Meteorological Applications最新文献

We employ a high-resolution Köppen climate classification dataset to examine shifts in Tundra zones within the Alps and Asia. Our analysis shows substantial reductions in Tundra areas by the mid-21st century under different Shared. Socioeconomic pathways (SSP1-2.6, SSP3-7.0, SSP5-8.5). Tundra zones in the Alps and the Tibetan Plateau are crucial for their unique climates and role as water reservoirs. Characterized by short, mild summers and long, severe winters, these zones are vital for the glaciers and perennial snow. The projected climate instability may significantly reduce alpine snow cover by mid-century with irreversible consequences. A 2°C temperature increase from the 1981–2010 baseline could eliminate the Tundra climate in the Alps and reduce it by over 70% in Asia. This is particularly concerning given that rivers from the Tibetan Plateau sustain nearly 40% of the global population.

Regional visibility forecasting encounters challenges due to data imbalance, temporal non-linearity and the consideration of multi-scale spatial factors. To tackle these challenges, this study introduces a novel approach for collaborative multi-site visibility forecasting based on spatiotemporal convolutional networks. Firstly, we preprocess the ERA5 reanalysis dataset and ground observation dataset, standardizing the spatiotemporal dimensions. We employ correlation coefficient analysis to select relevant meteorological factors. Subsequently, we create a spatiotemporal convolutional network model (TCN_GCN), which combines the power of temporal convolutional network (TCN) and graph convolutional network (GCN). Additionally, a weighted loss function is incorporated, accounting for the distribution of visibility values. The model is trained with multi-site data, enabling it to learn spatiotemporal visibility patterns across various sites. This empowers the model to generate multi-site visibility forecasts, thereby significantly improving regional visibility forecasting accuracy. Using 50 meteorological stations in Fujian Province, China, as a case study, we assess the model's predictions using key metrics such as mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R²). The experimental results demonstrate that the inclusion of both temporal and spatial features leads to a substantial enhancement in model prediction performance. The TCN_GCN model outperforms other deep learning methods in multi-site visibility forecasting, highlighting its effectiveness and superiority in improving regional visibility forecasting accuracy.

Albedo is one of the key parameters in climatic studies. Investigating its temporal and spatial behavior can be a tool for understanding environmental changes. The MODIS sensor continuously produces the land surface albedo on a global scale and with the appropriate spatial resolution and makes it available to researchers. In this study, to analyze Iran's surface albedo trend, first, the daily albedo data of the MODIS on Iran in the period from January 1, 2001 to December 30, 2021 with a spatial resolution of 500 m were prepared from the NASA website. After the necessary pre-processing, the long-term seasonal and annual trend of Iran's albedo was calculated at the 90% confidence level using the non-parametric Mann–Kendall test. The findings showed that the albedo trend is positive in the lowland interior areas of Iran and negative in the highland areas. Since the decreasing trend of albedo in highland areas indicates the reduction of snow cover in these areas, this issue can challenge the life and water resources of these areas that rely on the accumulation of snow.

Accurate predictions of streamflow and flood events are contingent upon the availability of reliable hydrometeorological data. In regions characterized by scarcity of ground-based hydrometeorological observations, satellite and reanalysis data assume prominence as alternative predictors. Floods and droughts have emerged as a significant concern in Northern Ghana, yet the scarcity of ground-based hydrometeorological data impedes effective prediction of these hydrological events. Consequently, the identification of suitable surrogate hydrometeorological data holds paramount importance in addressing these challenges. This study, therefore, assessed the accuracy of satellite and reanalysis data against ground-based data in Northern Ghana. Rainfall and mean temperature spanning from 1998 to 2019 and soil moisture datasets from 2019 to 2022 were collected from GMet, ISMN (ground-based), CHIRPS, PERSIANN-CDR, ERA5, ARC2, MERRA-2, TRMM and CFSR (satellite and reanalysis). Employing rigorous statistical measures, namely standard deviation, mean absolute error (MAE) and mean bias error (MBE), the accuracy of these datasets was thoroughly evaluated. The results revealed that CHIRPS and PERSIANN-CDR exhibited superior accuracy in rainfall simulation, with CHIRPS demonstrating particularly consistent congruence with observed data. In terms of mean temperature prediction, ERA5 surpassed MERRA-2 and CFSR. Regarding soil moisture assessments, both ERA5 and CFSR offered satisfactory simulations. Hence, our findings advocate for the preference of CHIRPS (for rainfall data), ERA5 (for temperature data) and a combination of CFSR/ERA5 (for soil moisture data) as dependable primary data sources for streamflow modelling, drought analysis, flood prediction and water resource management in the context of Northern Ghana.

