Meteorology and Atmospheric Physics最新文献

In this study, we have tried to find out the influence of air-sea heat fluxes (particularly the Surface Latent Heat Flux (SLHF) and the Surface Sensible Heat Flux (SSHF)) on the intensity of Intense Tropical Cyclones’ (ITCs’). We have analysed 32 ITCs which originated in the Bay of Bengal (BoB) during 1990–2019. We have used IMD best track data for track and vital parameters of ITCs. We have also used high resolution (0.25°×0.25°) ERA5 SLHF, SSHF and SST data for their variations during the lifetime of ITCs. It is observed that the SLHF_max during the whole lifetime and the study period is highly correlated with ITCs’ intensity (i.e. with the central pressure (CP) and the maximum sustained wind speed (MSW)) whereas the SSHF_max shows weak correlations with ITCs’ intensity. This suggests the strong association between the SLHF_max and ITCs intensity and strong latent heat flux exchange between the ocean and atmosphere during the whole lifetime and the study period. Similar results are observed in the pre-monsoon and the post-monsoon season. In the pre-monsoon season the association of SLHF_max with the ITCs intensity is stronger than the post-monsoon season due to strong background conditions, pointing towards the strong air-sea interaction. The SLHF_max in the growing and the decaying stage are also highly correlated with ITCs’ intensity but correlation coefficients of ITCs’ intensity with the SLHF_max in the decaying stage are slightly higher than those of in the growing stage. It is also found that the SSHF_max has an appreciable correlation with ITCs’ intensity during the growing stage whereas it has negligible correlation with ITCs’ intensity during the decaying stage pointing towards the influence of sensible heat flux in the growing stage of ITCs. It is also noticed that during rapid decay (RD) the SLHF_max is highly correlated with ITCs’ intensity possibly due to cold wakes which rapidly diminishes the SLHF but during rapid intensification the SLHF does not increase so rapidly due to the sluggish WISHE feedback, hence the SLHF_max during rapid intensification (RI) is not appreciably correlated with ITCs’ intensity.

Rainfall variability has a significant impact on hydrological cycle. Understanding rainfall variability over Fiji Islands is important for decision-making in the backdrop of global warming. Reanalysis rainfall products are commonly used to overcome observed data quality challenges especially over ungauged highland areas. However, an evaluation of reanalysed datasets is important to ensure accurate and reliable climate information generated using such datasets, especially for small Island with high variable topography like Fiji. This work aims to validate the spatiotemporal performance of European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis rainfall (ERA5) data against ground-based station data from 19 stations for the period 1971–2020 over Fiji Islands. Correlation coefficient and difference statistics: bias, and root mean square error, are used to assess the performance of the data. Further, common Empirical Orthogonal Function (common EOFs) analysis was used to evaluate spatiotemporal performance of ERA5 datasets. The results of the station-by-station comparison shows that interpolated ERA5 annual rainfall matches the corresponding results from rain gauges remarkably well for many stations. The correlation coefficient values range from 0.5 to 0.85, while the bias spans from a negative 282 to a positive 575, and the root mean square error (RMSE) varies between 285 and 662 mm for the annual rainfall across the study area. However, there is overestimation and underestimation of the observed rainfall by ERA5 datasets. The leading common EOF principal component for annual rainfall suggests that the inter-annual variability in ERA5 dataset is generally consistent with observed station datasets, cross validation results indicated high scores (correlations of 0.82), with limited spatial variation. This work presents a reliable data assessment of the ERA5 data over Fiji Islands, indicating there is good match of the annual observed rain gauged station data and ERA5. The findings give accuracy references for further use of the ERA5 data in understanding rainfall variability and change over the region.

On October 13th, 2020, at approximately 1940 UTC, a hailstorm struck the city of Itajubá, located in the south of the Minas Gerais state, Brazil. This hailstorm produced hail with a diameter of 5 cm causing damages in roofs of houses and shelters. In this sense, the objective of this study is to describe the synoptic-scale environment that lead to the “ingredients” necessary for the mesoscale development of the storm, and to provide a description of cloud microphysical and lightning properties. Several data sources were used in this study as: surface observations, reanalysis data, and atmospheric remote sensing information. The synoptic-scale environment conducive to storm formation was associated with an inverted trough at surface and a shortwave trough at upper-level levels, which were important to organize upward movements in the atmosphere. High reflectivity (> 60 dBZ) was registered in the convective cell from 1940 to 2010 UTC, according to the São Roque radar data, indicating the presence of large raindrops and/or hail on the ground. The total lightning rates increased from the beginning of the storm, reaching ~ 80 lightning/5 min around 20 min before the hail precipitation, which occurred at 1920 UTC. This study highlights the importance of associating synoptic and physical information for understanding the environment and the main features of hailstorms. It also emphasizes the significance of producing information that can aid in nowcasting tools.

The climate indices demonstrate temporal and spatial variability of large-scale ocean–atmosphere patterns. This study has been carried out to analyze the streamflow data in Turkey to understand the effects of climate indices such as the Southern Oscillation (SO), the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO). The periodical relationship of the streamflow data of Turkey over different atmospheric oscillations was investigated. For this purpose, the average annual and seasonal flows at the current 72 stations in other regions of Turkey have been studied. In this context, the correlation analysis determined the relationship between NAO, AO, SO indices, and stream-flows. Besides, the original observed data were separated into parts by discrete wavelet transform to obtain the periodic components. The correlations between the found periodical components and atmospheric indices were also examined. The correlations between the streamflow and the AO/NAO showed a strong negative relationship during the annual/winter and spring periods, especially in western Turkey. Besides, the periodic components showed us the multi-annual connections between the global indices and the streamflow data of Turkey.