基于CloudSat观测资料对WRF模式的冷锋降水过程云参数模拟效果检验

基于WRF数值模式,采用Lin微物理方案,对中国南方地区一次冷锋降水过程进行模拟试验,并用CloudSat观测数据对模式模拟的云量、云液态水和云冰水含量的垂直分布特征进行检验。结果表明:模式模拟云量的垂直分布范围小于CloudSat观测到的分布范围,模拟的云量在低空往往出现缺失,模式可以较好地模拟出CloudSat探测到的深对流云的分布,但对零散分布的小尺度云团模拟效果较差;模式模拟的云液态水分布范围也小于CloudSat观测到的分布范围,云液态水含量值略低于CloudSat观测值,对CloudSat观测的云液态水含量值较低的区域,模式往往不能模拟出云液态水的存在;模式模拟的云冰水垂直分布特征与CloudSat观测结果较为一致,特别是对冰水含量大值中心的位置模拟效果较好,但模式模拟的云冰水含量值远低于CloudSat观测值。整体来看,模式对云冰水垂直分布的模拟效果优于对云液态水的模拟,但Lin微物理方案对云液态水和云冰水的模拟还需进一步改进与完善。     

基于ERA5资料的陕西地区云水资源评估

利用1981—2020年欧洲中心高分辨率ERA5再分析资料及站点实测降水资料,通过中国气象局人工影响天气中心发布的云水资源监测评估方法,对陕西云水资源进行了评估。结果表明：陕西年均水汽总量28 6702×108 t,水凝物总量1 9384×108 t,降水总量1 3907×108 t,云水资源总量5477×108 t,“南多北少”的纬向分布特征明显;近40 a陕西全域云水资源呈下降趋势,含量丰沛的南部地区下降最为明显;液态水凝物主要位于低层850~600 hPa,固态水凝物主要位于中层650~350 hPa,根据水凝物垂直分布,春、秋两季人工影响天气的适宜催化高度约为3~5 km,冬季2~4 km,夏季4~8 km;陕西西、南边界水汽和水凝物净流入,东、北边界净流出,区域整体净收支为正,多年气候倾向率为负,呈下降趋势。     

中国地区夏季6～8月云水含量的垂直分布特征

基于观测资料的夏季云水含量时空分布情况对于数值天气预报、气候预测以及人工影响天气试验都十分重要。本文利用CloudSat卫星资料, 分析了2006～2008年中国地区夏季月平均云水含量的垂直和区域变化特征。结果显示, 青藏高原地形以及东亚夏季风对月平均云含水量分布具有明显影响。中国中部纬度上对流层中层的月平均液态水含量比南部及北部的量值大。各月平均云液水含量垂直廓线存在两个不同高度上的峰值区, 原因可能主要是受大尺度参数的控制, 以及受到青藏高原和东亚季风环流的影响。平均冰水含量纬向垂直分布的高值区主要在对流层中上部。本文中所揭示的云水含量特征为天气和气候模式改进、人工影响天气及云—辐射相互作用提供了重要的基础信息。     

有云环境下MODIS亮温资料的变分同化Ⅱ——对暴雨预报的影响

卫星资料提供了大量关于云和雨的观测信息,在暴雨预报中可发挥巨大的作用,然而在数值模式资料同化中的应用水平仍然不高,特别是红外辐射资料的应用。由于有云环境下辐射传输过程的模拟难度很大,因此通常只同化晴空环境下的红外辐射资料。基于GRAPES-3DVAR(Global and Regional Assimilation and Prediction Enhanced System,全球/区域同化预报系统),根据RTTOV辐射传输模式(fast radiative transfer model for TOVS,快速辐射传输模式)的特点,增加云水含量、云冰水含量和云量作为同化系统控制变量,在改进辐射传输模式对红外资料模拟的同时,利用红外资料调整初始云参数和大气参数。针对2007年5月26日南海季风爆发后广东地区的一次暴雨过程,选取MODIS(Moderate Resolution Imaging Spectroradiometer,中分辨成像光谱仪)传感器水汽(第27)和云顶观测(第36)通道进行了同化试验,利用WRF(Weather Research and Forecasting Model,天气研究和预报模式)进行了数值模拟,结果表明同化MODIS资料,可以改进初始场水汽和温度分布,间接调整高空风场,调整趋势符合卫星观测,对短时降水预报有正面影响。     

