Climate,conflict and forced migration

Despite the lack of robust empirical evidence, a growing number of media reports attempt to link climate change to the ongoing violent conflicts in Syria and other parts of the world, as well as to the migration crisis in Europe. Exploiting bilateral data on asylum seeking applications for 157 countries over the period 2006–2015, we assess the determinants of refugee flows using a gravity model which accounts for endogenous selection in order to examine the causal link between climate, conflict and forced migration. Our results indicate that climatic conditions, by affecting drought severity and the likelihood of armed conflict, played a significant role as an explanatory factor for asylum seeking in the period 2011–2015. The effect of climate on conflict occurrence is particularly relevant for countries in Western Asia in the period 2010–2012 during when many countries were undergoing political transformation. This finding suggests that the impact of climate on conflict and asylum seeking flows is limited to specific time period and contexts.     

The Meta-Gaussian Bayesian Processor of Forecasts and Associated Preliminary Experiments

Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast (BPF), a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast according to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting system in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hydrology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the deterministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.     

Nondispersive Periodic Solution of a Barotropic Semi-Geostrophic Model

The existence and solution of the non-dispersive periodic solution are achieved concerning nonlinear barotropic Rossby waves of a barotropic semi-geostrophic model, demonstrating the likelihood of the Taylor evolution, together with the related dimensionless a-criterion. Finally, the wave velocity expression is proposed with some diagnostic relations among the wave parameters.     

Study on Probability Distributions of Multi-Timescale Aerosol Optical Depth Using AERONET Data

The probability distribution analysis is per-formed for multi-timescale aerosol optical depth (AOD) using AErosol RObotic NETwork (AERONET) level 2.0 data.The maximum likelihood estimation is employed to determine the best-fit probability density function (PDF),and the statement that the fitting Weibull distribution will be light-tailed is proved true for these AOD samples.The best-fit PDF results for multi-site data show that the PDF of AOD samples with longer timescale in most sites tends to be stably represented by lognormal distribution,while Weibull distribution is a better fit for AOD samples with short timescales.The reason for this difference is ana-lyzed through tail characteristics of the two distributions,and an indicator for the selection between Weibull and lognormal distributions is suggested and validated.The result of this research is helpful for determining the most accurate AOD statistics for a given site and a given time-scale and for validating the retrieved AOD through its PDF.     

离散点气象要素平流外推模型

大气平流方程一般用于格点场数值预报的处理,鲜有应用到离散点要素外推。离散点平流外推模型不仅使时间方向站点资料补缺更具物理意义,而且为站点要素的预报奠定基础。模型首先根据站点离散特征,对平流方程进行改造,得到离散点风速、梯度本征向量,并通过频数统计拟合出平流项似然概率的高斯表达式,以此计算出新平流方程数值解。通过对单点和多点的实况试验,得出了模型的具体应用特点,并给出了一个实际的数据补缺场景,作用显著。该模型具有小巧、高效,易用等特点,有良好的普适性,作为一个通用的数学模型,为离散点的要素预报提供了一种新的思路。     

多尺度非静力通用模式框架的设计策略

针对目前国际上数值预报模式的最新发展趋势,综述了新形势下多尺度非静力通用数值预报模式框架的设计策略。目前世界上开发通用模式主要有两个途经,一是建立一个离散化方案和源程序代码共享的、全球和有限区通用的模式,二是建立一个单一的全球可变分辨率的模式。还从通用模式方程组的选取策略、模式网格属性的构造,时间积分方案、空间离散方案,垂直坐标的选取等方面进行了分析。     

基于遗传算法的特征选择方法在短时强降水预报中的应用北大核心

利用江苏省13个气象观测站历史上短时强降水观测资料,用遗传算法进行特征选择,选定影响短时强降水的950 hPa假相当位温、700 hPa比湿、500 hPa比湿、对流有效势能(Convective Available Potential Energy,CAPE)等14个特征为主要因素,将是否为短时强降水抽象成二元分类问题。借助机器学习中CART决策树算法进行分类分析,构建便于使用的短时强降水预报规则集。实验部分,随机选择5816条样本进行训练模型,得到适合江苏地区的短时强降水规则集,利用剩余的1454条数据进行实际检验,模型的短时强降水预报准确率为91.35%,非强降水预报准确率为97.11%,较特征选择之前分别提升了8.66%和1.05%。     

Statistical Downscaling of Pattern Projection Using Multi-Model Output Variables as Predictors

A pattern projection downscaling method is employed to predict monthly station precipitation. The predictand is the monthly precipitation at 1 station in China, 60 stations in Korea, and 8 stations in Thailand. The predictors are multiple variables from the output of operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction is made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of the model downscaled precipitation forecasts using the best predictors and is referred to as DMME. It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse resolution predictions of general circulation models. The correlation coefficient between the prediction of DMME and the observation in Beijing of China reaches 0.71; the skill is improved to 0.75 for Korea and 0.61 for Thailand. The improvement of the prediction skills for the first two cases is attributed to three steps: coupled pattern selection, optimal predictor selection, and multi-model downscaled precipitation ensemble. For Thailand, we use the single-predictor prediction, which results in a lower prediction skill than the other two cases. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected well, can be used to make skillful predictions of local precipitation by means of appropriate statistical downscaling.     

Coupling Ensemble Kalman Filter with Four-dimensional Variational Data Assimilation

This study examines the performance of coupling the deterministic four-dimensional variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme (E4DVAR) benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence: the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.     

响应变量随机缺失下部分线性单指标模型的非参数判别检验

研究了响应变量随机缺失情况下部分线性单指标模型的非参数部分检验问题,检验非参数部分预测变量同响应变量之间是否存在非线性关系.用参数和非参数函数的借补估计对缺失响应变量进行插值,并基于借补估计构造了广义似然比检验统计量,证明了其渐近分布性质.