均生函数逐步回归模型在降温、降水长期过程预测中的应用

应用均生函数逐步回归模型，经过十次变长序列动态建模进行多步预测，再对多个结果进行合成分析，可以更好地揭示、归纳及利用好自身的规律。对降温、降水等长期天气过程预测，得到较可信的结果。     

均生函数模型在气温和降水预测中的应用

应用均生函数对单一的气象要素序列建立预测模型,并进行试报与预测检验。实例计算分析表明,该方法对短期气候预测有较强的预测能力,均生函数模型预测结果具有一定的可信度。     

均生函数的改进模型在重庆地区主汛期降水预测中的应用试验

本文应用均生函数模型及其改进模型对重庆地区 主汛期(6～8月 )降水进行预测试验，结果表明：针对均生函数固有的缺点提出的改进方案所建立的数学预 报模型具有更好的预测效果，是一种具有较高使用价值的长期预报方法。     

均生函数模型在高原汛期降水预报中的应用

以均生函数为基函数,建立了正交化、主成分和最优子集回归三类预测模型。对云南昆明、滇中（包括昆明、玉溪和楚雄）及西藏林芝的汛期（６￣８）降水进行了模拟和预测、其拟合效果,特别是对极值的拟合十分理想。结果表明：在高原地区,均生函数模型预报降水具有较高的精度,拟合报准率和实际报准率分别为９７％￣１００％和６０％￣８０％,且具有多步预测能力。     

均生函数的改进模型在重庆地区主汛期降水预测中的应用试验

本文应用均生函数模型及其改进模型对重庆地区主汛期（6－8月）降水进行预测试验，结果表明：针对均生函数固有的缺点提出的改进方案所建立的数学预报模型具有更好的预测效果，是一种具有较高使用价值的长期预报方法。     

均生函数预测方法在青藏高原冬季降水预报中的应用及改进

均生函数预测方法在天气预报等领域得到了越来越广泛的应用，但预测方法没有涉及到图形处理。在对方法、原理及过程进行深入细致的研究后，重新编写了程序的头部、中部、尾部语句及子过程调用函数。经多次调试试验，实现了人口资料调用灵活、多变、计算模拟结果屏幕显示等，提高了预测方法的整体实用性和预测信息的可视化程度。检验得知：该方法对青藏高原冬季降水的预报具有较高的预报精度。     

多元均生函数模型及其在短期气候预测中的应用

在原均生数模型的基础上，对模型进行改进，首次引入预报因子变量，建立一个多元的均生函数模型。它适应于气温、降水、热带气旋个数等多种要素预报。新建的多元均生函数模型包含了原模型的优点。而且比原有均生函数模型具有更物理基础。     

均生函数残差预报模型在降水预报中的试验研究

在均生函预报模型的基础上，利用其残差数据序列对均生函数预报模型进行校正，提出了均生函数残差预报模型。运用两种模型对百色市6、7、8月月降雨量进行了历史样本拟合，并进行独立地样本预报试验。预报结果发现，均生函数残差预报模型对原有模型在预报精度上都有一定的改进，取得了较好的预报效果。同时，利用MannKendall法和Yamamoto法，可以明确突变开始的时间，指出突变区域，使待报时段与建模资料处在同一气候阶段则预报效果更为理想。     

时间序列均生函数模型在降水量预测中的应用

利用高要站1980—2015年冬季(12月—次年2月)的降雨量,构建均生函数模型,并采用粗选因子、逐步引入、逐步剔除等3组回归方案,对降雨量进行模型拟合,2016—2017年冬季降雨量进行模型预测结果的检验。结果表明:3组回归方程均能较好地拟合时间序列的变化趋势,对极值的把握表现较好,对短期气候预测业务有一定的现实意义。     

新阈值函数去噪方法在可降水量预报中的应用

大气观测数据中常含有噪声,如何减小噪声是大气观测数据应用的基础。本文在传统软、硬阈值函数去噪的基础上提出了新阈值函数去噪。首先,运用这三种方法对MATLAB中wnoise函数产生的bumps含噪信号进行去噪,并运用信噪比(SNR)和最小均方误差(MMSE)对去噪效果进行衡量。试验表明,新阈值函数去噪效果较好。最后,用新阈值函数去噪对可降水量时间序列进行处理,并用Kolmogorov熵和神经网络预测来衡量去噪效果。结果表明,新阈值函数同样适用于可降水量时间序列的去噪。     

