Meteorology

Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature revie...

One solution that can be taken to reduce GHG emissions is to reduce consumption of fossil fuels and replace them with renewable energy sources. Indonesia is rich in renewable energy sources, and one that has potential to be developed is solar energy. In line with Indonesia's development into a developed country, energy consumption is increasing. One of the activities that contributes to the largest energy use is cooking. The need for energy for cooking in Indonesia is large because the population and households are very large, No. 4 in the world. Solar Cooker is an alternative to reduce the use of fossil or traditional energy for cooking activities. Previous research has shown how the performance of a Solar Cooker can be improved if it is integrated with PCM thermal energy storage, making it possible to speed up cooking time, cook with low solar intensity and even make it possible to cook at night. However, the quantitative influence of the number of PCMs in a solar cooker has n...

Combined with the structure of the solar greenhouse, this paper proposed a new type of solar greenhouse heating and ventilation system with the framework of the solar greenhouse as the carrier of heating and ventilation. The system’s return air mode, tuyere type, tuyere installation position and aperture ratio were determined by computational fluid dynamics (CFD). The results showed that: 1) When the inlet wind speed was 10m/s and the temperature was 37°C, using the upper return air method, the average temperature of the greenhouse crop area increases by 14.7°C, which was greater than 1.3°C of the lower return air and 2.3°C of the middle return air, the heating effect was the best. 2) Based on the nozzle and slot-type tuyere, five kinds of air outlets were designed. It was found that the use of the nozzle-type air outlet could promote the circulation effect of the airflow in the greenhouse and send more heat to the crop area. 3) When the aperture ratio was controlled at 1.5, the num...

We analyzed two convective storms that passed over or near the Milešovka meteorological observatory. The observatory is located at the top of a hill and has been recently equipped with a Doppler polarimetric X-band radar FURUNO WR2120 for cloud investigations. Our analysis was based mainly on Doppler polarimetric radar data measured in vertical cross-sections (RHI-Range-Height Indicator). Radar data was also used for classifying hydrometeors by a newly developed XCLASS (X-band radar CLASSification) algorithm. We also used rapid scan data measured by the geostationary satellite Meteosat Second Generation to validate radar measurements at the upper parts of storms. Although an attenuation correction was applied to the reflectivity and differential reflectivity measurements, the attenuation typical of X-band radars was noticeable. It was mainly manifested in the differential reflectivity, co-polar correlation coefficient and specific differential phase. Nevertheless, radar measurements...

This research includes extreme spatial values. Application of the method with the Max-Stable (MSP) process. The approach will be used for the analysis of extreme rainfall in Medan City and Deli Serdang Regency. The extreme value sample selected by the Block Maxima Method, will be estimated to the spatial extreme value form by including location factors. The Max Stable Process (MSP) is an extension of the Multivariate distribution of extreme values into an infinite dimension of the theoretical extreme values. After selecting the best model, extreme rainfall data in Medan City and Deli Serdang Regency were obtained, then calculating predictions of extreme rainfall with certain time periods. Predictions are calculated using the rate of return, predictions of extreme rainfall using the return period for the next two years, in the city of Medan Belawan Maritime Station will reach 122.98 mm, BMKG Region 1 Station will reach a value of 139.13 mm. And the rain monitor in Deli Serdang Regency, the Deli Serdang Geophysics Station is predicted to be a maximum of 218.09 mm, the Kualanamu Meteorological Station is predicted to reach 91.87 mm, the North Sumatra Climatology Station will reach a maximum value of 174.18 mm. Predicted maximum Precipitation extremes can also be calculated for t (time) in future periods.