorms. Using cluster analysis on ERA5 reanalysis data and EUCLID (European Cooperation for Lightning Detection) lightning data, two major thunderstorm environments are found: wind-field thunderstorms, characterized by increased wind speeds, high shear, strong large-scale vertical velocities, and low CAPE values compared to other thunderstorms in the same region, and mass-field thunderstorms, characterized by large CAPE values, high dew point temperatures, and elevated isotherm heights. Wind-field thunderstorms occur mainly in winter and more over the seas, while mass-field thunderstorms occur more frequently in summer and over the European mainland. Several sub-environments of these two major thunderstorm environments exist. Principal component analysis is used to identify four topographically distinct regions in Europe that share similar thunderstorm characteristics: the Mediterranean, Alpine–central, continental, and coastal regions, respectively. Based on these results it is possible to differentiate lightning conditions in different seasons from coarse reanalysis data without a static threshold or a seasonal criterion.</p