Cloud type profoundly affects precipitation,but few studies have explored its impact on precipitation scale height.The authors calculated the ratio of the volume of each cloud type to the total cloud volume and partit...Cloud type profoundly affects precipitation,but few studies have explored its impact on precipitation scale height.The authors calculated the ratio of the volume of each cloud type to the total cloud volume and partitioned the tropical region based on the dominant cloud types.Based on this,tropical regions were categorized into altocumulus control regions,stratocumulus control regions,deep convective cloud control regions,and transition regions.These regions exhibit unique characteristics:high precipitation scale heights and low surface precipitation rates in altocumulus control regions;low precipitation scale heights and low surface precipitation rates in stratocumulus control regions;and moderate precipitation scale heights with high surface precipitation rates in deep convective cloud regions.These features arise from differences in cloud characteristics,precipitation probability,and intensity,influenced by varying water vapor structures.In terms of physical mechanisms,altocumulus,stratocumulus,and deep convective cloud regions are characterized by total dryness,upper-level dryness with lower-level wetness,and total wetness,respectively.Upper-layer dryness leads to low cloud and precipitation structures,reducing the precipitation scale height,while lower-layer dryness increases it.Different humidity conditions in the upper and lower layers lead to variations in cloud type and volume distribution,ultimately affecting precipitation scale heights.This finding aids the mechanistic study of cloud precipitation physics in the tropics,providing valuable insights for developing numerical models and parameterizations.展开更多
The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud typ...The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.展开更多
Accurate observation of clouds is challenging because of the high variability and complexity of cloud types and occurrences.By using the long-term cloud data collected during the ARM program at the Southern Great Plai...Accurate observation of clouds is challenging because of the high variability and complexity of cloud types and occurrences.By using the long-term cloud data collected during the ARM program at the Southern Great Plains central facility during 2001-2010,the consistencies and differences in the macrophysical properties of clouds between radiosonde and ground-based active remote sensing are quantitatively evaluated according to six cloud types:low;mid-low(ML);high-midlow;mid;high-mid(HM);and high.A similar variability trend is exhibited by the radiosonde and surface observations for the cloud fractions of the six cloud types.However,the magnitudes of the differences between the two methods are different among the six cloud types,with the largest difference seen in the high clouds.The distribution of the cloud-base height of the ML,mid,and HM clouds agrees in both methods,whereas large differences are seen in the cloud-top height for the ML and high clouds.The cloud thickness variations generally agree between the two datasets for the six cloud types.展开更多
By using four-year CloudSat/CALIPSO satellite data,the authors investigated cloud microphysical properties in three representative regions over East Asia,where models commonly suffer from great biases in simulations o...By using four-year CloudSat/CALIPSO satellite data,the authors investigated cloud microphysical properties in three representative regions over East Asia,where models commonly suffer from great biases in simulations of cloud radiative effects.This study aims to provide an observational basis of cloud microphysical properties for the modeling community,against which the model simulations can be validated.The analyzed cloud microphysical properties include mass,number concentration,and effective radius for both liquid and ice phases.For liquid clouds,both cloud mass and number concentration gradually decrease with height,leading to the effective radius being nearly uniformly spread in the range of 8-14μm.For ice clouds,the cloud mass and effective radius decrease with height,whereas the number concentration is nearly uniform in the vertical.The cloud microphysical properties show remarkable differences among different cloud types.Cloud mass and number concentration are larger in cumuliform clouds,whereas smaller in cirrus clouds.By comparing cloud properties among the Tibetan Plateau,East China,and the western North Pacific,results show the values are overall smaller for liquid clouds but larger for ice clouds over the Tibetan Plateau.展开更多
A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering me...A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus,cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency(24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.展开更多
We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Lang...We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language(IDL) and Visual C++(VC) code in combination to extend the technique in three dimensions(3-D),this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional(2-D) UFSCM.The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.展开更多
Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using sa...Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 μm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 μm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination is useful for delineating cumulonimbus underneath dense cirrus展开更多
As a basic property of cloud,accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons.Millimeter-wave cloud radar is an important means of identifying cloud type.Here,we dev...As a basic property of cloud,accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons.Millimeter-wave cloud radar is an important means of identifying cloud type.Here,we develop a fuzzy logic algorithm that depends on radar range-height-indicator(RHI)data and takes into account the fundamental physical features of different cloud types.The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar.The input parameters of the algorithm include average reflectivity factor intensity,ellipse long axis orientation,cloud base height,cloud thickness,presence/absence of precipitation,ratio of horizontal extent to vertical extent,maximum echo intensity,and standard variance of intensities.The identified cloud types are stratus(St),stratocumulus(Sc),cumulus(Cu),cumulonimbus(Cb),nimbostratus(Ns),altostratus(As),altocumulus(Ac)and high cloud.The cloud types identified using the algorithm are in good agreement with those identified by a human observer.As a case study,the algorithm was applied to typhoon Khanun(1720),which made landfall in south-eastern China in October 2017.Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.展开更多
In this study,cloud base height（CBH） and cloud top height（CTH） observed by the Ka-band（33.44 GHz） cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013（Septembe...In this study,cloud base height（CBH） and cloud top height（CTH） observed by the Ka-band（33.44 GHz） cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013（September-November） were verified and corrected.For comparative verification,CBH and CTH were obtained using a ceilometer（CL51） and the Communication,Ocean and Meteorological Satellite（COMS）.During rainfall,the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops,and this difference increased with rainfall intensity.During dry periods,however,the CBH and CTH observed by the cloud radar,ceilometer,and COMS were similar.Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS.In cases of rainfall or missing cloud radar data,the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data.These corrected cloud data were used to classify cloud types,which revealed that low clouds occurred most frequently.展开更多
During a research cruise over the Pacific Ocean in 1989, solar irradiance was measured with a broad-band pyranometer along the cruise track. Cloud cover was photographed with an all-sky time-lapse came ra. Cloud types...During a research cruise over the Pacific Ocean in 1989, solar irradiance was measured with a broad-band pyranometer along the cruise track. Cloud cover was photographed with an all-sky time-lapse came ra. Cloud types were observed and recorded. The data show that both the types and the amounts of clouds affect radiation fluxes on the sea surface. For low-level and middle-level clouds, the correlations (r) between measured irradiance (in Percent of calculated maximum irradiance) and cloud amount (in fraction of sky) were significant: r=-0. 79 and - 0. 66, respectively. For high-level clouds, the correlation was not significant: r=-0. 21. The results indicate that cloud shortwave forcing is a major modifier of the earth's surface insolation and change of cloud amount may affect global climate.展开更多
Water resources are one of the key factors restricting the development of arid areas,and cloud water resources is an important part of water resources.The arid region of central Asia is the core region of the current ...Water resources are one of the key factors restricting the development of arid areas,and cloud water resources is an important part of water resources.The arid region of central Asia is the core region of the current national green silk road construction,and is the largest arid region in the world.Based on cloud cover data of ECMWF,the current study analyzed temporal and spatial characteristics of cloud properties in arid regions of Central Asia between 1980 and 2019.Our findings show that:(1)From the point of view of spatial distribution,total cloudiness in arid regions of Central Asia was low in the south and high in the north.The distribution of high cloud frequency and medium cloud frequency was higher in the south and lower in the north,while low cloud frequency distribution was low in the south and high in the north.(2)In terms of time,the variation of cloud cover and cloud type frequency had obvious seasonal characteristics.From winter to spring,cloud cover increased,and the change of cloud type frequency increased.From spring to summer,cloud cover continued to increase and the change of cloud type frequency increased further.Cloud cover began to decrease from summer to autumn,and the change of cloud type frequency also decreased.(3)Generally,average total cloud cover decreased in most of central Asia,and high and medium cloud cover increased while low cloud cover decreased.This study provides a reference for the rational development of cloud resources in the region.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers 42175099 and 42027804]The appointment of Chunsong Lu at Nanjing University of Information Science&Technology was partially supported by the Jiangsu Specially-Appointed Professor[grant number R2024T01].
