冰核对云物理属性和降水影响的研究

系统回顾了冰核对云和降水影响的研究进展,详细分析了冰核对云的宏微观特征、对流系统结构和强度、辐射、雷电、降水量和降水强度的影响途径,并从云微物理过程的角度解释了其影响机制。得到如下结论:1通常情况下,冰核浓度增加,冰晶浓度增大,云滴浓度减小,云的生命史延长;2对于发展阶段的对流云,冰核在温度较高的过冷区活化使潜热在中低层提早释放,增加了对流系统中层不稳定能量,促进了对流系统的发展;3卷云中冰核浓度的增加,改变了冰相水物质与液相水物质的比例;削弱或抑制了卷云中同质核化,增大了卷云中冰晶粒子的平均半径;4冰核浓度的增加,能够使到达地气系统的净辐射增加;5冰核浓度的变化能够引起雷电活动发生频次和强度的变化;6冰核浓度增加,引起降水量的变化不确定,即降水量增加、减少或者变化不显著的情况都可能存在。这些结果为改进数值模式中冰核活化参数化方法提供指导,从而提高数值模式对云和降水的预报能力;同时为人工影响天气选择合适的人工催化剂和撒播时机提供参考。     

Using cloud water path and cloud top temperature for estimating convective and stratiform rainfall from SEVIRI daytime data

A satellite rainfall retrieval technique is proposed here. The relationships of rain rate with each of cloud water path (CWP) and cloud top temperature (CTT) are investigated. The CWP and CTT are retrieved from SEVIRI data (spinning enhanced visible and infrared imager), and corresponding rain rates are measured by weather radar. The rain rates are compared to corresponding CWP and then to corresponding CTT. The investigation demonstrates an exponential functional dependency between rain rates and CWP for low and moderate rain rates (stratiform rainfall). Conversely, the rain rates are more closely related to CTT for high rain rates (convective rainfall). Therefore, two separate relationships are established for rain rate retrievals. The results show rain rates estimated by the developed scheme are in good correlation with those observed by weather radar.     

Ideological and volume politics behind cloud water resource governance – Weather modification in China

Weather modification operations are the intentional alteration of weather and cloud water conditions using technologies such as cloud seeding. Post-socialist authoritarian China is the world’s leading user of state power for rainfall enhancement through weather modification, with diverse purposes including agriculture production, water security, ecological preservation, and mega events. We argue that weather modification in China needs to be understood as a facet of ecological modernization, in which the authoritarian state believes that precipitation can be controlled through the use of advanced technologies, thus transforming clouds into a kind of cloud water resource. Two political dimensions are highlighted to understand precipitation control and utilization of cloud water: the first is a new ideological politics of the changing human-weather relationship from ‘adaptation to the weather’ to ‘taming the weather”; the second is volume politics that presents unique characteristics of airborne water as opposed to terrestrial and groundwater.     

基于CloudSat卫星资料的新疆三大山区不同类型云高特征研究

作为空中水资源的重要组成部分,云在地球水循环过程和气候系统中扮演着重要角色,不同高度的云因其物理特性和动力过程的不同而对人工增水作业具有不同的指示意义. 采用2007年1月至2008年12月的美国宇航局（NASA）云卫星（CloudSat） 2B-CLDCLASS资料,从不同类型云的高度分布特征分析了新疆阿尔泰山、天山和昆仑山区的云水资源情况.结果表明：各个季节三大山区高层云所占比例均较大,在20%以上,其中,天山山区和昆仑山区雨层云所占比例也较大,在15%以上. 三大山区不同云的云顶和云底高度年变化趋势基本一致,昆仑山区各类型云的平均云顶和云底的高度最大,阿尔泰山区的最低.     

Retrieval of cloud classification parameters using two-dimensional fast Fourier transform

A method is presented for the retrieval of classification parameters of clouds observed by satellite-borne imaging systems. It is based on a two-dimensional fast Fourier transform of cloud images and an analysis of their power spectra. The parameters retrieved provide quantitative information on mean brightness, size, shape and directional properties of clouds. The efficacy of the subdivision of the original cloud image into smaller regions and the determination of individual parameters is demonstrated by applying this procedure to some NOAA and INSAT cloud images.     

