As one of the most influential and harmful disasters in extreme climate events, rainstorm and flood threaten people’s lives, property and social stability seriously. The research applied the historical disaster data to predict future risk assessment with deep learning method for timely response. Firstly, four disaster indicators (population disaster index, housing disaster index, agricultural disaster index and economic disaster index) were introduced to reflect different losses, which could form a comprehensive disaster index to quantify the overall loss degree; Second, with raster data and VGGNet, a lightweight regression convolutional neural network model VGG-Light was proposed to solve these problem; Third, focused on impact of precipitation on disaster situations, the ConvLSTM module was used to capture the spatiotemporal characteristics of precipitation data, and then the TSVGG-Light model was presented for feature fusion. Finally, the comprehensive disaster index obtained on the validation set had MSE and MAE of 0.012 and 0.007, R2 increased to 0.918, CS (0.05) increased to 97.50%, while CS (0.1) and CS (0.2) were both 100.00%. Therefore, TSVGG-Light model integrated with ConvLSTM has been proved efficiently for extracting the spatio-temporal characteristics of precipitation, which can accurately predict rainstorm and flood disasters compared with statistics characteristics of precipitation.

PM2.5 is extremely harmful to the atmospheric environment and human health, and a timely and accurate understanding of PM2.5 with high spatial and temporal resolution plays an important role in the prevention and control of air pollution. Based on multi-angle implementation of atmospheric correction algorithm (MAIAC), 1 km AOD products, ERA5 meteorological data, and pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5) in the Guangdong-Hong Kong-Macao Greater Bay Area during 2015-2020, a geographically and temporally weighted regression model (GTWR), BP neural network model (BPNN), support vector machine regression model (SVR), and random forest model (RF) were established, respectively, to estimate PM2.5 concentration. The results showed that the estimation ability of the RF model was better than that of the BPNN, SVR, and GTWR models. The correlation coefficients of the BPNN, SVR, GTWR, and RF models were 0.922, 0.920, 0.934, and 0.981, respectively. The RMSE values were 7.192, 7.101, 6.385, and 3.670 μg·m-3. The MAE values were 5.482, 5.450, 4.849, and 2.323 μg·m-3, respectively. The RF model had the best effect during winter, followed by that during summer, and again during spring and autumn, with correlation coefficients above 0.976 in the prediction of different seasons. The RF model could be used to predict the PM2.5 concentration in the Greater Bay Area. In terms of time, the daily ρ(PM2.5) of cities in the Greater Bay Area showed a trend of "decreasing first and then increasing" in 2021, with the highest values ranging from 65.550 μg·m-3 to 112.780 μg·m-3 and the lowest values ranging from 5.000 μg·m-3 to 7.899 μg·m-3. The monthly average concentration showed a U-shaped distribution, and the concentration began to decrease in January and gradually increased after reaching a trough in June. Seasonally, it was characterized by the highest concentration during winter, the lowest during summer, and the transition during spring and autumn. The annual average ρ(PM2.5) of the Greater Bay Area was 28.868 μg·m-3, which was lower than the secondary concentration limit. Spatially, there was a "northwest to southeast" decreasing distribution of PM2.5 in 2021, and the high-pollution areas clustered in the central part of the Greater Bay Area, represented by Foshan. Low concentration areas were mainly distributed in the eastern part of Huizhou, Hong Kong, Macao, Zhuhai, and other coastal areas. The spatial distribution of PM2.5 in different seasons also showed heterogeneity and regionality. The RF model estimated the PM2.5 concentration with high accuracy, which provides a scientific basis for the health risk assessment associated with PM2.5 pollution in the Greater Bay Area.

This research studied the risk assessment of geological hazards, such as landslides and debris flow, under the time series and trend characteristics of extreme precipitation events in the last 60 years in nine typical regions of the lower Jinshajiang River Basin. Nine indicators, including slope, engineering geological rock group, slope structure type, distance to road, topographic relief, distance to fault, distance to the water system, vegetation cover and profile curvature, were selected as the index factors for landslide susceptibility evaluation, and the information quantity method was used to obtain the landslide susceptibility evaluation of the study area. Based on the susceptibility evaluation, the spatial analysis function of GIS was used to derive the geological hazard zoning under the extreme rainfall trend. The results showed that the areas with high extreme rainfall trends have higher densities of geological hazard development and they are concentrated, while areas with low extreme rainfall trends have relatively less geological hazard development, and what development exists is scattered.