文摘Cloud type profoundly affects precipitation,but few studies have explored its impact on precipitation scale height.The authors calculated the ratio of the volume of each cloud type to the total cloud volume and partitioned the tropical region based on the dominant cloud types.Based on this,tropical regions were categorized into altocumulus control regions,stratocumulus control regions,deep convective cloud control regions,and transition regions.These regions exhibit unique characteristics:high precipitation scale heights and low surface precipitation rates in altocumulus control regions;low precipitation scale heights and low surface precipitation rates in stratocumulus control regions;and moderate precipitation scale heights with high surface precipitation rates in deep convective cloud regions.These features arise from differences in cloud characteristics,precipitation probability,and intensity,influenced by varying water vapor structures.In terms of physical mechanisms,altocumulus,stratocumulus,and deep convective cloud regions are characterized by total dryness,upper-level dryness with lower-level wetness,and total wetness,respectively.Upper-layer dryness leads to low cloud and precipitation structures,reducing the precipitation scale height,while lower-layer dryness increases it.Different humidity conditions in the upper and lower layers lead to variations in cloud type and volume distribution,ultimately affecting precipitation scale heights.This finding aids the mechanistic study of cloud precipitation physics in the tropics,providing valuable insights for developing numerical models and parameterizations.
基金supported in part by the National Natural Science Foundation of China (Grant No. 42105127)the Special Research Assistant Project of the Chinese Academy of Sciencesthe National Key Research and Development Plans of China (Grant Nos. 2019YFC1510304 and 2016YFE0201900-02)。
文摘The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.
基金supported by the National Natural Science Foundation of China[grant numbers 41275039,61327810 and91337214]
文摘Accurate observation of clouds is challenging because of the high variability and complexity of cloud types and occurrences.By using the long-term cloud data collected during the ARM program at the Southern Great Plains central facility during 2001-2010,the consistencies and differences in the macrophysical properties of clouds between radiosonde and ground-based active remote sensing are quantitatively evaluated according to six cloud types:low;mid-low(ML);high-midlow;mid;high-mid(HM);and high.A similar variability trend is exhibited by the radiosonde and surface observations for the cloud fractions of the six cloud types.However,the magnitudes of the differences between the two methods are different among the six cloud types,with the largest difference seen in the high clouds.The distribution of the cloud-base height of the ML,mid,and HM clouds agrees in both methods,whereas large differences are seen in the cloud-top height for the ML and high clouds.The cloud thickness variations generally agree between the two datasets for the six cloud types.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA20060501]the National Basic Research Program of China[grant numbers 2017YFA0604000 and 2016YFB0200800]the National Natural Science Foundation of China[grant number 41530426]。
文摘By using four-year CloudSat/CALIPSO satellite data,the authors investigated cloud microphysical properties in three representative regions over East Asia,where models commonly suffer from great biases in simulations of cloud radiative effects.This study aims to provide an observational basis of cloud microphysical properties for the modeling community,against which the model simulations can be validated.The analyzed cloud microphysical properties include mass,number concentration,and effective radius for both liquid and ice phases.For liquid clouds,both cloud mass and number concentration gradually decrease with height,leading to the effective radius being nearly uniformly spread in the range of 8-14μm.For ice clouds,the cloud mass and effective radius decrease with height,whereas the number concentration is nearly uniform in the vertical.The cloud microphysical properties show remarkable differences among different cloud types.Cloud mass and number concentration are larger in cumuliform clouds,whereas smaller in cirrus clouds.By comparing cloud properties among the Tibetan Plateau,East China,and the western North Pacific,results show the values are overall smaller for liquid clouds but larger for ice clouds over the Tibetan Plateau.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41775032 and 41275040)
文摘A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus,cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency(24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.
基金supported by the National Natural Science Foundation of China(Grant No.40875012)the National Basic Research Program of China(Grant No.2009CB421502)the Meteorology Open Fund of Huaihe River Basin(HRM200704).
