结合Gabor纹理与几何特征的高分辨率遥感影像城区道路提取方法

在充分考虑道路频谱在不同频带、不同方向上能量分布的基础上,提出一种基于Gabor纹理与几何特征相结合的高分辨率遥感影像城区道路提取方法。首先通过Gabor滤波器组得到遥感影像不同频带、不同方向上的Gabor纹理特征,并利用K-means方法对遥感影像进行分割;然后利用形态学方法分割与道路相连的地物,并选取适当的几何特征剔除非道路地物;最后利用形态学方法对道路网进行修整。实验结果表明,该方法可以有效、便捷地从高分辨率遥感影像中提取城区主干道路网。     

各向异性组合滤波法反演陆地水储量变化

地球时变重力场模型反演陆地水储量变化已为全球气候变化研究作出巨大贡献,考虑到时变重力场模型球谐系数中存在相关性,其高阶次项具有较大的误差,需采用最优的滤波方法进行空间平滑。本文提出一种新的各向异性组合滤波方法,其基本思想是将改进的高斯滤波法与均方根(root mean square,RMS)滤波法组合,即对球谐系数的低阶次采用改进的高斯滤波法,而高阶次采用RMS滤波法。首先分析了最新的GRACE RL05系列时变重力场模型系数误差特性,基于全球水储量变化反演结果,分析比较了高斯滤波、改进的高斯滤波、RMS滤波和DDK滤波与本文提出的组合滤波法的有效性及精度,并利用模型结果进行了验证,计算结果表明,组合滤波法的中误差最小。研究结果表明,本文提出的组合法相比于先前的滤波方法,可有效地过滤高阶次的噪声,消除南北条带误差,同时减少信号泄漏,提高信噪比,保留更多有效的地球物理信号,进而提高反演精度。     

Seismic random noise attenuation and signal-preserving by multiple directional time-frequency peak filtering

Time-frequency peak filtering (TFPF) is an effective method for seismic random noise attenuation. The linearity of the signal has a significant influence on the accuracy of the TFPF method. The higher the linearity of the signal to be filtered is, the better the denoising result is. With this in mind, and taking the lateral coherence of reflected events into account, we do TFPF along the reflected events to improve the degree of linearity and enhance the continuity of these events. The key factor to realize this idea is to find the traces of the reflected events. However, the traces of the events are too hard to obtain in the complicated field seismic data. In this paper, we propose a Multiple Directional TFPF (MD–TFPF), in which the filtering is performed in certain direction components of the seismic data. These components are obtained by a directional filter bank. In each direction component, we do TFPF along these decomposed reflected events (the local direction of the events) instead of the channel direction. The final result is achieved by adding up the filtering results of all decomposition directions of seismic data. In this way, filtering along the reflected events is implemented without accurately finding the directions. The effectiveness of the proposed method is tested on synthetic and field seismic data. The experimental results demonstrate that MD–TFPF can more effectively eliminate random noise and enhance the continuity of the reflected events with better preservation than the conventional TFPF, curvelet denoising method and F–X deconvolution method.     

2013—2017年夏季东北冷涡下东北地区MCS的统计特征

利用2013—2017年6—8月FY-2E和FY-2G地球静止卫星相当黑体温度（Black Body Temperature,TBB）资料、NCEP/NCAR再分析资料,对我国夏季东北冷涡下东北地区MCS的分布和活动特征进行了统计分析,结果表明：（1） MCS的活动具有明显的月际变化和日变化特征,6月对流活动最活跃。MCS的主要移向是东、东北和东南,平均移动距离3.99个经纬距。（2） MCS成熟时刻的面积、偏心率和生命史均小于江淮地区以及中国中东部,云顶高度低于江淮地区,整个生命史表现出发展快消亡慢的特征,与江淮地区相反。（3）基于MCS的定义得到的Z标准,对2016—2017年的MCS作了统计分析并与J标准统计得到的MCS进行对比,得出,两种定义下的MCS环境场特征基本一致,主要表现为MCS多生成于500 hPa槽前和槽后,对流层高层MCS位于双急流之间靠近北支急流的辐散区,南侧急流高度在200 hPa,北侧的急流高度在250 hPa。低层,位于低空急流左侧,低涡南侧、东南侧,有较强的水汽和动量输送。槽前生成的MCS南侧中层存在垂直反环流向MCS输送干暖空气与位涡,槽后生成的MCS两侧均有大值位涡向其输送,同时北侧冷干空气的输送使锋区及上升运动加强,更有利于MCS的形成。（4）两种标准下的MCS造成的降水明显不同,在统计强降水方面Z标准要优于J标准。由于Z标准空间与时间尺度较小,统计得到的MCS较多;但同时会遗漏部分相对弱的MCS。     

Assimilating Best Track Minimum Sea Level Pressure Data Together with Doppler Radar Data Using an Ensemble Kalman Filter for Hurricane Ike (2008) at a Cloud-Resolving Resolution