To better understand the characteristics of long-term change and variability in regional extreme precipitation and to examine possible regional responses to global climate warming, we analyzed temporal and spatial patterns of precipitation and extreme precipitation index changes in the Yangtze River Basin (YRB) over the last 120 yr. Based on the China Meteorological Administration’s daily precipitation data set of 60 city stations in mainland China from 1901-2020, we found that total annual precipitation and daily precipitation intensity of the Upper Reaches (UR) experienced a downward trend, but those of the Middle and Lower Reaches (MLR) showed an upward trend. Precipitation amount and intensity increased, while the number of precipitation days slightly decreased over the YRB during 1901-2020. Most extreme precipitation indices showed a decreasing trend in the first 60 yr that reversed after 1961 into slightly upward trends, except for consecutive dry days and wet days in the YRB during 1901-2020. There were significant downward trends for consecutive dry days and maximum 1 and 5 d precipitation during the first 60 yr, and significant upward trends for maximum 1 d precipitation, total extreme heavy precipitation, and very heavy precipitation days in the last 60 yr. The trends for extreme precipitation in the UR were lower than those in the MLR during 1901-2020, particularly in the last 60 yr, whereas the trends in the UR were higher than those in the MLR during 1901-1960. The fluctuations of extreme precipitation indices in the UR were more dramatic than those in the MLR, and the extreme precipitation indices all showed 5-10 or 20 yr periodic variabilities. The analysis also showed that observed changes in regional extreme events were directly related to rapid urbanization around the stations and multi-decadal variability of the East Asian monsoon.

Typhoons are frequent natural disasters in the southern coastal areas of China, which resulted in severe economic losses and casualties in affected areas. Therefore, typhoon risk assessment is critical for typhoon prevention and reduction. The research takes Guangdong as the experiment area and utilizes multi-source data from historical typhoon disasters for risk assessment. The indexes, such as Hazard, vulnerability, and sensitivity indicators, are selected to build index-based typhoon risk assessment model with Analytic Hierarchy Process (AHP) and entropy weight method. The research results show that the overall typhoon risk in Guangdong presents a trend from initially lowerto higher. The lowest point was reached in 2005 with low-risk areas accounting for 60.6%, while the highest point was reached in 2020 with high-risk areas accounting for 8.24%. The average proportion of low-risk areas over the 25-year period is 29.98%, while high-risk areas account for 4.8%. Typhoon risk shows a trend of low in the south and high in the north.

High-temperature extreme events are a major ecological and environmental problem faced by human beings. Focused on the risk assessments of such an extreme events, the research applied remote sensing, meteorological and socio-economic data from 2000 to 2020 with risk-exposure-vulnerability assessment framework to explore the spatial and temporal difference in the middle and lower reaches of the Yangtze River. Further, the spatial distribution of high-temperature risk has been obtained during three periods through analytic hierarchy process. The research results show that the high-risk areas are mainly concentrated in the provincial capital cities and around the Taihu Lake basin. Meanwhile, the high-risk presents an increasing trend, especially, the Taihu Lake basin shows the most obvious rising trend rate, as 7.33%.In addition, some higher-risk cities in the north tend to decrease, while the other in the south tend to increase, which show significant regional differences in the experiment area.

Much attention has been given to the change rule of a single extreme event, and there are few reports on comprehensive characteristics of multiple extreme events in a certain region. Based on the analyzes of annual frequency of extreme high temperature, extreme low temperature, extreme drought, extreme precipitation, and extreme typhoon events in China from 1961 to 2020, a multi extreme events composite risk grade index (MXCI) was constructed and applied to the comprehensive characteristics analyzes of multiple extreme events in China. The results show that the high value areas of MXCI were mainly located in southeast China and southwest China. Over the past 60 years, the MXCI has shown a decreasing trend in western China and most of southeastern China, and an increasing trend in the middle zone from southwest China to northeast China. Through comparative analysis, MXCI can objectively reflect the comprehensive characteristics of multiple extreme climate events in a region, which is helpful to understand regional extreme climate characteristics and effectively cope with extreme climate risks.

