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This study employs a newly defined regional-rainfall-event (RRE) concept to compare the hourly characteristics of warm-season (May-September) rainfall among rain gauge observations, China merged hourly precipitation analysis (CMPA-Hourly), and two commonly used satellite products (TRMM 3B42 and CMORPH). By considering the rainfall characteristics in a given limited area rather than a single point or grid, this method largely eliminates the differences in rainfall characteristics among different observations or measurements over central-eastern China. The results show that the spatial distribution and diurnal variation of RRE frequency and intensity are quite consistent among different datasets, and the performance of CMPA-Hourly is better than the satellite products when compared with station observations. A regional rainfall coefficient (RRC), which can be used to classify local rain and regional rain, is employed to represent the spatial spread of rainfall in the limited region defining the RRE. It is found that rainfall spread in the selected grid box is more uniform during the nocturnal to morning hours over central-eastern China. The RRC tends to reach its diurnal maximum several hours after the RRE intensity peaks, implying an intermediate transition stage from convective to stratiform rainfall. In the afternoon, the RRC reaches its minimum, implying the dominance of local convections on small spatial scale in those hours, which could cause large differences in rain gauge and satellite observations. Since the RRE method reflects the overall features of rainfall in a limited region rather than at a fixed point or in a single grid, the widely recognized overestimation of afternoon rainfall in satellite products is not obvious, and thus the satellite estimates are more reliable in representing sub-daily variation of rainfall from the RRE perspective. This study proposes a reasonable method to compare satellite products with rain gauge observations on the sub-daily scale, which also has great potential to be used in evaluating the spatiotemporal variation of cloud and rainfall in numerical models. 相似文献
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Level 3 (3A25) TRMM Precipitation Radar (PR) data are used for 13 years period (1998–2010) to prepare climatology of TRMM PR derived near surface rain (Total rain) and rain fractions for the 4-months duration of Indian Summer Monsoon season (June–September) as well as for individual months. It is found that the total rain is contributed mostly (99 %) by two rain fractions i.e. stratiform and convective rain fractions for the season as well as on the monthly basis. It is also found that total rain estimates by PR are about 65 % of the gauge measured rain over continental India as well as on sub-regional basis. Inter-annual variability of TRMM-PR rain estimates for India mainland and its sub-regions as well as over the neighboring oceanic regions, in terms of coefficient of variability (CV) is discussed. The heaviest rain region over north Bay of Bengal (BoB) is found to have the lowest CV. Another sub-region of low CV lies over the eastern equatorial Indian ocean (EEIO). The CVs of total rain as well as its two major constituents are found to be higher on monthly basis compared to seasonal basis. Existence of a well known dipole between the EEIO and the north BoB is well recognized in PR data also. Significant variation in PR rainfall is found over continental India between excess and deficit monsoon seasons as well as between excess and deficit rainfall months of July and August. Examination of rainfall fractions between the BoB and Central India on year to year basis shows that compensation in rainfall fractions exists on monthly scale on both the regions. Also on the seasonal and monthly scales, compensation is observed in extreme monsoon seasons between the two regions. However, much less compensation is observed between the north BoB