黄河利津水文站不同粒径悬浮颗粒物中有机碳含量的研究

估算不同粒径TSS中POC的入海日通量,对黄河利津水文站悬浮颗粒物(total suspended solid,TSS)样品,采用基于stokes原理的沉降法分为Ⅴ级,对分级后的样品测其有机碳(particulate organic carbon,POC)含量和粒度组成。在此基础上,用多元线性回归法对数据进一步处理,准确得到样品中不同粒径TSS中POC的含量。研究表明,黄河口淡水端水体TSS,<8μm的粘土和极细粉砂、8~16μm的细粉砂中,POC的含量为0.600%和0.400%左右;16~32μm的中粉砂中,POC的含量不足0.200%;而32~63μm的粗粉砂和>63μm砂中POC含量仅为0.050%和0.004%左右,80%以上的POC主要集中在<16μm的TSS中,而<32μm的TSS承载了95%以上的POC。     

基于EEMD和ARIMA的海温预测模型研究

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models： An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

基于回归模型的热带气旋条件下的海表降温参数化方案设计

Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature(SST) cooling induced by tropical cyclones(TCs). Three major dynamic and thermodynamic processes governing the TC-induced SST cooling(SSTC), vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables:sea surface height(SSH), wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001–2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72°C between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model,plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.     

海底双相随机介质声反射的SVM神经网络分类识别研究

考虑海底沉积介质为双相介质,为了更好地模拟实际海底底质的不均匀性,将随机介质理论引入双相介质理论。首先,通过基于随机-双相介质理论的高阶有限差分数值技术模拟计算海底底质分别为泥质砂、泥、泥质砾时的地震反射波信号。然后利用小波变换分别求取不同底质的一次反射波的包络作为其特征向量,最后利用基于粒子群智能算法优化的支持向量机神经网络对这些反射波信号进行分类识别。为了进一步考察所用方法的抗噪能力,对正演得到的海底底质反射波信号分别加入10%、30%、50%的高斯白噪音之后再进行分类,支持向量机仍然取得了较好的分类预测效果。基于上述正演模拟及分类识别方法的论证,提出了一套行之有效的微机软件模拟海底沉积物分类识别的一般化流程,这将有利于开展海底沉积物反射特征的进一步研究。     

基于GF5-AHSI遥感数据的横沙浅海水深反演

高光谱遥感水深反演是一种对传统水深测量方法的补充,具有方便、快捷、经济等突出优势。本文研究区位于上海横沙,属于典型滩涂浅水区,研究数据包括GF5-AHSI高光谱遥感数据和同时期的水深数据。通过数据变换和相关分析等方法提取建模参数,利用单波段比值模型、多元线性回归模型、最优标度回归模型和BP神经网络模型实现该区域水深反演,并对4种模型反演结果的准确性进行了验证和比较。研究发现：最优标度回归模型优于其他3种模型,R2达到了0.972,RMSE为0.47 m,适用于横沙浅海水深反演。     

八段锦联合同伴支持管理对COPD稳定期肺康复的效果评价

目的：探讨八段锦联合同伴支持管理对慢性阻塞性肺疾病（COPD）稳定期患者肺康复依从性及BODE指数的影响。方法：选择69 例由COPD急性加重进入稳定期的患者为研究对象,采用随机数字表法分为观察组（35 例）和对照组（34 例）。对照组给予常规护理、肺康复指导及发放康复处方执行记录单,观察组在对照组基础上采用八段锦运动训练,建立同伴支持管理微信群随访干预。比较2组患者肺康复依从性和BODE指数的变化情况。结果：干预后,观察组肺康复量表各项指标（除BMI）评分及总分、BODE指数各项指标及总分均优于对照组,差异有统计学意义(P＜0.01)。结论：慢性阻塞性肺疾病稳定期在肺康复过程中应用八段锦联合同伴支持管理,能激发患者康复依从性,改善BODE指数,提高患者生活质量。     

澳门单站气象资料变化趋势的预报模型之比较

以澳门单站年和四季降水和温度两个气象要素的气象资料(1952~2001年)为主要研究对象,利用3种统计预报模型(谐波分析外推模型、开环门限自回归模型和主分量因子的逐步回归模型)进行拟合及试报,对澳门的降水及温度的变化做较深入的分析及预测,比较3种统计预报模型在研究澳门单站气象资料的变化趋势中的优劣。结果发现,3种预报模型在预报年和四季平均气温的方面都比较理想,但对降水量变化序列做拟合及试报则差异较大。总体而言,气候场的主分量逐步回归法对相同的气候资料建模做拟合及试报,结果拟合率是三者中最高的,复相关为0.821~0.911,预报效果除秋季较差外,其余都非常好。     

1981—2018年内蒙古典型草原季节性冻土对气候变化的响应

以1981—2018年内蒙古典型草原季节性冻土为研究对象,通过气候倾向率、Mann-Kendall法、多元线性回归等方法,分析最大冻土时空分布特征、年际、年代际变化,研究影响最大冻土深度变化的气象因子。结果表明：（1） 内蒙古典型草原季节性冻土冻结初日在9—11月,终日在4—6月,年内最大冻土深度出现在2—3月,深度在100~280 cm之间。（2） 最大冻土深度年际变化分为下开口抛物线型、上开口抛物线型、正弦曲线型,从最大冻土深度气候倾向率看呈现减小趋势的站点有68%。（3） 最大冻土深度年代际变化分为逐年代递减、减-增型和无明显变化规律,50%的站点在1989年以后最大冻土深度发生突变。（4） 多元线性回归表明气温冻结指数、年平均风速、年极端最低气温对最大冻土深度产生显著影响。该研究揭示了最大冻土深度存在退化的事实,为草原应对气候变化提供指导,为陆地土壤和大气碳循环交换的研究给出提示。