Temperature, Relative Humidity, and Cloud Fraction Predicted by the NCEP Global Forecast System at the ARM SGP Site during 2001-2008:Comparison with ARM Observations

利用美国大气辐射测量项目(ARM)制作的“气候模拟最佳估计”(CMBE)观测数据集,检验美国环境预报中心(NCEP)全球预报系统(GFS)2001～2008年在ARM Southern Great Plains(SGP)站点预报的大气温度、相对湿度和云量的垂直分布,主要结论如下:(1)NCEP GFS较好地预报出了温度和相对湿度的季节变化.就各个季节平均而言,NCEP GFS高估了1.5～12km的大气温度,同时低估了春冬季13～16km和秋冬季1.5km以下的大气温度,各高度上温度偏差绝对值小于1℃;NCEP GFS预报结果再现了观测到的相对湿度垂直分布的双峰结构,但是高估了4～12 km的相对湿度.模式分辨率提高(T170L42更新为T254L64)显著改进了14～18 km相对湿度的预报.(2)预报的云量在10 km以下小于观测值,在10～13 km则高于观测值,而且,NCEPGFS没有预报出非降水性低云的云量,其预报的降水云的云量在8km以下也低于观测值,反映出NCEP GFS模式中浅对流和深对流活动不够活跃.(3)NCEP GFS模式用预报的相对湿度和云水/云冰混合比(qc)诊断云量,采用同样的诊断公式由观测的相对湿度和NCEP GFS预报输出的qc计算云量,得到的云量在11 km以下所有高度上都更加显著地小于观测值,即比NCEP GFS对云量的低估更加严重,说明NCEP GFS可能低估了此高度区间的qc.(4)2001～2008年间NCEP GFS预报的温度、湿度和云量改进不显著,其预报云量和qc的误差很可能与模式中深对流和浅对流方案、层云微物理方案的不确定性有关.     

地基探测与NCEP GFS模式预报云量在ARM SGP站点对比

使用大气辐射测量实验(Atmospheric Radiation Measurements:ARM)在美国南部大平原站点(Southern Great Plains:SGP)长时间序列(2001 2010年)的地基主动遥感云(Active Remote Sensing of Clouds:ARSCL)和美国国家环境预报中心(National Centers for Environmental Prediction:NCEP)全球预报系统(Global Forecast System:GFS)模式预报资料,对比分析了两者云量在不同时间尺度内(年际、月份和季节)的差异。结果表明,GFS模式预报总云量为83.8%,略高于地基观测结果(78.1%);两者总云量差异在秋季最大(8.8%),春季最小(2.2%)。在低垂直高度分辨率(≥3 km)时,地基探测低云、中云和高云的云量分别为46.1%、43.5%和61.2%;模式预报三类云的云量均要高于地基探测的云量,差异分别为9.6%、17.2%和9.1%。但是,在高垂直分辨率(250 m)时,地基探测云量在大多数高度层上要高于模式预报结果。这应该是两种资料廓线中有云出现的高度层数目存在差异引起的。地基观测和GFS模式预报同时表明,SGP站点上空云量垂直廓线呈现双峰结构,在边界层附近(1 km)和上对流层区域(8-12 km)云量较大,2-3 km高度范围内云量较小。在春夏秋冬四个季节内,两种资料在低层边界层附近的最大云量偏差分别为9.5%、8.8%、7.8%和11.2%。     

区域模式参考大气扰动量算法的预报试验

引进参考大气方法以降低垂直运动方程中的量级差,并设计了位温直接计算法、气压直接计算法和混合算法三种不同方案来改进预报效果。结果表明,这三种不同方案对500 hPa位势高度和温度的垂直分布的预报有较大影响,其中,混合算法的温度分布预报结果与NCEP分析场最接近。在三种方案的预报效果评估检验中,混合算法对风速、位势高度(hv)、降水量和2 m温度(T2m)的预报效果最好。在三种方案中,平均初始温度场的参考大气廓线的预报效果要优于等温参考大气廓线;在不同的参考大气状态下,混合算法在风速、位势高度、降水量和2 m温度的预报效果最好。      