Vertical structure of orographic precipitating clouds observed over south Asia during summer monsoon season

Orography profoundly influences seasonal rainfall amount in several places in south Asia by affecting rain intensity and duration. One of the fundamental questions concerning orographic rainfall is nature of the associated precipitating clouds in the absence of synoptic forcing. It is believed that these clouds are not very deep, however, there is not much information in the literature on their vertical structure. The present study explores the vertical structure of precipitating clouds associated with orographic features in south Asia using data collected with the precipitation radar on board the Tropical Rainfall Measuring Mission satellite. Two types of precipitating clouds have been defined based on cloud echo top height, namely, shallow echo-top cloud and medium echo-top cloud. In both, radar reflectivity factor is at least 30 dBZ at 1.5 km altitude, and tops of shallow and medium echo-top clouds lie below 4.5 km and between 4.5 and 8 km, respectively. The Western Ghats contains the highest fraction of the shallow echo-top clouds followed by the adjacent eastern Arabian Sea, while the Khasi Hills in Meghalaya and Cardamom Mountains in Cambodia contain the least fraction of them. Average vertical profiles of shallow echo-top clouds are similar in different mountainous areas while regional differences are observed in the medium echo-top clouds. Below 3 km, precipitation liquid water content in medium echo-top clouds is the highest over the Western Ghats and the eastern Arabian Sea. The average precipitation liquid water content increases by \(0.16\,\hbox { gm m}^{-3}\) for shallow echo-top clouds between 3 and 1.5 km altitude, while the corresponding increase for medium echo-top clouds is in 0.05–0.08 \(\hbox { gm m}^{-3}\) range.     

新疆天山山区降雨的微物理结构特征

使用GBPP-100型雨滴谱仪,于2001年6月12日至7月31日在天山北坡的小渠子气象站和牧业气象试验站,对27次降雨过程进行了雨滴谱观测,共获取了4 719个雨滴谱样本。通过观测资料分析新疆中天山山区积状云、层状云、积状-层状混合云降雨的微物理结构特征。观测分析表明,天山山区降雨雨滴的平均直径0.41~0.55 mm,以积状云最大,混合云次之,层状云最小。最大平均直径0.88~1.12 mm、平均雨强1.18~2.78 mm·h^-1、平均含水量5.23~11.62 g·m^-3,混合云的这三个特征量均为最大。三类云的雨强与数密度呈正相关。积状云、层状云降雨的雨滴谱服从M-P分布,混合云服从Γ分布。由于山区地形的作用,使云中降雨粒子的生长时间受到限制,天山山区降雨小滴浓度高、尺度小,人工降雨潜力大。     

Numerical simulation of deep convective cloud seeding using liquid carbon dioxide

In this study, a one-dimensional transient cumulonimbus cloud is modeled to be seeded by liquid CO₂. The model includes microphysical and dynamical processes associated with glaciogenic seeding by homogenous ice nucleation and two thermal terms associated with seeding by ?90 ºC liquid CO₂. For this model, the study concentrates on five types of hydrometeors, namely, cloud droplet, cloud ice, snow, hail/graupel, and rain. Point and horizontal seeding methods are implemented to observe their implications for rainfall enhancement, amount of hail/graupel production, vertical cloud extension, and radar’s reflectivity. In addition, the seeding temperature effects on the rainfall and microphysical processes are investigated. The results of the study show that, the rainfall enhancement and rainfall intensity in the point seeding case are more than those in the horizontal seeding. Moreover, the study reveals that, there is a vertical cloud extension enhancement of 0.5 km for clouds with top height of 10.5 km. The most important sources of the rain water production are found to be the accretion of cloud water by rain (P _RACW) and by snow (P _SACW), and for the graupel production is dry growth of the graupel (P _GDRY). The results of this study are confirmed by the results of other investigators and are found to be comparable with the recorded data at rain gauge stations.     