and EEIO belts in extreme rain months. This leads to speculation that the deficit and excess seasons over India may result from slight shift of the rainfall from Central India to the neighboring oceanic regions of north BoB. Contribution of stratiform and convective rain fractions have been also examined and the two fractions are found to contribute almost equally to the total rain. Results are further discussed in terms of the possible impact of the two rain fractions on circulation based on possible difference is vertical profiles of latent heat of two types of rain. Substantial differences in the lower and upper tropospheric circulation regimes are noticed in both deficit and excess monsoon months/seasons, emphasizing the interaction between rainfall (latent heat) and circulation. 相似文献
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TRMM测雨雷达对1998年东亚降水季节性特征的研究 总被引：16，自引：0，他引：16
利用热带测雨计划卫星上的测雨雷达得到的降水资料,对1998年东亚降水,特别是中国大陆东部、东海和南海的降水,进行了分析研究,并对比了热带降水研究结果。年统计结果表明,东亚地区层状云降水出现概率极高(比面积达83.7％),对流云降水的比面积仅占13.6％,然而两者对总降水量的贡献相当。结果还表明,暖对流云降水出现的比例和对总降水量的贡献很小。在季节尺度,对流云和层状云降水的比与两者的面积比成比例关系。除夏季外,测雨雷达降水量与GPCP降水量可比性好。研究结果还指出:在中纬度陆地和海洋上对流云和层状云的比降水量和比面积呈相反方向作季节性南北移动,这一活动与东亚季风变化一致;该地区降水的季节性变化还表现为降水垂直廓线的变化。除冬季外,南海地区降水垂直结构呈热带特征。CRAD分析表明,对流云降水的地面雨强变化大,尤其在陆地上,而层状云多表现为地面弱降水。 相似文献
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为掌握并改进青藏高原地区TRMM卫星降水雷达 (precipitation radar,PR) 地面降雨率准确度,统计分析了2005—2007年TRMM PR 2A25资料和逐小时地面雨量计,结果表明:青藏高原地区TRMM PR地面降雨率在层云降水时平均偏低35%,在对流云降水时平均偏高42%。Z-R关系的适用性是PR产生偏差的原因之一,研究将TRMM PR层云降水模型中20℃层Z-R关系的初始系数A和b分别修正为0.0288和0.6752,对流云降水模型中20℃层的初始系数A和b分别修正为0.0406和0.5809,得到两类降水模型0℃层与20℃层之间不同高度Z-R关系的更新系数。检验结果表明,修正降水模型后能够提高青藏高原地面降雨率测量的准确度。 相似文献
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This paper overviews observations and examines modeling issues associated with the mean state, climate variability and climate change in West Africa. The Tropical Rain Measuring Mission (TRMM) satellite allows for the first time estimates of Unconditional, Convective and Stratiform rain rates in West Africa. The 1998 estimated TRMM rates are compared to long-term observed rain rates and a merged rain data set (CMAP) during 1998. Further, the TRMM estimates are compared to the simulated rain rates from the Community Climate Model Version 3.6. The TRMM Precipitation Radar rain estimates are generally lower than either the long-term observations or the CMAP rates during 1998. Moreover, the TRMM rain estimates show a significant fraction of the total rain (convective + stratiform) is characterized as stratiform rain (30–40%). The CCM3 simulates primarily convective rain and negligible amounts of non-convective rain for West Africa. Furthermore, the TRMM high-resolution rain patterns strongly imply that rain in West Africa occurs on mesoscales in association with mesoscale convective systems (squall lines, mesoscale convective complexes and non-squall tropical clusters). We demonstrate this by briefly examining two mesoscale convective systems during May 1998 with METEOSAT data. Regional climate models may offer the best solution to understanding climate change in West Africa because of their ability to capture mesoscale systems and better their representation of orographic features. Adequate boundary conditions from Global Climate Models are still necessary for regional climate model simulations to successfully reproduce mean climate conditions and provide understanding with respect to future climate change. Observations in West Africa should be maintained or increased for monitoring climate variability and possibility of climate change in West Africa, proper initialization of numerical weather prediction models and the validation of climate models. 相似文献
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新一代全球降水观测计划GPM作为TRMM卫星的继承者,在物理探测和降水反演算法上具有明显进步。以广东省雨量自动站为基准,对2014—2018年间GPM的格点降水估测产品IMERG（V5B）的日变化特征和估测误差进行分析。结果表明,IMERG能清晰反映广东前、后汛期的降水双峰型特征,但对下午降水峰值明显高估,峰值出现时间滞后;而对于沿海早晨峰值降水则明显低估,对于降水极值,低估更加显著。IMERG对两个峰值的估测误差受不同因素影响,下午峰值降水的相对偏差与地形密切相关,珠江三角洲平原为稳定高估区,地形高度越高,低估幅度越大;而早晨峰值降水极值负偏差与地形高度、降水量的相关性均较小。对出现显著负偏差的早晨沿海降水样本日进行925 hPa风场合成,可知IMERG明显低估时,对应区域上游较强的超低空西南气流与风速夜间增长。IMERG对这一季风活动背景降水的低估构成了其估测早晨降水误差的主要来源。 相似文献
