Meteorological Applications最新文献

Current research on gust (GF) and peak (g) factors has mainly focused on landfalling typhoons, whereas studies related to offshore typhoons are relatively rare. They are critical in island architectural design. However, the underlying surfaces of islands have distinct characteristics compared to inland areas. Therefore, conclusions drawn from previous studies on coastal areas are less applicable to islands in open seas. This study explored the characteristics of GF and g in an island region based on tower observation data from an island in the South China Sea. The results indicate that: (1) Island topography can have a substantial influence on low-level winds during typhoons. The probability density function of ocean-side GF was concentrated around the low-value region. GF decreased with increasing height and conformed to generalized extreme value (GEV) distribution; (2) At wind speeds of about 12 m/s, GF was negatively correlated with mean wind speed (U), and the correlation was not significant when wind speeds were too fast or too slow. Under the influence of topography, the GF fluctuations of the wind field increased at each level; (3) When the ocean served as the underlying surface, the classic power-law model better described GF distribution changes with height. The exponent n of the power function model fitted to island-side wind was positive, whereas that fitted to ocean-side wind was negative, with higher wind speeds leading to larger absolute values of the exponent n; (4) The effect of island topography increased the slope k of the linear relationships for GF-$��I_u�$ and g-$��I_u�$.

Hail represents a major atmospheric peril in terms of monetary damages in many European countries. This highlights the potential benefit of hail-damage predictions, so-called impact-based hail forecasts, that can trigger preventive actions. The construction of impact-based forecasts is often complicated by a lack of data, especially with respect to impacts or damages. In this work, we implement and evaluate two types of impact-based hail forecasts and impact-oriented hail warnings that are tailored for specific user groups, using a uniquely comprehensive dataset comprising operational weather forecasts from the Swiss meteorological office and hail damage records provided by cantonal building insurance agencies in Switzerland. We first analyze impact-oriented hail warnings that are directed toward the general population. Although these warnings are based on meteorological thresholds (hail size), they are termed impact-oriented because the thresholds are chosen to represent the vulnerability of exposed assets. Second, we implement and evaluate impact-based forecasts that incorporate a complete impact model—including meteorological hazard, the (spatial) distribution of exposed assets, and their vulnerability—that can be of value to larger institutions and companies. Our study highlights how impact-based forecasts can be tailored to specific user groups, and how the availability of high-resolution data enables adjusting impact forecasts for optimal use. Given the limited predictability of hail as a thunderstorm-related phenomenon, the skill of impact forecasts remains below commonly accepted benchmarks for operational use warning products issued by authoritative warning agencies. Nonetheless, we argue that, in certain contexts, these products can still offer value despite their limited skill.

Dengue fever outbreaks impose a severe healthcare burden in Vietnam; therefore, the development of a Dengue early warning system is key to improve public health planning and mitigate this burden. This study assesses the ECMWF medium-range (up to 10 days) forecast skill for relative humidity in Vietnam—a key factor for vector-borne disease transmission—in re-forecasts between 2001 and 2020. Analysis focused on the rainy season (May–October) with ERA5 reanalysis as a reference dataset. Re-forecast data were pre-processed using a lead-time dependent quantile mapping technique to reduce the bias between forecasted and observational data, and skill was assessed using climatology and persistence as a reference. Rank histograms showed that the humidity forecast is reliable up to 10 days, and continuous ranked probability skill score (CRPSS) values show that the forecast is more skilful than the climatology up to 10 days. Nonetheless, when using persistence as a reference, CRPSS values are lower in South Vietnam, which was associated with the inaccurate representation of 2 m dew point temperature in the tropical regions, and the fact that persistence is a hard reference to beat in the tropics, hindering model forecast skill. Results from this study demonstrate that ECMWF ensemble forecasts of relative humidity are suitable to use as inputs for a Dengue early warning system up to 10 days in advance.