Strength of ensemble learning in multiclass classification of rockburst intensity

Rockbust is a violent expulsion of rock due to the extreme release of strain energy stored in surrounding rock mass, leading to considerable damages to underground strucures and equipment, and threatening workers' safety. As the operational depth of engineering projects increases, a larger number of factors influence the mechanism of rockburst. Therefore, accurate classification of rockburst intensity cannot be achieved based on conventional criteria. It is urgent to develop new models with high accuracy and ease to implement in practice. This study proposed an ensemble machine learning method by aggregating seven individual classifiers including back propagation neural network, support vector machine, decision tree, k-nearest neighbours, logistic regression, multiple linear regression and Naïve Bayes. In addition, we proposed nine data imputation methods to replace the missing values in the compiled database including 188 rockburst instances. Five-fold cross validation and the beetle antennae search algorithm are used to tune hyperparameters and voting weights of the individual classifiers. The results show that the rockburst classification accuracy obtained by the classifier ensemble has increased by 15.4% compared with the best individual classifier on the test set. The predictor importance obtained by the classifier ensemble shows that the elastic energy index is the most sensitive input variable for rockburst intensity classification. This robust ensemble method can be extended to solve other classification problems in underground engineering projects.     

Real-time nowcast of a cloudburst and a thunderstorm event with assimilation of Doppler weather radar data

Extreme weather events such as cloudburst and thunderstorms are great threat to life and property. It is a great challenge for the forecasters to nowcast such hazardous extreme weather events. Mesoscale model (ARPS) with real-time assimilation of DWR data has been operationally implemented in India Meteorological Department (IMD) for real-time nowcast of weather over Indian region. Three-dimensional variational (ARPS3DVAR) technique and cloud analysis procedure are utilized for real-time data assimilation in the model. The assimilation is performed as a sequence of intermittent cycles and complete process (starting from reception, processing and assimilation of DWR data, running of ARPS model and Web site updation) takes less than 20 minutes. Thus, real-time nowcast for next 3 h from ARPS model is available within 20 minutes of corresponding hour. Cloudburst event of September 15, 2011, and thunderstorm event of October 22, 2010, are considered to demonstrate the capability of ARPS model to nowcast the extreme weather events in real time over Indian region. Results show that in both the cases, ARPS3DVAR and cloud analysis technique are able to extract hydrometeors from radar data which are transported to upper levels by the strong upward motion resulting in the distribution of hydrometeors at various isobaric levels. Dynamic and thermodynamic structures of cloudburst and thunderstorm are also well simulated. Thus, significant improvement in the initial condition is noticed. In the case of cloudburst event, the model is able to capture the sudden collisions of two or more clouds during 09–10 UTC. Rainfall predicted by the model during cloudburst event is over 100 mm which is very close to the observed rainfall (117 mm). The model is able to predict the cloudburst with slight errors in time and space. Real-time nowcast of thunderstorm shows that movement, horizontal extension, and north–south orientation of thunderstorm are well captured during first hour and deteriorate thereafter. The amount of rainfall predicted by the model during thunderstorm closely matches with observation with slight errors in the location of rainfall area. The temporal and spatial information predicted by ARPS model about the sudden collision/merger and broken up of convective cells, intensification, weakening, and maintaining intensity of convective cells has added value to a human forecast.     

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

Observed rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3–12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R² = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R² = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R² = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through ‘Nowcasting’, thus increasing the response time for flood early warnings.     

Numerical simulation of cloud burst event on August 05, 2010, over Leh using WRF mesoscale model

Localized deep cumulus convective clouds have a capability of giving enormous amount of rainfall over a limited horizontal area, within a short span of time. Such types of extreme rainfall events are most common over the high elevated areas of Northern India during the Southwest monsoon season which causes widespread damage to the property and lives. Therefore, it is necessary to predict such extreme events accurately to avoid damage associated with them. The numerical mesoscale model Weather Research and Forecasting has been used to simulate the cloud burst event of Leh on August 05, 2010, so as to capture the main characteristics of the various parameters associated with this localized mesoscale phenomenon. The model has been integrated with four nested domains keeping Leh and its adjoining area as center. Two cloud microphysics parameterization schemes namely WSM3 and WSM6 have been used for the sensitivity experiments and results have been analyzed to examine the performance of both the schemes in capturing such extreme localized heavy rainfall events. Results show that the WSM6 microphysics was able to simulate the precipitation near to the observation. WSM3 microphysics simulated the location of the circulation near to the observation. In addition, the results also show that the maximum magnitudes of meridional and vertical wind as simulated with WSM3 microphysics are 12 and 4 m/s, respectively.     

