Journal of Meteorological Research最新文献

Under Arctic warming, near-surface energy transfers have significantly changed, but few studies have focused on energy exchange over Arctic glacier due to limitations in available observations. In this study, the atmospheric energy exchange processes over the Arctic glacier surface were analyzed by using observational data obtained in summer 2019 in comparison with those over the Arctic tundra surface. The energy budget over the glacier greatly differed from that over the tundra, characterized by less net shortwave radiation and downward sensible heat flux, due to the high albedo and icy surface. Most of the incoming solar radiation was injected into the glacier in summer, leading to snow ice melting. During the observation period, strong daily variations in near-surface heat transfer occurred over the Arctic glacier, with the maximum downward and upward heat fluxes occurring on 2 and 6 July 2019, respectively. Further analyses suggested that the maximum downward heat flux is mainly caused by the strong local thermal contrast above the glacier surface, while the maximum upward heat transfer cannot be explained by the classical turbulent heat transfer theory, possibly caused by countergradient heat transfer. Our results indicated that the near-surface energy exchange processes over Arctic glacier may be strongly related to local forcings, but a more in-depth investigation will be needed in the future when more observational data become available.

In this study, driven by ERA5 reanalysis data, the Weather Research and Forecasting (WRF) version 4.0 was used to investigate the eastward propagation of the Madden–Julian oscillation (MJO) in the tropical atmosphere during December–February (DJF) of 2007/2008. The experiment with 11 cumulus parameterization schemes respectively shows that the Grell 3D scheme is one of several worse ones in describing MJO activities. In addition, still by use of the Grell 3D scheme, four nudging assimilation experiments for water vapor in all model vertical layers (Ndg_all), lower layers (Ndg_low), middle layers (Ndg_mid), and upper layers (Ndg_upp) were conducted. It is found that when the water vapor in the model approaches to the observed value, the model performance for MJO activities is improved greatly. Among the four nudging simulations, Ndg_all certainly performs best. Although Ndg_mid is important for the MJO-filtered profiles related to moisture, Ndg_low and Ndg_upp exhibit superiority to Ndg_mid in simulating MJO eastward propagation. Ndg_low has advantages when MJO features are represented by zonal wind at 850 hPa and precipitation because the lower-level MJO-filtered moisture is conducive to the existence of lower-level condensational heating to the east of the MJO convective center. Ndg_upp performs better when describing the MJO eastward propagation features by outgoing longwave radiation (OLR) since it can capture the moisture and cloud top temperature of deep convection associated with MJO, as well as front Walker cell. These results suggest that the lower-level moisture is more important in regulating the MJO eastward propagation, and the observed maximum MJO-filtered moisture in the middle troposphere might be a phenomenon accompanying the MJO deep convection, but not a factor controlling its eastward propagation.

Temperature inversions are frequently observed in the boundary layer and lower troposphere of polar regions. Future variations of the low-level temperature inversions in these regions, especially the Antarctic, are still poorly understood. Due to the scarcity of observations in the Antarctic, reanalysis data and numerical simulations are often used in the study of Antarctic climate change. Based on ERA-Interim, ERA5, JRA-55, and NCEP–NCAR reanalysis products, this study examines temporal and spatial variations of Antarctic inversion depth in austral autumn and winter during 1979–2020. Deeper inversions are found to occur over the high plateau areas of the Antarctic continent. Based on the Mann–Kendall test, ERA-Interim and ERA5 data reveal that the Antarctic inversion depth in austral autumn and winter increased during 1992–2007, roughly maintained afterwards, and then significantly decreased since around 2016. The decrease trend is more obvious in the last two months of winter. Overall, JRA-55 better represents the spatial distribution of inversion depth, and ERA-Interim has better interannual variability. The Community Earth System Model Large Ensemble (CESM-LE) 30-member simulations in 1979–2005 were first verified against JRA-55, showing reasonable consistency, and were then used to project the future changes of Antarctic low-level inversion depth over 2031–2050 under RCP8.5. The CESM-LE projection results reveal that the temperature inversion will shallow in the Antarctic at the end of the 21st century, and the decrease in depth in autumn will be more pronounced than that in winter. In particular, the temperature inversion will weaken over the ice-free ocean, while it will remain stable over the ice sheet, showing certain spatial heterogeneity and seasonal dependence on the underlying cryospheric surface conditions. In addition, the decrease of inversion depth is found closely linked with the reduction in sea ice, suggesting the strong effect of global warming on the thermal structure change of the Antarctic.

