The wind profile radar data observed from the western mountainous and eastern plain areas of Chengdu from 1 Jan to 31 Dec 2022 are used to analyse the regional differences in the vertical structure and daily variation trend of the boundary layer wind field. The results indicate that: (1) There are regional differences in the prevailing wind direction of the lower boundary layer in Chengdu, with north-north-east winds prevailing in the western mountains area and northeast winds prevailing in the eastern plains, and without seasonal difference. While there is no regional difference in the prevailing wind direction between the east and west regions in the middle and upper boundary layer, both occur alternately with northeast and southwest winds that are consistent with the direction of the western mountain range. (2) The variation trend of the average wind speed profile in the boundary layer between the eastern and western areas of Chengdu is consistent, and the horizontal wind speed in the eastern plain area of the same detection height layer is greater than that in the western mountains area. (3) The local mountain-valley breeze is significant in the lower boundary layer of the western mountains area; the valley breeze with southeast winds prevails during the day; and the mountain breeze with northwest winds prevails at night. (4) There are no regional differences in the diurnal variation characteristics of horizontal wind speed at the same detection height layer, and it is a “single peak and single valley” type, with a peak during the day and a valley at night. (5) The differences in the wind field characteristics of the lower boundary layer between the east and west regions of Chengdu are mainly caused by the local complex terrain of the western mountains area.
NO2 is the common precursor of the secondary conversion of PM2.5 and O3. Understanding its change characteristics and influencing factors is of great significance for the collaborative treatment of PM2.5 and O3. Based on the VLF/LF three-dimensional lightning location monitoring system, SNPP/VIIRS satellite fire point data, NO2 column density in Sentinel-5P NRTI NO2 data products and other data, using various statistical methods, selecting February to April with high NO2 density and June to August with frequent lightning activities, this paper compares and analyses the influence of lightning activities on NO2 density in region A (96.5°-102°E, 20.5°-24°N) with high biomass burning in southwest Yunnan and its surrounding areas and region B (102°-104°E, 24°-26°N) with high human activities in central Yunnan. The results show that: (1) There are obvious differences in the spatial and temporal distribution of the number of lightning and NO2 column density in region A and region B. The NO2 column density outside area A is higher than that in China, but the distribution of lightning times is the opposite. The NO2 column density in region B decreases from Kunming to the surrounding area, and the number of lightning is less and more. In the dry season (November to April of the next year), the concentration of NO2 density is higher, and the rainy season (May to October) is lower, and the number of lightning is opposite. (2) From February to April, the NO2 column density in region A and region B has a significant positive spatial correlation with the number of fire points and anthropogenic CO2 emissions, respectively, but a significant negative correlation with the number of lightning. (3) Lightning activity is mostly accompanied by obvious rainfall (R≥1 mm). When the lightning activity is weak, the wet deposition effect of rainfall on the ground NO2 density is obvious, and the wet deposition effect of rain falling from the stronger lightning activity cannot completely offset the contribution of lightning to the increase of ground NO2 density. (4) The change of surface NO2 density during the first lightning from June to August is more regular than that from February to April, which shows that the ground NO2 density increases hourly in the first 6 hours and decreases slowly hourly in the last 3 hours. (5) The ground NO2 density on the lightning days in the two regions is generally higher than those on the days without lightning. Changes in biomass combustion intensity, rainfall intensity and planetary boundary layer height have a significant impact on the ground NO2 density.
To enhance the forecasting capability for daily extreme wind speeds, particularly for winds exceeding force 8, this paper uses the “past 3 h gust” wind speed forecast output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model as the primary input factor. Additionally, the paper addresses the extremely uneven sample distribution in the daily extreme wind speed series (samples with wind force above level 8 constitute a very small proportion of the total sample, while samples with wind force below level 5 constitute the vast majority). Moreover, the ECMWF model’s “past 3 h gust” wind speed forecast tends to overestimate low-level winds and underestimate high-level winds. Therefore, the paper leverages nearly five years of surface observations and ECMWF model “past 3 h gust” forecast data to develop a Tabnet-based daily extreme wind classification correction forecast model. The model’s input design includes previous observations, geographic information of the stations, ECMWF forecast fields, and previous forecast error terms. In the evaluation of an independent sample over one and a half years, the new correction forecast model reduces the mean absolute error (MAE) by 45.2% and the root mean square error (RMSE) by 25.7% compared to the interpolated ECMWF model. Furthermore, for wind force levels 1-5 and above 8-9, the new correction forecast model significantly improves the forecasting accuracy compared to the method using interpolated ECMWF forecast fields, demonstrating the feasibility of this forecasting approach.The model is constructed with a focus on overcoming the inherent limitations of the ECMWF model’s wind speed forecasts. By incorporating comprehensive input factors such as historical observation data, the geographical context of observation stations, and systematic forecast error corrections, the model aims to provide a more accurate prediction of extreme wind events. The primary challenge addressed by the model is the skewed distribution of wind force levels in the dataset, where extreme wind events are underrepresented. The innovative use of the Tabnet algorithm allows for a sophisticated analysis and adjustment of the forecast data, thus ensuring higher accuracy in predicting both low and high wind force levels. The independent validation over an extensive period highlights the robustness of the model. The significant reduction in MAE and RMSE underscores the model’s enhanced performance. Specifically, the accuracy improvements for the critical wind force levels 1-5 and 8-9 plus indicate the model’s practical applicability in real-world scenarios. This advancement is crucial for sectors reliant on precise wind forecasts, such as maritime operations, aviation, and disaster preparedness. The results clearly suggest that integrating historical data and addressing the ECMWF model’s biases can lead to substantial improvements in extreme wind speed forecasting. In conclusion, the development of the Tabnet-based correction forecast model represents a significant step forward in meteorological forecasting. By effectively addressing the biases and limitations of existing models, this new approach offers a more reliable tool for predicting extreme wind events.