基于CloudSat资料的中国地区深对流云物理特征研究

利用2007-2010年和2013-2014年Cloud Sat卫星资料,分析了中国11个地理区域的深对流云发生率、冰/液态水路径、冰/液态水含量等分布特征及其季节变化。结果显示,深对流云发生率整体呈现从东南到西北递减的趋势,高值区主要集中在西北地区东南部、西藏东南部、西南地区东部和南部、黄淮西部和南部、江汉、江淮、江南和华南等地,就各个地区不同季节而言,江南地区夏季的值最大,达到10.34%。在垂直高度上,深对流云发生率分布在18 km以下,最大值为11.31%,出现在江南夏季4.08~4.56 km高度上。深对流云中冰水路径最大值出现在华南夏季,液态水路径最大值出现在黄淮秋季,西藏地区的深对流云中冰水路径的比例明显高于液态水路径。冰水含量在垂直高度上存在两个高值区,分别位于6~8 km、14~18 km,最大值发生在江南夏季19.44 km左右高度上,达到1 018.87 mg·m~(-3),季节差异较大的高度位于14~18 km。液态水含量最大值发生在江淮冬季,达到411.50 mg·m~(-3),高度在9.36 km左右,垂直高度上最大值在2~6 km上均有出现。该结果可以更好地揭示深对流云的气候特征,并为人工影响天气以及数值模式中对深对流云物理量的模拟提供一定的参考依据。     

一次降水过程云液态水和降水演变特征的综合观测分析

利用IP/WVP-3000地基微波辐射计、多普勒雷达结合卫星及地面雨量计等多种遥感观测资料,对2007年6月30日北京地区的一次积层混合云降水过程的云液态水和降水的分布及演变特征进行了综合观测分析。分析表明:降水开始前,辐射计液态水含量有明显增加,地面降水的产生滞后于液态水含量增加一段时间,利用这种现象,可用于提前预测云系正处于降水产生的发展阶段,应用于人工增雨作业。在相同时间对同一位置进行探测的雷达和辐射计资料显示,雷达回波垂直分布趋势与辐射计液态水的垂直分布趋势有着较好的对应关系。此外空中液态水分布,特别是低层液态水的分布,与地面降雨的产生有着直接的关系。     

天山山区水汽和云水含量的空间分布特征

利用欧洲数值预报中心(ECMWF)发布的第一代全球分辨率ERA-Interim再分析数据,分析了1979～2014年天山山区水汽含量和云水含量的空间分布特征。结果显示：(1)水汽含量的高值中心出现在伊犁河谷地区,中心值域在10—11kg m-2之间,低值区位于天山中部的巴音布鲁克附近,中心值域在5—6kg m-2之间;夏季水汽含量最丰富,在8—11kg m-2之间。(2)云液水含量的高值区出现在博格达山北坡,而云冰水含量的高值区在西天山海拔较高的托木尔峰地区,低值区均在伊犁河谷等海拔低的地区;夏季云液水含量、云冰水含量均呈减少趋势,云冰水含量较云液水减少的更为明显,下降速率为0.28kg kg-1/10a;(3)垂直分布上,云液水含量在600hpa左右的高空出现高值区,中心最大值为10kg kg-1;云冰水含量的高值区则出现在500hpa左右的高空,为11kg kg-1;在对流层大气中云冰水含量值远大于云液水,且云冰水发展的高度较云液水更高。     

