Meteorological Applications最新文献

Land use and land cover (LULC) changes significantly influence the dynamics of weather systems, particularly in regions prone to rapid land cover changes and extreme weather events like monsoon depression (MD). This study employs the Weather Research and Forecasting (WRF) model with a high-resolution (2 km) configuration to investigate the impact of updated LULC data on the predictability of MDs over India. Two experiments were conducted with LULC data from different sources: (i) the United States Geological Survey (USGS) with 1 km resolution and (ii) the National Remote Sensing Centre (NRSC) with 56 m resolution. The simulations are validated against observational and reanalysis datasets, including Automated Weather Stations (AWS), ERA5 reanalysis, IMD best track data, and GPM IMERG rainfall estimates. The results indicate that the NRSC dataset, which reflects current updated land cover conditions, provides a more accurate representation of land surface-atmosphere interactions, leading to improved simulations of MDs' track, intensity, and associated rainfall. Key meteorological parameters such as wind profiles, potential vorticity, moisture transport, and diabatic heating exhibit better agreement with observed/reanalysis data in the NRSC experiment than in the USGS. The NRSC experiment consistently shows lower mean Direct Position Errors (DPEs) in MD tracks throughout the forecast period, with an average improvement of 45–60 km over USGS. Additionally, RMSE in surface variables like temperature, humidity, and wind is reduced by 5%–10%. This study highlights the critical role of accurate and up-to-date LULC data in numerical models for enhancing their forecast capability of MDs, particularly in regions undergoing rapid LULC changes.

Unmanned aerial vehicles (UAVs) play a significant role in the aviation industry nowadays. Their portability and lower cost compared to traditional meteorological towers mean that their use is gaining momentum in many meteorological applications. In particular, UAV-based wind measurements are exploited in atmospheric energy balance research, precision agriculture, climate change studies, among others. This work aims to review the state-of-the-art of UAV-based wind measurement techniques by comparing the different working principles and highlighting their main challenges. The analyzed methodologies are divided into two categories: direct wind measurements (using anemometers mounted on UAVs) and indirect wind measurements (using velocity and force balances). Key aspects, such as the use of computational fluid dynamics (CFD) simulations, the most common sensor onboarding strategies, and the set-up of experimental tests in wind tunnels or in the field to validate the wind measurement accuracy, are addressed. Furthermore, novel developments based on machine learning and data filtration techniques for data quality enhancement are detailed. Based on a quantitative analysis of the recent relevant literature on this topic, we can conclude that multirotor UAVs are preferred to fixed-wing UAVs for scientific purposes, with the main challenge being the effect of propeller perturbation in the case of direct method wind measurements. Finally, it is shown that in most of the studies analyzed, sonic anemometers are chosen among all other types of sensors. Alternatively, the simplest version of the indirect method, namely the tilt model, is a common choice.

Tropical cyclones (TCs) pose significant threats to life and property across global ocean basins, and forecasting their structural evolution, track, and intensity remains a major scientific challenge. This review synthesizes the current understanding of TCs across major basins, that is, the Pacific, Atlantic, and North Indian Oceans, with a focus on the key environmental factors influencing TC behavior, such as sea surface temperature (SST), vertical wind shear (VWS), mid-tropospheric moisture, and land surface conditions. A special emphasis is further placed on the comparative skill of operational numerical weather prediction (NWP) models employed globally for TC forecasting. The review also discusses TC tracking algorithms, structural diagnostics, and the evolution of forecasting frameworks, along with emerging research trends revealed through scientometric mapping. The 51 peer-reviewed studies were selected and analyzed, and scientometric analysis was conducted on these 51 studies. Out of these selected studies, 37.25% focused on the Pacific, 23.52% on the Atlantic, and 17.64% on the North Indian Ocean (NIO, that is, the Bay of Bengal (BoB) and Arabian Sea). Out of these 51 studies, it has been found that while most studies utilized satellite-based methods, data assimilation (DA) techniques were emerging during 2006–2013, gaining momentum with machine learning (ML) applications post-2019. Notably, research since 2019 highlights a shift toward machine-based algorithms aimed at improving intensity predictions. While these AI/ML-based TC prediction models show promise, challenges remain in scalability, interpretability, and integration into forecasting workflows. The review emphasizes the need for assimilating next-generation satellite datasets (e.g., CYGNSS, TROPICS, rapid-scan AMVs, LIDAR), improved storm surge modeling, and real-time ensemble forecasting with high spatiotemporal resolution. Ultimately, advancing TC forecasting requires a collaborative, interdisciplinary approach involving model developers, operational centers, and observational programs. Bridging short-term forecasting with climate-informed strategies will be pivotal in enhancing global resilience to cyclonic hazards in a warming world.

