ON THE VERTICAL DISTRIBUTION OF SUSPENDED SEDIMENT CONCENTRATION

The problem of sediment concentration distribution in a vertical for channel and pipe flows is studied in this paper. Considering the variation of the vertical component of the fluctuation velocity v as an independent variable, two types of sediment concentration distribution can be derived. However, only one type of distribution is commonly quoted while the other one rarely reported. In this paper explanation for such phenomena is given and conditions for the occurrence of both types are also discussed.     

我国单点动态垂直基准的建立

确定基准是研究地壳垂直运动的重要内容之一,在讨论均衡基准特点的同时,指出了在其研究我国大陆现今地壳垂直运动中可能带来的不适用性,从而提出了单点动态垂直基准。该基准在多次重合点变动情况下,能够保持前后基准一致,易于维护,与基本验潮站相联系,速率结果具有绝对性质,可以较好地适应未来,特别是具有适应采用空间技术手段的特点。全面叙述了利用国家高精度重复水准测量资料研究我国大陆现今地壳垂直运动过程中,建立单点动态垂直基准的情况。     

Two kinds of moment ratio diagrams and their applications in hydrology

We refocus attention on moment ratio diagrams and their uses in hydrology with four major objectives: (1) to summarize the information available in the literature about possible uses of the traditional moment ratio diagram introduced by Karl Pearson, which uses the coefficient of skewness and of kurtosis to compare the shapes of various distributions commonly used in hydrology; (2) to complete this traditional MRD by integrating into it the regions occupied by the log-Pearson Type III and generalized gamma distributions which are more and more used in hydrology; (3) to present another MRD which uses ratios of moments of orders –1 (harmonic mean), quasi zero (geometric mean) and 1 (arithmetic mean); (4) to stress the need to consider the different MRD's (along with the more recently introduced L-moment ratio diagrams) as complementary tools for choosing between distributions fitted to hydrologic data. Finally, using Monte Carlo simulation we compare the two types of diagrams as tools to identify and discriminate between different distributions.     

A theory of enhanced saturation of the gravity wave spectrum due to increases in atmospheric stability

In this paper we consider a vertical wavenumber spectrum of vertically propagating gravity waves impinging on a rapid increase in atmospheric stability. If the high-wavenumber range is saturated below the increase, as is usually observed, then the compression of vertical scales as the waves enter a region of higher stability results in that range becoming supersaturated, that is, the spectral amplitude becomes larger than the saturation limit. The supersaturated wave energy must then dissipate in a vertical distance of the order of a wavelength, resulting in an enhanced turbulent energy dissipation rate. If the wave spectrum is azimuthally anisotropic, the dissipation also results in an enhanced vertical divergence of the vertical flux of horizontal momentum and enhanced wave drag in the same region. Estimates of the enhanced dissipation rates and radar reflectivities appear to be consistent with the enhancements observed near the high-latitude summer mesopause. Estimates of the enhanced mean flow acceleration appear to be consistent with the wave drag that is needed near the tropopause and the high-latitude summer mesopause in large-scale models of the atmosphere. Thus, this process may play a significant role in determining the global effects of gravity waves on the large-scale circulation.     

利用不同模型计算的转换系数K的比较与分析

介绍了利用不同模型计算转换系数k和平均温度Tm的方法,并利用北京、武汉和拉萨站实测数据进行了计算,对计算结果进行了比较和分析。     

岩土力学参数空间变异性的集合卡尔曼滤波佶值

岩土参数具有结构性和随机性的空间变异特征，该特征导致岩土参数具有不确定性。以地质统计学作为岩土参数空间变异性分析的理论基础，将分布于研究区的岩土参数视为区域化变量，变异函数既描述了岩土参数整体的空间结构性变化，又描述了其局部的随机性变化，用变异函数理论模型作为描述岩土参数空间变异规律的数学模型。引入集合卡尔曼滤波（EnKF）分析方法，利用时空分布的观测数据，对岩土参数空间变异性进行估值。数值算例表明，EnKF能够有效地融合观测数据，较好地提供岩土参数空间变异性的估值。     

An ocean data assimilation system in the Indian Ocean and west Pacific Ocean

The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean(hereafter AIPOcean) constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving,hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles(mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity–temperature–depth, station data), remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993–2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges.     

The China Multi-Model Ensemble Prediction System and Its Application to Flood-Season Prediction in 2018

Multi-model ensemble prediction is an effective approach for improving the prediction skill short-term climate prediction and evaluating related uncertainties. Based on a combination of localized operation outputs of Chinese climate models and imported forecast data of some international operational models, the National Climate Center of the China Meteorological Administration has established the China multi-model ensemble prediction system version 1.0 (CMMEv1.0) for monthly-seasonal prediction of primary climate variability modes and climate elements. We verified the real-time forecasts of CMMEv1.0 for the 2018 flood season (June-August) starting from March 2018 and evaluated the 1991-2016 hindcasts of CMMEv1.0. The results show that CMMEv1.0 has a significantly high prediction skill for global sea surface temperature (SST) anomalies, especially for the El Nino-Southern Oscillation (ENSO) in the tropical central-eastern Pacific. Additionally, its prediction skill for the North Atlantic SST triple (NAST) mode is high, but is relatively low for the Indian Ocean Dipole (IOD) mode. Moreover, CMMEv1.0 has high skills in predicting the western Pacific subtropical high (WPSH) and East Asian summer monsoon (EASM) in the June-July-August (JJA) season. The JJA air temperature in the CMMEv1.0 is predicted with a fairly high skill in most regions of China, while the JJA precipitation exhibits some skills only in northwestern and eastern China. For real-time forecasts in March-August 2018, CMMEv1.0 has accurately predicted the ENSO phase transition from cold to neutral in the tropical central-eastern Pacific and captures evolutions of the NAST and IOD indices in general. The system has also captured the main features of the summer WPSH and EASM indices in 2018, except that the predicted EASM is slightly weaker than the observed. Furthermore, CMMEv1.0 has also successfully predicted warmer air temperatures in northern China and captured the primary rainbelt over northern China, except that it predicted much more precipitation in the middle and lower reaches of the Yangtze River than observation.     

中国区域性暴雨事件未来变化:RegCM4动力降尺度集合预估

区域性暴雨事件由于影响范围大、持续时间长,更易引发严重洪涝灾害,对经济社会可持续发展构成严重威胁。因此,预估区域性暴雨事件的未来变化对于气候变化适应和灾害风险管理意义重大。本文基于区域气候模式RegCM4对4个全球气候模式的动力降尺度模拟,利用“追踪式”客观识别方法,对我国区域性暴雨进行了识别,并从发生频次、持续时间、平均降水量、平均影响范围和综合强度5个方面预估了其在RCP4.5情景下的未来变化。多模式预估结果表明,我国平均区域性暴雨事件的发生频次、持续时间、平均降水量、平均影响范围和综合强度到21世纪末均呈不同程度的上升趋势。与1986—2005年相比,无论在21世纪中期(2046—2065年)还是末期(2080—2099年),位于“低值区”的事件出现频率减少,而位于“高值区”的事件出现频率增加。轻度区域性暴雨事件发生频次将减少,而中度、重度和严重的区域性暴雨事件发生频次将增加。在空间分布上,区域性暴雨事件的发生频次、持续时间和降水量均在我国东部区域大范围增加,并且三者增幅的空间分布型态较为一致。增加最显著的区域主要位于长江中下游、江南和华南地区,而且到21世纪末期的增加幅度大于中期。