有云环境下MODIS亮温资料的变分同化Ⅰ——方案设计与理想试验

云对红外辐射传输过程有十分重要的影响,在资料同化过程中考虑云的作用,不仅可以改进大气参数的同化效果,还可以利用红外资料调整部分云参数,减小云参数的初始误差。在GRAPES-3DVAR（Global and Regional Assimilation and Prediction Enhanced System,全球/区域同化预报系统）基础上,增加云水含量、云冰水含量和云量作为同化系统控制变量,针对晴空、部分有云和完全有云等环境对MODIS（Moderate Resolution Imaging Spectroradiometer,即中分辨成像光谱仪）的16个气象通道亮温进行了同化理想试验,结果表明当背景场模拟的亮温低于观测亮温时,分析场通过增温、减湿、减少云水含量、云冰水含量、云量可以达到升高模拟亮温的目的;反之可以降低模拟亮温;晴空环境下对温度、比湿的调整符合MODIS仪器的设计特点;有云环境下对云层以下的影响减弱。理想试验证实了在红外辐射资料同化过程中将云参数和大气参数同时作为同化系统控制变量,可以利用红外资料调整云参数和常规气象要素场,提高资料的利用率。     

基于CloudSat资料的青藏高原地区云微物理特征分析

青藏高原云物理特征的认识对高原天气和气候的研究有重要意义。利用2006年6月—2011年4月的CloudSat卫星资料,分析了青藏高原地区云的总云水路径、液态水路径、冰水路径及雷达反射率的分布特征,并对高原与东亚降水云的垂直结构进行对比,得到如下结论:(1) 总云水路径的大值区分布在高原西南坡、东南部及高原中部低值区分布在昆仑山脉、祁连山脉及其以北地区;暖季大于冷季;(2) 高原南部及东部为液水路径大值区,以液相云为主;高原中部、北部及西部为冰水路径大值区,以冰相云为主;(3) 雷达反射率的垂直分布主要介于-27～17 dBz,集中在3～9 km;云粒子群随高度先增大后减小,在4 km高度的大小和浓度最大;暖季云高大于冷季,对流活动旺盛;(4) 高原与东亚降水云的结构不同,季节变化也与东亚有差别。(5) 雷达反射率在近地面层随纬度的增大减小,垂直方向的递减率是暖季小于冷季;(6) 冷季的高原上与周边相比为丰水区,南坡的冰水路径与低层雷达反射率大值区对应,表明南坡阻挡作用促进云中冰粒子的形成。      

基于ERA-Interim资料的青海省空中云水资源评估

利用ERA-interim再分析资料分析2009—2018年青海省云液水含量和云冰水含量时空分布特征。结果表明:青海省云液水含量和云冰水含量自西北向东南逐渐增多,玉树南部、果洛东南部和祁连山区为云水资源较为丰富的地区,夏秋季节云水资源最为丰富,可达60～70 g·m~(-2)。从云水资源的垂直分布来看,云液水含量和云冰水含量随海拔高度增高呈先增多后减少的变化趋势,云液水含量在海拔4～6 km高度较多,云冰水含量在海拔7～8 km高度较多,云冰水含量峰值所在高度高于云液水含量峰值所在高度。夏秋季节,青南高原云液水含量和云冰水含量垂直变化幅度大,柴达木盆地云液水含量和云冰水含量垂直变化幅度小。从年际变化趋势来看,2009—2018年青海省大部地区云液水含量、云冰水含量呈增多趋势,且秋季增多趋势最为显著。从月际变化看,云液水含量和云冰水含量9月最高,1月最低。柴达木盆地云液水含量和云冰水含量的月际差异最小,东部农业区云液水含量月际差异最大,青南高原云冰水含量月际差异最大。     

青藏高原云水气候特征分析

利用2001~2016年MODIS月平均液相云水路径（Cloud Liquid Water Path,LWP）、冰相云水路径（Cloud Ice Water Path,IWP）资料和ERA-Interim再分析等资料,分析了青藏高原空中云水的分布特征、变化趋势以及与大气环流变化和水汽输送变化的关系。结果显示,LWP和IWP的年平均分布形态与降水、可降水量对应较好,林芝地区聚集了丰富的LWP、IWP、降水量和可降水量。受印度洋季风影响,LWP和IWP存在明显的季节变化,夏季LWP和IWP最丰富,冬季最少。水汽传输和高原的动力、热力作用是影响夏季LWP和IWP分布的主要因素,夏季高原南部相对湿度大,水汽抬升强烈,促进了LWP和IWP的形成和积累。LWP和IWP随海拔高度的变化特征较为相似,3000~5500 m海拔高度区间内二者的总体变化特征与青藏高原降水的梯度变化特征一致,为随高度先较快升高后保持稳定的分布特征。青藏高原年平均和季节平均LWP和IWP在2001~2016年间均以减少趋势为主,这一变化趋势与云量和降水变化趋势一致,LWP和IWP的减少趋势与水汽输送通量散度的增加密切相关。     

