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The errors in numerical weather forecasts resulting from limited ensemble size are explored using 1,000‐member forecasts of convective weather over Germany at 3‐km resolution. A large number of forecast variables at different lead times were examined, and their distributions could be classified into three categories: quasi‐normal (e.g., tropospheric temperature), highly skewed (e.g. precipitation), and mixtures (e.g., humidity). Dependence on ensemble size was examined in comparison to the asymptotic convergence law that the sampling error decreases proportional to N−1/2 for large enough ensemble size N, independent of the underlying distribution shape. The asymptotic convergence behavior was observed for the ensemble mean of all forecast variables, even for ensemble sizes less than 10. For the ensemble standard deviation, sizes of up to 100 were required for the convergence law to apply. In contrast, there was no clear sign of convergence for the 95th percentile even with 1,000 members. Methods such as neighborhood statistics or prediction of area‐averaged quantities were found to improve accuracy, but only for variables with random small‐scale variability, such as convective precipitation. [ABSTRACT FROM AUTHOR]

Background: Detecting changes in soil organic carbon (SOC) stock requires systematic and random sampling errors to be kept to a minimum. Especially in soil monitoring schemes based on soil profiles pits, it is important to understand if a minimum spatial shift of that profile pit during resampling could render resampling errors caused by spatial variability negligible. Aims: We aimed at (1) quantifying the random SOC stock error caused by a minimum shift in sampling location of one profile and (2) assessing whether an increase in the number of profile pits to three could significantly decrease the resampling error caused by spatial variability of the relevant parameters. Methods: Eight croplands and grasslands in northeast Germany were sampled. Three sampling designs were compared: one profile resampled (1) by one, (2) by three profiles or (3) three profiles resampled by three. In addition, 16 soil cores were taken per site to characterise overall plot‐scale heterogeneity and assess general patterns of spatial dependence of relevant parameters. Results: Spatial dependence of all assessed parameters was weak. Accordingly, the resampling of one profile by one induced a high mean absolute error of 5.1 and 7.6 Mg C ha–1 at a 0–30 cm depth for croplands and grasslands (7.5% and 8.5%). This error was reduced by approximately 50% when three profiles were resampled by three profiles. Conclusions: Even with the smallest spatial shifts possible, monitoring of SOC stocks relies on replicated resampling to detect management or climate change‐induced trends in reasonable and relevant timescales. [ABSTRACT FROM AUTHOR]

The emergence and rapid spread of novel variants of concern (VOC) of the coronavirus 2 constitute a major challenge for spatial disease surveillance. We explore the possibility to use close to real-time crowdsourced data on reported VOC cases (mainly the Alpha variant) at the local area level in Germany. The aim is to use these data for early-stage estimates of the statistical association between VOC reporting and the overall COVID-19 epidemiological development. For the first weeks in 2021 after international importation of VOC to Germany, our findings point to significant increases of up to 35–40% in the 7-day incidence rate and the hospitalization rate in regions with confirmed VOC cases compared to those without such cases. This is in line with simultaneously produced international evidence. We evaluate the sensitivity of our estimates to sampling errors associated with the collection of crowdsourced data. Overall, we find no statistical evidence for an over- or underestimation of effects once we account for differences in data representativeness at the regional level. This points to the potential use of crowdsourced data for spatial disease surveillance, local outbreak monitoring and public health decisions if no other data on new virus developments are available. [ABSTRACT FROM AUTHOR]

