Meteorological Applications最新文献

It is vital to analyze extreme wind speed in marine engineering designs. However, due to the lack of observational data, it is impossible to establish the measured long-term wind speed series. This study simulates the annual hourly wind field of every tropical cyclone (TC) with a resolution of 5 km in the Northwest Pacific (NWP) from 1981 to 2020. On this basis, combined with the sea surface wind speed data observed by the satellites and the ships, the 40-year annual maximum wind speed series of NWP are established. The Gumbel, three-parameter Weibull (Weibull-3par), two-parameter Weibull (Weibull-2par), generalized extreme-value (GEV) distribution, and the two parameter estimation methods are used to estimate the extreme wind speeds with different return periods (RPs) at four typical locations in the NWP. Meanwhile, the effects of different extreme-value distributions and different parameter estimation methods on the estimation results are discussed. Subsequently, the best distribution and parameter estimation method for each grid in the NWP are determined by the goodness-of-fit test, and then the spatial distributions of extreme wind speeds with different RPs along with uncertainty estimates in the entire NWP are obtained. The results show that extreme wind speeds with RPs of 5, 25, 50, and 100 years in the east of Taiwan and Philippines can reach a maximum of 43.8, 60.8, 70.4, and 81.4 m s⁻¹, respectively.

The land surface temperature (LT) is a crucial variable that governs the energy and radiation budget of the earth's atmosphere and influences land-atmosphere interactions. The LT plays a crucial role mainly in the short-range forecast of a numerical weather prediction (NWP) model. The primary research goal in this research work undertaken is to assess the impact of assimilation of LT data from the Indian satellite (INSAT-3D) into the NCMRWF global NWP model (NCUM) through a simplified Extended Kalman Filter (sEKF) land data assimilation system (LDAS), particularly important as there are few screen-level observations over the region. A dedicated stand-alone pre-processing system has been designed to prepare LT observations in a compatible format for the land surface assimilation system. The approach for LT data assimilation from the INSAT-3D satellite reduces the uncertainty associated with the initial state of LT analysis while simultaneously improving the accuracy of forecasts of near surface atmospheric variables. An observing system experiment (OSE) was carried out during both the summer (May) and winter (February) months by assimilating the INSAT-3D LT data in a coupled land-atmosphere analysis-forecast system. The results obtained from the OSE demonstrate that the use of INSAT-3D LT data improves the forecast skill of both maximum and minimum temperature over India, particularly in areas characterized by higher LT variability. The improvement is pronounced in forecasts of maximum (minimum) temperature during “Boreal” summer (“Boreal” winter) season. The verification scores also indicate that the incorporation of INSAT LT data substantially improves the NCUM model's forecast performance. By assimilating LT, the mean error of maximum and minimum temperature forecasts in India was decreased, accompanied by enhanced forecast accuracy within a time frame of approximately 24 h. The scores for the verification measures, specifically the Probability of Detection (POD), demonstrate a ~15% improvement in both the forecasts for maximum and minimum temperatures. This improves the temperature prediction as well as the ability to forecast intense weather episodes like cold spells and heat waves.

Operational forecasting systems often combine calibrated probabilistic outputs from several numerical weather prediction (NWP) models. A common approach is to use a weighted blend, with the more accurate models having higher weights. We show that this approach is not ideal and that using a simple neural network to combine forecasts yields better results. The sharpness of the forecast is increased, so that extreme events are more likely to be predicted. Improvements are also observed in accuracy as measured by the continuous rank probability score (CRPS) and reliability. The proposed neural network model has a simple architecture with few parameters, and training and inference can easily be done using a central processing unit. This makes it a practical option for improving the accuracy of blended operational forecasts.

Main, L., Sparrow, S., Weisheimer, A., & Wright, M. (2024) Skilful probabilistic medium-range precipitation and temperature forecasts over Vietnam for the development of a future dengue early warning system. Meteorological Applications, 31(4), e2222. Available from: https://doi.org/10.1002/met.2222

We apologize for this error.

Spring drought forecasting is essential in South Korea for managing water resources reliably and cultivating agricultural products efficiently, as seasonal rainfall difference often drives water shortage during spring. In the current study, a novel scheme for spring drought forecasting was suggested by extensively searching appropriate predictors from the global climate variable: here mean sea level pressure (MSLP) of the winter season due to its time lag for forecasting. The target series was estimated with the median of the spring precipitation series of the weather stations over South Korea, called the accumulated spring precipitation (ASP). A number of points of the MSLP data were detected as significant cross-correlation with the ASP and also the points were regionally grouped. Therefore, the regionalization for the high correlation points was performed, resulting in three regions, such as Arctic Ocean (R1), South Pacific (R2), and South Africa (R3). The R1 and R2 regions are located at the places where climate indices have been developed such as Arctic Oscillation and North Atlantic Oscillation for R1 and the indicator of El-Nino and Southern Oscillation for R2. The generalized linear model (GLM) was adopted in ASP drought forecasting with the driven three regionalized indices as the predictors of the ASP. The result indicates that the regionalized indices can produce a good performance in forecasting the ASP. The forecasting result can be employed as a good tool for managing water resources and planning better cultivation in agriculture industries.

