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Nowcasts (i.e., short-term forecasts from 5 min to 6 h) of heavy rainfall are important for applications such as flash flood predictions. However, current precipitation nowcasting methods based on the extrapolation of radar echoes have a limited ability to predict the growth and decay of rainfall. While deep learning applications have recently shown improvement compared to extrapolation-based methods, they still struggle to correctly nowcast small-scale high-intensity rainfall. To address this issue, we present a novel model called the Lagrangian convolutional neural network (L-CNN) that separates the growth and decay of rainfall from motion using the advection equation. In the model, differences between consecutive rain rate fields in Lagrangian coordinates are fed into a U-Net-based CNN, known as RainNet, that was trained with the root-mean-squared-error loss function. This results in a better representation of rainfall temporal evolution compared to the RainNet and the extrapolation-based LINDA model that were used as reference models. On Finnish weather radar data, the L-CNN underestimates rainfall less than RainNet, demonstrated by greater POD (29% at 30 min at 1 mm·h$^{-1}$ threshold) and smaller bias (98% at 15 min). The increased ETS values over LINDA for leadtimes under 15 min, with maximum increases of 7% (5 mm·h$^{-1}$ threshold) and 10% (10 mm·h$^{-1}$), show that the L-CNN represents the growth and decay of heavy rainfall more accurately than LINDA. This implies that nowcasting of heavy rainfall is improved when growth and decay are predicted using a deep learning model.

Accurate wind profile measurements are important for applications ranging from aviation to numerical weather prediction. The spatial pattern of winds can be obtained with ground-based remote sensing instruments, such as weather radars and Doppler lidars. As the return signal in weather radars is mostly due to hydrometeors or insects, and in Doppler lidars due to aerosols, the instruments provide wind measurements in different weather conditions. However, the effect of various weather conditions on the measurement capabilities of these instruments has not been previously extensively quantified. Here we present results from a 7-month measurement campaign that took place in Vantaa, Finland, where a co-located Vaisala WRS400 X-band weather radar and WindCube 400S Doppler lidar were employed continuously to perform wind measurements. Both instruments measured plan position indicator (PPI) scans at 2.0∘ elevation from the horizontal. Direct comparison of radial Doppler velocities from both instruments showed good agreement with R2=0.96. We then examined the effect of horizontal visibility, cloud base height, and precipitation intensity on the measurement availability of each instrument. The Doppler lidar displayed good availability in clear air situations and the X-band radar in precipitation. Both instruments exhibited high availability in clear air conditions in summer when insects were present. The complementary performance in the measurement availability of the two instruments means that their combination substantially increases the spatial coverage of wind observations across a wide range of weather conditions.

Transient Eulerian simulations of multiphase flow inside a laboratory-scale circulating fluidized bed (CFB) riser were performed with air, bed material, and a secondary solid phase to evaluate the mixing of the secondary solid phase. This simulation data can be applied in model development or for computing terms that are commonly used when modeling mixing with simplified models (pseudo-steady state, non-convective models, etc.). The data was produced with transient Eulerian modeling using Ansys Fluent 19.2. The simulations were done with one fluidization velocity and bed material, while the density, particle size, and inlet velocity of the secondary solid phase was varied and 10 simulations per each secondary solid phase case were simulated for 1 s, each simulation having different starting conditions (flow state of the air and bed material) inside the riser. These 10 cases were then averaged to provide an average mixing profile for each secondary solid phase. Both the averaged and un-average data are included. The details of the modeling, averaging, geometry, materials, and cases are described in the open-access publication by Nikku et al. (Chem. Eng. Sci. 269, 118503).

Lipid-related thiamine (vitamin B1) deficiency of Baltic salmon (Salmo salar), the M74 syndrome, is generally caused by feeding on abundant young sprat (Sprattus sprattus) in the Baltic Proper, the main foraging area of these salmon. In 2014, a strong year-class of sprat was hatched in the Baltic Proper, and a strong herring (Clupea harengus) year-class was hatched in the Gulf of Bothnia, where herring is the dominant salmon prey. The fatty acid (FA) signatures of prey fish in muscle or eggs of second sea-year spawners suggested that 27% of wild River Simojoki and 68% of reared River Dal salmon remained in the Gulf of Bothnia in 2014 instead of continuing to the Baltic Proper. In 2016, 23% of the M74 females of the River Simojoki and 58% of the River Dal originated from the Gulf of Bothnia, and 13% and 16%, respectively, originated from the Baltic Proper. Some salmon from the River Neris in the southern Baltic Proper had also been feeding in the Gulf of Bothnia. In general, low free thiamine (THIAM) concentration in eggs was associated with high lipid content and high docosahexaenoic acid (DHA, 22:6n−3) and n−3 polyunsaturated FA (n−3 PUFA) concentrations in muscle but not in eggs. A higher THIAM concentration and lower proportions of DHA and n−3 PUFAs in Arctic Ocean salmon eggs, despite higher egg lipid content, indicated that their diet contained fewer fatty fish than the Baltic salmon diet. Hence, M74 originated by foraging heavily on young fatty sprat in the Baltic Proper or herring in the Gulf of Bothnia.