东印度洋天气和风暴潮预报系统的初步评估

东印度洋天气和风暴潮实时预报系统（EPMEF_EIO）由区域大气模式和区域风暴潮模型组成,每天实时运行4次.大气初边场来自美国国家环境预报中心（NCEP）的全球预测系统（GFS）,通过区域嵌套得到印度洋-东印度洋-斯里兰卡区域的3 d预报结果.大气模式的10 m预报风场驱动风暴潮模式,得到东印度洋-斯里兰卡区域的潮汐和风暴潮3 d预报结果.通过与中国科学院南海海洋研究所斯里兰卡站气象塔观测数据、最优台风路径数据和科伦坡水位站数据对比,发现模式预报气温和相对湿度的日变化较观测值偏小,气温总体RMSE为1.26℃,相关系数为0.8,相对湿度的总体RMSE为7.0%,相关系数为0.7;模式预报风速以整体偏大为主,总体RMSE为2.3 m/s,相关系数为0.65;模式预报风向能把握主要的变化趋势,RMSE在20°~32°之间,相关系数约0.65;模式24、48和72 h路径预报平均误差分别为110.5、166.4和181.0 km.此外,模式水位预报的RMSE为0.035 m,占最大振幅约5%,与观测的相关系数达到0.996.这说明了模式可以用于预报潮汐和风暴潮过程.     

GFS对我国南方两次持续性降水过程的预报技巧评估

采用美国全球预报系统（Global Forecasting System,GFS）资料,利用谐波滤波提取空间长波、超长波分量,检验评估了GFS对2012年7月11—31日东亚地区大气环流场和降水的可预报能力。结果表明：GFS模式对东亚地区的中低层高度场预报可靠时效维持6 d以上,高层预报可靠时效可达10 d;长波、超长波的可预报效果显著,其中高度场长波5～8波的预报效果好于3～6波,风场则相反;GFS对我国南方两次持续性降水过程的可预报天数维持在8 d左右,并可提前2天预报出强降水带位置;模式对持续性降水过程预报相对站点观测降水量整体偏强。     

基于复卡尔曼滤波技术的华东区域风的多模式集成预报研究

基于欧洲中期天气预报中心的业务预报系统(EC)、美国国家环境预报中心的全球预报系统(GFS)、我国的中尺度数值业务预报系统(CMA-MESO)和全球预报系统(CMA-GFS)这4个预报系统的华东及周边地区(20°～40°N、110°～130°E)2020年1—4月逐日地面和高空风的0～72 h预报资料,利用复卡尔曼滤波...     

数值模式对强台风“菲特”登陆期间预报能力评述

基于自动站观测和ECMWF再分析资料,针对中国气象局上海台风研究所区域高分辨率台风模式(Shanghai Tropical Cyclone High Resolution Analysis and Prediction System,STI-THRAPS)和业务常用的4个数值模式,即欧洲中期天气预报中心(European Centre for Medium-range Weather Forecasts,EC)全球模式,美国全球预报系统(The Global Forecast System,GFS),日本气象厅(Japan Meteorological Agency,JMA)全球模式和我国T639(T639L60)全球模式,对1323号台风"菲特"登陆过程的预报性能进行了综合检验评估。结果显示:对暴雨以上的强降水预报STI-THRAPS有明显优势。仅有该模式对超过500 mm的极端降水做出预报,且各项评分均好于全球模式,漏报率也明显优于其他模式。对暴雨以下的降水预报各个模式差距不大。美国GFS和STI-THRAPS较好地预报了大风区,STI-THRAPS预报的风场与实况的空间相关程度最高。从漏报率上来看,STI-THRAPS模式的风场预报具有明显优势。虽然预报最大风速偏大,但是STI-THRAPS在24 h后的路径预报有较大优势。     

