Meteorological Applications最新文献

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.

Weather regimes and weather patterns, here jointly referred to as circulation types, are used to generate forecasts for a variety of applications, such as energy demand and flood risk. However, there are usually many different choices available for precisely which circulation types to use. Ideally, one would like to use circulation types that are both highly informative for the application and also highly predictable, but in practice, there is often a tradeoff between informativity and predictability. We present a simple, general framework for how to construct a circulation type forecast that optimally balances these factors by segueing between different choices of circulation types at different lead times based on information-theoretic considerations. As an example, we apply this framework to the case of forecasting energy demand in Great British winters. We compare a set of 30 weather patterns produced by the UK Met Office with the much simpler two-state framework consisting of a positive and negative North Atlantic Oscillation (NAO) regime and show how to optimally combine the two across a winter season.

Understanding human thermal comfort is essential for assessing environmental conditions and their implications for well-being, particularly in the context of global climate change. This study examines the influence of 30 climatic and ecological factors, including temperature, humidity, atmospheric pressure, solar radiation, wind dynamics, and topographical characteristics, on human thermal comfort across Iran. A multidisciplinary approach was employed, integrating principal component analysis (PCA) for feature selection, multivariate regression (MR) for impact quantification, cluster analysis (CA) for climate classification, and spatial modeling (SMA) to assess regional disparities. Furthermore, machine learning models (MLMs) and artificial neural networks (ANNs) were utilized to capture complex, nonlinear relationships in climate–comfort interactions. Based on a comprehensive data set spanning 38 years (1984–2022), the findings reveal significant spatial variations in climate sensitivity. Weighted indices such as predicted mean vote (PMV), physiologically equivalent temperature (PET), and thermal discomfort index (TDI) enhance the precision of comfort assessments. The results indicate that northern Iran, particularly the western coastal region of the Caspian Sea, exhibits the most favorable climatic conditions, whereas arid and semi-arid areas experience heightened thermal stress. These insights advance biometeorological research by linking climate variability to human physiological responses and provide practical implications for urban planning, public health policies, and climate adaptation strategies. By integrating high-dimensional climate data with advanced computational techniques, this study highlights the necessity of adaptive measures to mitigate the impacts of climate change on human thermal comfort.

An optimal transport (OT) problem seeks to find the cheapest mapping between two distributions with equal total density, given the cost of transporting density from one place to another. Unbalanced OT allows for different total density in each distribution. This is the typical setting for precipitation forecast and observation data, when considering the densities as accumulated rainfall, or intensity. True OT problems are computationally expensive, however through entropic regularisation it is possible to obtain an approximation maintaining many of the underlying attributes of the true problem. In this work, entropic unbalanced OT and its associated Sinkhorn divergence are examined as a spatial forecast verification method for precipitation data. The latter being a novel introduction to the forecast verification literature. It offers many attractive features, such as morphing one field into another, defining a distance between fields and providing feature based optimal assignment. This method joins the growing research by the Spatial Forecast Verification Methods Inter-Comparison Project (ICP) which aims to unite spatial verification approaches. After testing this methodology's behaviour on numerous ICP test sets, it is found that the Sinkhorn divergence is robust against the common double penalty problem (a form of phase error), on average aligns with expert assessments of model performance, and allows for a variety of novel pictorial illustrations of error. It provides informative summary scores, and has few limitations to its application. Combined, these findings place unbalanced entropy regularised optimal transport and the Sinkhorn divergence as an informative method which follows geometric intuition.

Statistical methods can be used to create bias correction models that learn from past forecast errors and reduce systematic errors in real-time forecasts. This study presents a machine learning (ML) approach using extreme gradient-boosted (XGBoost) trees to address biases in a numerical weather prediction (NWP) nowcast model for key meteorological parameters: 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind gust. These ML models have been integrated into the Finnish Meteorological Institute's (FMI) operational nowcasting framework, Smartmet nowcast. Results show that, even with a relatively modest set of meteorological predictors, the ML bias correction method significantly improves forecast accuracy, reducing the root mean square error (RMSE) by 24%–29% compared to the direct NWP model output. The implementation of this new bias correction method not only improves the quality of FMI's short-range forecasts, but also extends the availability of bias-corrected data for longer forecast lead times, offering substantial improvements over the previously implemented bias correction method. The codebase for this machine learning bias correction is available at (https://github.com/fmidev/snwc_bc).