Background: Haemoglobin levels guide many diagnostic and therapeutic interventions. They form part of the basic daily management of ICU patients. Laboratory results are frequently delayed. This can have important clinical consequences, including withholding potentially life-saving interventions. Point-of-care devices, if accurate, provide a solution to this problem. Inaccurate devices may provide misleading results leading to unnecessary and hazardous blood transfusions or under-estimation of disease severity. The aim was to compare the accuracy and reliability of the DiaSpect Hemoglobin T (DiaSpect. Medical GmbH, Sailauf, Germany) haemoglobinometer with the HemoCue (HemoCue AB, Ängelholm Sweden) haemoglobinometer and the gold standard laboratory analyser for this trial (XT-200i Sysmex/Coulter LH750). Methods: Venous blood samples were collected via a central venous line from Intensive Care Unit (ICU) patients (n = 265) for determination of haemoglobin (Hb) concentration by DiaSpect as well as the HemoCue, and the automated haemoglobin analysers: XT-200i Sysmex or the Coulter LH750. Agreement between the methods was tested using Bland-Altman plots. A Hb variation of 0.5 g/dl was considered clinically significant. There were a significant number of wasted samples with the DiaSpect, as a total of 350 cuvettes were used but only 214 complete samples obtained. This wastage was attributed to errors in sampling, cuvette shortage, lack of experience with the device and lack of 'user-friendliness'. Results: A total of 265 samples were obtained, of which 207 had complete data sets and were analysed further in the study. There were 58 incomplete samples, of which 51 were DiaSpect samples. Mean haemoglobin was 9.11 (standard deviation ± 1.74), 9.07 (standard deviation ± 1.96) and 9.02 (standard deviation ± 2.06) using the laboratory analyser, HemoCue and DiaSpect respectively. Laboratory and HemoCue: the mean difference between the two readings was -0.04 (95% limits of agreement -2.15 to 2.07 g/dl) with an average between 5.00 g/dl and 15.10 g/dl. Laboratory and DiaSpect: the mean difference between the readings was -0.09 (95% limits of agreement -2.64 to 2.46 g/dl) with an average between 5.10 g/dl and 14.9 g/dl. HemoCue and DiaSpect: the mean difference between the two haemoglobin levels was 0.04 (95% limits of agreement 2.73 to -2.64 g/dl). Both methods returned measurements within the pre-defined clinical acceptable limits of ± 0.5 g/dl. Conclusion: The haemoglobin concentration assessment by the HemoCue and DiaSpect showed an acceptable limit of agreement when compared against the laboratory analyser. There were a significant number of wasted samples when using the DiaSpect. [ABSTRACT FROM AUTHOR]

An accumulating number of studies are reporting severe insect declines. These studies aim to quantify temporal changes in invertebrate populations and community composition and attribute them to anthropogenic drivers.Seibold et al. 2019 (Nature, 574, 671–674) analysed arthropod biomass, abundance and species richness from forest and grassland plots in a region of Germany and reported declines of up to 78% between 2008 and 2018. However, their analysis did not account for the confounding effects of temporal pseudoreplication.We show that simply by including a year random effect in the statistical models and thereby accounting for the common conditions experienced by proximal sites in the same years, four of the five reported declines become non‐significant out of six tests overall.To place recent estimates of insect trends in a broader context, we analysed invertebrate biomass, abundance and richness from 640 time series from 1167 sites around the world. We found that the average trends across the terrestrial and freshwater realms were not significantly distinguishable from no net change. Shorter time series that are likely most affected by sampling error variance – such as those in Seibold et al. 2019 (Nature, 574, 671–674) – yielded the most extreme decline and increase estimates.We suggest that the media uptake of negative trends from short time series may be serving to exaggerate the 'insect Armageddon' and could undermine public confidence in research. We advocate that future research uses appropriate model structures to build a more robust understanding of biodiversity change. [ABSTRACT FROM AUTHOR]

Rainfall-induced attenuation is a major source of underestimation for radar-based precipitation estimation at C-band. Unconstrained gate-by-gate correction procedures are known to be inherently unstable and thus not suited for unsupervised attenuation correction. In this study, we evaluate three different procedures to constrain gate-by-gate attenuation correction using reflectivity as the only input. These procedures are benchmarked against rainfall estimates from uncorrected radar data, using six years of radar observations from the single-polarized C-band radar in South-West Germany. The precipitation estimation error is obtained by comparing the radar-based estimates to rain gauge observations. All attenuation correction procedures benchmarked in this study lead to an effective improvement of precipitation estimation. The first method caps the corrections if the rain intensity increase exceeds a factor of two. The second method decreases the parameters of the attenuation correction iteratively for every radar beam calculation until attaining a stability criterion. The second method outperforms the first method and leads to a consistent distribution of path-integrated attenuation along the radar beam. As a third method, we propose a slight modification of Kraemer's approach which allows users to exert better control over attenuation correction by introducing an additional constraint that prevents unplausible corrections in cases of dramatic signal losses. [ABSTRACT FROM AUTHOR]

In a New Keynesian DSGE model with non-Ricardian consumers, we show that automatic stabilization according to a countercyclical spending rule following the idea of the debt brake is well suited both to steer the economy and in terms of welfare. In particular, the adjustment account set up to record public deficits and surpluses serves well to keep the level of government debt stable. However, it is essential to design its feedback to government spending correctly, where discretionary lapses should be corrected faster than lapses due to estimation errors. [ABSTRACT FROM AUTHOR]