美国ARW模式系统简介

1概述1997年美国国家大气研究中心(NCAR)中小尺度气象处(MMM)、国家环境预报中心(NCEP)的环境模拟中心(EMC)、预报系统试验室的预报研究处(FRD)和俄克拉荷马大学的风暴分析预报中心(CAPS)四部门联合发起新一代高分辨率中尺度天气研究预报模式WRF(Weather Research Forecast)开发计划,拟重点解决分辨率为1～10Km、时效为60h以内的有限区域天气预报和模拟问题。该计划由美国国家自然科学基金会(NSF)和美国国家海洋和大气管理局(NOAA)共同支持,1998年已形成共同开发的标准,2000年2月被确定为实现美国天气研究计划(USWRP)主要目…     

3个全球模式对近地层风场预报能力的对比检验

为了使检验的预报风速具有可比性,首先利用WRF模式对3种常用全球环流预报场(ECMWF、GFS、T639)在中国区域进行4个典型月的10 km水平分辨率的降尺度计算,再利用中国400座测风塔同期观测资料对24 h内70 m高度的风速预报性能进行了对比检验。结果表明:(1)从4个典型月的全风速检验情况来看,ECMWF预报效果略好于GFS,T639稍差,但三者做简单的集合平均可获得最优的预报结果;(2)3种全球预报场降尺度后的预报风速误差平面分布比较相似,整体来看,内蒙古、东北和沿海部分区域误差较小,内陆地区误差较大,尤其在高原和内陆复杂地形下预报效果不佳;(3)从预报风速在4个风速区间的平均检验情况来看,在(0,3]m·s-1和(3,15]m·s-1区间内,ECMWF的TS评分略好一些,在(15,25]m·s-1区间GFS的TS评分最高,在25 m·s-1以上ECMWF具有明显优势;(4)在(3,15]m·s-1区间,3种全球环流预报场的风速预报误差平均在35%左右,ECMWF略好于GFS,GFS略好于T639;(5)从各测风塔点位最优预报效果的全球场统计情况来看,在(3,15]m·s-1区间内,有55.5%的点是ECMWF效果最优,24.8%是GFS最优,19.7%是T639最优。     

二阶位涡在暴雨预报中的应用

提出并推导二阶位涡物理量,并利用美国NCEP/NCAR 0.5°×0.5°GFS的24 h预报资料,计算了东北冷涡暴雨、锋面暴雨、低槽暴雨、台风暴雨等类型暴雨500—850 h Pa二阶位涡绝对值的垂直积分,与相对应时刻的24 h累积地面观测降水量进行对比。结果表明：二阶位涡的水平分布与暴雨落区有较好的对应关系,其对观测降水具有指示预测作用。2013年6—8月华南地区（20°—35°N,105°—125°E）24 h预报的6 h累计降水量大于10 mm的ETS评分表明,二阶位涡预报降水的平均ETS评分高于美国GFS预报降水的平均ETS评分,其对降水有较好的指示作用。     

Diagnosis and testing of low-level cloud parameterizations for the NCEP/GFS model using satellite and ground-based measurements

The objective of this study is to investigate the quality of clouds simulated by the National Centers for Environmental Prediction global forecast system (GFS) model and to examine the causes for some systematic errors seen in the simulations through use of satellite and ground-based measurements. In general, clouds simulated by the GFS model had similar spatial patterns and seasonal trends as those retrieved from passive and active satellite sensors, but large systematic biases exist for certain cloud regimes especially underestimation of low-level marine stratocumulus clouds in the eastern Pacific and Atlantic oceans. This led to the overestimation (underestimation) of outgoing longwave (shortwave) fluxes at the top-of-atmosphere. While temperature profiles from the GFS model were comparable to those obtained from different observational sources, the GFS model overestimated the relative humidity field in the upper and lower troposphere. The cloud condensed water mixing ratio, which is a key input variable in the current GFS cloud scheme, was largely underestimated due presumably to excessive removal of cloud condensate water through strong turbulent diffusion and/or an improper boundary layer scheme. To circumvent the problem associated with modeled cloud mixing ratios, we tested an alternative cloud parameterization scheme that requires inputs of atmospheric dynamic and thermodynamic variables. Much closer agreements were reached in cloud amounts, especially for marine stratocumulus clouds. We also evaluate the impact of cloud overlap on cloud fraction by applying a linear combination of maximum and random overlap assumptions with a de-correlation length determined from satellite products. Significantly better improvements were found for high-level clouds than for low-level clouds, due to differences in the dominant cloud geometry between these two distinct cloud types.     

