Meteorological Applications最新文献

This study investigates meteorological drought in sub-Saharan Africa within the context of elephant conservation. Prolonged drought significantly impacts elephants, leading to increased mortality rates and heightened human–elephant conflicts. We assess both the anticipated 21st century changes in impact-relevant meteorological drought metrics and the efficacy of existing forecasting systems in predicting such droughts on seasonal time scales. The climate change element of our study uses the 6th Coupled Model Intercomparison Project (CMIP6) ensemble to evaluate projected change in 3-month Standardized Precipitation Index (SPI3). We then carry out a quantitative assessment of seasonal forecast skill, utilizing 110 years of precipitation hindcasts generated by the European Centre for Medium Range Forecasting (ECMWF) system. Our findings indicate that persistent drought is projected to become more frequent over the 21st century in southern Africa, where the majority of elephants reside. Analysis of seasonal hindcasts indicates that, while the forecasts have greater skill than climatology, they remain highly uncertain. Previous work suggests that it may be possible to reduce this uncertainty by contextualizing forecasts within specific climate regimes. However, even with improved forecast skill, effective action hinges on the alignment of forecasts with the practical needs of conservation practitioners. Over the next decades, a co-production approach will be critical for leveraging seasonal forecasts for climate change adaptation within the conservation sector.

An operational scheme for predicting the symmetric R30 and R50 of tropical cyclones (TCs) in the western North Pacific was developed using a statistical regression method and track pattern clustering (four clusters). The statistical–dynamical model employs multiple linear regressions of two to eight variables at each cluster and forecast lead time. The dependent variable for prediction was the change in the 5-kt wind radius (R5)—a proxy of TC size—relative to the initial time. The performance of the model was compared for the training (2008–2016) and testing (2017–2018) periods. The effect of clustering on TC size prediction was evaluated by comparing the performance of the non-clustering and clustering models. The clustering model improved the prediction of TC size by 3%–24% at all lead times during the training period, especially with a significant improvement of up to 43% in Cluster 2. In Cluster 2, because most TCs tend to develop strongly and continue to increase in size, it greatly reduced the variability in TC size through clustering, allowed for smarter predictor selection, and ultimately improved TC size prediction. In the real-time R30 and R50 predictions for the 2017 and 2018 TCs, the error of the clustered model was 18%–19% less than that of the non-clustered model. The analysis results revealed that the real-time prediction errors of the current model increase when the TC tracks are difficult to classify into specific clusters, the predicted environments and TC tracks are inaccurate, and the size and intensity of a TC rapidly increase.

Advection is a prevailing meteorological phenomenon in the arid and semi-arid environments that directly affects the crop and soil hydrology. It could have a great impact on the rate of standard crop evapotranspiration (ET_c) in the irrigated areas. Therefore, it is required to take it into consideration while computing crop water requirement through the physically based meteorological procedures. The objectives of this study are (1) simple modification of the Penman–Monteith (PM) equation in calculation of the grass reference evapotranspiration (ET_o) to implement the local advection, (2) comparing the single (K_c) and dual crop coefficient (K_cb) of two quinoa cultivars (Titicaca, and Q5) with and without advection correction and (3) presenting a simple model to calculate the advection factor using the maximum air temperature and mean relative humidity for the future crop growth modelling studies. Both Q5 and Titicaca showed unexpectedly very high ET_c and potential transpiration (T_p) as 1568 mm and 1003 mm, and 1156 mm and 829 mm, respectively, due to occurrence of regional advection, whereas very high unrealistic K_c and K_cb values for Q5 revealed the impact of strong local advection and external energy. Therefore, we modified ET_o to implement the advection effect through introducing the advection factor, ET_c/R_n (R_n is the net radiation), as a function of maximum air temperature and mean relative air humidity during the growing season [ET_c/R_n = exp (0.025T_max – 0.015RH_avg)] which resulted in higher ET_o values, and consequently lower and more realistic K_c and K_cb. As a result, modified maximum K_c values of 1.14 and 1.55 and the modified maximum K_cb of 0.94 and 1.0 were obtained for Titicaca and Q5 cultivars, respectively. This procedure leads to a more accurate site-specific K_c estimation and indirectly reliable ET_c estimation as a function of advection factor and its multiplication by the non-modified ET_o. Furthermore, for direct estimation of ET_c through the PM equation, the coefficients of aerodynamic and canopy resistance components of PM equation were modified for estimation of ET_c by the non-modified R_n, which resulted in accurate estimation of ET_c.

