秦巴山区降水量分布综合模式

用山地旬降水量的综合模式，根据汉中站的大样本资料，估算秦巴山区考察站（小样本）旬降水量的分布。推证表明该模式起到了对小样本资料的补差和订正作用，可获得旬降水量的全部统计参数，可以推广应用到其它山区降水量的研究中去。     

降水量概率分布的一种间接模式

本文提出一种相邻测站月(年)降水量Γ分布的间接估计模式。根据该模式,借助长序列(大样本)测站资料可间接估计短序列(小样本)测站的理论分布。推证表明,间接模式比直接由本站短序列记录估计理论分布提高了有效率并简化了拟合分布模式的计算。     

雨季划分及雨带变动的研究

1.雨季划分标准 鉴于月、旬、候降水量的分布都不是正态的,用多年平均资料划分雨季并不能代表多数年份的情形。因此本文定义某旬降水量相对系数C=某旬降水量÷(该旬天数×多年平均年降水量/365),用相对系数的好处,在于它能避免因各旬所包括的日数不同而引起的差异。用历年逐旬降水量计算相对系数,并且定义C≥1的旬为多雨旬。统计每一旬为     

柴达木盆地降水的时空分布特征

利用柴达木盆地11个国家气象站(2017年3月—2018年2月)及28个区域气象站(2017年6—8月)月降水量资料,运用线性回归订正法和比值订正法推算柴达木盆地的年降水量,进一步分析柴达木盆地降水量季节变化及空间分布特征。结果表明:(1)柴达木盆地降水量年内分配极不均匀,呈单峰性,峰值出现在7月,5—9月(汛期)降水量占全年的87.4%。季节差异非常明显,降水主要集中在夏季;(2)年降水量空间分布特征:柴达木盆地年降水量各地差异极为显著,降水量整体表现为从东向西逐渐减少。最大值出现在天峻,最小值出现在冷湖。用2种方法推算的年降水量最大值出现在柴达木盆地东北部祁连山南麓的木里镇,其次在格尔木市南部出现了两个相对的大值中心,中间区域(93°～97°E)由四周山区向盆地中心逐渐减少的形势表现得更加清晰。夏季降水量的空间分布与年降水量的空间分布完全一致。(3)国家气象站模型中降水量分布只受经度和海拔高度的影响,而线性回归法和比值订正法模型中降水量的分布不仅受经度和海拔高度的影响,还受纬度的影响,三者的贡献率由大到小的排序是经度海拔高度纬度。     

北疆天然草场牧草产量分布模型

本文首先建立新疆北部气候要素的垂直分布模型，结果表明，在北疆地区，气温随海拔高度上升而凝减，降水量呈物线分布，在中山带达到最大。然后利用天然草场资源调查资料和相应气象站及推算的资料，建立天然草场牧草产量与气象条件的分布模型，年降水量每增加1毫米，天山北坡中部和西部的天然草场平均单产可增加0．94和0．82公斤，生存气候模型可用于推算天然草场牧草产量空间分布。     

我国南方旬降水量的统计特征

本文对秦岭—淮河以南地区旬降水量的时空分布特征进行了初步统计分析。指出论域内各旬降水量大多为正偏态和尖峰态分布。谐波分析表明,旬降水量年变化以1波为主。1波振幅和位相的自然正交分析显示出其年际变化趋势的空间分布各具特色。     

GM（0，2）模型在遥感估算区域降水中的应用

根据遥感植被指数信息，基于灰色累加生成原理，利用植被指数增长与旬累积降水量的同步性，建立旱年、正常年的GM(0，2)模型，归纳为2种不同气候类型模式，并利用旬植被指数来估计旬降水累积量，可通过累减还原估算出旬降水量。     

甘肃中部春夏干旱气候分析与预测系统

利用甘肃中部13个测站历年4～6月的旬降水、温度资料，分析了干旱年、月、(旬)降水量、平均温度等气候变化特征及其空间分布。在此基础上，建立了相应的春、夏干旱短期气候预测系统模型。经业务使用，准确率较高。     

近45年来中国旱涝分析及影响

根据１９５１－１９９５年我国３３６个站逐旬降水量资料，采用服从皮尔逊Ⅲ型分布的旱涝Ｚ指数，分析了４０多年来我国的旱涝事实，并对旱涝灾害的影响进行了评述。     

基于TRMM数据的秦巴山区降水特征分析

本文利用1998～2015年TRMM卫星3B42的日降水资料，对秦巴山区的年降水量、季降水量、各个等级降雨日数、整个区域降水量的年际变化和季节变化进行了统计，同时还对各个不同降水类型的倾向率进行了统计，再根据他们的时空分布进行分析。结果表明:1)1998～2015年秦巴山区年平均降水量从东南到西北呈现依次递减的变化特征且呈带状分布, 山区南部的降水量明显高于北部，山区东西部降水差别不大；2)四个季节的平均降水均呈现“南高北低”的空间分布特征；3)无雨日数在整个山区呈增大的趋势，小雨日数在整个山区呈减少的趋势，中雨日数除山区东南外呈增大的趋势，大雨日数除重庆、河南、四川北部外基本呈增大的趋势，暴雨日数除山区东西部暴雨日数减少外其他地方都呈增加的趋势；4)整个山区年降水量随着时间的增长在缓慢的增多。      

