Meteorological Applications最新文献

Jeddah, the second-largest city in the Kingdom of Saudi Arabia, experienced an unprecedented 220 mm of rainfall on November 24, 2022. This extreme rainfall, which was four times the climatological monthly mean rainfall for November, resulted in severe flooding and significant damage to infrastructure. This study investigates the underlying physical mechanisms contributing to this extreme event and its predictability using in situ and satellite observations and numerical modeling. Our analysis reveals the event initially developed as a frontal system over the northwest regions of the Red Sea through interactions between cold air from mid-latitudes and warm air from the southeast. It reached Jeddah at 0600 UTC, November 24, accompanied by strong surface convergence, which is typical of winter rainfall in Jeddah. The system was further fueled by persistent moisture intrusion from the Mediterranean and the southern Red Sea, driven by the southeast movement of the Arabian Anticyclone. We evaluated the predictive capability of the Weather Research and Forecasting (WRF) model to forecast this extreme event at different lead times, utilizing a cloud-resolving 1-km configuration. The WRF model, driven by the National Centers for Environmental Prediction operational Global Forecasts, successfully reproduced the extreme rainfall event up to 5 days in advance. Even at a 5-day lead time, the model captured the storm's movement from northwest to southeast and the qualitative spatial distribution of rainfall, consistent with satellite observations and radar reflectivity. Additionally, the predicted distribution of total precipitable water vapor aligned closely with Meteosat brightness temperatures. This demonstrates that the high predictive skill of the WRF model is due to its high-resolution configuration, careful selection of the domain, and physical parameterizations. By addressing both the physical mechanisms and the model's performance, this work provides valuable insights into extreme rainfall forecasting and highlights the potential for mitigating the impacts of such extreme events in the Jeddah region.

It is often argued that numerical weather prediction models remain deficient in forecasting specific weather features and that such deficiencies contribute significantly to overall forecast errors. To clarify these claims, we quantify how cyclones, fronts, upper tropospheric jets, moisture transport axes (MTAs), and cold-air outbreaks (CAOs) contribute to short-term (12-h) forecast errors and biases in the ERA5 reanalysis dataset from 1979 to 2022. Employing a feature-based attribution method, we evaluate errors globally, focusing particularly on temperature, moisture, and wind fields, and examine regional and seasonal variations during winter (DJF) and summer (JJA). The presence of weather features is generally associated with increased forecast errors (RMSEs) compared to feature-free conditions. RMSEs are especially pronounced for moisture fields in conjunction with fronts and MTAs, where errors in total column water vapor can be twice as large. Cyclone-related errors are more pronounced in the low-level wind field. During CAOs, on the other hand, errors are reduced. In terms of systematic biases, wind speeds and moisture are underestimated along western boundary currents, together with insufficient moisture transport along MTAs. Wintertime temperature biases over the Northern Hemisphere oceans have stronger associations with fronts and MTAs than those over the Southern Hemisphere oceans. A persistence analysis confirms that for some features and specific variables, forecasts yield less added value relative to non-feature conditions. Cyclones are the most notable example, where forecasts provide less added value in most cases. In contrast, jets and CAOs are features where forecasts consistently add more added value. The identified feature-based error diagnostics can aid targeted efforts to improve numerical weather prediction systems.

Power spectra are evaluated for a range of ensemble systems run at the European Centre for Medium-Range Weather Forecasts (ECMWF). These spectra allow us to chart and compare the spatial–temporal evolution of ensemble spread and error, and to evaluate the impact of model and observational changes. We investigate whether differences between spread and error indicate issues of reliability or other deficiencies. In agreement with previous studies, for ensembles made with the physics-based model, extratropical variances (of 250 hPa geopotential height) saturate quickly at small scales, while planetary scale errors are far from saturated at day 10. At intermediate lead-times, forecasts are over-dispersive at synoptic scales. Tropical errors (for 200 hPa velocity potential) grow most rapidly over the first day, but are not fully saturated even by day 40. Tropical differences between spread and error at scales below 500 km are thought to reflect a need for more observations of tropical (divergent) winds, rather than a lack of reliability. Forecast variances in a “near perfect twin” ensemble suggest there is the potential to improve predictive skill by 5 days. Error variances highlight the substantial observational and modeling developments required to ensure that such forecasts are reliable. The impact of a recent system upgrade (which includes a change to the formulation of model uncertainty) and results from an experiment where additional radio occultation observations are assimilated, demonstrate that progress can be made when developments are focused on synoptic scale uncertainty and error-growth. Power spectra for two prototype data-driven ensembles show similar spatial–temporal evolution at large scales to that of the physics-based model; one has better overall reliability, and the other has reduced error. At smaller scales, the prototypes display a tendency for small-scale forecast variance and error to increase with lead-time beyond their theoretical limits. With the speed and breadth of ensemble development, these results illustrate the potential utility of power spectra diagnostics for comparing and developing ensemble systems.