This paper uses the daily observation data of 669 meteorological stations in the Yangtze River Basin for the past 60 years（1961—2020） after homogenization and correction, and adopts the extreme value analysis method combining relative threshold and absolute threshold to identify the extreme high temperature events, extreme low temperature events, extreme drought events and extreme precipitation events in the Yangtze River Basin in the past 60 years, also the annual occurrence probability and linear change trend were analyzed. Considering the occurrence of extreme climate events across the country based on this, Muti-Extreme events Composite grads Index（MECI） was constructed, and the comprehensive risk level of extreme climate events in the Yangtze River Basin was analyzed. The research results show that compared with other regions of the country, the comprehensive risk level of extreme climate in most parts of the Yangtze River Basin was higher. Although the comprehensive annual occurrence frequency has shown a linear decrease trend since 1961, the comprehensive risk level of extreme climate events in Yangtze River Basin was significantly higher than that of other parts of the country since the 1990s. Through the individual analysis of different extreme climate events, the results show that the annual frequency of extreme drought events in the Yangtze River Basin has shown a linear decreasing trend in the past 60 years. Compared with other regions of this country, the risk level of extreme drought in most parts of the Yangtze River Basin was above the intermediate level, indicating that Yangtze River Basin was prone to extreme drought events. The annual frequency of extreme precipitation events in the Yangtze River Basin showed a weak increasing trend in the past 60 years, MECI analysis showed that the high-risk areas are mainly located in the middle and lower reaches of the Yangtze River, the risk of extreme precipitation events was very high in western Hunan, most of Jiangxi and southern Hubei. Extreme high temperature events in most areas of the Yangtze River Basin had increased significantly in the past 60 years, especially since the beginning of this century. But the risk level of extreme high temperature events was relatively low compared with other areas in China. In the past 60 years, extreme low temperature events in the Yangtze River Basin had decreased significantly, but compared with other parts of the country, the risk level was increased. Since the beginning of the 21st century, the comprehensive risk level of extreme climatic events in the Yangtze River Basin was increasing, high temperature and drought compound type events occurred frequently. The proportion of extreme precipitation events and extreme low temperature events in the country was continued to increase. In recent years, the social and economic impacts caused by extreme climate events have become more and more serious, indicating that it was of great importance and urgency to strengthen the risk prevention of extreme climate events in the Yangtze River Basin.

在全球气候变暖背景下,我国极端气候事件增加,特别是干旱灾害发生频率和强度呈明显上升的态势,已经对我国农业的可持续发展、粮食安全和社会稳定构成了潜在威胁,尤其是我国北方受水资源条件限制,一旦遇到重大干旱事件,农业生产必然会受到严重影响.1961年以来,华北大部、西北地区东部、西南大部地区降水量减少,出现干旱化趋势,特别是甘肃中东部、宁夏大部、陕西北部、山西中部、河北南部、河南北部、山东大部、吉林西部等地是干旱高危险区.日益增加的干旱灾害让部分地区出现水资源短缺,对工农业生产、生态环境和群众生活造成严重影响.根据未来气候变化情景预估,到21世纪末我国北方地区干旱化趋势还将加剧.在此背景下,作为对气候变化最为敏感和相对脆弱的产业,农业灾害风险明显增加,直接危及我国粮食安全,急需加强干旱监测预警方法和指标体系研究,尽快建立我国干旱灾害风险评估及预警体系,及时识别重大干旱事件的发生和发展,加强农业干旱灾害风险动态评估与预警基础研究,解决农业防旱减灾过程中的关键性问题,提高气候变化影响定量化评估水平,为我国应对气候变化和防灾减灾提供技术支撑.

Based on the daily observation data of 2254 meteorological stations in China from 1961 to 2021 after homogenization and correction, the extreme value analysis method that combines relative threshold and absolute threshold was used to identify extreme climate events in the mainland of China, namely extreme high temperature, extreme low temperature, extreme drought, extreme precipitation and extreme typhoon events. The frequency and linear variation trends of individual extreme events in the past 60 years were investigated. On this basis, the multi extreme events composite risk grade index (MXCI) was constructed, which can objectively reflect the comprehensive characteristics of multi extreme climate events in different regions. The results show that the high value areas of MXCI were mainly located in Southeast China and Southwest China. The MXCI presented a decreasing trend in most of the western and southeastern regions of China, while an increasing trend from Southwest China to the south of Northeast China, due to the frequent occurrence of extreme high temperature, extreme drought and other extreme events in these regions.