Extending an earlier study, the best track minimum sea level pressure (MSLP) data are assimilated for landfalling Hurricane Ike (2008) using an ensemble Kalman filter (EnKF), in addition to data from two coastal ground-based Doppler radars, at a 4-km grid spacing. Treated as a sea level pressure observation, the MSLP assimilation by the EnKF enhances the hurricane warm core structure and results in a stronger and deeper analyzed vortex than that in the GFS (Global Forecast System) analysis; it also improves the subsequent 18-h hurricane intensity and track forecasts. With a 2-h total assimilation window length, the assimilation of MSLP data interpolated to 10-min intervals results in more balanced analyses with smaller subsequent forecast error growth and better intensity and track forecasts than when the data are assimilated every 60 minutes. Radar data are always assimilated at 10-min intervals. For both intensity and track forecasts, assimilating MSLP only outperforms assimilating radar reflectivity (Z) only. For intensity forecast, assimilating MSLP at 10-min intervals outperforms radar radial wind (Vr) data (assimilated at 10-min intervals), but assimilating MSLP at 60-min intervals fails to beat Vr data. For track forecast, MSLP assimilation has a slightly (noticeably) larger positive impact than Vr(Z) data. When Vr or Z is combined with MSLP, both intensity and track forecasts are improved more than the assimilation of individual observation type. When the total assimilation window length is reduced to 1h or less, the assimilation of MSLP alone even at 10-min intervals produces poorer 18-h intensity forecasts than assimilating Vr only, indicating that many assimilation cycles are needed to establish balanced analyses when MSLP data alone are assimilated; this is due to the very limited pieces of information that MSLP data provide.     

Effect of doubling the ensemble size on the performance of ensemble prediction in the warm season using MOGREPS implemented at the KMA

Using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) implemented at the Korea Meteorological Administration (KMA), the effect of doubling the ensemble size on the performance of ensemble prediction in the warm season was evaluated. Because a finite ensemble size causes sampling error in the full forecast probability distribution function (PDF), ensemble size is closely related to the efficiency of the ensemble prediction system. Prediction capability according to doubling the ensemble size was evaluated by increasing the number of ensembles from 24 to 48 in MOGREPS implemented at the KMA. The initial analysis perturbations generated by the Ensemble Transform Kalman Filter (ETKF) were integrated for 10 days from 22 May to 23 June 2009. Several statistical verification scores were used to measure the accuracy, reliability, and resolution of ensemble probabilistic forecasts for 24 and 48 ensemble member forecasts. Even though the results were not significant, the accuracy of ensemble prediction improved slightly as ensemble size increased, especially for longer forecast times in the Northern Hemisphere. While increasing the number of ensemble members resulted in a slight improvement in resolution as forecast time increased, inconsistent results were obtained for the scores assessing the reliability of ensemble prediction. The overall performance of ensemble prediction in terms of accuracy, resolution, and reliability increased slightly with ensemble size, especially for longer forecast times.     

Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction

A conceptual coupled ocean-atmosphere model was used to study coupled ensemble data assimilation schemes with a focus on the role of ocean-atmosphere interaction in the assimilation. The optimal scheme was the fully coupled data assimilation scheme that employs the coupled covariance matrix and assimilates observations in both the atmosphere and ocean. The assimilation of synoptic atmospheric variability that captures the temporal fluctuation of the weather noise was found to be critical for the estimation of not only the atmospheric, but also oceanic states. The synoptic atmosphere observation was especially important in the mid-latitude system, where oceanic variability is driven by weather noise. The assimilation of synoptic atmospheric variability in the coupled model improved the atmospheric variability in the analysis and the subsequent forecasts, reducing error in the surface forcing and, in turn, in the ocean state. Atmospheric observation was able to further improve the oceanic state estimation directly through the coupled covariance between the atmosphere and ocean states. Relative to the mid-latitude system, the tropical system was influenced more by ocean-atmosphere interaction and, thus, the assimilation of oceanic observation becomes more important for the estimation of the ocean and atmosphere.     

基于DDS技术的Loran—C信号源的杂散信号抑制的分析与实现

采用直接数字频率合成（DDS）技术设计的Loran—C信号源,具有输出杂散多且难以预测的缺点。基于对DDS基本原理的研究分析,针对DDS输出信号存在的相位舍位杂散问题,对其关键部位的相位累加模块进行优化设计,并基于FPGA技术,在QuartusII环境下完成了对Loran．C信号源的实现与仿真验证。结果表明,通过优化的设计算法能够产生失真小,稳定度好的输出波形,从而验证了该方法抑制杂散的有效性与可行性。     

星载GPS自主定轨的长期稳定性

通过模拟长达100天的星载GPS伪距观测资料,进行卡尔曼滤波定轨仿真计算．重点研究：1)采用简化的动力学模型与简化状态转移矩阵,是否保证滤波的长期稳定性；2)模型误差矩阵Q的选取对滤波定轨精度的影响；3)与事后最小二乘批处理相比较,在简化模型下自主定轨的精度．同时给出了相应的结论．     

An acousto-optical imaging spectrophotometer for astrophysical observations

We present the results of our tests of an acousto-optical imaging spectrophotometer with a CCD detector for astronomical observations. The tunable acousto-optical filter, based on a paratellurite single crystal with a 13 Å pass band operates in the wavelength range 6300–11000 Å. We obtained image spectra for the planetary nebula NGC 7027 in the Hα line and for Saturn in the methane absorption band, as well as Hα and continuum images for the nuclear region of the Seyfert galaxy NGC 1068.