A Comparison of Four Precipitation Distribution Models Used in Daily Stochastic Models

Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain-dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August.     

利用随机模拟研究极值天气的预报模型试验

将随机过程的交叉理论应用于天气气候极值分析，以长江三角洲地区逐月最高最低气温为例，说明了交叉理论在极值研究中的作用。基于该理论，对上海市近100a一月气温序列，用随机模拟的方法讨论了极端温度出现的频数、持续时间、时间间隔等参数对于气候变化的敏感性，并根据气候变化趋势，预测了未来气候极值统计特征的变化规律。     

基于气象要素的逐日玉米产量气象影响指数

利用1981—2020年5—9月气象数据与玉米产量数据,通过改进逐日降水适宜度并构建逐日气候适宜度模型,建立基于相似年逐日气象要素的作物生育期气候适宜度序列,利用气象产量与气候适宜指数建立模型,设计逐日作物产量气象影响指数以表征气象条件对作物的影响程度,基于该指数构建东北地区玉米逐日产量预报模型并分析其逐日预报准确率,用以表明该指数的准确性。结果表明：利用3个相似年预报结果加权集成综合相似年逐日作物产量气象影响指数可提高逐日预报准确率,黑龙江年尺度逐日预报准确率年际间波动小于东北其他地区。综合相似年月尺度下,随着玉米发育期的推进和实时气象数据的引入,月尺度平均预报准确率逐渐提高。东北地区玉米产量8月31日的日尺度预报准确率普遍高于7月31日;辽宁日尺度预报差异较大,但随着玉米发育期推进逐日预报产量和实际产量接近,准确率也提高。基于气象要素构建的逐日作物产量影响指数和同期气象影响指数可以定量评估不同时段气象条件对作物产量的影响程度,在一定程度上可提高农业气象业务定量化评价水平。     

中国地面月气候资料质量控制方法的研究

针对湿度质量控制中的主要问题,利用3种温湿关系,设计了温湿跳变一致性检查、湿度传感器僵死现象检查、温湿空间质量控制参数一致性检查3种检测方法,对全国2 428站相对湿度资料的质量检测。结果表明：在检测的42个月内,平均每站每年未通过跳变一致性检查为1.2次,且大都集中出现在少部分台站;出现湿度传感器僵死现象共22站次,僵死过程平均持续时间为17 d,最长超过了65 d;温湿空间质量控制参数一致性检查方法,降低了相对湿度空间一致性检查方法的误检率。

     

中国北方沙尘天气气象场的数值模拟

利用PSU/NCAR的非静力中尺度气象预报模式MM5对北方地区气象场进行模拟,通过对2001年3月21日发生并影响北京地区的沙尘过程的数值实验表明,该模式能够较好地模拟和预测北方沙尘天气的时空演变特征。低值系统以及其低层辐合、高层辐散的垂直结构,可以产生很强的上升气流,形成地面起沙和沙尘上扬,而强风是沙尘输送的动力条件。利用轨迹模式对空气质点轨迹进行了模拟研究,可以较好地反映沙尘的发生源区和输送路径,与卫星监测和天气系统分析是一致的。并编写软件用于演示气象场的动态效果。     

几个常用气象要素相关场显著性检验的统计模拟

对天气预报,特别是气候分析和预测中经常用到的几个气象要素场,应用相关场显著性检验的统计模拟方法,计算出了某一单点或区域平均气象要素与全球ＯＬＲ,北太平洋ＳＳＴ和北半球５００ｈＰａ高度的相关场的显著性检验判据。并对气象统计预报中相关场显著性检验的问题进行了讨论。     

多元判据综合评估中期天气客观相似预报模式

提出一个对多元判据综合评估的中期天气客观相似预报模式。模式应用同中期天气过程的时间—空间尺度相适应的经过时间滤波的大气环流背景为客观判据，比较全面地评估了预报时刻前后多层次、多要素的大气环流动态变化的相似，通过定义相似指数综合评估在多元判据下样本的相似程度，从相似时域的历史样本中找到较佳相似的中期过程，预测未来l～10天的中期天气过程。模式检验和预报试验表明该模式具有预报技巧。     

天山北坡一次大雾天气的数值模拟分析

利用常规气象观测数据、NCEP再分析资料和WRF4.0中尺度数值模式,对2019年12月8—15日新疆天山北坡出现的一次持续性大雾天气的成因进行分析。结果表明：此次大雾天气出现在500 hPa新疆脊控制、850 hPa暖中心维持、地面蒙古冷高压影响的环流背景下。雾开始和维持阶段,地面1 200 m存在逆温强度为0.9℃/100 m的强逆温层,为大雾的形成和维持提供了静力稳定条件;大雾一般出现在辐射降温最明显的傍晚前后;大雾天气出现后2 m气温和地面温度温差始终维持在5℃左右,地、气温差使地面积雪一直升华,为大雾天气持续提供了充足的水汽条件;近地层大气一直存在2.0 m/s以下的微风,形成的湍流维持了雾滴悬浮的平衡状态。当逆温层中上层出现6～10 m/s偏东风时,雾层厚度增加;中上层风速过大或地面～600 m风向一致时,雾减弱或消散。     