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Validation of Daily Precipitation from Two High-Resolution Satellite Precipitation Datasets over the Tibetan Plateau and the Regions to Its East 总被引：1，自引：0，他引：1 
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下载免费PDF全文 Daily precipitation amounts and frequencies from the CMORPH (Climate Prediction Center Morphing Technique) and TRMM (Tropical Rainfall Measuring Mission) 3B42 precipitation products are validated against warm season in-situ precipitation observations from 2003 to 2008 over the Tibetan Plateau and the regions to its east. The results indicate that these two satellite datasets can better detect daily precipitation frequency than daily precipitation amount. The ability of CMORPH and TRMM 3B42 to accurately detect daily precipitation amount is dependent on the underlying terrain. Both datasets are more reliable over the relatively flat terrain of the northeastern Tibetan Plateau, the Sichuan basin, and the mid-lower reaches of the Yangtze River than over the complex terrain of the Tibetan Plateau. Both satellite products are able to detect the occurrence of daily rainfall events; however, their performance is worse in regions of complex topography, such as the Tibetan Plateau. Regional distributions of precipitation amount by precipitation intensity based on TRMM 3B42 are close to those based on rain gauge data. By contrast, similar distributions based on CMORPH differ substantially. CMORPH overestimates the amount of rain associated with the most intense precipitation events over the mid-lower reaches of the Yangtze River while underestimating the amount of rain associated with lighter precipitation events. CMORPH underestimates the amount of intense precipitation and overestimates the amount of lighter precipitation over the other analyzed regions. TRMM 3B42 underestimates the frequency of light precipitation over the Sichuan basin and the mid-lower reaches of the Yangtze River. CMORPH overestimates the frequencies of weak and intense precipitation over the mid-lower reaches of the Yangtze River, and underestimates the frequencies of moderate and heavy precipitation. CMORPH also overestimates the frequency of light precipitation and underestimates the frequency of intense precipitation over the other three regions. The TRMM 3B42 product provides better characterizations of the regional gamma distributions of daily precipitation amount than the CMORPH product, for which the cumulative distribution functions are biased toward lighter precipitation events. 相似文献
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On the Tropical Rainfall Measuring Mission (TRMM) 总被引：6，自引：0，他引：6
Dr. J. Simpson C. Kummerow W. -K. Tao R. F. Adler 《Meteorology and Atmospheric Physics》1996,60(1-3):19-36
Summary The importance of quantitative knowledge of tropical rainfall, its associated latent heating and variability is summarized in the context of climate change. Since the tropics are mainly covered with oceans, with some deserts and jungles, the monthly precipitation is not known within a factor of two. Hence the only way to measure it adequately for climate and general circulation models is from space. The paper describes the Tropical Rainfall Measuring Mission (TRMM). This joint Japan-U.S. cooperative Earth Probe satellite will be launched from Japan in 1997 for a three-year mission. The scientific basis of the instrument and orbit selection is explained. The precipitation instrument complement comprises the first rain radar to be flown in space (PR), and a multi-channel passive microwave sensor (TMI) improved relative to the SSM/I1 by an additional channel at 10 GHz. The third rain instrument is a five-channel VIS/IR (VIRS) sensor. Progress in construction of instruments, observatory, data system, and the ground validation program is summarized. A report is also given concerning development of the algorithms by which rainfall and its associated latent heat release will be calculated from the several instruments, separately and in combination, and how the scientists will interact with the data system to obtain the 32 rain data products necessary to fulfill the science requirements.With 9 FiguresSSM/I stands for Special Sensor Microwave Imager. 相似文献