云南夏季降水异常的影响因子及物理统计预测方法

云南夏季降水年际变化较大,影响因子众多,夏季降水的预测较为困难。使用1965—2017年云南省122个气象观测站的逐日降水资料和NCEP大气环流资料,采用年际增量的方法来预测云南夏季降水。文中基于云南夏季降水年际增量变化规律和影响夏季降水的环流形势及物理过程,选取了6个具有物理意义的预测因子,包括:前期2月南太平洋海温异常、前期2月东亚北部海平面气压异常、前期4月北美500hPa位势高度异常、前期5月太平洋北部海平面气压异常、前期1月印度半岛北部500hPa位势高度异常及前期2月澳洲以南地区200hPa高度场偶极子异常,来建立云南夏季降水预测模型。并对预测模型进行逐年交叉检验和1998—2017年逐年独立样本检验。交叉检验中夏季降水年际增量预测值和观测值的相关系数为0.85,相对均方根误差为8.0%。回报检验中夏季降水年际增量的相对均方根误差为9.1%,63.0%的异常年份预测值能够准确地预报出夏季降水异常。该预测模型有较好的预测能力。     

The Application of a Meteo-hydrological Forecasting System with Rainfall Bias Correction in a Small and Medium-sized Catchment

Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast. The quality of rainfall data in terms of both intensity and distribution is very important for establishing a reliable meteo-hydrological forecasting model. To improve the accuracy of rainfall data, the successive correction method is introduced to correct the bias of rainfall, and a meteo-hydrological forecasting model based on WRF and WRF-Hydro is applied for streamflow forecast over the Zhanghe River catchment in China. The performance of WRF rainfall is compared with the China Meteorological Administration Multi-source Precipitation Analysis System (CMPAS), and the simulated streamflow from the model is further studied. It shows that the corrected WRF rainfall is more similar to the CMPAS in both temporal and spatial distribution than the original WRF rainfall. By contrast, the statistical metrics of the corrected WRF rainfall are better. When the corrected WRF rainfall is used to drive the WRF-Hydro model, the simulated streamflow of most events is significantly improved in both hydrographs and volume than that of using the original WRF rainfall. Among the studied events, the largest improvement of the NSE is from -0.68 to 0.67. It proves that correcting the bias of WRF rainfall with the successive correction method can greatly improve the performance of streamflow forecast. In general, the WRF / WRF-Hydro meteo-hydrological forecasting model based on the successive correction method has the potential to provide better streamflow forecast in the Zhanghe River catchment.     

A Modeling Study of Surface Rainfall Processes Associated with a Torrential Rainfall Event over Hubei, China, during July 2007

The surface rainfall processes associated with the torrential rainfall event over Hubei,China,during July 2007 were investigated using a two-dimensional cloud-resolving model.The model integrated the large-scale vertical velocity and zonal wind data from National Centers for Environmental Prediction(NCEP)/Global Data Assimilation System(GDAS) for 5 days.The time and model domain mean surface rain rate was used to identify the onset,mature,and decay periods of rainfall.During the onset period,the descending motion data imposed in the lower troposphere led to a large contribution of stratiform rainfall to the model domain mean surface rainfall.The local atmospheric drying and transport of rain from convective regions mainly contributes to the stratiform rainfall.During the mature periods,the ascending motion data integrated into the model was so strong that water vapor convergence was the dominant process for both convective and stratiform rainfall.Both convective and stratiform rainfalls made important contributions to the model domain mean surface rainfall.During the decay period,descending motion data input into the model prevailed,making stratiform rainfall dominant.Stratiform rainfall was mainly caused by the water vapor convergence over raining stratiform regions.     

Rainfall generator with spatial and temporal characteristics

The aim of this paper is to present a rainfall generator which takes both spatial and temporal characteristics into account. The statistical model behind the generator is based on long-time experience with statistical analyses of empirical rainfall data and represents, therefore, a genuine theoretical point for simulating series of precipitation values. The sites of the recording gauges and the actual time (day, month, year etc.) enter a statistical model as systematic components for the prediction of expected rainfall in a two-way analysis of variance design. Beyond these systematic components, spatial correlations among the observations are still present in nature, a feature which is modelled also by the rainfall generator.     

