Meteorological Applications最新文献

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.

Nadimpalli, R., Y. S. Nekkali, K. K. Osuri, M. Mohapatra, D. Niyogi. 2025. “Understanding the Role of Antecedent Land Conditions on Rapid Intensity Changes in Landfalling Tropical Cyclones Over the Bay of Bengal.” Meteorological Applications 32, no. 6: e70134. https://doi.org/10.1002/met.70134.

In the published article, the funding details were missing. The following funding information should be included:

Funding: This work benefited in part from Monsoon Mission–III (IITM/MM-III/2023/IND-2/Sanction Order), NASA (80NSSC21K1008), NSF 2502272 and 241387, the UNESCO Chair, Farish Endownment and Oliver Fellowship at Jackson School of Geosciences, and the UT–UNESCO India International Initiative (U2I2 S. Kumar and R. Bashyam Gift).

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Soil moisture is classified as an essential climate variable (ECV) and is relevant to understanding hydrological, agricultural and ecological processes. Yet, in spite of its importance, direct observations of soil moisture remain limited globally—those that exist are typically limited in duration and spatial extent. Consequently, alternative approaches for estimating soil moisture have been developed, including water balance (‘bucket’) models, the use of remotely sensed information and the application of land surface modelling techniques. Spaceborne and land surface modelling based methods offer significant potential for monitoring and modelling soil moisture at a variety of spatial scales; however, their resolution remains relatively coarse for global and continental scale applications. At country scale, land surface models have demonstrated their potential but they require access to computational resources to deliver high resolution products. With the advent of machine- and deep- learning and data fusion techniques, high resolution global and regional soil moisture datasets are increasingly becoming available. Here, we evaluated a statistical machine learning approach to downscale the European Space Agency's (ESA) Climate Change Initiative (CCI) combined passive and active soil moisture product for Ireland using covariates that included both static (e.g., topography) and dynamic (e.g., gridded rainfall and temperature) variables. The model was developed using in situ cosmic ray neutron sensor (CRNS) measurements obtained from a network of sites in the United Kingdom, justified on the basis that the United Kingdom is geographically similar to Ireland in terms of its climate, soil types and land cover management practices. The model was found to perform reasonably well when validated against limited in situ data obtained from available time domain reflectometry (TDR) measurements available from Ireland. The developed model was subsequently used to derive spatial estimates of soil moisture on a 1 km grid across the Republic of Ireland.

Arid and Semi-Arid Lands have witnessed a surge in extreme climatic events with devastating environmental and livelihood effects. Understanding the dynamics of these extreme events, including drought, is essential for anticipatory action among resource-dependent communities. This study utilised Earth Observatory Systems and Google Earth Engine to analyse 24 years of Normalised Difference Drought Index trends in the Narok West landscape of Kenya across six timeframes (2000, 2005, 2010, 2015, 2020, and 2024). It revealed that the Normalised Difference Drought Index ranged from −0.489 (April 2000) to 0.469 (August 2005). Additionally, it established that during June–July–August dry seasons, there was an increase in the proportionate area under severe drought from 11% in 2000 to 24% in 2024 (average 19.17%, SD: 8.43%); and a decrease in the proportionate area under non-drought (good conditions) from 57.5% in 2000 to 40.5% in 2024 (average 40.5%, SD: 7.43%) respectively. Temporal increase in drought events was observed to be increasing from 2015, with extremes witnessed in 2020. Moreover, we established that season dry season rainfall averages 147.2 mm (95% CI: 100.7–193.8) and is decreasing at a rate of 1.25 mm annually. It is anticipated that the frequency and severity of drought across the landscape might increase due to weather variability, predominantly attributed to climate change. The increase could have a detrimental effect on water quality and quantity, public and ecosystem health, mental health and wellness, peace and protection, and rangeland ecology. Our study contributes to the body of research on future drought scenarios, which could assist with methodological and empirical studies and corrective actions. To adapt to and manage the effects of changing climate, these scenarios necessitate interdisciplinary community and landscape strategies, including the need for communities to develop a comprehensive understanding of the impacts of climate change and plan for the sustainable management of water resources.

Climate change is one of the biggest challenges of the 21st century. It severely affects many developing countries whose economy depends on climate-sensitive sectors with low adaptive capacity. Studies in northeastern Ethiopia have not addressed the future climate conditions well, using the recently released CMIP6 global climate models. This study focused on projections of precipitation and temperature changes and trends using CMIP6 GCMs in the eastern Amhara, Northeastern, Ethiopia. The gridded temperature and precipitation data were extracted from the Climatic Research Unit (CRU TS4.07) and Global Precipitation Climatology Centre (GPCCv2020) for 1984–2014, respectively. The historical and projected data were retrieved from the Earth Systems Grid Federation (ESGF). The projections were computed under SSP2-4.5 and SSP5-8.5 scenarios for two future periods: 2040s (2030–2060) and 2080s (2070–2100). The modified Mann–Kendall's test and Sen's slope were used to detect precipitation and temperature trends. The annual and seasonal projected precipitation and temperature results showed significant increasing trends at a 5% probability level. The annual precipitation will increase by 7.77% and 13.74% under the SSP2-4.5 scenario and by 14.02% and 28.48% under the SSP5-8.5 scenario for the 2040s and 2080s, respectively. The annual maximum temperature will increase by 0.92°C and 1.86°C under SSP2-4.5 and by 1.25°C and 3.39°C under the SSP5-8.5 scenario. Likewise, the annual minimum temperature will increase by 1.62°C and 1.97°C in the 2040s and by 2.56°C and 4.48°C in the 2080s under SSP2-4.5 and SSP5-8.5 scenarios, respectively. Regarding spatial distribution, the most significant precipitation and temperature changes are projected in the west and central parts of the study area. Increasing precipitation trends and temperature changes are projected under both scenarios and periods. Thus, an analysis of the impacts of climate change and the design of solutions would be very relevant.