文章选取西藏南木林县的1个国家气象观测站(1980-2021年)以及艾玛乡等15个省级乡镇地面天气站(2020-2021年)的逐日气象数据、土地利用类型栅格和基础地理信息等数据.主要通过SPSS软件构建马铃薯气候适宜度数据同高相关性的气象要素、经纬度、海拔DEM等地理信息的多元回归模型,通过GIS栅格计算器等技术实现"400×400米"的精细化拟合区划,并在此基础上叠加土地利用类型数据,估算出不同气候适宜度分布区域下实际可以用于播种马铃薯的耕地分布及面积数据,结果得出:南木林县可种植马铃薯的耕地总面积为167.1km2,其中以艾玛乡为主的马铃薯气候最适宜区耕地占40.9km2,占比为24.5％;以湘河河谷流域为主的马铃薯气候适宜区耕地占100.8km2,占比为60.3％;值得一提的是艾玛乡总耕地面积26.9km2中处于马铃薯气候最适区达20.3km2,占比达75.4％.而享誉西藏自治区的"艾玛马铃薯",就产自此带.这除了与此带土质、种植方式有关,还与其气候条件高度相关,气候适宜度高,对马铃薯生长发育为正贡献,因此能够促进其产量和品质的提升.文章提出的高原马铃薯气候适宜度评价模型乡镇精细化区划,可以为高原马铃薯主产县产业调整、合理种植布局提供更为准确的理论参考.

客观定量监测和评估雨涝灾害的强度,可为防御洪涝灾害提供技术支撑.基于极端降水事件中持续时间和强度关系理论,采用包含当日近5d最强日相当降水量作为日降水强度指标,构建了雨涝指数和区域雨涝过程强度算法.利用1961—2018年云南省逐日降水资料,分析了云南雨涝过程特征.结果表明:①在云南地区确定25 mm日降水量作为雨涝过程出现的起始日降水量阈值是符合实际的,而非50 mm;②1961—2018年云南共发生320次雨涝过程,年均5.5次,其中特强34次、强60次、较强105次,一般121次.云南雨涝强度无明显趋势变化,但雨涝过程强度波动变化剧烈;③云南年均发生轻涝34.0站日,中涝6.4站日,重涝0.9站日,特涝0.1站日.云南雨涝发生月际变化大,全年除2—3月外,其余月份均可发生,主要集中在6—8月;④云南暴雨洪涝存在明显的年际和年代际变化特征,20世纪60—70年代、21世纪以来处于偏少期,20世纪80—90年代处于偏多期.总体来看,雨涝指数能更好地反映雨涝过程和区域雨涝强度,其结果与实际洪涝灾情较为吻合.

Abstract Shallow soil refers to the soil layer within the 50 cm depth. Shallow soil temperature (ST) directly or indirectly affects many processes in the soil, such as seed germination, plant growth, and water evaporation. Therefore, the study of shallow ST is of great significance in understanding the surface energy, water cycle, ecology and climate change. This work collected observational data from 141 meteorological stations on the Qinghai-Tibet Plateau from 1981 to 2020 and ERA5 reanalysis data, used the “Moving Surface Spline Interpolation Algorithm Based on Green’s Function” and “Fuzzy C-means algorithm”, and analyzed the temporal and spatial change characteristics of ST at different levels. The results showed that 1) the temperature increase of 0–20 cm (the surface layer of the shallow soil) was roughly the same. The average annual ST was 9.15–9.57°, and the interdecadal variabilities were 0.49–0.53 K/10a. The average annual ST of 40 cm (the bottom layer) was 8.69°, and the interdecadal variability reached 0.98 K/10a. 2) Considering the 7 regions, the warming trend was obvious, and there were certain regional differences. The average annual ST in different regions ranged from 5.2 (northeastern Plateau) to 17.1 °C (western Sichuan Plateau), with a difference of nearly 12 K. The standard deviation ranged from 0.40 (western Sichuan Plateau) to 0.61 K (Qiangtang Plateau), with a difference of 0.21 K. 3) The errors of the obtained grid data were basically less than 3%, which were much smaller than the errors obtained from the ERA5 reanalysis data. This work is significant for understanding the characteristics of ST evolution and land‒atmosphere interactions on the Qinghai-Tibet Plateau and provides important data support for improving the underlying surface boundary conditions of models.