In this study, a regional Parsivel OTT disdrometer network covering urban Zhengzhou and adjacent areas is employed to investigate the temporal–spatial variability of raindrop size distributions (DSDs) in the Zhengzhou extreme rainfall event on 20 July 2021. The rain rates observed by disdrometers and rain gauges from six operational sites are in good agreement, despite significant site-to-site variations of 24-h accumulated rainfall ranging from 198.3 to 624.1 mm. The Parsivel OTT observations show prominent temporal–spatial variations of DSDs, and the most drastic change was registered at Zhengzhou Station where the record-breaking hourly rainfall of 201.9 mm over 1500–1600 LST (local standard time) was reported. This hourly rainfall is characterized by fairly high concentrations of large raindrops, and the mass-weighted raindrop diameter generally increases with the rain rate before reaching the equilibrium state of DSDs with the rain rate of about 50 mm h⁻¹. Besides, polarimetric radar observations show the highest differential phase shift (K_dp) and differential reflectivity (Z_dr) near surface over Zhengzhou Station from 1500 to 1600 LST. In light of the remarkable temporal–spatial variability of DSDs, a reflectivity-grouped fitting approach is proposed to optimize the reflectivity–rain rate (Z–R) parameterization for radar quantitative precipitation estimation (QPE), and the rain gauge measurements are used for validation. The results show an increase of mean bias ratio from 0.57 to 0.79 and a decrease of root-mean-square error from 23.69 to 18.36 for the rainfall intensity above 20.0 mm h⁻¹, as compared with the fixed Z–R parameterization. This study reveals the drastic temporal–spatial variations of rain microphysics during the Zhengzhou extreme rainfall event and warrants the promise of using reflectivity-grouped fitting Z–R relationships for radar QPE of such events.

Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo (Time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data from multiple stations in the complex terrain of an international airport, TELMo incorporates the horizontal u (east–west) and v (north–south) wind components to significantly reduce forecasting errors. On a day with high wind direction variability, TELMo achieved mean absolute error values of 5.66 for 2-min, 10.59 for 10-min, and 14.79 for 20-min forecasts, processed within a swift 9-ms/step timeframe. Standard degree-based analysis, in comparison, yielded lower performance, emphasizing the effectiveness of the u and v components. In contrast, a Vanilla neural network, representing a shallow-learning approach, underperformed in all analyses, highlighting the superiority of deep learning methodologies in wind direction nowcasting. TELMo is an efficient model, capable of accurately forecasting wind direction for air traffic operations, with an error less than 20° in 97.49% of the predictions, aligning with recommended international thresholds. This model design enables its applicability across various geographical locations, making it a versatile tool in global aviation meteorology.

Accurate forecasting of heavy precipitation in central China is still a challenge, within which a key issue is our still incomplete understanding of the convective systems (CSs) responsible for such events. In this study, through use of an iterative rain-cell tracking algorithm, the macroscale characteristics (scale, intensity, duration, etc.) of the CSs that produced 595 short-term heavy precipitation events in Hunan Province, central China, are quantitatively analyzed, based on radar reflectivity, echo top, and rainfall observations at 1-km and 6-min intervals in April–September of 2016–2018. The results show that CSs present significant seasonal and diurnal features. Spring CSs usually cover a larger echo area with stronger convective cores and thus generate more precipitation than summer CSs, though summer CSs develop more vigorously and frequently. CSs initiated at 1400–1600 local time are characterized by the strongest convection and a smaller spatiotemporal scale, causing violent and transient showers with typical areal precipitation of 0.5–1 mm km⁻², but less total precipitation. Further analyses of the relationships among the scale, intensity, duration, and total precipitation of CSs reveal that the convective intensity is linearly correlated to the spatiotemporal scale of CSs, with the duration increasing on average by 0.0372 h dBZ⁻¹; the echo area is significantly correlated to the total precipitation, and the duration and rainfall amount are connected with the area expansion rate (AER) of CSs: when the AER exceeds 50%, CSs expand rapidly with increasing total precipitation, but the duration is shorter. These findings provide a helpful reference for the forecasting of short-term heavy precipitation induced by CSs in central China.

