京津冀及周边地区冬季能见度与PM2.5浓度和环境湿度的多元回归分析

2013年至今,中国冬季与雾霾相伴的低能见度事件频发,京津冀及周边地区尤为严重。PM_2.5浓度与环境湿度是导致低能见度的最关键影响因素。为了深入研究PM_2.5浓度与环境湿度对大气能见度的影响,利用2017年1月京津冀及周边地区MICAPS气象数据与PM_2.5观测数据,运用天气学诊断分析方法讨论了不同相对湿度下PM_2.5浓度、环境湿度对冬季能见度变化的相对贡献,按照地理环境与污染程度差异将京津冀及周边地区划分为北京-天津地区与河北-山东地区,建立了PM_2.5浓度与环境湿度（由露点温度、温度代表）对能见度的多元回归方程,并对2015、2016、2018、2019年冬季能见度进行了回算检验。结果显示:相对湿度低于70%、PM_2.5浓度低于75 μg/m3时,北京-天津地区与河北-山东地区能见度多高于10 km,PM_2.5浓度升高是此时能见度迅速降低的主导因素;相对湿度从70%上升至85%和PM_2.5浓度从75 μg/m3升高200 μg/m3的共同作用导致了能见度降低到10 km至5 km;能见度进一步从5 km下降至2 km则更多依赖于相对湿度进一步从85%升高至95%,PM_2.5浓度与此时能见度相关减弱;能见度降低至2 km甚至更低主要是由于水汽近饱和状态下（相对湿度95%以上）的雾滴消光引起,与PM_2.5浓度的变化关系不大。与不分组直接拟合相比,以相对湿度85%为界线,分别拟合能见度能够很大程度优化多元回归模型,相对湿度高于85%时能见度拟合值的均方根误差从9.2和5.2 km下降至0.5和0.7 km,5 km以下拟合能见度的误差大幅度减小。按相对湿度85%将数据分组所得的拟合方程对2015、2016、2018、2019年1月能见度估算结果较好,观测值与拟合值相关系数均高于0.91,为雾-霾数值预报系统提供了新的能见度参数化算法。      

A Spatial Consistency Quality Control Method for Daily Surface Temperature Observations

A new spatial consistency quality control method (SRF) based on the spatial regression test (SRT) and random forest (RF) was adapted to identify potential outliers in daily surface temperature observations in this article. For the new method, the SRT method was used to filter the data and the RF method was used to conduct regression. To evaluate the performance of the quality control method, the SRF, SRT and RF methods were applied to a surface temperature dataset with seeded errors from different regions of China from 2005 to 2014. Compared to SRT and RF, the results indicate that the SRF method outperforms the other two methods for the most cases. And the results of the comparison led to the recommendation that the SRF method improves the regression accuracy of traditional spatial consistency quality control methods and reduces the runtime of random forest through data refinement.     

A simple gravity wave drag parametrization for use in medium‐range weather forecast models

Abstract

A simple gravity wave drag parametriiation over mountainous terrain is tested for its ability to reduce the systematic errors of medium‐range weather forecasts. Following Boer et al. (1984), this parametrization is a function of the low‐level wind speed and stability, the local Froude number, and the variance of the subgrid‐scale orographie features.

A comparison study of ten 7‐day forecasts obtained with envelope orography, wave drag or standard orography, shows that wave drag is as effective as envelope orography in reducing the systematic errors. A further comparison where the combined effects of the wave drag and that of a complementary enhanced orography (that is one that includes only the subgrid‐scale elements not treated separately by wave drag) are taken into account shows this latter approach to be the most promising in reducing orographically‐related systematic errors.     

基于遥感的土壤电阻率估算研究

以我国长江中下游的南京及其周边15个市(县)为例,采用野外测定、室内分析与遥感反演相结合的方法,开展了土壤电阻率估算研究.选用影响土壤电阻率的土壤水分、土壤温度、土壤可溶盐总量与土壤阳离子交换量等4个主要因子,遥感反演土壤水分和温度空间分布,以获取估算土壤电阻率所需要的主要参数;采用偏最小二乘二次多项式(PLSQM)模型对不同地表覆盖类型下的土壤电阻率进行估算,PLSQM估算模型的估算值与实测值的相关系数达到0.85,平均相对误差(MRE)为19.02%,均方根误差(RMSE)为7.79.结果表明,草地、农田、林地3种不同地表覆盖类型下土壤电阻率有明显差异,PLSQM模型实现了较高估算精度,具有较好的应用潜力.     

基于MonoRTM模型的微波辐射计反演方法研究

基于辐射传输模型MonoRTM计算天空亮温度,使用多元线性统计回归方法和BP神经网络方法分别对大气温度和水汽密度廓线进行了反演,检验并分析了两种方法的反演精度.结果表明,多元线性回归方法反演的温度偏差总体不大于6K,反演的水汽密度偏差小于4 g/m3;神经网络方法反演的温度偏差小于2K,反演的水汽密度误差总体不大于2 g/m3.与探空数据的对比表明,对于大气温度和水汽密度反演,BP神经网络方法的反演结果都要比多元线性回归方法的反演结果更接近探空资料值.     

Ekman层风速随高度分布方程的数值解试验

根据大气水平运动方程推导出Ekman层风速随高度分布方程,该分布可用二阶线性微分方程表示,用有限差分法求解该二阶线性微分方程的边值问题的数值解,并给出计算程序,输出结果。将结果与经典解析解比较,讨论了经典解的正确性和适用范围。     

往复活塞式制冷压缩机性能系数的曲面回归方程

采用非线性二乘法对国产１２．５系列往复活塞式制冷压缩机的实测性能数据进行拟合，得出描述该系列制冷压缩机性能系数与运行工况之间相关关系的曲面回归方程，为制冷系统的数值计算、动态特性模拟和优化设计提供数学模型。     

箭竹生长及开花的气象条件研究

通过对我国历史上箭竹开花及王朗自然保护区箭竹生长气象条件的研究认为，箭竹开花多在冬暖夏凉的年份；箭竹的生长量可由气象因素的变化来进行预测。研究结果可为保护大熊猫提供一定的气象依据。     

THE FREE-WILSON PARADIGM REDUX:SIGNIFICANCE OF THE FREE-WILSON COEFFICIENTS,INSIGNIFICANCE OF COEFFICIENT ‘UNCERTAINTIES’ AND STATISTICAL SINS

The Free-Wilson paradigm is an established and powerful tool for quantitatively relating activity withchemical structure.Current implementations of the paradigm,however,are flawed both conceptually andin execution.As part of an attempt to more fully realize the promise of the paradigm,it was necessaryto examine these limitations in detail.This report introduces a robust,theory-founded Free-Wilson implementation:stepwise principalcomponents regression analysis(SPCRA).SPCRA is computationally superior to previousimplementations but does not in itself correct their conceptual flaws.The development of SPCRA did,however,facilitate derivation of a simple and chemically significantinterpretation of the Free-Wilson structure-activity model.A number of statistical aspects of this modelcommonly misused in previous applications are discussed at length.These discussions provide criticalbackground for the development of an alternative implementation of the Free-Wilson paradigm.