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The capacity of Tropical Rainfall Measuring Mission (TRMM) Satellite for measuring rainfall
was examined by using TMI-85.5 GHz microwave image data and precipitation data during a heavy rainfall
experiment in southern China. From comparisons with the distribution of rain amount in an hour with BB T of
85.5 GHz microwave, it is clear that the center of heavy rain corresponds with an area of low BB T value. The
location and shape of BB T distribution is similar to that of precipitation, and the larger the rainfall rates, the
lower the BB T . A statistic analysis shows that the correlation coefficients between BB T and rain rates is
negative and significant. Especially, when the rain rate is over 7 mm/h, the correlation degree between BB T
and rain rates is more significant. The results shows that TRMM/TMI-85.5 G has great ability to measure convective
heavy rain. 相似文献
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Satya Prakash C. Mahesh R. M. Gairola P. K. Pal 《Meteorology and Atmospheric Physics》2010,110(1-2):45-57
In the present study, an attempt has been made to estimate and validate the daily and monthly rainfall during the Indian summer monsoon seasons of 2008 and 2009 using INSAT (Indian National Satellite System) Multispectral Rainfall Algorithm (IMSRA) technique utilizing Kalpana-1 very high resolution radiometer (VHRR) measurements. In contrary to infrared (IR), microwave (MW) rain rates are based on measurements that sense precipitation in clouds and do not rely merely on cloud top temperature. Geostationary satellites provide broad coverage and frequent refresh measurements but microwave measurements are accurate but sparse. IMSRA technique is the combination of the infrared and microwave measurements which make use of the best features of both IR- and MW-based rainfall estimates. The development of this algorithm included two major steps: (a) classification of rain-bearing clouds using proper cloud classification scheme utilizing Kalpana-1 IR and water vapor (WV) brightness temperatures (Tb) and (b) collocation of Kalpana-1 IR brightness temperature with Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) surface rain rate and establishment of a regression relation between them. In this paper, the capability of IMSRA as an operational algorithm has been tested for the two monsoon seasons 2008 and 2009. For this, IMSRA has been used to estimate daily and monthly rainfall and has been intercompared on daily and monthly scales with TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 V6 product and Global Precipitation Climatology Project (GPCP) rain product during these two monsoon years. The daily and monthly IMSRA rainfall has also been validated against ground-based observations from Automatic Weather Station (AWS) Rain Gauge and Buoy data. The algorithm proved to be in good correlation with AWS data over land up to 0.70 for daily rain estimates except orographic regions like North-East and South-West India and 0.72 for monthly rain estimates. The validation with Buoys gives the reasonable correlation of 0.49 for daily rain estimates and 0.66 for monthly rain estimates over Tropical Indian Ocean. 相似文献
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中国区域逐日融合降水数据集与国际降水产品的对比评估 总被引：12，自引：3，他引：9