Evaluation of cloud properties in the NOAA/NCEP global forecast system using multiple satellite products

Knowledge of cloud properties and their vertical structure is important for meteorological studies due to their impact on both the Earth’s radiation budget and adiabatic heating within the atmosphere. The objective of this study is to evaluate bulk cloud properties and vertical distribution simulated by the US National Oceanic and Atmospheric Administration National Centers for Environmental Prediction Global Forecast System (GFS) using three global satellite products. Cloud variables evaluated include the occurrence and fraction of clouds in up to three layers, cloud optical depth, liquid water path, and ice water path. Cloud vertical structure data are retrieved from both active (CloudSat/CALIPSO) and passive sensors and are subsequently compared with GFS model results. In general, the GFS model captures the spatial patterns of hydrometeors reasonably well and follows the general features seen in satellite measurements, but large discrepancies exist in low-level cloud properties. More boundary layer clouds over the interior continents were generated by the GFS model whereas satellite retrievals showed more low-level clouds over oceans. Although the frequencies of global multi-layer clouds from observations are similar to those from the model, latitudinal variations show discrepancies in terms of structure and pattern. The modeled cloud optical depth over storm track region and subtropical region is less than that from the passive sensor and is overestimated for deep convective clouds. The distributions of ice water path (IWP) agree better with satellite observations than do liquid water path (LWP) distributions. Discrepancies in LWP/IWP distributions between observations and the model are attributed to differences in cloud water mixing ratio and mean relative humidity fields, which are major control variables determining the formation of clouds.     

测量云液水柱含量的一个设想

云液态水柱含量是一个重要的气象学和云雾物理参数.对于云液水柱含量测量已发展了多种技术,但由于云在时空上变化很大,目前地基、飞机以及卫星测量的全部资料,都不能满足数值天气预报、人工增雨及气候变化研究等方面的工作需要.作者提出一种测云水的新方法,即从卫星-地面的微波衰减来确定云水(斜)柱量,并研究了此方法中的测量通道选择及测量方式问题,进行了初步的误差分析研究.结果表明,此方法在现有技术条件下可行,云水的测量精度不难达到20%～30%的水平.与卫星被动微波遥感结合起来,可获得精度更高的云水全球分布资料.     

Application of aircraft observations over Beijing in cloud microphysical property retrievals from CloudSat

Cloud microphysical property retrievals from the active microwave instrument on a satellite require the cloud droplet size distribution obtained from aircraft observations as a priori data in the iteration procedure.The cloud lognormal size distributions derived from 12 flights over Beijing,China,in 2008-09 were characterized to evaluate and improve regional CloudSat cloud water content retrievals.We present the distribution parameters of stratiform cloud droplet （diameter ＜500 tm and ＜1500 μm） and discuss the effect of large particles on distribution parameter fitting.Based on three retrieval schemes with different lognormal size distribution parameters,the vertical distribution of cloud liquid and ice water content were derived and then compared with the aircraft observations.The results showed that the liquid water content （LWC） retrievals from large particle size distributions were more consistent with the vertical distribution of cloud water content profiles derived from in situ data on 25 September 2006.We then applied two schemes with different a priori data derived from flight data to CloudSat overpasses in northern China during April-October in 2008 and 2009.The CloudSat cloud water path （CWP） retrievals were compared with Moderate Resolution Imaging Spectroradiometer （MODIS） liquid water path （LWP） data.The results indicated that considering a priori data including large particle size information can significantly improve the consistency between the CloudSat CWP and MODIS CWP.These results strongly suggest that it is necessary to consider particles with diameters greater than 50 tm in CloudSat LWC retrievals.     