文摘We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language(IDL) and Visual C++(VC) code in combination to extend the technique in three dimensions(3-D),this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional(2-D) UFSCM.The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.
文摘Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 μm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 μm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination is useful for delineating cumulonimbus underneath dense cirrus
基金This work was supported by the National Natural Science Foundation of China(Grant No.41675029)the National Basic Research Program of China(No.2013CB430102).
文摘As a basic property of cloud,accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons.Millimeter-wave cloud radar is an important means of identifying cloud type.Here,we develop a fuzzy logic algorithm that depends on radar range-height-indicator(RHI)data and takes into account the fundamental physical features of different cloud types.The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar.The input parameters of the algorithm include average reflectivity factor intensity,ellipse long axis orientation,cloud base height,cloud thickness,presence/absence of precipitation,ratio of horizontal extent to vertical extent,maximum echo intensity,and standard variance of intensities.The identified cloud types are stratus(St),stratocumulus(Sc),cumulus(Cu),cumulonimbus(Cb),nimbostratus(Ns),altostratus(As),altocumulus(Ac)and high cloud.The cloud types identified using the algorithm are in good agreement with those identified by a human observer.As a case study,the algorithm was applied to typhoon Khanun(1720),which made landfall in south-eastern China in October 2017.Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.
基金supported by the principal project, “Development and application of technology for weather forecasting (NIMR-2012-B-1)” of the National Institute of Meteorological Sciences of the Korea Meteorological Administration
文摘In this study,cloud base height（CBH） and cloud top height（CTH） observed by the Ka-band（33.44 GHz） cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013（September-November） were verified and corrected.For comparative verification,CBH and CTH were obtained using a ceilometer（CL51） and the Communication,Ocean and Meteorological Satellite（COMS）.During rainfall,the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops,and this difference increased with rainfall intensity.During dry periods,however,the CBH and CTH observed by the cloud radar,ceilometer,and COMS were similar.Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS.In cases of rainfall or missing cloud radar data,the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data.These corrected cloud data were used to classify cloud types,which revealed that low clouds occurred most frequently.
文摘During a research cruise over the Pacific Ocean in 1989, solar irradiance was measured with a broad-band pyranometer along the cruise track. Cloud cover was photographed with an all-sky time-lapse came ra. Cloud types were observed and recorded. The data show that both the types and the amounts of clouds affect radiation fluxes on the sea surface. For low-level and middle-level clouds, the correlations (r) between measured irradiance (in Percent of calculated maximum irradiance) and cloud amount (in fraction of sky) were significant: r=-0. 79 and - 0. 66, respectively. For high-level clouds, the correlation was not significant: r=-0. 21. The results indicate that cloud shortwave forcing is a major modifier of the earth's surface insolation and change of cloud amount may affect global climate.
基金financially supported by the National Natural Science Foundation of China (41867030, 41971036)the National Natural Science Foundation innovation research group science foundation of China (41421061)
文摘Water resources are one of the key factors restricting the development of arid areas,and cloud water resources is an important part of water resources.The arid region of central Asia is the core region of the current national green silk road construction,and is the largest arid region in the world.Based on cloud cover data of ECMWF,the current study analyzed temporal and spatial characteristics of cloud properties in arid regions of Central Asia between 1980 and 2019.Our findings show that:(1)From the point of view of spatial distribution,total cloudiness in arid regions of Central Asia was low in the south and high in the north.The distribution of high cloud frequency and medium cloud frequency was higher in the south and lower in the north,while low cloud frequency distribution was low in the south and high in the north.(2)In terms of time,the variation of cloud cover and cloud type frequency had obvious seasonal characteristics.From winter to spring,cloud cover increased,and the change of cloud type frequency increased.From spring to summer,cloud cover continued to increase and the change of cloud type frequency increased further.Cloud cover began to decrease from summer to autumn,and the change of cloud type frequency also decreased.(3)Generally,average total cloud cover decreased in most of central Asia,and high and medium cloud cover increased while low cloud cover decreased.This study provides a reference for the rational development of cloud resources in the region.
