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The rapid temporal evolution of convective rainfall poses a challenge for rainfall nowcasting models. With the growing potential of machine learning models for precipitation nowcasting to produce realistic-looking nowcasts for long lead times, it is important to investigate whether the nowcasts also produce realistic development for convective rainfall. Common verification metrics traditionally used to validate nowcasting models are often dominated by large-scale stratiform rainfall, and averaging the metrics across entire precipitation fields obscures how accurately the models replicate individual convective cells, which makes it difficult to distinguish the model skill for the growth and decay of convective rainfall. In this study, we present a convective cell tracking-based framework to investigate how accurately nowcasting models reproduce the development of convective rainfall. The framework consists of first identifying and tracking the convective cells in the input observation rainfall fields, and then identifying and tracking the cells separately in the target observations and the nowcast rainfall fields by continuing the cell tracks identified in the observations. Features describing the cells and cell tracks, such as the cell volume rain rate and area, are then extracted. In addition to the errors in these feature values, the models' skill in reproducing the existence of convective cells is estimated by calculating several contingency-table metrics, such as the Critical Success Index. The results allow the analysis of how accurately the models reproduce the growth and decay of convective rainfall and quantify the differences between the models, for example, due to differences in how the models smooth the nowcasts, i.e., blurring. The framework also allows differentiation of the results based on the initial conditions of the cell tracks, demonstrated here by separating the tracks into decaying or growing cell tracks based on the cell status when the nowcast is created. The framework is demonstrated using four open-source advection-based models: the advection nowcast, S-PROG, and LINDA implemented in the pysteps library, and L-CNN, with data from the Swiss radar network. The results indicate that the L-CNN model reproduced the existence of convective cells best among the models and had smaller errors in the cell volume rain rate than LINDA and S-PROG. LINDA had the smallest underestimation in the cell mean rain rate, whereas S-PROG significantly overestimated the cell volume rain rate and area because of blurring. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting (forecasting locally for 0–6 h) serves both public security and industries, facilitating the mitigation of losses incurred due to, e.g., flash floods and is usually done by predicting weather radar echoes, which provide better performance than numerical weather prediction (NWP) at that scale. Probabilistic nowcasts are especially useful as they provide a desirable framework for operational decision-making. Many extrapolation-based statistical nowcasting methods exist, but they all suffer from a limited ability to capture the nonlinear growth and decay of precipitation, leading to a recent paradigm shift towards deep-learning methods which are more capable of representing these patterns. Despite its potential advantages, the application of deep learning in probabilistic nowcasting has only recently started to be explored. Here we develop a novel probabilistic precipitation nowcasting method, based on Bayesian neural networks with variational inference and the U-Net architecture, named DEUCE. The method estimates the total predictive uncertainty in the precipitation by combining estimates of the epistemic (knowledge-related and reducible) and heteroscedastic aleatoric (data-dependent and irreducible) uncertainties, using them to produce an ensemble of development scenarios for the following 60 min. DEUCE is trained and verified using Finnish Meteorological Institute radar composites compared to established classical models. Our model is found to produce both skillful and reliable probabilistic nowcasts based on various evaluation criteria. It improves the receiver operating characteristic (ROC) area under the curve scores 1 %–5 % over STEPS and LINDA-P baselines and comes close to the best-performer STEPS on a continuous ranked probability score (CRPS) metric. The reliability of DEUCE is demonstrated with, e.g., having the lowest expected calibration error at 20 and 25 dBZ reflectivity thresholds and coming second at 35 dBZ. On the other hand, the deterministic performance of ensemble means is found to be worse than that of extrapolation and LINDA-D baselines. Last, the composition of the predictive uncertainty is analyzed and described, with the conclusion that aleatoric uncertainty is more significant and informative than epistemic uncertainty in the DEUCE model. [ABSTRACT FROM AUTHOR]

Study aim: Physically active adults have been shown to have higher heart rate variability (HRV) than less active adults, but less is known about children in this regard. In adults, training-induced changes in physical performance have been shown to be related to increase in HRV, especially in its high frequency component (HF), which is a marker of parasympathetic activity. The purpose of this study was to examine whether 8 weeks of instructed physical training would improve physical performance and cardiac autonomic function (HRV) in secondary school pupils and to examine the relationship between changes in physical performance and the function of the autonomic nervous system. Material and methods: The test group included 12 girls and 12 boys and the control group 7 girls and 7 boys. All the sub­jects were 13-15 years old. Physical training included warm up, circuit training, endurance training, stretching and relaxation 3 times a week for eight weeks. Endurance training intensity was 70-75% of maximal heart rate. Endurance, flexibility, speed and power were measured before and after training. The low frequency (LF) and high frequency (HF) components of HRV were recorded in supine rest and in standing conditions before and after the eight-week period. Results: Time to exhaustion in the endurance test increased in the test group (p < 0.001), flexibility and ball throwing improved in the test group (p < 0.05), while no changes were observed in the control group. No significant changes were observed in HRV in either group. Conclusions: In conclusion, eight weeks of physical training improves physical performance in children, but it might not affect autonomic cardiac function.

The Finnish educational system is known for its equality. However, in many key areas in national and international assessments, Swedish-language schools in Finland have lagged behind their Finnish-language counterparts. So far there is little research into the underlying reasons for this discrepancy. In this article, in order to illuminate the phenomenon, we take a closer look at a number of school-level indices based on the Finnish OECD Programme for International Student Assessment (PISA) 2009 data and compare school principals' responses across these two school categories. Our findings reveal differences in school policies and practices, in school climate, and in school resources between the two parallel language-specific sectors of basic education in Finland.

Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by moving to higher resolution and making use of dual-polarization, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. But since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 hours. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, 6 different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events for each country were used to quantify the overall agreement between radar and gauges and the errors affecting the peaks. Results show that the overall agreement between radar and gauges in heavy rain is fair, with multiplicative biases in the order of 1.41-1.66 (i.e., radar underestimates by 29-39.8 %) and correlation coefficients of 0.71-0.83 across countries. However, the bias increases with intensity, reaching 45.9 %-66.2 % during the peaks. Only part of the bias (i.e., roughly 13 %-30 % depending on the radar product) can be explained by differences in measurement areas between gauges and radar. Radar products with higher spatial and temporal resolutions agreed better with the gauges, highlighting the importance of high-resolution radar for urban hydrology. However, for capturing peak intensity and reducing the bias during the most intense part of a storm, the ability to combine measurements from multiple overlapping radars to help mitigate attenuation seemed to play a more important role than resolution. The use of dual-polarization and phase information (e.g., Kdp) in the experimental Finnish OSAPOL product also seemed to provide a slight advantage in heavy rain. But improvements were hard to quantify and similarly good results were achieved in the Netherlands by applying a simple Z-R relation together with a mean field bias-correction. [ABSTRACT FROM AUTHOR]