2017年5月7日广州特大暴雨模拟中的背景场影响分析

2017年5月7日广州发生了特大暴雨,为研究不同背景场资料对这次暴雨过程的影响,模式背景场分别采用美国NCEP的GFS资料和中国的T639资料,利用GRAPES_Meso模式对这次暴雨过程进行了数值模拟和影响分析;数值试验结果表明,采用不同的背景场对这次暴雨过程具有显著影响,用T639资料(T639_run)作为模式背景场大致模拟出了这次暴雨过程,而采用NCEP GFS数据(GFS_run)模拟的降水明显偏北。其原因是,采用T639资料做背景场时,华南暴雨区域存在深厚的水汽输送,同时存在强烈的上升运动,可以产生极端强降水;而采用GFS资料进行数值模拟时,实际暴雨区上空的上升气流较弱,水汽输送也较弱,使强降水落区偏北。GRAPES_Meso模式模拟的华南地区的云顶高度整体偏高,云顶温度整体偏低,相对来说,采用T639_run的模拟结果优于GFSrun的结果,该研究结果可以为云降水方案中的水物质和云量计算方案的改进和优化提供一定的参考。     

Application of Radar Reflectivity Factor in Initializing Cloud-Resolving Mesoscale Model. Part Ⅰ: Retrieval of Microphysical Elements and Vertical Velocity

Assuming that cloud reaches static state in the warm microphysical processes, water vapor mixing ratio(qv), cloud water mixing ratio (qc), and vertical velocity (w) can be calculated from rain water mixing ratio (qr)- Through relation of Z-qr, qr can be retrieved by radar reflectivity factor (Z). Retrieval results indicate that the distributions of mixing ratios of vapor, cloud, rain, and vertical velocity are consistent with radar images, and the three-dimensional spatial structure of the convective cloud is presented. Treating q,v saturated at the echo area, the retrieved qr is about 0.1 g kg-1, qc is always less than 0.3 g kg-1, w is usually below 0.5 m s-1, and rain droplet terminal velocity (vr) is around 5.0 m s-1 in the place where radar reflectivity factor is about 25 dBz; in the place where echo is 45 dBz, the retrieved qr and qc are always about 3.0 g kg-1, w is greater than 5.0 m s-1, and vr is around 7.0 m s-1. In the vertical, the maximum updraft velocity is greater than 3.0 m s-1 at the height of around 5.0 kin, the maximum cloud water content is about 3.0 g kg-1 above 5 km and the maximum rain water content is about 3.0 g kg-1 below 6 kin. Due to the assumption that the cloud is in static state, there will be some errors in the retrieved variables within the clouds which axe rapidly growing or dying-out, and in such cases, more sophisticated radar data control technique will help to improve the retrieval results.     

雷达反射率因子在中尺度云分辨模式初始化中的应用Ⅰ：云微物理量和垂直速度的反演

对于水平网格距小于10 km的高分辨率非静力平衡的显式云分辨模式,云微物理量的初始化以及物理量之间的相互协调是十分重要的,并且一直是云分析领域的一个难题。考虑在云雨处于定常状态的前提下,根据暖云过程,可得到云中水成物之间相互转化以及与垂直速度的约束关系,而雨水含量跟雷达反射率因子(Z)有关。因此,采用合肥新一代天气雷达2003年7月5日02时(北京时)的观测资料,针对雷达探测的特点对雷达原始数据进行了坐标转换,并进行了插值处理和简单的质量控制,然后依据Z-qr关系和定常暖云方案,反演了雨水混合比(qr)、云水混合比(qc)、水汽混合比(qv)和垂直速度(w)。结果表明,由此得到的雷达回波主要特征及量值分布与雷达站的CAPPI图像基本一致;水汽、云水、雨水和垂直速度量值大小的分布与雷达回波强度的分布也是吻合的,体现出了云中水成物和垂直速度的三维分布结构;各物理量的量值也符合梅雨锋暴雨的特点,梅雨锋积层混合云系中层状云和对流云的差别十分明显。在较强的回波区,雨水在6 km以下含量较大,最大值位于4 km左右,可超过3.0 g/kg;上升速度在5 km左右最大,最大值超过5 m/s;云水含量大值区位于5 km以上,在7 km高度上达到最大,超过3.0 g/kg;雨滴末速度虽然上下比较一致,一般几米每秒,但在5 km左右为大值区。     

