Journal of Meteorological Research最新文献

Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) observations of brightness temperature (TB) are used to examine the temporal evolutions of convection-affected structures of Hurricane Irma (2017) during its rapid intensification (RI) period from 0600 to 1800 UTC 4 September 2017. The ABI observations reveal that both an elliptical eye and a spiral rainband that originated from Irma’s eyewall obviously exhibit wavenumber-2 TB asymmetries. The elliptical eye underwent a counterclockwise rotation at a mean speed of a wavenumber-2 vortex Rossby edge wave from 0815 to 1005 UTC 4 September. In the following about 2 hours (1025–1255 UTC 4 September), an inner spiral rainband originated from the eyewall and propagated at a phase speed that approximates the vortex Rossby wave (VRW) phase speed calculated from the aircraft reconnaissance data. During the RI period of Irma, ABI TB observations show an on–off occurrence of low TB intrusions into the eye, accompanying a phase lock of eyewall TB asymmetries of wavenumbers 1 and 2 and an outward propagation of VRW-like inner spiral rainbands from the eyewall. The phase lock leads to an energy growth of Irma’s eyewall asymmetries. Although the eye remained clear from 1415 to 1725 UTC 4 September, an inner spiral rainband that originated from a large convective area also had a VRW-like outward propagation, which is probably due to a vertical tilt of Irma. This study suggests a potential link between convection sensitive GOES imager observations and hurricane dynamics.

North China experienced devastating rainfall from 29 July to 1 August 2023, which caused substantial flooding and damage. This study analyzed observations from surface rain gauges and S-band dual-polarization radars to reveal the following unique features of the precipitation evolution from the plain to the mountains during this event. (1) The total rainfall was found concentrated along the Taihang Mountains at elevations generally > 200 m, and its spatiotemporal evolution was closely associated with northward-moving low-level jets. (2) Storms propagated northwestward with southeasterly steering winds, producing continuous rainfall along the eastern slopes of the Taihang Mountains owing to mountain blocking, which resulted in the formation of local centers of precipitation maxima. However, most rainfall episodes with an extreme hourly rainfall rate (HRR), corresponding to large horizontal wind shear at low levels, actively occurred in the plain area to the east of the Taihang Mountains. (3) The western portion of the extreme heavy rain belt in the north was mainly caused by long-lasting cumulus–stratus mixed precipitation with HRR < 20 mm h⁻¹; the eastern portion was dominated by short-duration convective precipitation with HRR > 20 mm h⁻¹. The contributions of convective precipitation and cumulus–stratus mixed precipitation to the total rainfall of the southern and middle rain belts were broadly equivalent. (4) The local HRR maxima located at the transition zone from the plain to the mountains were induced by moderate storm-scale convective cells with active warm-rain processes and large number of small-sized rain droplets. (5) During the devastating rainfall event, it was observed that the rainfall peaked at around 1800 local time (LT) every day over the upstream plain area (no diurnal cycle of rainfall was observed in relation to the accumulated rainfall centers over mountain areas). This was attributable to convective activities along the storm propagation path, which was a result of the more unstable stratification with a suitable steering mechanism that was related to afternoon solar heating and enhanced water vapor. The findings of this study improve our understanding and knowledge of the extreme precipitation that can develop from the plain to the mountains in North China.

Microwave radiometer (MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles. A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset. However, this is challenging due to limitations in the temporal and spatial resolution of available sounding data, which often results in a lack of coincident data with MWR deployment locations. Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting (WRF) model’s renowned simulation capabilities, which offer high temporal and spatial resolution. By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data, our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites, which enables reliable MWR retrieval in diverse geographical settings. Different machine learning (ML) algorithms including extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), extra trees (ET), and backpropagation neural network (BPNN) are tested by using WRF simulations, among which BPNN appears as the most superior, achieving an accuracy with a root-mean-square error (RMSE) of 2.05 K for temperature, 0.67 g m⁻³ for water vapor density (WVD), and 13.98% for relative humidity (RH). Comparisons of temperature, RH, and WVD retrievals between our algorithm and the sounding-trained (RAD) algorithm indicate that our algorithm remarkably outperforms the latter. This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms, thus opening up new possibilities for MWR deployment and airborne observations in global locations.