Whirlwinds were photographically captured in Stok, Choglamsar, and Nubra valleys in Ladakh, India, in June 2018. It is estimated that the spatial extent of these whirlwinds was ~50 m², vertical extent ~0.5–1 km, and lasted for ~15 min. To assess the meteorological setup that could have contributed to the occurrence of the whirlwinds, Advanced Research Weather Research and Forecasting (ARW) model (v4.3) was run in a three nested domain setup with 3 km, 1 km, and 333 m resolution. The model could simulate the whirlwinds at finer grid spacing (~333 m). The whirlwinds are formed in a strongly sheared environment of ~22 m s⁻¹, and the storm-relative shear direction is ~80°. These events appear to be initiated as feedback of localized heterogeneity in a convective setting with increased winds and directional change with height. The surface wind convergence due to the temperature gradient at the surface also contributes to whirlwind initiation. The temperature gradient aligns with recently developed landscape heterogeneity and could be due to increasing urbanization. This study reports on the first evidence of whirlwinds in the Himalayan region and demonstrates the ability of the ARW model in representing/simulating whirlwinds in the complex orography of the Himalayan region.

Over the last decade, tropical cyclone (TC) track and intensity predictions have improved by nearly 50% in the Atlantic and Northern Indian Ocean, driven by advancements in ocean-coupled numerical models, data assimilation techniques, and an expanding network of observations. However, the prediction of severe weather events driven by convection, particularly those associated with heavy precipitation over land, has not kept pace with these improvements in TC forecasting. While 1–2 km horizontal resolutions are crucial for capturing convection over land and ocean, seamless prediction across scales demands an accurate representation of the coupled evolution of ocean, land, and atmospheric states. To address the complex problem of severe weather across a spectrum of atmospheric motions—including TCs over the ocean and severe convective systems over coastal and inland regions—we have developed the Indian Ocean–Land–Atmosphere (IOLA) Coupled Mesoscale Prediction Framework. This Framework integrates the well-tested nonhydrostatic model (NMM) dynamical core with advanced nesting techniques from the hurricane weather research and forecast (HWRF) system. It further incorporates ocean coupling from HWRF and physics packages adopted from the WRF community model. This represents the first-ever coupled modeling system explicitly designed to tackle extreme weather events across multiple domains and scales. Extensive testing of this novel modeling framework demonstrates that a high-resolution (1–2 km) “all-purpose” severe weather prediction system can effectively address the challenges of forecasting extreme weather over the Indian region. One of the key focuses of this work is the application of 1-km horizontal resolution moving nests over the monsoon region, where synoptic-scale interactions play a critical role in modulating severe weather and heavy precipitation events. With this configuration, the model provides a high equitable threat score (ETS) > 0.18 for heavy to extreme rainfall events for 48 h and above lead times. This framework enables a unified approach to simulating severe weather phenomena accurately and flexibly. Also, it sets a new benchmark for seamless prediction of extreme weather, paving the way for improved resilience against coastal hazards and inland severe weather events.

Reference evapotranspiration (ET_o) is a critical parameter for assessing crop water requirement and formulating irrigation scheduling and water management practices under climate change conditions and water shortage. Classical approaches e.g., the FAO-Penman-Monteith (FPM-56) equation generally require several meteorological data inputs, which are often unavailable or limited. In the present study, CNN-RNN and GPU-accelerated CNN (CNN-GPU) models were utilized for temperature-dependent ET_o estimating. ‘SHapley Additive exPlanations’ (SHAP) analysis revealed that solar radiation and wind speed exerted high degrees of influence, even after their exclusion from the input matrix, which clarified these implicit nonlinear relationships captured by the model. CNN-GPU model outperformed CNN-RNN in both accuracy (RMSE = 0.23 mm/day, NS = 0.98) and computational efficiency with a faster training time by 20.4%. Despite training with limited input variables (temperature records), the proposed DL-based models successfully captured complex temporal and spatial meteorological patterns in the study region.