Vehicle-based mobile observations are taken across the world every day by operational and research meteorological organizations, public transportation agencies, and private car manufacturers. Whether directly weather-related (e.g., air temperature) or not (e.g., wiper speed), the coverage and frequency of these observations holds the promise of filling in gaps between fixed observing stations and greatly improving situational awareness and weather forecasting, from road surface condition-specific applications and winter road maintenance to urban and street-level numerical weather prediction and beyond. However, in order to take advantage of these observations, the weather, water, and climate enterprise must work together with the transportation enterprise across academic, public, and private sectors to provide a mechanism for obtaining these data, so that the benefits of using these unconventional observations may be realized.

This study employed a network comprising 16 fixed “Ecosmart” sensors deployed in the Cergy-Pontoise conurbation. Continuous measurements of PM₁₀, PM_2.5, and NO₂, significant pollutants in the Paris region, were conducted from April 8 to June 6, 2022. The collected data were represented as statistically composite spatial matrices due to the heterogeneous urban landscape and the overlapping of multiple pollution sources. Temporal variations on a daily basis were influenced by both traffic and meteorological conditions. Daytime, characterized by denser traffic compared to nighttime, exhibited higher concentrations of PM₁₀ and PM_2.5. Conversely, NO₂ concentration levels displayed two peaks associated with traffic volume, and relatively elevated nocturnal values compared with midday due to atmospheric vertical stability during the nighttime phase. The analysis of weather-type impacts revealed that during unstable weather conditions, elevated particle concentrations stemmed from dust resuspension from the ground and long-range transport. Maximum NO₂ concentrations were observed during stable weather conditions, whereas minimum concentrations occurred during unstable weather.

Dengue fever is a source of substantial health burden in Vietnam. Given the well-established influence of temperature and precipitation on vector biology and disease transmission, predictions of meteorological variables, such as those issued by ECMWF as a world-leading provider of global ensemble forecasts, are likely to be valuable model inputs to a future dengue early warning system. In the absence of established verification at municipal and regional scales, this study assesses the skill of rainy season (May–October) ensemble precipitation and 2-m temperature retrospective forecasts over North and South Vietnam initialized for dates during the period 2001–2020, evaluated against the ERA5 reanalysis for the same period. Forecasts are found to be significantly skilful compared with both climatology and persistence for lead times up to 10 days, including for cumulative precipitation values considered against independent rain gauge data. Rank histograms demonstrate that ensembles generally avoid excessive bias and consistently positive CRPSS values indicate substantial skill for temperature and cumulative precipitation forecasts for all spatial scales considered, despite differences in rainy season characteristics between North and South Vietnam. This forecast reliability demonstrates that meteorological input data based on ECMWF ensemble forecasts would add appreciably more value to the development of a future dengue early warning system compared to reference forecasts like climatology or persistence. These results raise hope for further exploration of predictive skill for relevant meteorological variables, particularly focused on their downscaling to produce district-level epidemiological forecasts for urban areas where dengue is most prevalent.