青藏高原那曲地区一次深对流云垂直结构的多源卫星和地基雷达观测对比分析

为了加强对青藏高原深对流云垂直结构的深入认识,利用TRMM、CloudSat和Aqua多源卫星观测资料及地基垂直指向雷达（C波段调频连续波雷达和KA波段毫米波云雷达）资料,对第三次青藏高原大气科学试验期间2014年7月9日13-16时（北京时）发生在那曲气象站附近的深厚强对流云和那曲气象站以西100 km左右的深厚弱对流云的垂直结构特征进行了分析,得到的结果如下:（1）深厚强对流云和深厚弱对流云的水平尺度均较小（10-20 km）,垂直发展高度较高（15-16 km,均指海拔高度）;深厚强对流云在0℃层以下雷达反射率因子递增非常快,表明对流云内固态降水粒子下落至0℃层以下后融化过程有很重要的作用;在对流减弱阶段有明显的0℃层亮带出现,亮带位于5.5 km左右（距地1 km）;（2）对比TRMM测雨雷达和C波段调频连续波雷达观测到的雷达反射率因子,发现TRMM测雨雷达在11 km以下存在高估;（3）深对流云主要为冰相云,云内10 km以上主要是丰富小冰粒子,而10 km以下是较少的大冰晶粒子;深厚强对流云和深厚弱对流云的微物理过程都主要包括混合相过程和冰化过程,混合相过程分为两种:一种是-25℃（深厚强对流云）或-29℃（深厚弱对流云）高度以下以凇附增长为主,另一种是该高度以上主要以冰晶聚合、凝华增长为主,该过程冰晶粒子有效半径增长较快。这些空基和地基的观测证据进一步揭示了青藏高原深对流云的垂直结构特征,为模式模拟青藏高原深对流云的检验提供了依据。      

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.     

Evaluation of cloud and precipitation parameterization using a single-column model: A TWP-ICE case study

Cloud and precipitation parameterization schemes are evaluated, and their sensitivity to the method and/or parameters used to determine cloud physical processes is examined using a singlecolumn version of the Unified Model (SCUM). In the experiment for TWP-ICE, cloud fraction is overestimated (underestimated) in the upper (lower) troposphere due to the wet (dry) bias. The precipitation rate is well simulated during the active monsoon period, but overestimated during the suppressed monsoon and clear skies periods. In the moist convection scheme, trigger condition and entrainment process affect the lower tropospheric humidity through the impact on convective occurrence frequency and intensity, respectively. Strengthening the trigger condition and using the adaptive entrainment method alleviate the low-level dry bias. In the microphysics scheme, more large-scale precipitation is produced with prognostic rain, due to rain sedimentation considering vertical velocity of rain drop, than with diagnostic rain. Less ice/snow deposition with the prognostic two-ice category results in lower ice water content and upper-level cloud fraction than with the diagnostic splitting method for the twoice category. In the cloud macrophysics scheme, the prognostic cloud fraction and cloud/ice water content scheme produces a larger cloud fraction and more cloud/ice water content than the diagnostic scheme, mainly due to detrainment from moist convection (cloud source) that surpasses the effect of convective heating and drying (cloud sink). This affects temperature by influencing the radiative, convective, and microphysical processes. The experiment with combined modifications in cloud and precipitation schemes shows that interaction between modified moist convection and cloud macrophysics schemes results in more alleviation of the cold bias not only at the lower levels but also at the upper levels.     