基于粤港澳珠三角洲空气质量监测数据,利用克里金插值法和Spearman秩系数相关法分析了2015-2021年大湾区空气污染物的时空分布特征,利用灰色关联分析法分析了自然、社会因素对空气污染物的影响.结果表明,近7年大湾区的空气质量整体向好,PM10、PM2.5年均值分别为40.32、23.38μg·m-3,均低于新标准二级限值,O3、NO2、SO2年均值分别为51.68、33.45和6.61μg·m-3.其中,SO2质量浓度低于新标准一级限值,CO年均值为0.68mg·m-3,CO、PM2.5、NO2、PM10和SO2年均质量浓度呈下降趋势,降幅分别为23.67％、39.48％、27.02％、33.75％和41.21％,季节上表现为冬季最高、春秋季次之、夏季最低.O3年均质量浓度呈波浪上升趋势,升幅为14.54％,季节变化为:秋季＞春季＞冬季＞夏季,O3质量浓度升高的原因可能是副热带高气压带和台风外围的大气环流形式等不利气象条件和O3前体物高排放逐渐升高等人为排放因素等共同影响的结果.O3的空间分布表现为东莞、深圳、港澳和江门南部等东部沿海地区高于中西部内陆地区,高值质量浓度达48～70μg.m-3,其他污染物质量浓度呈"中西—东部"递减趋势.气温、日照时数和风速与CO、NO2、PM10、PM2.5和SO2质量浓度呈负相关,与O3呈正相关,相对湿度与O3呈负相关,与其他污染物呈正相关,降水量对珠港澳三地污染物质量浓度影响不一.能源消耗、工业生产、人口和机动车数据等是影响大湾区空气质量的主要因素.本研究结果可为粤港澳大湾区在大气污染防治、环保策略制定等方面提供参考.

Based on the daily temperature and precipitation data from 1961 to 2021 obtained from 2019 national meteorological stations of China, and by means of Meteorological Drought Comprehensive Index (MCI) and some improved identification methods, we identified all drought event processes in all the seven regions of China in this paper. We also carefully analyzed these regional droughts and made following conclusions: drought was spreading towards southern China with an increasing frequency; consecutive drought year group occurred in all the seven regions of China with an increasing frequency under the background of global warming; both drought duration and comprehensive intensity consistently changed with the area affected by drought, with a correlation coefficient of 0.52–0.67 and 0.88–0.99 respectively, and passed the significance test of 0.05. The method used in this paper could be employed to effectively monitor and evaluate drought processes from multiple dimensions including duration, comprehensive intensity and area affected by drought. Thus, it actually provided a helpful decision-making basis for governments to prevent the risk of drought.

Sea ice has important effect on the marine ecosystem and people living in the surrounding regions in winter. However, the understanding on changes of sea ice in the Bohai and northern Huanghai Sea (BNHS), China is still limited. Based on the images from Visible and InfraRed Radiometer (VIRR) onboard Chinese second generation polar-orbit meteorological series satellites FY-3A/B/C, the sea ice areas in the BNHS were extracted from December 2008 to March 2019, the spatio-temporal distribution characteristics of sea ice and the relationship between sea ice area and climatic factors were analyzed, then a preliminary sea ice forecast model based on the climatic factors was developed. The results showed that sea ice area in the BNHS in each December was relatively small and rather high sea ice occurrence probability appeared in the offshore areas in Liaodong Bay and northern Huanghai Sea. The sea ice area in January or February each year was the largest, and sea ice occurred in most of areas in Liaodong Bay and northern Huanghai Sea with rather high probability and in some areas in Bohai Bay and Laizhou Bay with relatively high probability. However, the sea ice area in each March was the smallest, and sea ice was even melted completely occasionally, hence with relatively low occurrence probability in Liaodong Bay. As for the inter-annual variability of sea ice in the BNHS during the research period, the sea ice area was largest in winter 2010/11 and smallest in winter 2014/15, and annual sea ice area presented a decreasing trend. The atmospheric temperature, western Pacific subtropical high (WPSH), Asia polar vortex (APV), Asian monsoon circulation (AMC) and Eurasian monsoon circulation (EMC) were very important climatic factors for sea ice formation and they had significant correlations with sea ice area. Therefore, a preliminary sea ice forecast model was constructed by using eight climatic factors including western Pacific subtropical high area index (WPSHAI), western Pacific subtropical high intensity index (WPSHII), western Pacific subtropical high northern boundary position index (WPSHNBPI), Asia polar vortex area index (APVAI), Asian zonal circulation index (AZCI), Asian meridional circulation index (AMCI), Eurasian zonal circulation index (EZCI) and mean minimum atmospheric temperature (MMAT). The model was confirmed to have a robust forecast effect by using F -test and validated sample data. The results are useful for monitoring sea ice with remote sensed data and forecasting sea ice conditions by climatic indices.