This paper provides a physically based approach to assess Land Surface Temperature (LST) in an urban context, to analyze the Surface Urban Heat Island Intensity (SUHII). We developed and tested a joint hydrological–energy balance model, Poli-HE, to compute surface energy and mass fluxes between soil surfaces and shallow atmospheric layers in the city of Milano, Italy. Land Surface Temperature (LST) was calculated under given climate conditions and land cover, and spatially distributed with a resolution of 500 m. For mixed paved/green pixels, Vegetation Fraction (VF) was applied. Energy and water balances were integrated, linking water content and latent heat flux to LST. Data from 9 meteorological stations in Milano provided inputs of radiation, air temperature, precipitation, wind speed, and relative humidity during 2010–2022. LST estimated by MODIS satellite were used for model tuning, where the Poli-HE model effectively replicated the spatial distribution of urban LST. During summer, when LST in Milano reaches +35°C, paved and green surfaces differ by about + 3.7°C, reaching up to + 4.5°C at times. The Poli-HE outcomes indicate that the presence of green areas can provide a cooling effect and reduce LST, as also shown by satellite observations. Particularly, we showed that an increase of green share, ΔVF = + 10%, may correspond to a decrease of ΔLST = −0.26°C. Our quantitative approach may support urban authorities and professionals, providing a practical tool for current and future planning and projects for adaptation and mitigation under the framework of national and international efforts.

The East Asian monsoon rainfall is crucial and serves as a sufficient water resource for reservoir watersheds in East Asia. Among them, the Feitsui Reservoir (FR) is the primary source of water supply in northern Taiwan, especially for Taipei City. As a result of global warming, Taiwan has experienced major droughts, resulting in insufficient water being stored in reservoirs, except in the FR. This study discovered that precipitation from tropical cyclones (TCs; 29.97% of annual rainfall) was not the dominant source of water in the Feitsui Reservoir Watershed (FRW); instead, the results indicated that the water resources of FRW were contributed by non-TC rainfall (~70%), where the northeasterly monsoon resulted in heavy rainfall in autumn–spring (48.24%) and the mei-yu and summer monsoon contributed to the others (21.79%). Due to the interaction between the monsoon and topography, asymmetric rainfall patterns were observed in this study. Specifically, rainfall from autumn to spring was concentrated in the eastern part of the FRW and northern Taiwan, while it was distributed on the opposite side during the mei-yu and summer seasons. Under global warming conditions, spring rainfall in the FRW has shown a decreasing trend from 1990 to 2020, whereas a significant increase was observed in mei-yu rainfall. Our findings explain the seasonal rainfall characteristics and regional climate variability in the FRW and northern Taiwan. This study can be used as a reference for evaluating strategies for adjusting water resources to achieve the ultimate goal of a stable water supply in Taiwan.

Soil moisture (SM) is one of the crucial variables of the earth system that needs to be accurately initialized in a numerical weather model for accurate weather predictions. As the availability of in situ SM observations is sparse in space and time, satellite-derived SM estimates are widely used to create model surface boundary conditions through assimilation techniques. SM retrievals from the soil moisture active passive (SMAP) satellite have been assimilated into the high-resolution NCUM-R operational forecasting system over the Indian region for the first time in this study. The simplified extended Kalman filter (sEKF) based Land Data Assimilation System (LDAS) creates land surface SM initial conditions for NCUM-R by assimilating SMAP-derived SM and screen-level observations. Two numerical experiments, namely CTL (incorporating only screen level observations in LDAS) and SMP (assimilating both SMAP SM and screen level observations in LDAS), are carried out to assess the model's forecast skill improvement by assimilating SM. The validation analysis with the SM in situ observations network indicates skill improvement of 0.013 and 0.002 for anomaly correlation and unbiased RMSE in the accuracy of SM estimates with assimilation. The skill improvement is found to be higher in the wetter SM regions. Furthermore, the positive impact of SM assimilation on the forecast of surface air temperature is also noted. Finally, we demonstrated that the SMAP assimilation has led to a more realistic representation of SM than in the control simulation for various precipitation events, suggesting its usage for drought/flood monitoring in the long term.