As meteorological organisations transition to high-resolution ensemble-based forecasting, they risk leaving behind downstream users who rely on deterministic data: a need that may arise from the inability to process large volumes of data or difficulty integrating probabilistic information into decision-making processes. Proposed solutions for such users typically involve providing the control (unperturbed) member of the ensemble or deriving a forecast through the independent treatment of variables (such as the median). However, relying solely on the control member undermines the benefits of ensemble forecasting, while univariate approaches can result in forecasts that lack physical consistency across variables. To address this, we propose a novel method to select ‘most-likely’ ensemble realisations, combining techniques from pre-existing ensemble post-processing methods. For a given location, we construct a timeseries of ‘most-likely values’ for variables of interest by extracting the mode from multivariate probability density distributions created at each timestep. We then select the ensemble member most similar to this timeseries using clustering techniques. Since the chosen realisation is a complete forecast from an individual model run, this allows us to deliver a spot forecast for that location that maintains physical consistency across all variables, including those not directly analysed. As a demonstration, we apply this method to output from the Met Office convective-scale ensemble MOGREPS-UK at 240 locations across the Met Office synoptic observation network, focusing on near-surface air temperature and windspeed. We find that the chosen member performs comparably to the control member at short lead times, but is able to outperform the control member at longer lead times. This is an important finding as it demonstrates an alternative to the control member for users who require physically consistent spot forecasts, utilising the additional information available in the ensemble. In addition to improving forecast accuracy, this method also offers the ability to tailor solutions for individual users.

Radar echo maps are essential for precipitation forecasting, providing visual representations of rainfall patterns, including spatial distribution and intensity. To enhance radar echo prediction, this study introduces the MSIM–MIM model, which integrates the MFEF and SIM modules within the MIM framework. The MFEF module utilizes dilated convolutions to capture multi-scale features while maintaining spatial details, improving contextual understanding, and boosting prediction accuracy, all without increasing computational cost. The SIM module employs a gating mechanism to selectively extract and process spatiotemporal context, thereby enhancing the model's ability to represent these patterns. This results in more refined state representations, allowing the MSIM–MIM model to retain and leverage context more effectively, thus reducing prediction errors. Experimental results demonstrate that MSIM–MIM outperforms other spatiotemporal models, achieving lower MSE and MAE in radar echo predictions across multiple datasets.

This study analyzed the circulation patterns and micro-physical features of mountain fog in Southern Fujian using fog droplet spectrum data from meteorological stations, sounding data, and ERA5 reanalysis. Results suggested that both the convergence of cold and warm air in spring and the presence of southwestern warm moist airflow can lead to the formation of mountain fog in Southern Fujian. The former featured lower temperatures and denser isotherms in low levels compared to the latter. This resulted in an increase of supersaturation in the coastal atmosphere, thereby accelerating particle nucleation and condensation growth, forming larger droplets or even precipitation particles. Mountain fog in Southern Fujian has an average total particle number concentration of 314 cm⁻³ and an average total liquid water content of 0.1721 g·m⁻³. Average fog droplet spectrum features an unimodal distribution, with a peak at 5–6 μm. However, the average liquid water content spectrum showed a bimodal distribution, with the main peak at 8–9 μm interval and a secondary peak at 22–24 μm, indicating that total particle number concentration in fog was mainly controlled by small particles, but particles smaller than 10 μm and those in the 20–30 μm intervals both contributed significantly to the total liquid water content. Four parameterization schemes were used to fit visibility. Results showed that fitted coefficients differ significantly from those in other regions; hence, establishing local parameterization schemes for visibility was very important. In the evaluation results, fitting using the total particle number concentration as a factor showed the best performance, with a determination coefficient of up to 0.7. Mean absolute errors were significantly higher between 200 and 1000 m, especially in the 200–500 m interval. This was attributed to the larger ratio of standard deviation to the average value of particle concentration and liquid water content in this interval, indicating more uneven distributions of micro-physical parameters.

