Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment

The spatial dependence of meteorological variables is crucial for many impacts, for example, droughts, floods, river flows, energy demand, and crop yield. There is thus a need to understand how well it is represented in downscaling (DS) products. Within the COST Action VALUE, we have conducted a comprehensive analysis of spatial variability in the output of over 40 different DS methods in a perfect predictor setup. The DS output is evaluated against daily precipitation and temperature observations for the period 1979?2008 at 86 sites across Europe and 53 sites across Germany. We have analysed the dependency of correlations of daily temperature and precipitation series at station pairs on the distance between the stations. For the European data set, we have also investigated the complexity of the downscaled data by calculating the number of independent spatial degrees of freedom. For daily precipitation at the German network, we have additionally evaluated the dependency of the joint exceedance of the wet day threshold and of the local 90th percentile on the distance between the stations. Finally, we have investigated regional patterns of European monthly precipitation obtained from rotated principal component analysis. We analysed Perfect Prog (PP) methods, which are based on statistical relationships derived from observations, as well as Model Output Statistics (MOS) approaches, which attempt to correct simulated variables. In summary, we found that most PP DS methods, with the exception of multisite analog methods and a method that explicitly models spatial dependence yield unrealistic spatial characteristics. Regional climate model?based MOS methods showed good performance with respect to correlation lengths and the joint occurrence of wet days, but a substantial overestimation of the joint occurrence of heavy precipitation events. These findings apply to the spatial scales that are resolved by our observation network, and similar studies with higher resolutions, which are relevant for small hydrological catchment, are desirable.Funding Information: EU. Grant Number: EU COST Action ES110

Development of a GEM-TPC prototype

The use of GEM foils for the amplification stage of a TPC instead of a con- ventional MWPC allows one to bypass the necessity of gating, as the backdrift is suppressed thanks to the asymmetric field configuration. This way, a novel continuously running TPC, which represents one option for the PANDA central tracker, can be realized. A medium sized prototype with a diameter of 300 mm and a length of 600 mm will be tested inside the FOPI spectrometer at GSI using a carbon or lithium beam at intermediate energies (E = 1-3AGeV). This detector test under realistic experimental conditions should allow us to verify the spatial resolution for single tracks and the reconstruction capability for displaced vertexes. A series of physics measurement implying pion beams is scheduled with the FOPI spectrometer together with the GEM-TPC as well.Comment: 5 pages, 4 figures, Proceedings for 11th ICATTP conference in como (italy

The VALUE perfect predictor experiment: Evaluation of temporal variability

Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short-term variations, spells and variability from interannual to long-term trends. The EU-COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long-term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors.EU COST Association, Grant/Award Number: ES1102; Ministry of Education, Youth, and Sports of the Czech Republic, Grant/AwardNumber:LD1202

CPT and Lorentz symmetry tests with hydrogen using a novel in-beam hyperfine spectroscopy method applicable to antihydrogen experiments

We present a Rabi-type measurement of two ground-state hydrogen hyperfine transitions performed in two opposite external magnetic field directions. This puts first constraints at the level of 2.3 × 10^{−21} GeV on a set of coefficients of the Standard Model Extension, which were not measured by previous experiments. Moreover, we introduce a novel method, applicable to antihydrogen hyperfine spectroscopy in a beam, that determines the zero-field hyperfine transition frequency from the two transitions measured at the same magnetic field. Our value, ν_0 = 1.420 405 751 63(63) GHz, is in agreement with literature at a relative precision of 0.44 ppb. This is the highest precision achieved on hydrogen in a beam, improving over previous results by a factor of 6

An intercomparison of a large ensemble of statistical downscaling methods over Europe : Results from the VALUE perfect predictor cross‐validation experiment

VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value‐cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value‐cost.eu/validationportal.Peer reviewe

TuringLang/MCMCChains.jl: v6.0.4

<h2>MCMCChains v6.0.4</h2> <p><a href="https://github.com/TuringLang/MCMCChains.jl/compare/v6.0.3...v6.0.4">Diff since v6.0.3</a></p> <p><strong>Merged pull requests:</strong></p> <ul> <li>CompatHelper: bump compat for Documenter to 1 for package docs, (keep existing compat) (#432) (@github-actions[bot])</li> <li>CompatHelper: bump compat for Documenter to 1 for package test, (keep existing compat) (#433) (@github-actions[bot])</li> <li>CompatHelper: bump compat for MLJBase to 1 for package docs, (keep existing compat) (#434) (@github-actions[bot])</li> <li>CompatHelper: bump compat for MLJBase to 1 for package test, (keep existing compat) (#435) (@github-actions[bot])</li> <li>Update chains.jl (#437) (@sunxd3)</li> <li>CompatHelper: bump compat for AbstractMCMC to 5, (keep existing compat) (#438) (@github-actions[bot])</li> <li>CompatHelper: bump compat for AbstractMCMC to 5 for package test, (keep existing compat) (#439) (@github-actions[bot])</li> </ul> <p><strong>Closed issues:</strong></p> <ul> <li>write(file, chn) fails due to missing method (#425)</li> <li>Array(chn) drops third dimension for nchain==1 (#429)</li> </ul&gt