Meteorology and Atmospheric Physics最新文献

Based on hourly rain gauge observation, cloud amount, and radiative fluxes data from the Clouds and the Earth’s Radiant Energy System (CERES) and ECMWF Reanalysis v5 (ERA5) dataset, the precipitation process during the Meiyu period in the middle and lower reaches of the Yangtze River in 2020 was simulated in WRF to reveal the influence of cloud radiative heating process on the diurnal variation of precipitation using multiple cloud microphysical schemes. The statistical evaluation of three microphysical parameterization schemes shows that the two-moment scheme WDM6 is more reasonable than the other two schemes in simulating the precipitation distribution, central intensity, and cloud characteristic distribution. There are significant bimodal characteristics in the diurnal variation of precipitation during the Meiyu period by analyzing the observation data. The numerical experiment accurately simulated the time and magnitude of the early morning peak in the heavy rain area but failed to reproduce the peak in the late afternoon, resulting in a false weak rainfall accumulation. The comparison of simulation results with the observed cloud macroscale and microscale characteristics revealed that the reason for the deviation of precipitation simulation was closely related to the inaccurate description of cloud microphysical quantities. The lack of ice phase cloud droplets led to excessively strong radiative heating rate at 200–500 hPa, resulting in anomalous warming in the mid-upper troposphere. Meanwhile, the cold advection at 850 hPa led to anomalous cooling in the lower troposphere, increasing atmospheric stability and further inhibiting the development of the afternoon thermal convection process.

The prolonged positive phase of the Arctic Oscillation (AO) and associated quasi-stationary equivalent barotropic Euro-Atlantic blocking high (EABH) and the Siberian high (SH) existed during January 2020. The presence of the persistent quasi-stationary barotropic high resulted in higher (lower) than normal surface temperatures in most parts of northern (southern) Eurasia. The large-scale analysis suggests the detouring of the mid-latitudinal westerlies from EABH and the formation of the west–east trough from East Atlantic to Northwest Pacific across North Africa, the Middle East, North India and China. The convergence of the moisture and positive convection anomalies along the region of the trough is perceived from the analysis. In the backdrop of this large-scale circulation anomalies, higher than normal precipitation was received in most of the central and north Indian regions with thunderstorms/hailstorm events. The variations in the AO index (AOI) and EABH were found to be in concurrence with the precipitation anomalies over the Indian region. The detailed analysis of a selected thunderstorm/hailstorm case suggests that the lowering of the 0 °C isotherm due to the intrusion of mid-latitudinal westerlies and the development of atmospheric instability with moisture supply from the adjacent seas facilitated the occurrence of the thunderstorms/Hailstorm events during January 2020.

This study focuses on the importance of reliable surface wind forecasts for various sectors, particularly energy production. Traditional numerical weather prediction models are facing limitations and increasing complexity, leading to the development of machine learning models as alternatives or supplements. The research consists of two stages. In the first stage, the ERA5 database is used to evaluate the long-term performance of different combinations of features and two tree-based algorithms for predicting surface wind characteristics (speed and direction) in Cairo. The XGBoost algorithm slightly outperforms the Random Forest algorithm, especially when combined with appropriate feature selection. Even three years after the training period, the results remain very good, with an RMSE of 0.59 m/s, rRMSE of 17%, and R² of 0.84. The second stage assesses the multivariate approach's ability to forecast wind speed evolution at different time horizons (1–12 h) during a week characterized by significant wind dynamics. The forecasts demonstrate excellent agreement with observations at a 1-h time horizon, with an RMSE of 0.35 m/s, rRMSE of 7.6%, and R² of 0.98, surpassing or comparable to other literature results. However, as the time lag increases, the RMSE (0.86, 1.14, and 1.51 m/s for 3, 6, and 12 h, respectively) and rRMSE (18.7%, 24.8%, and 32.9% for 3, 6, and 12 h, respectively) also increase, while R² decreases (0.86, 0.79, and 0.60). Furthermore, the wind variations' amplitude is underestimated. To address this bias, a simple correction method is proposed.