Seismic Hazard Estimation in Northern Algeria

In the present study, the seismic hazard in northern Algeria is estimated using both physical strain energy release and Gumbel's extreme values approaches. For six of the most industrial and populated cities in Algeria, seismic hazard is assessed and examined in greater detail. Gumbel's extreme values approach has been used to estimate seismic hazard in terms of magnitude and P.G.A at each point of an equispaced grid all over the north of Algeria. An average attenuation relationship for PGA has been provided using known relations which have been established in regions with similar attenuation characteristics.The results are presented mainly in the form of graphs and contour maps of magnitudes (respectively PGA) with a 60% probability of not being exceeded in the next 100 and 200 years. Globally, they give main features of northern Algeria in terms of zoning (as well as in terms of magnitude and in terms of PGA). They corroborate the ones obtained through other works, especially in the basin areas (Mitidja, Cheliff, Soumam and Constantine Basin).     

Research Status and Progress of Balloon-borne Cloud and Precipitation Particles Probe

The accurate observation of the microphysical structure of cloud and precipitation plays an important role in understanding the formation of clouds and precipitation. In-situ measurement using measuring instruments carried by meteorological balloons is an effective way to obtain the microphysical properties of cloud and precipitation particles, which is a supplementary means for aircraft to observe cloud and precipitation particles. This observation method plays a more and more important role in in-situ measurement. According to the difference of the working principle of the existing balloon-borne cloud and precipitation particles probes, the detectors can be divided into particle impact-sampling sensors, particle imaging sensors, light-scattering sensors, light intensity attenuation sensors and charge measurement sensors. The working principles, key technologies and main advantages and disadvantages of typical instruments were summarized, and their applications to detailed cloud structure acquisition, cloud remote sensing method establishment, cloud and precipitation physical process research and parameterization, and scientific observation of thunderstorm clouds were briefly introduced. Finally, the development trend of balloon-borne cloud precipitation particle detectors was prospected, which will provide reference for related technical research and equipment development.     

祁连山夏季地形云综合探测试验

2006年和2007年夏季在祁连山冷龙岭西段开展了地形云云量、云状、大气水汽、风场、雨滴谱和雨强等的综合探测试验,以分析祁连山地形云的特征。结果表明：①祁连山区夏季云量丰富,平均云量在6成以上。西南气流天气背景下总云量多达8成;②祁连山夏季无降水日大气水汽非常少,700 hPa以上层大气相对湿度大多在20%以下;③西南气流背景下祁连山南北侧山谷风的共同作用,气流昼间向山顶辐合,夜间向山谷辐散,当水汽条件充足时,极易抬升形成可以产生降水的地形云;④祁连山降水主要由小于1 mm的雨滴组成。     

Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India

Spatial and temporal variability of rainfall over different seasons influence physical, social and economic parameters. Pre-monsoon (March, April and May – MAM) rainfall over the country is highly variable. Since heat lows and convective rainfall in MAM have an impact on the intensity of the ensuing monsoons, hence the pre-monsoon period was chosen for the study. The pre-whitened Mann Kendall test was used to explore presence of rainfall trend during MAM. The results indicate presence of significant (at 10% level) increasing trend in two stations (Ajmer, Bikaner). The practical significance of the change in rainfall was also explored as percentage changes over long term mean, using Theil and Sen's median slope estimator. Forecast using univariate ARIMA model for pre-monsoon months indicates that there is a significant rise in the pre-monsoon rainfall over the northwest part of the country.     