中国国家气象信息中心基于2400多个国家级台站观测日降水量和CMORPH卫星反演降水产品,采用概率密度匹配和最优插值相结合的两步数据融合方法,研制了中国区域1998年以来的0.25°×0.25°分辨率的逐日融合降水产品(CMPA_Daily)。通过该数据集与广泛应用于中国天气气候领域的两种国际上降水融合产品TRMM 3B42(Tropical Rainfall Measuring Mission, 3B42)和GPCP(Global Precipitation Climatology Project, 1 degree daily)的对比评估,考察CMPA_Daily产品的质量,评价其能否合理体现中国降水的天气气候特征。首先利用2008—2010年5—9月独立检验数据定量对比了CMPA_Daily、TRMM 3B42和GPCP 三种降水产品的误差,结果表明,在误差的时间变化和空间分布上,CMPA_Daily均具有最高的相关系数和最小的平均偏差及均方根误差,TRMM 3B42其次,GPCP的误差相对较大。CMPA_Daily只低估了大暴雨,TRMM 3B42低估了大雨以上量级的降水,而GPCP低估了除小雨以外的所有降水。CMPA_Daily产品因融入了更多的站点观测信息,不论在中国东部沿海,还是中西部地形复杂区,其精度均优于TRMM 3B42和GPCP产品,即使在站点稀疏的青藏高原地区,CMPA_Daily降水量也更加接近站点观测,呈现明显的高相关。CMPA_Daily与独立检验数据的高相关在地形起伏时效果也较稳定,TRMM和GPCP的相关系数则随着地形变化幅度陡变而非常明显地降低。进一步通过对比分析各降水产品1998—2012年的气候平均降水特征表明,3种资料对中国区域气候平均降水量、降水强度、频率分布以及年际变化的总体描述基本一致,因有效融入了更多的中国站点观测信息,不论降水空间分布还是降水量,CMPA_Daily与地面观测均最为接近,在中国的中东部大部分地区对降水的估计精度明显更高,而在站点分布较稀疏的青藏高原地区,CMPA_Daily的降水分布型与TRMM、GPCP卫星融合资料类似,较地面站点插值产品更能体现出合理的降水分布。对中国强降水事件监测对比表明,CMPA_Daily产品可以更加准确地描述降水的强度变化,细致刻画降水空间分布,在把握降水小尺度特征上具有明显的优势,体现出高分辨率、高精度降水产品的特点。 相似文献
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Mekonnen Gebremichael Witold F. Krajewski Thomas M. Over Yukari N. Takayabu Phillip Arkin M. Katayama 《Atmospheric Research》2008,88(3-4):337-354
We used a three-year (1998–2000) dataset of TRMM Precipitation Radar observations to investigate the scaling properties of spatial rainfall fields. This dataset allows consideration of spatial scales ranging from about 4.3 km to 138 km and short temporal scales corresponding to the sensor overpasses. The focus is on the marginal spatial moment scaling, which allows estimation of the scaling parameters from a single scene of data. Here we present a global perspective of the scaling properties of tropical rainfall in terms of its spatial variability, atmospheric forcing, predictability, and applicability. Our results reveal the following: 1) the scaling parameters exhibit strong variability associated with land/ocean contrast and mean precipitation at the synoptic scale; 2) there exists a one-to-one relationship between the scaling parameters and the large-scale spatial average rain rate of a universal functional form; 3) the majority of the scenes are consistent with the hypothesis of scale invariance at the moment orders of 0 and 2; 4) relatively there are more scale-invariant rain scenes over land than over ocean; and 5) for the scenes that are non-scale-invariant, deviation from scale-invariance mainly arises from the increasingly intermittent behavior of rainfall as spatial scale decreases. These results have important implications for the development and calibration of downscaling procedures designed to reproduce rainfall properties at different spatial scales and lead to a better understanding of the nature of tropical rainfall at various spatial resolutions. 相似文献
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In this study, tropical monthly mean precipitation estimated by the latest Global Precipitation Climatology Project （GPCP） version 2 dataset and Tropical Rainfall Measurement Mission Precipitation Radar （TRMM PR） are compared in temporal and spatial scales in order to comprehend tropical rainfall climatologically. Reasons for the rainfall differences derived from both datasets are discussed. Results show that GPCP and TRMM PR datasets present similar distribution patterns over the Tropics but with some differences in amplitude and location. Generally, the average difference over the ocean of about 0.5 mm d^-1 is larger than that of about 0.1 mm d^-1 over land. Results also show that GPCP tends to underestimate the monthly precipitation over the land region with sparse rain gauges in contrast to regions with a higher density of rain gauge stations. A Probability Distribution Function （PDF） analysis indicates that the GPCP rain rate at its maximum PDF is generally consistent with the TRMM PR rain rate as the latter is less than 8 mm d^-1. When the TRMM PR rain rate is greater than 8 mm d^-1, the GPCP rain rate at its maximum PDF is less by at least 1 mm d^-1 compared to TRMM PR estimates. Results also show an absolute bias of less than 1 mm d^-1 between the two datasets when the rain rate is less than 10 mm d^-1. A large relative bias of the two datasets occurs at weak and heavy rain rates. 相似文献
17.