Cloud microphysical properties in tropical convective and stratiform regions

Summary Cloud microphysical properties in tropical convective and stratiform regions are examined based on hourly zonal-mean data from a two-dimensional cloud-resolving simulation. The model is integrated for 21 days with the imposed large-scale vertical velocity, zonal wind and horizontal advections obtained from Tropical Ocean Global Atmosphere Coupled Ocean-atmosphere Response Experiment (TOGA COARE). Time-mean cloud microphysical budgets are analyzed in raining stratiform regions, convective regions, and non-raining stratiform regions, respectively. In raining stratiform regions, ice water path (IWP) and liquid water path (LWP) have similar magnitudes. The collection process contributes slightly more to the growth of raindrops than the melting processes do, and surface rain rate is higher than the raindrop-related microphysical rate, indicating that the hydrometeor convergence from the convective regions plays a role in surface rainfall processes. In convective regions, IWP is much smaller than LWP, the collection process is dominant in producing raindrops, and surface rain rate is lower than the raindrop-related microphysical rate. In non-raining stratiform regions, IWP is much larger than LWP, and the melting processes are important in maintaining the raindrop budget. The statistical analysis of hourly data suggests that the slopes of linear regression equations between IWP and LWP in three regions are different. Rain producing processes in convective regions are associated with the water cloud processes regardless of convection intensity.     

CONVECTIVE-STRATIFORM RAINFALL PARTITION BY RADIANCE-DERIVED CLOUD CONTENT: A MODELING STUDY

A new scheme that separates convective-stratiform rainfall is developed using threshold values of liquid water path (LWP) and ice water path (IWP). These cloud contents can be predicted with radiances at the Advanced Microwave Sounding Unit (AMSU) channels (23.8, 31.4, 89, and 150 GHz) through linear regression models. The scheme is demonstrated by an analysis of a two-dimensional cloud resolving model simulation that is imposed by a forcing derived from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE). The rainfall is considered convective if associated LWP is larger than 1.91 mm or IWP is larger than 1.70 mm. Otherwise, the rainfall is stratiform. The analysis of surface rainfall budget demonstrates that this new scheme is physically meaningful.     

东亚地区云微物理量分布特征的CloudSat卫星观测研究

本文利用2007~2010年整四年最新可利用的CloudSat卫星资料, 对东亚地区(15°~60°N, 70°~150°E)云的微物理量包括冰/液态水含量、冰/液态水路径、云滴数浓度和有效半径等的分布特征和季节变化进行了分析。本文将整个东亚地区划分为北方、南方、西北、青藏高原地区和东部海域五个子区域进行研究, 结果显示:东亚地区冰水路径值的范围基本在700 g m-2以下, 高值区分布在北纬40度以南区域, 在南方地区夏季的平均值最大, 为394.3 g m-2, 而在西北地区冬季的平均值最小, 为78.5 g m-2;而液态水路径的范围基本在600 g m-2以下, 冬季在东部海域的值最大, 达到300.8 g m-2, 夏季最大值为281.5 g m-2, 分布在南方地区上空。冰水含量的最高值为170 mg m-3, 发生在8 km附近, 南方地区夏季的值达到最大, 青藏高原地区的季节差异最大;而液态水含量在东亚地区的范围小于360 mg m-3, 垂直廓线从10 km向下基本呈现逐渐增大的趋势, 峰值位于1~2 km高度上。冰云云滴数浓度在东亚地区的范围在150 L-1以下, 水云云滴数浓度的值小于80 cm-3, 垂直廓线的峰值均在夏季最大。冰云有效半径在东亚地区的最大值为90 μm, 发生在5 km左右;水云有效半径在东亚地区的值分布在10 km以下, 最大值为10~12 μm, 基本位于1~2 km高度上。从概率分布函数来看, 东亚地区冰/水云云滴数浓度的分布呈现明显的双峰型, 其他量基本为单峰型。本文的结果可以为全球和区域气候模式在东亚地区对以上云微物理量的模拟提供一定的观测参考依据。     