Precipitation rate climatology related to different cloud types using satellite imagery over Iran

Cloud types have a substantial influence on precipitation. This paper presents a study of the monthly variations of daytime different cloud types over Iran using data collected from Moderate-resolution Imaging Spectroradiometer (MODIS) aboard Terra during 2001–2015, MODIS aboard Aqua during 2002–2015, International Satellite Cloud Climatology Project (ISCCP) H-series cloud type data during 2001–2009 and precipitation rate associated with different cloud types using Tropical Rainfall Measuring Mission (TRMM) satellite products during 2001–2009. Different cloud types were determined using MODIS cloud optical thickness and cloud top pressure data based on ISCCP algorithm. The results showed that stratocumulus and cumulus clouds have maximum occurrence frequency over marine areas especially southern seas. The maximum frequency of nimbostratus and deep convective occurrence occurred over mountainous regions particularly at the time of Aqua overpass and cirrus and cirrostratus are observed over southeast of Iran during warm months due to monsoon system. Altostratus cloud is extended in each month except January, at the time of Terra overpass while nimbostratus is seen at the time of Aqua overpass during warm months in the study area. Cumulus and altocumulus clouds have shown remarkable frequency in all months especially over marine regions during warm and fall months. The higher value of precipitation rate is related to altostratus with a rate approximately 7 mm/h at the time of Terra overpass during April. Altostratus has the maximum recorded precipitation rate except in Nov., Dec., Sep., and Jan. at the time of Terra overpass, whereas the maximum precipitation rate is linked to nimbostratus cloud activity (up to 5 mm/h) except for March, April, and Sep. at the time of Aqua overpass. Deep convective (up to 1.32 mm/h), cirrostratus (up to 1.11 mm/h), and cirrus (0.02 mm/h) are observed only in warm months. The results were compared with ISCCP cloud types so that precipitation rate classified from low to high and Spearman rank correlation was calculated. The results showed good agreement between these two cloud type data; however, there were few notable difference between them.     

Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data

With the acceleration of urbanisation in China, preventing and reducing the economic losses and casualties caused by urban rainstorm waterlogging disasters have become a critical and difficult issue that the government is concerned about. As urban storms are sudden, clustered, continuous, and cause huge economic losses, it is difficult to conduct emergency management. Developing a more scientific method for real-time disaster identification will help prevent losses over time. Examining social media big data is a feasible method for obtaining on-site disaster data and carrying out disaster risk assessments. This paper presents a real-time identification method for urban-storm disasters using Weibo data. Taking the June 2016 heavy rainstorm in Nanjing as an example, the obtained Weibo data are divided into eight parts for the training data set and two parts for the testing data set. It then performs text pre-processing using the Jieba segmentation module for word segmentation. Then, the term frequency–inverse document frequency method is used to calculate the feature items weights and extract the features. Hashing algorithms are introduced for processing high-dimensional sparse vector matrices. Finally, the naive Bayes, support vector machine, and random forest text classification algorithms are used to train the model, and a test set sample is introduced for testing the model to select the optimal classification algorithm. The experiments showed that the naive Bayes algorithm had the highest macro-average accuracy.     

A novel study of artificial bee colony with clustering technique on paddy rice image classification

Rice is a crop of global importance. To predict the area of paddy rice and thus its production, it draws great attraction of using data mining approaches on remote sensing data, which are well accepted. Many approaches based on supervised and unsupervised learning techniques have been developed over the years. Artificial bee colony (ABC) algorithm with a clustering technique is one of the most popular swarm-based algorithms. In this study, ABC algorithm is used to perform the rice image classification based on remote sensing imagery. This study comprises two stages. In the first part of the study, the ancillary information composed from the original spectra is applied to increase the performance of classification. As the other parts of the study, an efficient unsupervised classifier is developed to evaluate the performance of the incorporated ancillary information. This study integrates the ABC algorithm into a clustering process to build a land cover classifier system. On the other hand, a parallel approach using ant colony optimization (ACO) is studied for comparison. Two significant contributions are presented in this study: (1) a paddy rice image classifier is built with ABC algorithm and (2) the outcome of classifier using ABC algorithm outperforms that using ACO algorithm.