We investigate the boundary-layer (BL) height at Hyytiälä in southern Finland diagnosed from radiosonde observations, a microwave radiometer (MWR) and ERA5 reanalysis. Four different, pre-existing algorithms are used to diagnose the BL height from the radiosondes. The diagnosed BL height is sensitive to the method used. The level of agreement, and the sign of systematic bias between the four different methods, depends on the surface-layer stability. For very unstable situations, the median BL height diagnosed from the radiosondes varies from 600 to 1500 m depending on which method is applied. Good agreement between the BL height in ERA5 and diagnosed from the radiosondes using Richardson-number-based methods is found for almost all stability classes, suggesting that ERA5 has adequate vertical resolution near the surface to resolve the BL structure. However, ERA5 overestimates the BL height in very stable conditions, highlighting the ongoing challenge for numerical models to correctly resolve the stable BL. Furthermore, ERA5 BL height differs most from the radiosondes at 18:00 UTC, suggesting ERA5 does not resolve the evening transition correctly. BL height estimates from the MWR are also found to be reliable in unstable situations but often are inaccurate under stable conditions when, in comparison to ERA5 BL heights, they are much deeper. The errors in the MWR BL height estimates originate from the limitations of the manufacturer's algorithm for stable conditions and also the misidentification of the type of BL. A climatology of the annual and diurnal cycle of BL height, based on ERA5 data, and surface-layer stability, based on eddy covariance observations, was created. The shallowest (353 m) monthly median BL height occurs in February and the deepest (576 m) in June. In winter there is no diurnal cycle in BL height; unstable BLs are rare, yet so are very stable BLs. The shallowest BLs occur at night in spring and summer, and very stable conditions are most common at night in the warm season. Finally, using ERA5 gridded data, we determined that the BL height observed at Hyytiälä is representative of most land areas in southern and central Finland. However, the spatial variability of the BL height is largest during daytime in summer, reducing the area over which BL height observations from Hyytiälä would be representative.

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]

Background Our aim was to evaluate the prevalence, mortality, regional and sex distribution of neural tube defects (NTDs) in Finland. Methods Data for this population-based study were collected from 1987 to 2018 from the national health and social welfare registers. Results There were in total 1634 cases of NTDs, of which 511 were live births, 72 pregnancies ended in stillbirth and 1051 were terminations of pregnancy due to fetal anomaly (TOPFA). The total prevalence of NTDs was 8.6 per 10 000 births and it increased slightly annually (OR 1.008; 95% CI: 1.002, 1.013) during the 32-year study period. The birth prevalence of NTDs decreased (OR 0.979; 95% CI: 0.970, 0.987), but the prevalence of TOPFA increased annually (OR 1.024; 95% CI 1.017, 1.031). The perinatal mortality of NTD children was 260.7 per 1000 births and the infant mortality was 184.0 per 1000 live births, whereas these measures in the general population were 4.6 per 1000 births and 3.3 per 1000 live births, respectively. There was no difference in the NTD prevalence between males and females (P- value 0.77). The total prevalence of NTDs varied from 7.1 to 9.4 per 10 000 births in Finland by region. Conclusions Although the majority of NTDs are preventable with an adequate folic acid supplementation, the total prevalence increased in Finland during the study period when folic acid supplementation was mainly recommended to high-risk families and to women with folic acid deficiency. NTDs remain an important cause of infant morbidity and mortality in Finland. [ABSTRACT FROM AUTHOR]

Signs of impaired thiamine (vitamin B1) status in feeding-migrating Atlantic salmon (Salmo salar) were studied in three Baltic Sea areas, which differ in the proportion and nutritional composition of prey fish sprat (Sprattus sprattus) and herring (Clupea harengus). The concentration of n−3 polyunsaturated fatty acids (n−3 PUFAs) increased in salmon with dietary lipids and n−3 PUFAs, and the hepatic peroxidation product malondialdehyde (MDA) concentration increased exponentially with increasing n−3 PUFA and docosahexaenoic acid (DHA, 22:6n−3) concentration, whereas hepatic total thiamine concentration, a sensitive indicator of thiamine status, decreased with the increase in both body lipid and n−3 PUFA or DHA concentration. The hepatic glucose 6-phosphate dehydrogenase activity was suppressed by high dietary lipids. In salmon muscle and in prey fish, the proportion of thiamine pyrophosphate increased, and that of free thiamine decreased, with increasing body lipid content or PUFAs, or merely DHA. The thiamine status of salmon was impaired mainly due to the peroxidation of n−3 PUFAs, whereas lipids as a source of metabolic energy had less effect. Organochlorines or general oxidative stress did not affect the thiamine status. The amount of lipids, and, specifically, their long-chain n−3 PUFAs, are thus responsible for generating thiamine deficiency, and not a prey fish species per se.

The Kjeldahl method is the most utilized method for total protein content analysis in food. Using the universal nitrogen-to-protein conversion factor of 6.25, the protein content of insects is likely to be overestimated due to their chitin content. We have calculated nitrogen-to-protein conversion factors for two crickets, house cricket (Acheta domesticus) and field cricket (Gryllus bimaculatus), which are used for food and feed in Europe. By analyzing their nitrogen and amino acid content we were able to show that a conversion factor of 5.09 could be used for house cricket and 5.00 for field cricket in protein content calculation. Based on these results with a reservation about slight variation in farming conditions, we suggest a nitrogen conversion factor of 5.0 for both crickets.