全球地面降水月值历史数据集研制

全球降水历史数据是开展气候、水循环等研究的基础。收集整理全球12个数据源降水历史月值资料,通过站号、站名甄别不同数据源中相同台站,对344个通过相关系数、一致率、均值t检验、方差F检验的台站多源资料进行拼接,尽可能多地融合各套数据产品优势,最终形成全球降水历史月值数据集(CMA Global PrecipitationV1.0, CGP)。数据集重点解决当前国际数据产品在东亚地区站点稀少、同时应用多套数据应用门槛较高等问题。数据集收录3.1万个台站共计1.87×107组月降水记录, 4152个台站序列长度达百年。与美国大气海洋局(NOAA)的全球降水数据集(GHCN-M V2.0)对比,CGP新增1万个站点、0.5×107组有效观测记录和1030条百年序列,其中141条百年序列通过多源整合技术获取。CGP的站点和数据量优势主要体现在东亚、东欧、西伯利亚等站点稀疏地区。基于CGP分析的全球降水时空特征与国际同类产品的结果较一致。新增的数据虽然没有改变全球降水分布的总体特征,但对区域性的百年降水变化检测有一定影响。基于CGP的全球降水百年序列结果显示,20世纪前半叶全球降水量偏小,近20年是1900年以来全球降水量最大的时期,各纬度带、各个国家或地区的降水长期变化趋势呈现显著的差异。      

Main types of synoptic processes and circulation types generating heavy precipitation events in Armenia

The results of synoptic analysis and objective Lamb circulation classification scheme (LAMB) during the days with heavy precipitation events in Armenia are presented. An analysis of synoptic situations in the area of Armenia and Southern Caucasus for the researched period 2001–2009 has shown that there are seven types of synoptic process characteristic of days with heavy precipitation events in Armenia. Adoption of the LAMB made it possible to identify the typical circulation types for each type of the synoptic process singled out. The results suggest that the LAMB is able to recognize the typical pattern of the distribution of sea level pressure field for each type of the synoptic processes. However, the use of the LAMB is further complicated in Southern Caucasus which can be explained by the existence of regional peculiarities of atmospheric circulation associated with significant influence of the Caucasian ridge. The LAMB may be recommended as an extra tool for synoptic analysis as well as for developing of synoptic climatology and statistical downscaling methods for Armenia and Southern Caucasus.     

Localized climate change scenarios of mean temperature and precipitation over Switzerland

There is a growing need of the climate change impact modeling and adaptation community to have more localized climate change scenario information available over complex topography such as in Switzerland. A gridded dataset of expected future climate change signals for seasonal averages of daily mean temperature and precipitation in Switzerland is presented. The basic scenarios are taken from the CH2011 initiative. In CH2011, a Bayesian framework was applied to obtain probabilistic scenarios for three regions within Switzerland. Here, the results for two additional Alpine sub-regions are presented. The regional estimates have then been downscaled onto a regular latitude-longitude grid with a resolution of 0.02° or roughly 2 km. The downscaling procedure is based on the spatial structure of the climate change signals as simulated by the underlying regional climate models and relies on a Kriging with external drift using height as auxiliary predictor. The considered emission scenarios are A1B, A2 and the mitigation scenario RCP3PD. The new dataset shows an expected warming of about 1 to 6 °C until the end of the 21st century, strongly depending on the scenario and the lead time. Owing to a large vertical gradient, the warming is about 1 °C stronger in the Alps than in the Swiss lowlands. In case of precipitation, the projection uncertainty is large and in most seasons precipitation can increase or decrease. In summer a distinct decrease of precipitation can be found, again strongly depending on the emission scenario.     

A nonlinear time-lag differential equation model for predicting monthly precipitation

This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space con-tinuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms of the time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthly rainfall datasets from Changsha station Results show that the model is likely to describe the nonlinearity of the an-nual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating for the wet season.     

A step-response approach for predicting and understanding non-linear precipitation changes

Future changes in precipitation represent one of the most important and uncertain possible effects of future climate change. We demonstrate a new approach based on idealised CO₂ step-change general circulation model (GCM) experiments, and test it using the HadCM3 GCM. The approach has two purposes: to help understand GCM projections, and to build and test a fast simple model for precipitation projections under a wide range of forcing scenarios. Overall, we find that the CO₂ step experiments contain much information that is relevant to transient projections, but that is more easily extracted due to the idealised experimental design. We find that the temporary acceleration of global-mean precipitation in this GCM following CO₂ ramp-down cannot be fully explained simply using linear responses to CO₂ and temperature. A more complete explanation can be achieved with an additional term representing interaction between CO₂ and temperature effects. Energy budget analysis of this term is dominated by clear-sky outgoing long-wave radiation (CSOLR) and sensible heating, but cloud and short-wave terms also contribute. The dominant CSOLR interaction is attributable to increased CO₂ raising the mean emission level to colder altitudes, which reduces the rate of increase of OLR with warming. This behaviour can be reproduced by our simple model. On regional scales, we compare our approach with linear ‘pattern-scaling’ (scaling regional responses by global-mean temperature change). In regions where our model predicts linear change, pattern-scaling works equally well. In some regions, however, substantial deviations from linear scaling with global-mean temperature are found, and our simple model provides more accurate projections. The idealised experiments reveal a complex pattern of non-linear behaviour. There are likely to be a range of controlling physical mechanisms, different from those dominating the global-mean response, requiring focussed investigation for individual regions, and in other GCMs.