Convection-permitting modeling allows us to understand mechanisms that influence rainfall in specific regions. However, microphysics parameterization (MP) and planetary boundary layer (PBL) schemes remain an important source of uncertainty, affecting rainfall intensity, occurrence, duration, and propagation. Here, we study the sensitivity of rainfall to three MP [Weather Research and Forecasting (WRF) Single-Moment 6-class (WSM6), Thompson, and Morrison] and two PBL [the Yonsei University (YSU) and Mellor–Yamada Nakanishi Niino (MYNN)] schemes with a convection-permitting resolution (4 km) over northwestern South America (NWSA). Simulations were performed by using the WRF model and the results were evaluated against soundings, rain gauges, and satellite data, considering the spatio-temporal variability of rainfall over diverse regions prone to deep convection in NWSA. MP and PBL schemes largely influenced simulated rainfall, with better results for the less computationally expensive WSM6 MP and YSU PBL schemes. Regarding rain gauges and satellite estimates, simulations with Morrison MP overestimated rainfall, especially westward of the Andes, whereas the MYNN PBL underestimated precipitation in the Amazon–Savannas flatlands. We found that the uncertainty in the rainfall representation is highly dependent on the region, with a higher influence of MP in the Colombian Pacific and PBL in the Amazon–Savannas flatlands. When analyzing rainfall-related processes, the selection of both MP and PBL parameterizations exerted a large influence on the simulated lower tropospheric moisture flux and moisture convergence. PBL schemes significantly influenced the downward shortwave radiation, with MYNN simulating a greater amount of low clouds, which decreased the radiation income. Furthermore, latent heat fluxes were greater for YSU, favoring moist convection and rainfall. MP schemes had a marked impact on vertical velocity. Specifically, Morrison MP showed stronger convection and higher precipitation rates, which is associated with a greater latent heat release due to solid-phase hydrometeor formation. This study provides insights into assessing physical parameterizations in numerical models and suggests key processes for rainfall representation in NWSA.

The sparse and uneven placement of rain gauges across the Tibetan Plateau (TP) impedes the acquisition of precise, high-resolution precipitation measurements, thus challenging the reliability of forecast data. To address such a challenge, we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory (GAN-ConvLSTM) for Precipitation Nowcasting (MGCPN), which utilizes data products from the Integrated Multi-satellite Retrievals for global precipitation measurement (IMERG) data, offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 min. The results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast time. This issue is a common challenge in precipitation forecasting models. Furthermore, we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model accuracy. The model demonstrates its competence in generating precise precipitation nowcasting with IMERG data, offering valuable support for precipitation research and forecasting in the TP region. The metrics results obtained from our study further emphasize the notable advantages of the MGCPN model; it outperforms the other considered models in the probability of detection (POD), critical success index, Heidke Skill Score, and mean absolute error, especially showing improvements in POD by approximately 33%, 19%, and 8% compared to Convolutional Gated Recurrent Unit (ConvGRU), ConvLSTM, and small Attention-UNet (SmaAt-UNet) models.

Considerable spring precipitation occurs over South China (SC), a region that is adjacent to large-scale Asian topography and oceans. Its reasonable simulation is crucial for improving regional climate predictability. This study investigates spring precipitation biases over SC and their possible causes in atmosphere-only and coupled Flexible Global Ocean–Atmosphere–Land System finite-volume version 3 (FGOALS-f3) models with different horizontal resolutions. The performance of spring precipitation simulation over SC varies across different FGOALS-f3 model versions, with the best reproducibility in the high-resolution coupled model (25 km). In the low-resolution atmosphere-only model (100–125 km), the precipitation dry bias over SC is closely linked to overestimated surface sensible forcing over the eastern Tibetan Plateau (TP), which weakens the subtropical anticyclone over the western Pacific (SAWP) through regional circulation responses. By contrast, the high-resolution atmosphere-only model further amplifies surface thermal forcing in the Asian continents, causing intensified land-sea thermal contrast between the Southeast Asian continents and western Pacific, enhanced southerly winds and SAWP, and increased water vapor transport into SC. Meanwhile, the reduced middle-high level cold bias over 10°–30°N in the high-resolution atmosphere-only model intensifies the East Asian westerly jet and ascent over SC, leading to enhanced spring precipitation there. The high-resolution coupled model simulation not only reduces sea surface cold bias over the Bay of Bengal, thus intensifying the Indian-Burma trough and strengthening low-level water vapor transport into SC, but also enhances ascent over SC. As a result, the high-resolution coupled model better reproduces the magnitude and pattern of spring precipitation over SC than its atmosphere-only model. Compared with low-resolution models, the domain-mean spring precipitation dry bias decreases by 11.2% over SC in the high-resolution atmosphere-only model and by 35.9% in the coupled model. These results demonstrate that the high-resolution FGOALS-f3 models can improve simulations of the influencing atmospheric circulations and spring precipitation over SC.