EVALUATION OF CONVECTIVE-STRATIFORM RAINFALL SEPARATION SCHEMES BY PRECIPITATION AND CLOUD STATISTICS

In this study,two convective-stratiform rainfall partitioning schemes are evaluated using precipitation and cloud statistics for different rainfall types categorized by applying surface rainfall equation on grid-scale data from a two-dimensional cloud-resolving model simulation.One scheme is based on surface rainfall intensity whereas the other is based on cloud content information.The model is largely forced by the large-scale vertical velocity derived from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment(TOGA COARE).The results reveal that over 40% of convective rainfall is associated with water vapor divergence,which primarily comes from the rainfall type with local atmospheric drying and water hydrometeor loss/convergence,caused by precipitation and evaporation of rain.More than 40% of stratiform rainfall is related to water vapor convergence,which largely comes from the rainfall type with local atmospheric moistening and hydrometeor loss/convergence attributable to water clouds through precipitation and the evaporation of rain and ice clouds through the conversion from ice hydrometeor to water hydrometeor.This implies that the separation methods based on surface rainfall and cloud content may not clearly separate convective and stratiform rainfall.     

基于WRF模式的云南短时强降水物理量特征

为了解云南短时强降水发生前本地化中尺度WRF(Weather Research Forecast)模式输出结果的物理量特征及其对短时强降水预报的作用,使用WRF模式对2016年云南主汛期(6—8月)5次短时强降水过程进行模拟,利用模式输出的高时空分辨率资料计算5次过程中85个样本在短时强降水发生前6 h水汽类、动力类及不稳定条件类的部分物理量值,使用箱线图分析各物理量的分布特征及其与短时强降水的关系,应用经验累积分布函数图确定各物理量的阈值。研究表明,水汽类物理量样本数据值分布较为集中,随着短时强降水的临近数值逐渐增大;动力类的6 km垂直风切变中位数值及平均值随时间变化很小,所有时次的6 km垂直风切变阈值均低于12 m/s,表明短时强降水发生前有弱垂直风切变;不稳定条件类中对流有效位能样本数据的离散程度较大,对短时强降水无指示意义;LI指数、K指数和700 hPa假相当位温样本数据离散度较小,其中K指数中位数值、平均值及阈值的上下限在短时强降水发生前1 h有显著增大的特征,且数据集中度达到最高,大的K指数值与短时强降水有较好的对应关系。使用物理量阈值推算短时强降水落点的方法对云南本地化WRF模式短时强降水的预报性能有改进作用。     

Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre’s climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is under-estimated (over-estimated) over wet (dry) regions of southern Africa.     

Evaluation of precipitation prediction skill of IMD operational NWP system over Indian monsoon region

Summary The study provides a concise and synthesized documentation of the current level of skill of the operational NWP model of India Meteorological Department based on daily 24 hours forecast run of the model during two normal monsoon years 2001 and 2003 making detailed inter-comparison with daily rainfall analysis from the use of high dense land rain gauge observations. The study shows that the model, in general, is able to capture three regions of climatologically heavy rainfall domains along Western Ghats, Northeast India and over east central India, over the domain of monsoon trough. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The inter-comparison reveals that performance of the model rainfall forecast deteriorated in 2003 when rainfall over most parts of the region was significantly under-predicted. These features are also reflected in the error statistics. The study suggests that there is a need to maximize the data ingest in the model with a better data assimilation scheme to improve the rainfall forecast skill.     

A comparative study of mixed exponential and Weibull distributions in a stochastic model replicating a tropical rainfall process

A stochastic rainfall model is presented for the generation of hourly rainfall data in an urban area in Malaysia. In view of the high temporal and spatial variability of rainfall within the tropical rain belt, the Spatial–Temporal Neyman–Scott Rectangular Pulse model was used. The model, which is governed by the Neyman–Scott process, employs a reasonable number of parameters to represent the physical attributes of rainfall. A common approach is to attach each attribute to a mathematical distribution. With respect to rain cell intensity, this study proposes the use of a mixed exponential distribution. The performance of the proposed model was compared to a model that employs the Weibull distribution. Hourly and daily rainfall data from four stations in the Damansara River basin in Malaysia were used as input to the models, and simulations of hourly series were performed for an independent site within the basin. The performance of the models was assessed based on how closely the statistical characteristics of the simulated series resembled the statistics of the observed series. The findings obtained based on graphical representation revealed that the statistical characteristics of the simulated series for both models compared reasonably well with the observed series. However, a further assessment using the AIC, BIC and RMSE showed that the proposed model yields better results. The results of this study indicate that for tropical climates, the proposed model, using a mixed exponential distribution, is the best choice for generation of synthetic data for ungauged sites or for sites with insufficient data within the limit of the fitted region.