Machine Learning-Based Front Detection in Central Europe

Extreme weather phenomena such as wind gusts, heavy precipitation, hail, thunderstorms, tornadoes, and many others usually occur when there is a change in air mass and the passing of a weather front over a certain region. The climatology of weather fronts is difficult, since they are usually drawn onto maps manually by forecasters; therefore, the data concerning them are limited and the process itself is very subjective in nature. In this article, we propose an objective method for determining the position of weather fronts based on the random forest machine learning technique, digitized fronts from the DWD database, and ERA5 meteorological reanalysis. Several aspects leading to the improvement of scores are presented, such as adding new fields or dates to the training database or using the gradients of fields.

Associations of weather fronts with pig reproduction and mortality

ABSTRACTFarmers and practising veterinarians have long suspected the impact of weather fronts on production and animal health. A common impression is that sows will farrow earlier in connection with a cold front. There might be a correlation between daily mortality and the occurrence of a strong atmospheric front. Population-based quantitative studies on weather fronts’ effects on animal health and production are very sparse in the scientific literature. In this study, the associations between the weather fronts and daily farrowing incidence, the pregnancy length and the daily death incidence were analysed. The results show that cold front increased the odds of more than daily six farrowings on the day of the front (with at least 3°C cooling OR: 4.79, 95%CI: 1.08-21.21, p=0.039). On the day of the front, with at least 3°C temperature change both the cold and the warm front increased the odds of the farrowing on the day ≥ 118th day of the gestation (OR: 3.10, 95%CI: 1.04-9.30, p=0.43 and OR: 4.39, 95%CI: 1.73-11.15, p=0.002, respectively). On the day after the day of front, the odds of farrowing on the ≤ 113th day of gestation are increased, if the temperature decrease was at least 2°C the OR: 2.30 (95%CI: 1.04-5.06, p=0.039). On the day after the warm front with at least 1°C temperature increase the odds of more than daily three deaths is increased (OR: 5.44, 95%CI: 1.23-24.05, p=0.025).

Guide to Identifying and Controlling Postharvest Tomato Diseases in Florida

Pathogens are present in all tomato production areas and are most numerous when the weather becomes warm and wet. Movement of weather fronts or tropical storms through production areas can also affect the susceptibility of tomato fruit to decay. Fruit decays can be minimized by the employment of strict sanitation measures along with careful handling. This bulletin is designed to supplement field scouting and identification guides by a) describing postharvest decay pathogens important to Florida tomato packers and shippers, b) presenting sanitation guidelines for controlling decay pathogens during harvest and handling operations, c) offering appropriate storage temperature options.

Automated detection of weather fronts using a deep learning neural network

Deep learning (DL) methods were used to develop an algorithm to automatically detect weather fronts in fields of atmospheric surface variables. An algorithm (DL-FRONT) for the automatic detection of fronts was developed by training a two-dimensional convolutional neural network (2-D CNN) with 5 years (2003–2007) of manually analyzed fronts and surface fields of five atmospheric variables: temperature, specific humidity, mean sea level pressure, and the two components of the wind vector. An analysis of the period 2008–2015 indicates that DL-FRONT detects nearly 90 % of the manually analyzed fronts over North America and adjacent coastal ocean areas. An analysis of fronts associated with extreme precipitation events shows that the detection rate may be substantially higher for important weather-producing fronts. Since DL-FRONT was trained on a North American dataset, its extensibility to other parts of the globe has not been tested, but the basic frontal structure of extratropical cyclones has been applied to global daily weather maps for decades. On that basis, we expect that DL-FRONT will detect most fronts, and certainly most fronts with significant weather. However, where complex terrain plays a role in frontal orientation or other characteristics, it might be less successful.

Formation and Development of Convective Storms

Cumulus clouds are pervasive on earth, and play important roles in the transfer of energy through the atmosphere. Under certain conditions, shallow, nonprecipitating cumuli may grow vertically to occupy a significant depth of the troposphere, and subsequently may evolve into convective storms. The qualifier “convective” implies that the storms have vertical accelerations that are driven primarily, though not exclusively, by buoyancy over a deep layer. Such buoyancy in the atmosphere arises from local density variations relative to some base state density; the base state is typically idealized as a horizontal average over a large area, which is also considered the environment. Quantifications of atmospheric buoyancy are typically expressed in terms of temperature and humidity, and allow for an assessment of the likelihood that convective clouds will form or initiate. Convection initiation is intimately linked to existence of a mechanism by which air is vertically lifted to realize this buoyancy and thus accelerations. Weather fronts and orography are the canonical lifting mechanisms. As modulated by an ambient or environmental distribution of temperature, humidity, and wind, weather fronts also facilitate the transition of convective clouds into storms with locally heavy rain, lightning, and other possible hazards. For example, in an environment characterized by winds that are weak and change little with distance above the ground, the storms tend to be short lived and benign. The structure of the vertical drafts and other internal storm processes under weak wind shear—i.e., a small change in the horizontal wind over some vertical distance—are distinct relative to those when the environmental wind shear is strong. In particular, strong wind shear in combination with large buoyancy favors the development of squall lines and supercells, both of which are highly coherent storm types. Besides having durations that may exceed a few hours, both of these storm types tend to be particularly hazardous: squall lines are most apt to generate swaths of damaging “straight-line” winds, and supercells spawn the most intense tornadoes and are responsible for the largest hail. Methods used to predict convective-storm hazards capitalize on this knowledge of storm formation and development.

Snowstorm at the geomagnetic observatory

The Sinji Vrh Geomagnetic Observatory (hereinafter the Observatory) is situated on Gora above Ajdovščina, a highland karst plateau, in the southwestern part of Slovenia. The Observatory operates in exceptional geological and meteorological conditions due to its location. The very first measurements at the time of initial tests showed that weather fronts induce changes in the local magnetic field. The first measurements intended to determine the value of this influence were carried out at the end of summer 2011. In 2013 the first such measurements were carried out in January. This article presents the results of these measurements, showing how the snowstorm induced changes in Earth's magnetic field.