There are many ships and ports in Shanghai coastal zones, where disastrous weather occurs frequently. Meteorological disasters often threaten the safety of people’s lives and properties along the coast and in the ports. In the past, the meteorological warnings for Shanghai coastal zones are mainly based on those issued for Yangshan Port by Shanghai Marine Meteorological Centre (SMMC), which are called “unified warnings”. However, there are obvious differences in the time and intensity of meteorological disasters in each region, and the unified warnings cannot meet the needs of the production and operation of the shipping and ports. In 2020, Shanghai coastal zones were divided into five sub-zones, where the meteorological forecast and warnings were carried out separately from July 2020. Based on hourly observational data of the representative stations in Shanghai coastal zones and warning signal data from 2016 to 2022, the gale events are selected to analyse the statistical characteristics of temporal and spatial distribution and evaluate the forecast quality and the economic benefits of the zonal warnings. The results show that: (1) The farther away from the coastline, the more gale days, the higher the wind speed and the longer the duration; the higher the wind speed during the process, the more obvious the difference of wind scale. In particular, the wind scale caused by typhoons can range up to 5 levels. (2) Compared with the “unified warning”, the missing alarm rate (MAR) of gale warnings in each sea area has been reduced significantly, by up to 5%, the false alarm rate (FAR) for the western part of Yangtze River estuary is reduced by more than 8% and the TS score is significantly improved by more than 10%. (3) The advance time of gale warnings has been reduced by more than 3 hours, the maintaining duration has been shortened by more than 16 hours at most, which can reduce the loss of nearly 18 million RMB and improve the production efficiency of the coastal zones of Shanghai greatly. The results of this paper show that refined marine meteorological forecasts and early warnings provide a safety guarantee for marine transportation and port production operations, resulting in significant social and economic benefits and the enhancement of the comprehensive guarantee level of marine meteorological services in Shanghai. In the next step, we will continue to research and develop more refined objective forecast methods adapted to this business, then build a regional shared operational system platform and extend it to the Yangtze River Delta region, so as to promote the high-quality development of shipping meteorological integration in the Yangtze River Delta region.
The increasing frequency, intensity and scope of extreme heat events due to climate change, which is mainly characterised by significant warming, is one of the current key climate stressors for sustainable development in terms of socio-economics, ecological balance and agricultural production in Jiangxi Province. High-temperature dangerousness evaluation is the basic work of high-temperature disaster risk assessment. However, in Jiangxi Province, the current research on high-temperature hazards mainly focuses on the analysis of trends and spatial distribution patterns, and few studies are conducted to reveal the risk of high-temperature occurrence through disaster risk theory. In this paper, based on the daily maximum temperature data of 79 meteorological stations in Jiangxi Province from 1961 to 2022, the trends of three disaster-inducing factors (the number of high-temperature days, the extreme maximum temperature and the high-temperature intensity) and their values under four return periods (1 in 5 years, 1 in 10 years, 1 in 20 years, and 1 in 50 years, respectively) are analysed using the least square method and the Kernel density estimation method, respectively. Then, through K-mean cluster analysis, the dangerousness distribution of each disaster-causing factor is obtained and a comprehensive high-temperature dangerousness map is produced. Finally, according to the disaster risk theory, the agricultural heat risk is assessed by the product of high-temperature dangerousness, agricultural exposure (quantified by land use cover) and agricultural fragility (quantified by gross domestic product kilometre gridded data). The results show that: (1) The overall trend of the number of high-temperature days, extreme maximum temperature and high-temperature intensity in Jiangxi Province during 1961-2022 shows an increasing trend, but the trend has a phased character, with a decreasing trend before 1997. (2) The dangerousness of each disaster-inducing factor is relatively high, with the proportion of high-risk areas in the province ranging from 41.7% to 61.4%. (3) The comprehensive dangerousness shows a spatial distribution pattern of low in the north and low in the centre, and the high-risk areas are mainly concentrated in the eastern part of Shangrao and most parts of Ji’an. (4) Agricultural medium-high risk zones are consistent with the spatial distribution of the dangerousness map. However, due to the uneven distribution of agricultural fragility, the low-risk zone is more surrounded by cities, and is mainly concentrated in southern Ganzhou, most of Xinyu, north-central Nanchang, and eastern Jiujiang. This paper can provide some reference for the comprehensive risk assessment of meteorological disasters.