Accurate short-term prediction of typhoon 10-m wind fields is crucial for early warning and risk reduction. We propose a lightweight spatiotemporal deep-learning model that couples a convolutional neural network (CNN) for spatial features with a long short-term memory (LSTM) network for temporal dynamics, augmented by squeeze-and-excitation (SE) channel attention and a multi-branch feature fusion network (MBFN). Using ERA5 winds and China Meteorological Administration best-track records over East Asia (2020–2023), the model ingests four hourly frames to predict the 10-m wind field 1-h ahead. Across root mean square error (RMSE), mean absolute error (MAE), and average wind speed error (AWSE), the approach consistently outperforms U-Net, ConvLSTM, and Transformer baselines and better reconstructs high-wind structures near typhoon cores; relative to a plain CNN–LSTM baseline, average RMSE and MAE decrease by 0.90% and 0.68% over 2020–2023. Ablation studies isolate the effects of SE and MBFN, evidencing robust generalization and computational efficiency suitable for near-real-time operations. A supplementary 6-h experiment shows only modest, consistent increases across years—RMSE by 0.54% on average, MAE by 0.50%, and AWSE by 0.41%—indicating robustness at longer lead times.

There are 22 tropical cyclones (TCs) affecting Shanghai from 2012 to 2024, which are categorized into four groups in terms of track, that is, landing in Shanghai (LD), moving northward across the sea east of Shanghai (NAE), moving northward (NAW) and westward (WAW) across the land west of Shanghai. What's more, Shanghai is spatially divided into 10 districts, urban areas (UB), Pudong, Baoshan, Minhang, Fengxian, Qingpu, Jinshan, Songjiang, Jiading, and Chongming. The district-scale characteristics of the observed total accumulative precipitation (P_total), maximum hourly accumulative precipitation (P_1h-max), and extreme wind (WS_3s-max) are analyzed. Results show that the underlying surface in Shanghai significantly decreases the mean WS_3s-max, resulting in the lowest mean WS_3s-max of 9.2 m·s⁻¹ in UB. Regarding the spatial distribution of mean P_total, both the underlying surface and TC structure exerted a significant influence, resulting in the mean P_total exceeding 110 mm in both UB and four suburban districts. TC track can also influence the spatial pattern of the mean P_total, P_1h-max, and WS_3s-max. The key TC tracks for mean P_total and mean P_1h-max are NAW TCs. The coastal districts always have higher mean WS_3s-max regardless of TC track. The spatial distribution of maximum P_total, P_1h-max, and WS_3s-max may be partly affected by the underlying surface in Shanghai and more by the TC structure. Overall, the impact TC of P_total and P_1h-max is not exactly one-to-one, that is, the TCs that cause the maximum P_total do not necessarily produce the maximum P_1h-max, and most of the time the maximum precipitation and wind do not occur in the same TC case.

In Zimbabwe, the production, dissemination and use of seasonal weather forecasts in maize production is a system that involves the flow of information from a production point to a final point for farmers, through dissemination channels such as agricultural extension officers and more experienced farmers and elders, in the case of indigenous seasonal weather forecasts. This paper examines the perspectives of maize farmers (the general public or the masses) alongside the views of agricultural extension officers, policy shapers and influencers (key informants or elites) regarding seasonal weather forecasts and their role in improving farmers' access to this information. The findings reveal a broad consensus that indigenous seasonal weather forecasts can complement modern forecasts, aiding farmers' adaptation to climate change mainly through selecting suitable crop varieties, scheduling planting dates and planning other agricultural activities. Both farmers and key informants agreed on the need to downscale and disseminate locality-specific seasonal weather forecasts and co-production involving the integration of indigenous seasonal forecasts with modern seasonal weather forecasts. However, many farmers feel marginalised, with limited access to localised and customised forecasts. Elites often underestimate this marginalisation, creating asymmetric information gaps. This asymmetry in information between farmers and elites highlights the need for more frequent interaction between the two groups, especially through co-production processes, to enhance access to seasonal weather forecasts and strengthen climate adaptation.