Cold surge events in the South China Sea (over 110–117.5° E longitude at 15° N latitude) from 1979 to 2019 are divided into four types depending on their correlation with North Pacific extratropical cyclones (ECs). Climatologic relationships between the two phenomena reveal that 92% (39%) of all cold surges are accompanied by ECs (explosive extratropical cyclones, abbreviated as EC-E), while 31% of ECs are accompanied by cold surges. The occurrence and development of ECs favour eruptions of cold air from higher latitudes, which in turn produce conditions conducive to cold surges. In the North Pacific, ECs travel in a northeastward direction, ultimately contributing to the Aleutian low. Meanwhile, the westerly jet is observed to strengthen following cold surge events. Both actions drive further EC activity, which in turn facilitates subsequent cold surges. Therefore, when ECs occur before and after cold surges, the cold surge event itself tends to be relatively strong and long lived. The transmission of energy by ECs is a primary link between the high and middle latitudes and contributes to the impact of cold surges on low latitudes. This study also explores the respective influences of the Siberia High and Aleutian Low on cold surges. The majority of cold surges that do not involve ECs occur in the context of the weaker Siberia High and Aleutian Low. Cold surges accompanied with ECs can be even stronger and longer lasting when the Siberia High and Aleutian Low strengthen.

The Italian territory is located at the heart of one of the global hot spots of climate change, where the implementation of climate-smart land management practices is imperative to guarantee the present and future maintenance of ecosystem functions as well as the sustainability of human socioeconomic activities. The project HIGHLANDER (HIGH performance computing to support smart LAND sERvices) led by Cineca aims at building a comprehensive and multi-sector framework for land-management decision-making in Italy. The project relies on high quality information on different components of the landscape, with a focus on climate-driven processes, and state-of-the-art computing infrastructures. The HIGHLANDER Data Portal maximizes the impact of HIGHLANDER results by providing access to data products and services. In this article, we describe the architectural features of the platform, as well as the available HIGHLANDER datasets and downstream applications.

Using 22-year ground observations on meteorological variables, we conducted a statistical analysis to reveal fog characteristics at Shanghai Pudong International Airport (SPIA). Fog events were classified by fog types using an objective method. Two types of advection fogs are dominant in fog events at SPIA, followed by radiation fogs. Different fogs have evident annual and monthly variations in frequency. Advection fog mostly appear from February to April, while radiation fog is mainly between November and February. Six synoptic patterns associated with fogs were determine by a self-organizing maps cluster method, two of which, namely ‘west of marine high pressure’ and ‘inverted trough’, are most favourable for the formation of advection fogs, whereas ‘bottom of weak cold high-pressure’ is favourable for radiation fogs. The frequency of advection fog occurrence exhibits temporal fluctuations with distinct peaks occurring 2–3 h after sunset, around midnight, and 1–2 h before sunrise, while almost all radiation fog occur in the second half of the night, and favourable conditions for fog onset become more effective with the length of the night. Radiation fog exhibits a longer duration than advection fog and tends to result in lower visibility. The prevalent wind direction for the formation of advection fog is generally from the east to the southeast, whereas radiation fog is typically associated with westerly winds. The temperature and air pressure during the formation of both advection and radiation fogs follow a normal distribution in most seasons. More fog characteristics were categorized by fog types and seasons.

Owing to climate change, abnormal climate phenomena occur frequently, damaging aging highway facilities. Therefore, it is necessary to develop adaptation projects for highway systems. Adaptation projects focus on mitigating the impact of climate change on highway facilities and enabling safe use. The present study aims to develop quantified evaluation indicators that are necessary for planning and selecting appropriate climate change adaptation projects. By analysing previous studies, the most important factors to be considered in the evaluation process of climate change adaptation projects were defined, and 20 preliminary evaluation indicators were divided into three layers. A feasibility study for each class was performed on the preliminary evaluation indicators by an expert panel; 16 evaluation indicators were selected through a feasibility study, and the weight of each indicator was calculated using the analytical hierarchy process (AHP) method. The priority of the evaluation indicators was service improvement through adaptation projects (weight 8.76), cost reduction and job creation (weight 8.46), and climate change impact reduction and vulnerability reduction (weight 8.31). These quantified evaluation indicators can help organizations that manage highways respond to climate change and establish adaptation projects.