This work presents a novel scheme to retrieve soil moisture (SM) from the Cyclone Global Navigation Satellite System (CyGNSS) data, which is accomplished by using a bagged regression trees (BRT) algorithm with the inputs being the CyGNSS-derived products, the corresponding geolocation, and associated climate type. This algorithm is validated with the in situ hourly SM data acquired by China’s automatic SM observation stations throughout the year 2018. High consistency between the retrieved SM results and the measured SM is achieved, with a correlation coefficient of 0.86 and a root-mean-square error of 0.05 cm3/cm3. The results obtained in this work indicate that the proposed BRT-based method can effectively estimate SM from CyGNSS data in different scenarios of various station locations and climate types in a near real-time manner.

COSMIC掩星大气产品的时空分辨率和垂直分辨率较高,可提高数值天气预报模型的精度并用于气候气象研究,但目前鲜有在全球范围内的验证分析.该文利用无线电探空数据评估COSMIC掩星探测系统反演的大气水汽压在不同高度、季节和纬度上的数据质量,并以COSMIC在近地面(925 hpa)和对流层顶(300 hpa)两个典型高度的水汽压数据分析全球水汽的时空分布特征.结果表明:1)对流层顶以下的COSMIC水汽压略低于探空水汽压值;COSMIC水汽压在近地面偏差较大,且随着高度的上升而减小,高空COSMIC水汽压与探空水汽压基本一致,对改进数值天气预报精度具有重要意义.2)COSMIC水汽压与探空水汽压的差异以夏季最大,且随着纬度升高偏差逐渐减小.3)COSMIC水汽压相对于探空水汽压的平均偏差、均方根误差和平均绝对百分比误差分别为-1.657～-1.088×10-5 hpa、0.005～4.189 hpa和24.553％～37.956％;全球COSMIC水汽压在两个典型高度的分布特征相似,且存在南北半球不对称性.

Sichuan region as an important hub of western China population, its phenological change has a great influence on the western economic construction and social development, so the phenological response under the background of global warming and change, the ecological balance, scientific research and agricultural production is of great significance [1]. Taking MODIS remote sensing satellite images of Sichuan province as the data set, aiming at the inversion problem of vegetation phenology, the machine learning method--Extreme gradient boosting(XGBoost) was used to build the vegetation phenology prediction model, and the results were compared with the traditional methods. The results show that the prediction model of machine learning method has a certain accuracy. The experimental results show that the XGBoost is able to achieve an acceptable accuracy, the average root mean squared error (RMSE), mean absolute error (MAE) and coefficient of correlation (R) were 4.684/4.413, 4.353/4.297, and 0.7725/0.7812 respectively.

针对CYGNSS卫星风速产品的适用性问题,以美国国家数据浮标中心(NDBC)的实测风速与美国国家飓风中心(NHC)的最佳路径风速为参照,选取美国西南部海域为研究区,通过数据匹配和对比分析,评估了CYGNSS不同模型估算风速产品的精度.结果 表明,CYGNSS FDS模型估算的中、低风速产品与NDBC浮标实测风速具有较好的一致性,CYGNSS风速与浮标风速的差异在春夏季稍高、秋冬季略低;CYGNSS YSLF模型估算的高风速产品与NHC最大风速存在较大差异,CYGNSS风速低于NHC最大风速;对于CYGNSS两种模型估算的风速产品,利用遥感观测量NBRCS反演出的风速都比LES反演出的风速具有更好的精度.总体而言,本研究验证了CYGNSS风速产品的真实有效性,对提高海洋数值预报能力具有一定的意义.