突发性强对流天气快速识别预警改进方法

传统多普勒天气雷达强对流灾害性天气监测采用固定阈值判别法给出强风暴的冰雹闪电灾害预警结果,该方法不适用于不同经纬度、季节和复杂地形条件下的强对流天气识别预警。本文利用循环递归的区域生长法对TITAN算法进行改进,从而快速识别三维强风暴单体及其雷达特征物理量;使用多普勒天气雷达和TRMM星载气象雷达的历史观测数据反演河北石家庄地区春夏两季复杂地形条件下的强风暴灾害性天气Logistics多元线性回归概率预警模型。对发生在河北石家庄夏季的一次强飑线天气和发生在春季的一次超级多单体风暴天气进行冰雹闪电灾害性天气识别预警实验,并与传统算法进行误差对比分析。实验结果表明:与传统算法对比,该方法对强风暴天气识别预警的定位精度较高,并且其漏报率和虚报率较低,有助于快速识别预警强对流灾害性天气。     

美国WRF模式的进展和应用前景

WRF(Weather Research Forecast)模式系统是美国气象界联合开发的新一代中尺度预报模式和同化系统，2004年6月对外发布了第二版和三维变分同化系统。这个模式采用高度模块化、并行化和分层设计技术，集成了迄今为止在中尺度方面的研究成果。模拟和实时预报试验表明，WRF模式系统在预报各种天气中都具有较好的性能，同时实现在线完全嵌套大气化学模式，不仅具有较好的天气预报水平，而且具有预报空气质量的能力，具有广阔的应用前景。     

CINRAD/SA天气雷达灯丝电源改进电路

针对SA雷达发射机灯丝电源在实际应用过程中进行了跟踪,对出现的电源驱动电路板被烧焦的故障原因和处理办法进行了阐述。现有的SA雷达发射机灯丝电源存在保护性设计缺陷,经常出现破坏性的故障,特别是电源在驱动信号不稳的情况下,两个半桥驱动开关管被击穿,引起驱动电流过大,并烧焦电路板。通过对电路实际维修以及分析,发现此问题是可以克服的,只要适当增加灯丝电源的自我保护功能,并对控制电路进行部分改进,在驱动信号不稳引起电源输出异常时,电源快速收到雷达系统发出的响应信号,自动掐断强电电源,阻止了大电流在驱动信号非稳定期持续经过驱动电路,减少对开关管的冲击,避免随后产生破坏性故障。改进后灯丝电源在部分雷达上进行试用,效果非常理想。     

参考大气在中期天气预报和气候模拟中的应用

本文概述了根据应用参考大气概念提出的参考大气谱模式计算格式（或称静力扣除法）在国内外一些气象机构的中期天气预报、后处理和四维同化、以及气候模拟中的应用。计算表明，这种参考大气谱模式计算格式，能有效的减少截谱误差，明显地改进中期天气预报和气候模拟。     

Application of Multiple Analysis of Series for Homogenization to Beijing Daily Temperature Series （1960-2006）

Homogenization of climate observations remains a challenge to climatechange researchers, especially in cases where metadata (e.g., probable dates ofbreak points) are not always available. To examine the influence of metadata onhomogenizing climate data, the authors applied the recently developed MultipleAnalysis of Series for Homogenization (MASH) method to the Beijing (BJ) dailytemperature series for 1960--2006 in three cases with different references: (1)13M---considering metadata at BJ and 12 nearby stations; (2)13NOM---considering the same 13 stations without metadata; and (3)21NOM---considering 20 further stations and BJ without metadata. The estimatedmean annual, seasonal, and monthly inhomogeneities are similar between the 13Mand 13NOM cases, while those in the 21NOM case are slightly different. Thedetected biases in the BJ series corresponding to the documented relocationdates are as low as -0.71^oC, -0.79^oC, and -0.5^oCfor the annual mean in the 3 cases, respectively. Otherbiases, including those undocumented in metadata, are minor. The resultssuggest that any major inhomogeneity could be detected via MASH, albeit withminor differences in estimating inhomogeneities based on the differentreferences. The adjusted annual series showed a warming trend of 0.337, 0.316,and 0.365^oC (10 yr)^-1 for the three cases, respectively, smallerthan the estimate of 0.453^oC (10 yr)^-1 in the original series,mainly due to the relocation-induced biases. The impact of the MASH-typehomogenization on estimates of climate extremes in the daily temperature seriesis also discussed.     