Subseasonal-to-seasonal (S2S) prediction has gained momentum in the recent past as a need for predictions between the weather forecasting timescale and seasonal timescale exists. The availability of S2S databases makes prediction and predictability studies possible over all the regions of the globe. Most S2S studies are, however, relevant to the northern hemisphere. In this review, the S2S literature relevant to the southern hemisphere (SH) are presented. Predictive skill, sources of predictability, and the application of S2S predictions are discussed. Indications from the subseasonal predictability studies for the SH regions suggest that predictive skill is limited to 2 weeks in general, particularly for temperature and rainfall, which are the variables most frequently investigated. However, temperature has enhanced skill compared to rainfall. More S2S prediction studies that include the quantification of the sources of predictability and the identification of windows of opportunity need to be conducted for the SH, particularly for the southern African region. The African continent is vulnerable to weather- and climate-related disasters, and S2S forecasts can assist in alleviating the risk of such disasters.

Lightning often causes death, injury, and damage to various facilities and equipment. Accurately detecting the spatial location of lightning occurrence by predicting thunderstorms and lightning is of great significance. Traditional lightning detection systems detect lightning by measuring the sound, light, and electromagnetic field information radiated by lightning. These methods typically have two problems. First, the detection process of lightning signals is susceptible to electromagnetic interference. Second, the equipment cost is high and is not friendly to some lightning detection tasks only targeted at specific scenarios. In order to detect lightning more conveniently, we propose a lightning detection model based on deep learning networks. With the increase in the use of cameras in modern society, designing lightning object detection networks based on deep learning is possible. However, two problems have been found in existing practice: (1) When strong lightning meteorological phenomena occur, the lightning features in the image are covered by bright electric lights, and convolutional neural networks cannot distinguish between strong lightning scenes and strong ultraviolet scenes. (2) The performance of convolutional neural networks is often related to the model's size. The larger the model, the stronger the performance of the network. However, in practical application scenarios, computing resources are insufficient to use sufficiently large networks. In this paper, we propose a simple and effective lightning object detection network (LD-Net) and use a foreground-background segmentation algorithm to locate frames containing lightning in the video. After using the knowledge distillation-based model compression method, the mAP of the lightning object detection network with a backbone net of resnet with 18-layer (LD-Net-18) can reach 82.4%. We hope that the proposed LD-Net can serve as a simple and powerful alternative to traditional lightning detection methods, enhancing efficiency in lightning detection tasks.

The probabilistic forecast skill level of statistically downscaled European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal-to-seasonal (S2S) forecasts is determined in predicting maximum and minimum temperatures for weeks 1–4 lead times during 20-year December–January–February (DJF) seasons from 2001 to 2020 over South Africa. Skilful S2S forecasts are vital in assisting decision-makers in the development of contingency planning for any eventualities that may arise because of weather and climate phenomena. Extreme high- and low-temperature events over a prolonged period can lead to hyperthermia and hypothermia, respectively, and can lead to loss of life. The results from the relative operating characteristic (ROC) and reliability diagrams indicate that the ECMWF S2S model has skill in predicting maximum temperature up to week 3 ahead, particularly over the central and eastern parts of South Africa. The ROC scores indicate that the model has skill in predicting minimum temperature up to week 4 ahead for the above-normal category, particularly over the central and eastern parts of South Africa. Reliability diagrams indicate that the model has a tendency of overestimating the below-normal category when predicting both maximum and minimum temperatures for weeks 1–4 lead times over South Africa. Furthermore, canonical correlation analysis (CCA) pattern analysis suggests that when there are anomalously positive and negative predicted 850-hPa geopotential heights located over South Africa, there are anomalously hot and cold conditions during the DJF seasons over most parts of South Africa, respectively. These results suggests that statistical downscaling of model forecasts can improve forecast skill. Moreover, the results suggest that there is potential for S2S predictions in South Africa, and as such, S2S prediction system for maximum and minimum temperatures can be developed.