Orellana-Samaniego, M. L., R. Célleri, J. Bendix, N. Turini, and D. Ballari. 2025. “Can Global Products Capture Precipitation Variability in the Galápagos Islands? An Assessment Based on Climatic Time-Series Components.” Meteorological Applications 32, no. 4: e70085. https://doi.org/10.1002/met.70085.

In the published article, the below Acknowledgements section is missing.

During July 19–21, 2021, Henan Province in China experienced a historically rare heavy rainfall event, with the maximum hourly rainfall amount appearing in Zhengzhou City, the capital of Henan Province, on 20 July (hereafter “7.20” HRE). In this study, the “7.20” HRE is analyzed based on the observations of 215 ground-based GNSS stations and 118 national meteorological stations in Henan Province, and ERA5 reanalysis data. By comparing the surface precipitation intensity, water vapor, and atmospheric energy conditions across temporal and spatial scales, it is shown that the area with heavy rainfall near Zhengzhou did not exhibit extreme atmospheric energy values or vertical environmental instability. The environmental conditions in the southeast of Zhengzhou were more conducive to the occurrence and development of precipitation, but there was no obvious precipitation on the ground. The analysis of water vapor consumption rate (V_c) and precipitation flux (F) reveals that a large amount of water vapor was consumed in the southeast of Zhengzhou, resulting in the formation of substantial precipitation above the 600 hPa level. The precipitation was carried to Zhengzhou by the southeast wind, leading to the precipitation content over Zhengzhou and its nearby areas increasing rapidly as altitude decreased from 600 hPa to 1000 hPa. The overlay of precipitation provided by both dynamic transport from the southeast and cloud microphysical production over Zhengzhou was the main cause of the “7.20” HRE under the background of an atypical weak environmental field.

Cloud-to-ground (CG) lightning occurs frequently in Jiangsu Province, China. High-speed rail (HSR) spans across the province, covering a large geographic area. Studying the occurrence of lightning and the characteristics of strikes in HSR corridors is of great significance for the lightning protection and safe operation of the HSR. Based on the terrain, lightning detection data, and catenary engineering parameters along 12 HSR corridors in Jiangsu Province, this study provides detailed analyses of the lightning characteristics, the cumulative probability distribution (CPD) of lightning current amplitudes, and the lightning strike characteristics on the catenary in these areas. The results show that CG lightning mainly occurs from 05:00 a.m. to 10:00 a.m. and mostly happens in the summer season. The CG lightning density along the HSR corridors in southern Jiangsu Province is relatively high. According to the CPD of lightning current amplitudes and the fitting curves obtained by the Levenberg–Marquardt method, the “a” values are relatively larger for the Nanjing–Anqing, Nanjing–Hangzhou, and Shanghai–Chengdu HSR lines, which indicates that the lightning current amplitudes along these three HSR corridors are larger than those along other lines. In terms of the CG lightning intensity index, which is the combination of lightning current intensity and CG lightning frequency, its values are relatively larger in the Zhenjiang section of the Shanghai–Nanjing riverside line, the Wuxi section of the Shanghai–Nanjing intercity line, and the Yangzhou section of the Lianyungang–Zhenjiang line. The large-value areas of the tripping rate of the catenary caused by direct lightning strikes are relatively consistent with those of CG lightning density. The direct-lightning-strike tripping rate along the feeder F wire is considerably larger than that along the trolley wire T. In the absence of overhead lightning shield wires along HSR lines, the maximum tripping rate of wire F caused by direct lightning strikes is 24.3 times (100 km)⁻¹ a⁻¹, the minimum tripping rate is 2.6 times (100 km)⁻¹ a⁻¹, and the average tripping rate is 10.6 times (100 km)⁻¹ a⁻¹. In contrast, along wire T, the maximum tripping rate caused by direct lightning strikes is 7.6 times (100 km)⁻¹ a⁻¹, the minimum value is 0.8 times (100 km)⁻¹ a⁻¹, and the average value is 3.3 times (100 km)⁻¹ a⁻¹. When considering overhead lightning shield wires, the probability of direct lightning strikes on wire F drops to 0.12 times (100 km)⁻¹ a⁻¹, and that on wire T is negligible. Accordingly, the average tripping rate caused by backflashovers is 3.5 times (100 km)⁻¹ a⁻¹.