This is the first attempt to merge highly accurate precipitation estimates from Global Precipitation Measurement (GPM) with gap free satellite observations from Meteosat to develop a regional rainfall monitoring algorithm to estimate heavy rainfall over India and nearby oceanic regions. Rainfall signature is derived from Meteosat observations and is co-located against rainfall from GPM to establish a relationship between rainfall and signature for various rainy seasons. This relationship can be used to monitor rainfall over India and nearby oceanic regions. Performance of this technique was tested by applying it to monitor heavy precipitation over India. It is reported that our algorithm is able to detect heavy rainfall. It is also reported that present algorithm overestimates rainfall areal spread as compared to rain gauge based rainfall product. This deficiency may arise from various factors including uncertainty caused by use of different sensors from different platforms (difference in viewing geometry from MFG and GPM), poor relationship between warm rain (light rain) and IR brightness temperature, and weak characterization of orographic rain from IR signature. We validated hourly rainfall estimated from the present approach with independent observations from GPM. We also validated daily rainfall from this approach with rain gauge based product from India Meteorological Department (IMD). Present technique shows a Correlation Coefficient (CC) of 0.76, a bias of −2.72 mm, a Root Mean Square Error (RMSE) of 10.82 mm, Probability of Detection (POD) of 0.74, False Alarm Ratio (FAR) of 0.34 and a Skill score of 0.36 with daily rainfall from rain gauge based product of IMD at 0.25° resolution. However, FAR reduces to 0.24 for heavy rainfall events. Validation results with rain gauge observations reveal that present technique outperforms available satellite based rainfall estimates for monitoring heavy rainfall over Indian region. 相似文献
18.
Simulations of precipitation using NRCM and comparisons with satellite observations and CAM: annual cycle 总被引：1，自引：1，他引：0
The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36?km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000?C2005) from within the Tropics (30°S?C30°N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the long-time mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36?km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations. 相似文献
19.
TRMM星载测雨雷达和地基雷达反射率因子数据的三维融合 总被引：3，自引：1，他引：2
TRMM卫星上的测雨雷达(TRMMPR)探测资料分布均匀且具有很高的垂直分辨率,但灵敏度较低;地基雷达(GR)水平分辨率较高且具有较高的灵敏度,但其垂直分辨率低。通过将TRMM PR与GR反射率因子数据的三维数据融合,得到了更优的反射率因子图像。测雨雷达与地基雷达三维数据融合主要分为以下几步:测雨雷达与地基雷达数据预处理——如去杂波、衰减校正;测雨雷达与地基雷达时空匹配;选取和应用合适的三维图像融合算法;对融合后的图像进行效果评估。试验结果表明:融合后的图像不仅增大了信息量,更好地检测弱降水,还提高了空间三维(3D)分辨率,能更好地反映降水区域细节,且使得数据总体上具有更高的完整性和可靠性。此外,还将基于雷达资料估测的降水数据与地面雨量计数据进行对比,估计反射率因子数据融合在降水测量上的有效性。 相似文献
20.
0302号(鲸鱼)台风降水和水粒子空间分布的三维结构特征 总被引：5，自引：8，他引：5
由于缺乏关于台风结构信息的高分辨率资料,即探测台风云系内部结构特征的技术限制,造成了进一步理解台风的动力传送特征的困难.作者用热带测雨卫星(TRMM,Tropical Rainfall Measuring Mission)的测雨雷达(PR,Precipitation Radar)和TRMM微波图像仪(TMI,TRMM Microwave Imager)资料详细研究了"鲸鱼"台风(0302号)于2003年4月16日1105 UTC的降水和降水云系中各种水粒子的三维结构特征.通过分析发现该时刻:(1)台风降水中大部分区域为层性降水(占总降水面积的85.5%),对流性降水占总降水面积的13.1%,但对流性降水的贡献却达到41.8%,所以,虽然对流性降水所占面积比例很少,但是它对总降水量的贡献却很大.(2)60%降水主要集中在距离台风中心100 km以内的区域,约占总降水量的60%.(3)各种水粒子含量随着与台风中心距离的增加而减少.降水云系中水粒子最大含量出现高度与水粒子的种类和与台风中心的距离有关.最后,分析了台风降水和降水云系中三维分布的成因. 相似文献