Persistent cold air pools (CAPs) trap pollutants in valleys for extended periods, leading to reduced visibility and increased air pollution within these valleys. The structure of the persistent cold air pool that occurred in the Lanzhou Valley in December 2016 was simulated using different Planetary Boundary Layer (PBL) scenarios of the Weather Research and Forecasting (WRF) model, and the simulation of the persistent cold air pool was further optimized in these PBL scenarios. The simulation results indicated that weather-scale dry subsidence and nighttime ground radiation cooling were significant factors contributing to the accumulation of persistent CAPs and pollutants in the Lanzhou Valley. In contrast, convective lifting from the ground led to the dissipation of persistent CAPs and a reduction in pollution within the valley. During persistent CAPs, the PM_2.5 concentration and valley heat deficit (Q) were 66.7% and 62% higher, respectively, than during non-CAP. In the original MYNN scheme, the average PBL height, double turbulent kinetic energy (QKE), and turbulence length scale during persistent CAPs decreased by 30.79%, 50.5%, and 34.4%, respectively, compared to non-CAP. Compared with the original MYNN scheme, the optimized MYNN scheme shows a significant improvement in the turbulence simulation during the sustained CAPs, resulting in a more stable atmosphere. The PBL height during the sustained CAPs is reduced by 28 m, the diurnal turbulence length scale is reduced by 31.62%, the stability parameter is reduced by 39%, the diurnal mean QKE is reduced by 27.45%, and the QKE impact height is reduced by 100–400 m.

Summer 2023 saw record high temperatures across several European countries with these heat events being unofficially dubbed Cerberus and Charon. This has led to discussion about whether naming severe heat events is an effective way to convey the risks posed. In online experiments with regionally representative sample of members of the public in England and Italy, we assessed the effect of giving a heat event a mythological, non-mythological or no name on anticipated severity, concern, trust/confidence and behavioural intention. We find that while naming alone does not have a strong effect on anticipated response to severe heat events in either country, going against the established trend of using mythological names in Italy could diminish concern.

In our contemporary era, meteorological weather forecasts increasingly incorporate ensemble predictions of visibility—a parameter of great importance in aviation, maritime navigation, and air quality assessment, with direct implications for public health. However, this weather variable falls short of the predictive accuracy achieved for other quantities issued by meteorological centers. Therefore, statistical post-processing is recommended to enhance the reliability and accuracy of predictions. By estimating the predictive distributions of the variables with the aid of historical observations and forecasts, one can achieve statistical consistency between true observations and ensemble predictions. Visibility observations, following the recommendation of the World Meteorological Organization, are typically reported in discrete values; hence, the predictive distribution of the weather quantity takes the form of a discrete parametric law. Recent studies demonstrated that the application of classification algorithms can successfully improve the skill of such discrete forecasts; however, a frequently emerging issue is that certain spatial and/or temporal dependencies could be lost between marginals. Based on visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts for 30 locations in Central Europe, we investigate whether the inclusion of Copernicus Atmosphere Monitoring Service (CAMS) predictions of the same weather quantity as an additional covariate could enhance the skill of the post-processing methods and whether it contributes to the successful integration of spatial dependence between marginals. Our study confirms that post-processed forecasts are substantially superior to raw and climatological predictions, and the utilization of CAMS forecasts provides a further significant enhancement both in the univariate and multivariate setup. We also demonstrate that post-processing significantly improves the predictions of low visibility events, which opens the door for aeronautical applications.

Atmospheric icing on ground structures is a concern from design, operation, and safety perspectives. Supercooled water droplets size and liquid water content (LWC) are important weather parameters to better understand the ice accretion physics on ground structures. Most existing studies are based on measurements at high altitude. The study is based on the field results of a specific event (from 9:30 to 22:27 h on October 29, 2022) in Arctic region of northern Norway. The data from this event are presented and used for analytical validation and simulation. Field measurements of different meteorological weather parameters including the droplet size and LWC are carried out leading to recording of resultant atmospheric ice load and intensity. A comprehensive study is also carried out to validate droplet collision efficiency and ice load using the existing analytical model ISO-12494 and computational fluid dynamics (CFD)–based numerical simulations. Furthermore, the differences in icing simulation using parameters such as median volume diameter (MVD), Langmuir B –J as alternatives to the actual droplet size distribution (DSD) spectrum are also analyzed. The results show that under natural meteorological conditions, the characteristics of water DSD change in real time. Using MVD alone to calculate the water droplet collision efficiency on circular cylinders can lead to significant errors. Accurately selecting the Langmuir distribution as a substitute for the actual DSD can reduce simulation errors to within 5%. Compared to the analytical model, the numerical simulations result better reflects the collision characteristics of water droplets of different sizes on the cylindrical object.

Present-day air quality is known through dense monitoring and extensive pollution control mechanisms. In contrast, knowledge of historical pollution, particularly before the industrial revolution, is accessible only through occasional reports of singular local events and through natural archives such as ice or sediment cores that record global-scale pollution. However, the regular local to regional pollution that most affects human life is hardly known. Historical sciences have argued both for and against significant air pollution in and around historic cities and manufacturing sites. For the Roman era, it has been hypothesized that air quality played a role in several patterns of action of the period. However, to the author's knowledge, there are no quantitative studies of Roman emissions. Using the results of modern experimental archaeology, this study attempts to quantify the emissions from Roman pottery kilns and their impact on surrounding human settlements. It is shown that although the pollution did not reach today's limits, it must have approached levels known to cause adverse health effects. A series of additional test simulations have been conducted to determine how these first results might be improved in the future.