基于星载微波雷达和激光雷达探测的夏季云顶高度及云量差异分析

云是天气与气候变化的重要影响因子,准确估量云顶高度和云量对分析云特性、降水及强天气预报、估算云辐射强迫等都具有重要意义。利用2006-2010年6-8月CloudSat卫星搭载的微波云廓线雷达（CPR,简称微波雷达）和CALIPSO卫星搭载的云-气溶胶偏振激光雷达（CALIOP,简称激光雷达）的探测资料,分析了全球云顶高度及云量的空间分布特征。结果表明,热带地区微波雷达探测云顶高度平均比激光雷达低约4 km,但均超过12 km;副热带洋面云顶高度在4 km以下,且两部雷达探测的云顶高度差异存在地域性。微波雷达对薄云、云砧及云顶高度低于2.5 km的低云存在漏判,对厚云的云顶高度偏低估;微波雷达探测的全球总云量均值为51.1%,比激光雷达少23.3%;两者给出的云量分布也存在显著的海-陆差异,其中洋面云量差异更大,如微波雷达测出局部洋面云量为80%,而激光雷达的探测结果却超过90%。由于激光雷达发射波长短,对云顶微小粒子比较敏感,而微波雷达波长较长,对相对较小粒子的探测存在局限性。因此,激光雷达对云顶高度的探测优于微波雷达。此结果不仅加强了对激光雷达和微波雷达探测原理的认识,而且进一步理解了云的气候特征。     

ENERGY BUDGET BIAS IN GLOBAL COUPLED OCEAN-ATMOSPHERE-LAND MODEL*

The energy budget of the two versions of the GOALS model (GOALS-1.1 and GOALS-2) is described and compared to observational estimates.The results illustrate that the simulated surface net shortwave radiation flux is underestimated in the high-latitude regions while the surface net longwave radiation flux is substantially overestimated in that region,which results in the lower surface air temperature (SAT) of the polar region and the stronger negative sensible heat flux in high latitudes.The overestimated sensible heat flux from surface to atmosphere in the continents causes the much warmer SAT centers,which may be the reason for the bias of the model SAT. The bias that the simulated precipitation is less than observation in most regions is closely related to the underestimated latent heat flux over most of the Eurasian Continent and the oceans, especially over the subtropical oceans.It can be seen that the bias in the OLR of the two models lies in low and middle latitudes,where the absorbed solar shortwave radiation flux at the top of the atmosphere is comparable to the NCEP reanalysis,but much less than ERBE data.This indicates that the improvement of cloud-radiation parameterization scheme in low and middle latitudes is of critical importance to the simulation of global energy budget.The simulated cloud cover from the GOALS-2 model with diagnosed cloud scheme is generally less except at equatorial areas, especially in the mid-latitude areas,which causes the large bias of energy budget there.It is suggested that the refinement of cloud parameterization is one of the most important tasks in the model's future development.     

海-陆-气全球耦合模式能量收支的误差

通过分析GOALS模式两个版本GOALS 1.1和GOALS 2的能量收支 ,并与观测对比 ,结果表明 :模式模拟的地表净短波辐射通量在高纬地区偏低 ,而净长波辐射通量又偏高 ,导致极地表面温度偏低 ,感热通量在高纬地区为很高的负值。而在陆地上感热加热作用显著偏强 ,使地表有较大的向上净能量给大气 ,引起陆地上有些暖中心也偏强 ,这也解释了模式模拟地表面空气温度场的误差原因。海洋上潜热通量偏低 ,特别是在副热带洋面上偏少更明显。陆地上的欧亚和北美大陆大部分地区潜热通量仍偏低。这也是模式降水在大部分地区偏少的重要原因。两模式大气顶OLR偏低的模拟主要是在中低纬度 ,大气顶净短波辐射通量的模拟在中低纬度虽然与NCEP结果接近 ,但与地球辐射收支试验ERBE资料比较仍偏小较多 ,说明改进中低纬度云 辐射参数化方案对改进全球能量收支的模拟有重要意义。GOALS 2模式中诊断云方案模拟的云量除赤道地区外普遍偏小 ,尤以中纬度为甚 ,造成那里能量收支出现大的误差 ,这表明了更好的云参数化方案的引入是今后模式发展的重要任务之一