The Asian monsoon (AM) region is a well-known region with prevailing stratosphere–troposphere exchange (STE). However, how the STE across this region changes with the weakening AM remains unclear. Here, we particularly diagnose the air mass transport between the planetary boundary layer (PBL) and the stratosphere over the AM region during 1992–2017 using the Lagrangian particle dispersion model FLEXPART based on the ERA-Interim reanalysis data. The results show that both the downward and upward deep STEs exhibit a detectable increasing trend, while the latter, namely, the deep troposphere-to-stratosphere transport (DTST), is relatively more significant. Further analysis reveals that the long-term trend of DTST over the AM region could be partly attributed to changes in the Pacific Walker circulation and the air temperature (especially at upper levels). Additionally, it is found that DTST increases markedly over the tropical oceanic regions, while the increasing DTST into the stratosphere can be attributed to the enhanced air masses originated from the PBL over the terrestrial regions, where large amounts of pollutant emissions occur. The results imply that the influence of the DTST on the chemical composition and the climate of the stratosphere over the AM region is expected to become increasingly important, and is thereby of relevance to climate projection in an evolving climate.

This study first utilizes four well-performing pre-trained convolutional neural networks (CNNs) to gauge the intensity of tropical cyclones (TCs) using geostationary satellite infrared (IR) imagery. The models are trained and tested on TC cases spanning from 2004 to 2022 over the western North Pacific Ocean. To enhance the models performance, various techniques are employed, including fine-tuning the original CNN models, introducing rotation augmentation to the initial dataset, temporal enhancement via sequential imagery, integrating auxiliary physical information, and adjusting hyperparameters. An optimized CNN model, i.e., visual geometry group network (VGGNet), for TC intensity estimation is ultimately obtained. When applied to the test data, the model achieves a relatively low mean absolute error (MAE) of 4.05 m s⁻¹. To improve the interpretability of the model, the SmoothGrad combined with the Integrated Gradients approach is employed. The analyses reveal that the VGGNet model places significant emphasis on the distinct inner core region of a TC when estimating its intensity. Additionally, it partly takes into account the configuration of cloud systems as input features for the model, aligning well with meteorological principles. The several improvements made to this model’s performance offer valuable insights for enhancing TC intensity forecasts through deep learning.

A comparative analysis of the spatiotemporal distribution characteristics of rainfall and lightning in coastal and inland areas of Guangdong Province of China during the pre-summer rainy season (PSRS) from 2008 to 2017 reveals distinct patterns. In the inland target region (ITR), rainfall is concentrated in the central and eastern mountainous areas. It exhibits a bimodal diurnal variation, with peaks in the afternoon and morning. The afternoon peak becomes more pronounced during the post-monsoon-onset period because of the increased rainfall frequency. Similarly, in the coastal target region (CTR), rainfall concentrates around mountainous peripheries. However, CTR’s rainfall is weaker than ITR’s during the pre-monsoon-onset period, primarily associated with the lower-level moisture outflow in CTR, but it strengthens significantly during the post-monsoon-onset period owing to enhanced moisture inflow. CTR’s diurnal rainfall variation transitions from bimodal to a single broad peak during the post-monsoon-onset period, influenced by changes in both rainfall frequency and intensity. In contrast to rainfall, the spatiotemporal distribution of lightning centers remains relatively stable during the PSRS. The strongest center is located over ITR’s plains west of the rainfall center, with a secondary center in the western plains of CTR. Lightning activity significantly increases during the post-monsoon-onset period, particularly in ITR, primarily because of the increased lightning hours. The diurnal lightning flash density and lightning hours show a single afternoon peak in the two target regions, and the timing of the peak in ITR is approximately two hours later than in CTR. Composite circulation analysis indicates that during early morning, the lower atmosphere is nearly neutral in stratification. The advected warm, moist, unstable airflow, combined with topography, favors convection initiation. In the afternoon, solar radiation increases thermal instability, further enhancing the convection frequency and intensity. Improved moisture and thermal conditions contribute to an increase in rainfall and lightning during the post-monsoon-onset period. Moreover, the occurrence of lightning is found to be closely linked to the most unstable convective available potential energy, low-level vertical wind shear, and updraft intensity.

Monitoring and predicting highly localized weather events over a very short-term period, typically ranging from minutes to a few hours, are very important for decision makers and public action. Nowcasting these events usually relies on radar observations through monitoring and extrapolation. With advanced high-resolution imaging and sounding observations from weather satellites, nowcasting can be enhanced by combining radar, satellite, and other data, while quantitative applications of those data for nowcasting are advanced through using machine learning techniques. Those applications include monitoring the location, impact area, intensity, water vapor, atmospheric instability, precipitation, physical properties, and optical properties of the severe storm at different stages (pre-convection, initiation, development, and decaying), identification of storm types (wind, snow, hail, etc.), and predicting the occurrence and evolution of the storm. Satellite observations can provide information on the environmental characteristics in the preconvection stage and are very useful for situational awareness and storm warning. This paper provides an overview of recent progress on quantitative applications of satellite data in nowcasting and its challenges, and future perspectives are also addressed and discussed.