Analysis of Ice Water Path Retrieval Errors Over Tropical Ocean

Retrieval of multi-layered cloud properties, especially ice water path (IWP), is one of the most perplexing problems in satellite cloud remote sensing. This paper develops a method for improving the IWP retrievals for ice-over-water overlapped cloud systems using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Visible and Infrared Scanner (VIRS) data. A combined microwave, visible and infrared algorithm is used to identify overlapped clouds and estimate IWP separately from liquid water path. The retrieval error of IWP is then evaluated by comparing the IWP to that retrieved from single-layer ice clouds surrounding the observed overlapping systems. The major IWP retrieval errors of overlapped clouds are primarily controlled by the errors in estimating the visible optical depth. Optical depths are overestimated by about 10–40% due to the influence of the underlying cloud. For the ice-over-warm-water cloud systems (cloud water temperature Tw > 273 K), the globally averaged IWP retrieval error is about 10%. This cloud type accounts for about 15% of all high-cloud overlapping cases. Ice-over-super-cooled water clouds are the predominant overlapped cloud system, accounting for 55% of the cases. Their global averaged error is 17.2%. The largest IWP retrieval error results when ice clouds occur over extremely super-cooled water clouds (Tw 6 255 K). Overall, roughly 33% of the VIRS IWP retrievals are overestimated due to the effects of the liquid water clouds beneath the cirrus clouds. To improve the accuracy of the IWP retrievals, correction models are developed and applied to all three types of overlapped clouds. The preliminary results indicate that the correction models reduce part of the retrieval error.     

Latitudinal and Scan-dependent Biases of Microwave Humidity Sounder Measurements and Their Dependences on Cloud Ice Water Path

The relationship between differences in microwave humidity sounder(MHS)–channel biases which represent measured brightness temperatures and model-simulated brightness temperatures, and cloud ice water path(IWP) as well as the influence of the cloud liquid water path(LWP) on the relationship is examined. Seven years(2011–17) of NOAA-18 MHS-derived measured brightness temperatures and IWP/LWP data generated by the NOAA Comprehensive Large Array-data Stewardship System Microwave Surface and Precipitation Products System are used. The Community Radiative Transfer Model, version2.2.4, is used to simulate model-simulated brightness temperatures using European Center for Medium-Range Weather Forecasts reanalysis data as background fields. Scan-angle deviations of the MHS window channel biases range from-1.7 K to1.0 K. The relationships between channels 2, 4, and 5 biases and scan angle are symmetrical about the nadir. The latitudedependent deviations of MHS window channel biases are positive and range from 0–7 K. For MHS non-window channels,the latitudinal deviations between measured brightness temperatures and model-simulated brightness temperatures are larger when the detection height is higher. No systematic warm or cold deviations are found in the global spatial distribution of difference between measured brightness temperatures and model-simulated brightness temperatures over oceans after removing scan-angle and latitudinal deviations. The corrected biases of five different MHS channels decrease differently with respect to the increase in IWP. This decrease is stronger when LWP values are higher.     

基于多源卫星资料的中国地区云宏微观特征研究

利用2007—2016年国际卫星云气候计划（International Satellite Cloud Climatology Project,ISCCP）、云和地球辐射能量系统（Clouds and the Earth''s Radiant Energy System,CERES）和中分辨率成像光谱仪（Moderate Resolution Imaging Spectroradiometer,MODIS）卫星反演云产品,对比分析了不同数据反演的中国地区云系结构的宏微观特征,并采用复合评价指标定量评估了不同数据之间时间和空间上的一致性。结果表明：三套卫星数据都能较好地反演出中国地区总云量呈南高北低、东高西低、夏高冬低的分布特征,但通过比较时间技巧（Temporal Skill,S_T）及空间技巧（Spatial Skill,S_S）复合评价指标及其各项分量发现,与MODIS相比,CERES与ISCCP数据反演的总云量时间序列演变特征明显更为一致,且其评分均有南方优于北方,夏季优于冬季的特征;进一步分析不同高度云量的S_T评分发现,CERES和ISCCP两套数据在南方地区的总云量差异主要来自于低云量的绝对偏差,而北方地区的偏差则同时存在于低云和中云;对比分析MODIS和CERES反演的云滴有效半径发现,高云对应的冰相云一致性较高,而中低云相对应的液相云的偏差则有夏季高于冬季的规律。针对夏季液相和冰相云滴粒径及概率密度分析则表明,相比CERES数据,MODIS对夏季液水和冰水粒子的有效半径在不同地区均有不同程度的高估,液（冰）水谱宽则更宽（窄）。