This study analyzes projected summer climate changes (June to September) for 2100 in the Côte d'Azur (CAZ) based on atmospheric variables linked to high temperatures recorded by a network of Météo-France (MF) stations. These variables were projected using different Shared Socioeconomic Pathways (SSPs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Recognized as a region particularly vulnerable to climate change due to its dense and aging population, the CAZ faces increasing risks of heat-related stress. This research aims to better understand the future relationships between general atmospheric circulation and local climate changes in the study area. In the context of climate change marked by shifts in global atmospheric circulations, this study uses a multivariable classification based on four key parameters: geopotential height at 500 hPa, specific humidity at 850 hPa, the meridional wind component (V) at 850 hPa, and temperature at 850 hPa. These parameters help identify synoptic dynamics influencing local temperatures. Using historical data and SSP projections, the study evaluates the frequency and structure of atmospheric circulations projected for 2100. The results reveal that, for all time horizons and scenarios tested, the study area experiences a significant increase in the frequency of conditions favorable to very hot days, accompanied by a strengthening of anticyclonic conditions, rising temperatures, and drying of air masses at altitude. Meanwhile, very cool days become increasingly rare. These climatic changes exacerbate health risks, particularly for vulnerable populations, increasing the likelihood of heatstroke, dehydration, and premature mortality.

Severe hail storms are a leading cause of building damages in Switzerland, yet accurately observing hail using weather radar remains challenging. Opportunely, Switzerland benefits from a uniquely dense network of crowdsourced hail reports, providing an additional data source. Since 2021, over 50,000 reports were submitted each hail season through the national weather service's mobile application, including some false reports. In this study, we apply a rigorous filtering approach to these reports, including the implementation of a 4D-DBSCAN clustering algorithm, to develop a gridded hail size product. Using 65,000 hail damage claims from August 2020 to September 2023, an impact function is calibrated and used to model hail damage to buildings. The new crowdsource-based hail size product improves hail damage estimates in comparison to the radar-based data, largely due to an improved distinction of severe and sub-severe hail within a storm. The model can approximate the number and cost of hail damages to any user-provided building portfolio in real time, facilitating the management of the aftermath of a hail storm.

A forecast is reliable if it is statistically indistinguishable from the observation in a distributional sense. In probabilistic forecasting, reliability is a necessary (but not sufficient) condition for optimal decision-making. In ensemble forecasting, reliability is the sign of a well-designed system. Tools for assessing reliability in the univariate case exist and have proved to be popular. One well-known example of a tool for ensemble forecasts is the rank histogram. Although univariate probabilistic forecasts are historically the most commonly used, multivariate forecasting is fundamental when multiple variables that influence each other play a role in a decision-making process. The simplest of the multivariate cases is the bivariate one, where only two interdependent variables are forecast. Here, we discuss how assessing the reliability of bivariate ensemble forecasts can be performed using generalisations of univariate diagnostic tools. We introduce the 2-D rank histogram, a simple and non-restrictive generalisation of the univariate rank histogram. A summary statistic of the ensemble reliability in the bivariate space is also suggested together with a strategy to disentangle marginal and dependency contributions. The interpretation of 2-D rank histograms is illustrated with synthetic data and ECMWF ensemble forecasts. Toy-model experiments are used to help associate histogram patterns with typical reliability misspecifications in a fully controlled environment, while an application to the ECMWF ensemble shows how reliability issues can be diagnosed with this versatile tool.

Thunderstorms can produce hazards to society such as tornadoes and flash floods, occasionally at the same time. These storms can be categorized by their convective mode, largely through their appearance on radar. Convective mode is an important factor in how forecasters analyze these threats and warn the public when necessary. This study uses a random forest classification technique to categorize two sets of storms in the Southeastern United States: one comprised of storms warned for tornadoes and flash floods at the same time, and the other warned for tornadoes without necessarily having a concurrent flash flood warning. The goal of these classifications was to use information about each storm's meteorological environment to identify (1) its mode and (2) whether the hazard warning(s) issued by the National Weather Service verified, or whether the warning was a “false alarm.” The models predicting mode generally exhibited more skill and identified differences between discrete modes and linear modes, particularly in upper-level humidity, lapse rates, and low-level wind speeds. The models predicting whether the warnings verified exhibited less skill, but indicated that environments favorable for tornadoes were characterized by stronger wind speeds, lower upper-level moisture, and higher supercell composite parameter, while environments favorable for flash floods were characterized by greater moisture, lower wind speeds, and slower storm motion. These results are of note to researchers and forecasters seeking to better anticipate hazards, identify hazards, increase warning accuracy, and minimize false alarms as the implementation of artificial intelligence into the forecasting process continues.