The Google Earth Engine (GEE) was used to investigate the performance of the Global Land Data Assimilation System (GLDAS) soil temperature (ST) data against observed ST from 13 synoptic stations over a semiarid region in Iran. Three-hourly ST data were collected and analyzed in two depths (0–10 cm; 40–100 cm) and 5 years. In each depth, GLDAS-Noah ST data were evaluated for daily minimum, maximum, and average ST (i.e., T_min, T_max, and T_avg). Based on the correlation coefficient, Kling–Gupta Efficiency, and Nash–Sutcliffe Efficiency the overall performance of the GLDAS-Noah is 0.96, 0.66, and 0.79 for T_min; 0.97, 0.84, and 0.89 for T_avg; and 0.95, 0.89, and 0.89 for T_max, respectively in the first layer. Likewise, 0.97, 0.85, and 0.86 for T_min; 0.97, 0.77, and 0.80 for T_avg; and 0.97, 0.69, and 0.69 for T_max are obtained in the second layer. However, there is a significant negative bias which tends to underestimate ST in the two investigated layers, given by an average bias over all the stations analyzed of −24%, −12%, and −5% for T_min, T_avg, and T_max in the first layer, and average bias of −8%, −13%, and −17% for T_min, T_avg, and T_max in the second layer. This study reveals that GLDAS-Noah-derived ST can be used in arid regions where little or no observation data is available. Moreover, GEE performed as an advanced geospatial processing tool in regional scale analysis of ST in different layers.

Low-cost sensing and the Internet of Things (IoT), present new possibilities for unconventional monitoring of environmental parameters. This paper describes a series of intersecting networks of particulate matter sensors that were deployed across the Birmingham conurbation for a 12-month period. The networks consisted of a combination of commercially available sensors and University developed sensors. Data from these networks were assimilated with data from a third-party Zephyr deployment, along with the DEFRA AURN network, which was hosted on an open-source online platform. This nesting of sensor networks allowed for new insights into sensor performance, including the accuracy of a large network to detect regional concentrations and the number of sensors needed for effective monitoring beyond indicative measurements. After comprehensive data validation steps, the sensors were shown to perform well during co-location with reference instrumentation (exhibiting slopes of 0.74–1.3). The sensors demonstrated good capability of detecting temporal patterns of regional PM_2.5 with the mean of the entire sensor network recording an annual mean PM_2.5 concentration within 0.2 μgm⁻³ of the regulatory network annual mean observation. Network-derived statistics for estimating urban background concentrations compared to a reference site increase in-line with the number of sensors available, however when assessing this for near-source concentrations the importance of sensor location rather than the number of sensors is highlighted. Overall, the network provided novel insights into local concentrations, detecting similar hotspots to those identified by a high-resolution model. The increased spatial coverage afforded by the sensor network has the potential to support higher resolution evaluation of models and provide unprecedented spatial evidence for air pollution management interventions.

Britain's power system has shifted towards a major contribution from wind energy. However, wind is highly variable, and exceptionally low wind events can simultaneously occur with cold conditions, which increase demand. These conditions can pose a threat for the security of energy supply. Here we use bias-corrected wind supply data and the estimated temperature-related part of demand to analyse events of potential weather-related energy shortfall based on the historic meteorological record. We conduct sensitivity studies with varying scenarios of Britain's total wind energy capacity and the temperature sensitivity of national demand. These scenarios are estimates for present-day conditions as well as potential future changes of the power system. We apply a new methodology to estimate the potential severity of an event for the power system, and analyse the atmospheric conditions associated with the most severe events. We find that events of potentially severe shortfall are relatively rare and short-lived, and often occur with an atmospheric pattern broadly resembling a negative North Atlantic Oscillation. This broad tendency emerges from a wide range of individual daily weather patterns that cause cold and still conditions. With an increase in wind capacity, it is likely that severe events will become rarer, although the most severe days of the record are relatively insensitive to changes in wind supply and temperature sensitivity of demand under our assumptions.