飞机空中积冰的气象条件分析及数值预报试验

利用2000年3月至2005年6月山东飞机增雨作业季节,90架次飞机宏观观测资料,对飞机积冰的气象条件和积冰特征进行了分析;并利用PSU/NCAR MM5非静力数值模式,对2005年5月16日增雨作业过程进行了个例研究,在模式准确地预测出了降水的发展趋势和降水范围的基础上,用模式预报场计算的积冰指数和实际飞行观测资料进行了对比试验。结果表明,地面倒槽和冷锋,是山东飞机增雨作业季节的主要作业天气;低压倒槽出现轻度及中度以上积冰所占的比率最大,但南方气旋出现积冰的概率最高,达53.3%;MM5模式预报场计算的积冰指数和垂直上升运动场的叠加,能较好地反映最可能发生积冰的区域、时间及积冰强度,可作为确定飞机增雨作业安全飞行航线的依据。     

Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer

Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality, gene flow(through pollen drift), and plant disease transmission(spore dispersion). Although Lagrangian stochastic(LS) models describe stochastic wind behaviors, such models assume that wind velocities follow Gaussian distributions. However,measured surface-layer wind velocities show a strong skewness and kurtosis. This paper presents an improved model, a non-Gaussian LS model, which inco...     

Experimental Simulations of Extreme Precipitation Based on the Multi-Status Markov Chain Model

A multi-status Markov chain model is proposed to produce daily rainfall, and based on which extreme rainfall is simulated with the generalized Pareto distribution (GPD). The simulated daily rainfall shows high precision at most stations, especially in pluvial regions of East China. The analysis reveals that the multistatus Markov chain model excels the bi-status Markov chain model in simulating climatic features of extreme rainfall. Results from the selected six stations demonstrate excellent simulations in the following aspects:standard deviation of monthly precipitation,daily maximum precipitation,the monthly mean rainfall days,standard deviation of daily precipitation and mean daily precipitation, which are proved to be consistent with the observations. A comparative study involving 78 stations in East China also reveals good consistency in monthly mean rainfall days and mean daily maximum rainfall, except mean daily rainfall. Simulation results at the above 6 stations have shown satisfactory fitting capability of the extreme precipitation GPD method. Good analogy is also found between simulation and observation in threshold and return values. As the errors of the threshold decrease, so do the di?erences between the return and real values. All the above demonstrates the applicability of the Markov chain model to extreme rainfall simulations.     

ECMWF模式的延伸期日最高气温预报偏差估计及订正分析

使用欧洲中期天气预报中心（ECMWF）的20年集合预报回算数据,检验分析了延伸期第16天至第30天预报时效其对我国日最高气温的预报性能。结果表明,西部地区预报误差明显大于中东部地区。全国平均而言,模式预报较实况偏低1.1℃~1.39℃,均方根误差为4.6℃~4.9℃。进一步分析指出,第16天均方根误差最小、且随着时效的延长其略有增大。夏季模式预报效果最好,春季和秋季的部分时段预报效果较差。基于历史偏差订方法,本文还对2018年6月至2019年6月的日最高气温预报进行了误差订正试验。结果显示,订正后的预报准确率提升了15.2%~19.2%。聚焦2018年7月的一次中东部地区大范围高温过程,模式原始预报明显低估了高温强度,订正预报更接近实况,显示其具有一定的订正效果。     

复杂陆面模式与中期数值天气预报模式的耦合

将复杂陆面模式(CLM3)耦合到中期数值预报模式YHMPT106中,替换原来的简单3层陆面过程参数化方案.对比分析表明,较为复杂的陆面过程模式CLM3与中期预报业务模式YHMPT106的耦合是成功的,耦合模式可稳定运行,其预报效果也好.这个试验亦为将来气候预测模式中使用精细的大气环流模式提供了有用的经验.     

Parameterized Forward Operators for Simulation and Assimilation of Polarimetric Radar Data with Numerical Weather Predictions

Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP).To realize this potential,an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables.For this purpose,a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain,snow,hail,and graupel.The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution.The calculated polarimetric variables are then fitted to simple functions of water content and volumeweighted mean diameter of the hydrometeor particle size distribution.The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting(WRF)model to have simulated PRD,which are compared with existing operators and real observations to show their validity and applicability.The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations,making it efficient in PRD simulation and assimilation usage.     

Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec,Canada

Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m^?2?d^?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m^?2?d^?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.