Cloud microphysics plays an important role in determining the intensity and precipitation of tropical cyclones (TCs). In this study, a high-resolution numerical simulation by WRF (version 4.2) of Typhoon Meranti (2016) during its rapid intensification (RI) period was conducted and validated by multi-source observations including Cloud-Sat and Global Precipitation Mission satellite data. The snow and ice particles content were found to increase most rapidly compared with other hydrometeors during the RI process. Not all hydrometeors continued to increase. The graupel content only increased in the initial RI stage, and then decreased afterwards due to precipitation during the RI process. In addition, sea surface temperature (SST) sensitivity experiments showed that, although the intensity of the TC increased with a higher SST, not all hydrometeors increased. The graupel content continued to increase with the increase in SST, mainly due to the accumulation of more lower-temperature supercooled water vapor at the corresponding height. The content of snow decreased with the increase in SST because stronger vertical motion at the corresponding height affected the aggregation of ice crystals.

Changing meteorological conditions during autumn and winter have considerable impact on air quality in the Yangtze River Delta (YRD) region. External climatic factors, such as sea surface temperature and sea ice, together with the atmospheric circulation, directly affect meteorological conditions in the YRD region, thereby modulating the variation in atmospheric PM_2.5 concentration. This study used the evolutionary modeling machine learning technique to investigate the lag relationship between 144 climate system monitoring indices and autumn/winter PM_2.5 concentration over 0–12 months in the YRD region. After calculating the contribution ratios and lagged correlation coefficients of all indices over the previous 12 months, the top 36 indices were selected for model training. Then, the nine indices that contributed most to the PM_2.5 concentration in the YRD region, including the decadal oscillation index of the Atlantic Ocean and the consistent warm ocean temperature index of the entire tropical Indian Ocean, were selected for physical mechanism analysis. An evolutionary model was developed to forecast the average PM_2.5 concentration in major cities of the YRD in autumn and winter, with a correlation coefficient of 0.91. In model testing, the correlation coefficient between the predicted and observed PM_2.5 concentrations was in the range of 0.73–0.83 and the root-mean-square error was in the range of 9.5–11.6 µg m⁻³, indicating high predictive accuracy. The model performed exceptionally well in capturing abnormal changes in PM_2.5 concentration in the YRD region up to 50 days in advance.

The original vector discrete ordinate radiative transfer (VDISORT) model takes into account Stokes radiance vector but derives its solution assuming azimuthal symmetric surface reflective matrix and atmospheric scattering phase matrix, such as the phase matrix derived from spherical particles or randomly oriented non-spherical particles. In this study, a new VDISORT is developed for general atmospheric scattering and boundary conditions. Stokes vector is decomposed into both sinusoidal and cosinusoidal harmonic modes, and the radiance at arbitrary viewing geometry is solved directly by adding two zero-weighted points in the Gaussian quadrature scheme. The complex eigenvalues in homogeneous solutions are also taken into full consideration. The accuracy of VDISORT model is comprehensively validated by four cases: Rayleigh scattering case, the spherical particle scattering case with the Legendre expansion coefficients of 0th–13th orders of the phase matrix (hereinafter L13), L13 with a polarized source, and the random-oriented oblate particle scattering case with the Legendre expansion coefficients of 0th–11th orders of the phase matrix (hereinafter L11). In all cases, the simulated radiances agree well with the benchmarks, with absolute biases less than 0.0065, 0.0006, and 0.0008 for Rayleigh, unpolarized L13, and L11, respectively. Since a polarized bidirectional reflection distribution function (pBRDF) matrix is used as the lower boundary condition, VDISORT is now able to handle fully coupled atmospheric and surface polarimetric radiative transfer processes.

The Bay of Bengal (BoB) tropical cyclones (TCs) and the Tibetan Plateau vortices (TPVs) are two crucial weather systems influencing the Tibetan Plateau (TP). Their synergistic effects can lead to widespread heavy precipitation events on the TP. In this study, we employ the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to track the trajectory of water vapor transport during three large-scale precipitation events on the TP under the combined influence of BoB TCs and TPVs. The results indicate that low-level water vapor from the BoB under the influence of BoB TCs was cyclonically entangled into the cyclonic circulation, lifted and transported northward by southwesterly flow to the southeastern part of the TP, which contributes to the moistening of the entire troposphere there. Additionally, convergence of the cyclonic circulation of the TPVs on the northern TP further transports water vapor collected in the southeastern TP northward, conducive to the maintenance and development of precipitation systems, thus inducing widespread heavy precipitation events over the TP.