几种建立DEM模型插值方法精度的交叉验证

建立DEM模型时需要对离散的高程点进行空间插值,实现这一目的的插值方法有很多种。文章主要选择了6种常用的插值方法,分别在平原、丘陵和高山几种不同复杂程度的环境下对其插值的精度使用交叉验证方法进行评估。结果表明,同样的插值方法对不同复杂程度的地形效果是不一致的,多种插值方法中以克里金插值方法的插值适用性最强,精度最高。     

全站仪坐标导线的施测与平差探讨

提出了全站仪坐标导线外业施测时的检核方法和内业几种严密平差方法。     

Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC (National Meteorological Center) method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.     

Regional Temperature Forecast for the Next Day in Hong Kong

For a century or so, the Hong Kong Observatory (HKO) has been providing temperature forecast for the whole of Hong Kong with the HKO Headquarters as the reference location. In recent decades, due to spreading of population from the main urban center to satellite towns, there is an increasing demand for regional temperature forecasts. To support such provision, the HKO has developed a regression model to provide objective guidance to forecasters in formulating forecasts of maximum and minimum temperatures for the next day at various locations in Hong Kong. In this paper, the regression model is presented, together with the assessment of its performance. Based on the verification of one year of forecasts, it is found that the root mean square errors (RMSEs) of maximum (minimum) temperature forecasts are from about 1.3 to 2.1 (1.1 to 1.4) degrees, respectively. The regression model is shown to have generally out-performed the operational regional spectral model then operated by HKO. Regional temperature forecast methods of other meteorological or research centers are also surveyed. Equipped with the regression model, the HKO has launched an online regional temperature forecast service for the next day in Hong Kong since March 2008.     

Seasonal Prediction of Spring Dust Weather Frequency in Beijing

In this paper, seasonal prediction of spring dust weather frequency (DWF) in Beijing during 1982-2008 has been performed. First, correlation analyses are conducted to identify antecedent climate signals during last winter that are statistically significantly related to spring DWF in Beijing. Then, a seasonal prediction model of spring DWF in Beijing is established through multivariate linear regression analysis, in which the systematic error between the result of original prediction model and the observation, averaged over the last 10 years, is corrected. In addition, it is found that climate signals occurring synchronously with spring dust weather, particularly meridional wind at 850 hPa over western Mongolian Plateau, are also linked closely to spring DWF in Beijing. As such, statistical and dynamic prediction approaches should be combined to include these synchronous predictors into the prediction model in the real-time operational prediction, so as to further improve the prediction accuracy of spring DWF in Beijing, even over North China. However, realizing such a prediction idea in practice depends essentially on the ability of climate models in predicting key climate signals associated with spring DWF in Beijing.     

地震红外长波辐射短期异常特征分析

利用NOAA卫星资料反演的地震长波辐射值OLR对黑龙江及其周边2003年以来8次中强地震进行了追踪研究.结果显示,震前2个月左右OLR场值存在明显的高值异常区,未来震中距离高值区中心点250km(优势距离为160km).该方法对黑龙江及其周边4级以上地震短期判定可能具有一定的指示价值,也为利用卫星遥感技术预测未来可能发震区域并及时做出短期预测预警提供了新途径.     

山东数字地震台网定位子台网 选取对定位结果的影响

选取山东及邻区4个不同区域的地震,分析“定位子台网”分布对地震定位精度的影响,研究“定位子台网”的选取规则.探讨两种台站分布情况下定位偏差、水平误差、拟合残差随空隙角、近台距离、远台距离的变化;定位偏差及水平误差与拟合残差的关系.结果显示,速报时选择4-9个分布相对合理的“定位子台网”参加定位,结果较好,使用水平误差估...     

OLR方法在黑龙江地区中强地震前异常现象的探讨

利用卫星遥感长波辐射(OLR)资料,分析黑龙江省2004年-2008年发生的几次ML 4.0以上中强地震前的OLR资料,发现地震发生前,在震中附近地区有长波辐射增强的特征,可能与强震孕育过程中的热状态有关,识别和提取卫星对地遥感红外波段的热兆信息,可望在中强地震监测预报的层面上有新的突破.     

双能CT成像基材料分解法的理论误差分析

双能X射线计算机断层成像技术(DECT)能够准确地重建物质的有效原子序数和电子密度信息,是一种有效的物质辨别技术.基材料分解法是求解DECT重建问题的主要方法之一.但目前应用此方法得到的重建结果误差较大,并且对于金属伪影抑制能力也较差.本文分析了DECT基材料分解法重建结果的误差来源和形成机制,阐释了其对金属伪影抑制作...     

Characteristics of Quasi-Periodic Oscillations During Sea Fog Events

Based on the high-resolution datasets collected in a sea fog field experiment at the northern coast of South China Sea, the variations of liquid water content (LWC) and net longwave radiation flux (NLRF) during two sea fog events on 16th-17th and 18th-19th March, 2008 are exaimeed by wavelet analysis, and the cooling mechanisms for fog formation and persistence are also investigated. The main results are shown as follows. (1) Sea fog may develop and persist whether it is cloudy or not aloft. However, when there is cloud aloft the LWC is less and wind speed in sea fog is higher than that in the clear sky. (2) The quasi-periodic oscillations (QPOs) of NLRF are observed in the formation stage of the two fogs. QPOs of LWC are only found in the developing stage no matter with cloudless or cloudy condition. (3) It is likely that sea fog forms by the cooling effects of longwave radiation and develops through the vertical mixing induced by the radiative cooling at the upper level. (4) During sea fog development and persistence, other mechanisms could also play important roles in fog-layer cooling, such as turbulent heat transport and radiation transport between air-sea interfaces.