灰色模型在快速卫星钟差预报中的应用

GPS实时精密单点定位需要实时的、精确的、可靠的预报卫星钟差预报,因此卫星钟差的预报是一项非常重要的工作,它对实时的高精度导航定位具有重要意义。为导航定位提供时间标准的导航卫星原子钟是非常精密的仪器,对外界环境非常敏感,无法将卫星钟差作为普通的白噪声处理,可以但可将卫星钟差看作是灰色系统来进行研究。本文根据灰色系统相关理论,将灰色系统模型GM(1,1)应用到卫星钟差的预报,并用IGS超快速星历建立了预报卫星钟差的灰色预测模型,研究了卫星钟差的变化规律。结果表明:灰色模型可用于卫星钟差的短期预报,它对超快速星历的预报精度与IGS产品中的IGU超快速星历本身的预报精度相当。     

Bias and precision of percentiles of bulk grain size distributions

Percentiles such as D₅₀ and D₈₄, calculated from weights retained on different sieves, are widely used to characterize grain size distributions (GSDs) of bulk samples of sedimentary deposits or sediment fluxes. The sampling variability of such percentiles is not well known, and few sampling guidelines exist for reliable characterization of GSDs. We report results from computer sampling experiments on the variability of sample percentiles in different-sized samples from populations with a log-normal GSD by weight and different sorting coefficients. Sample sizes are scaled by the volume of a median-sized grain so that results can be applied to any log-normal GSD. Sampling is random for the GSD by number that is equivalent to a specified GSD by weight. Results show important differences from standard sampling theory applicable to pebble-count GSDs. In small bulk samples all percentiles, including the median, are underestimated (more so for smaller samples, coarser percentiles and poorer sorting), and precision does not improve with the square root of sample size until fairly large sample sizes are exceeded. Non-dimensional equations fitted by eye to the results give good approximations to expected bias and precision in any percentile from 50 to 95 for any given sample size and population sorting coefficient. They are inverted to estimate the sample size required to avoid significant bias, or achieve specified precision, in any percentile of interest given estimates of the population D₅₀ and sorting coefficient. Target sample sizes are sometimes considerably smaller, but in other circumstances larger, than suggested by previous guidelines relating to estimation of the entire grain size distribution. Bias is likely in small samples of river bedload and good precision requires very large samples of poorly sorted gravel deposits. © 1997 John Wiley & Sons, Ltd.     

基于RBF神经网络的多系统钟差预报算法

针对海上条件下,对于实时定位应用,实时数据流无法下载的情况,文中提出一种基于RBF神经网络的卫星钟差预报算法,给出基函数的中心、方差以及隐含层到输出层的权值的计算方法,采用滑动窗口的方法,用样本数据训练后的网络预测下一个历元的钟差值,依次往后训练网络直到预测完整个时间段,通过实验验证了算法的可用性。短期预报中,GPS预报精度在1 ns以下,BDS和GLONASS在2～3 ns左右;长期预报中,GPS预报精度在几十纳秒左右,而BDS和GLONASS在几百纳秒左右,文中给出了相应的结果分析。     

点火脉冲对GRACE加速度计校准的影响

GRACE卫星搭载的高精度SuperSTAR加速度计观测数据包含点火脉冲信号,本文分析点火脉冲数据特点,讨论点火脉冲对加速度计校准结果(尺度参数和偏差参数)的影响。2008-04一个月的加速度计实测数据分析表明,点火脉冲对尺度参数、比例参数的影响最大分别为0.02、10-7m·s-2,对加速度校准结果的影响在Y、Z方向上高达10-10m·s-2。     

SSR改正数更新频率及中断时间间隔对实时PPP的影响分析

实验模拟服务器端不同更新频率的SSR改正数对实时精密单点定位的影响,以及客户端改正数中断时长对实时精密单点定位的影响。结果表明,服务器端SSR改正数的播发频率控制在30 s以内可获得cm级定位精度;客户端的SSR改正数中断在150 s之内可获得cm级定位精度,改正数中断200 s可获得20 cm的定位精度,改正数中断300 s可获得亚m级定位精度。     

Evaluation of bias on the probability of collapse from amplitude scaling using spectral-shape-matched records

Although for many years it was thought that amplitude scaling of acceleration time series to reach a target intensity did not introduce any bias in the results of nonlinear response history analyses, recent studies have showed that scaling can lead to an overestimation of deformation demands with increasing scale factors. Some studies have suggested that the bias can be explained by differences in spectral shape between the response spectra of unscaled and scaled records. On the basis of these studies, some record selection procedures assume that if records are selected using spectral-shape-matching procedures, amplitude scaling does not induce any bias on the structural response. This study evaluates if bias is introduced on lateral displacement demands and seismic collapse risk estimates even when spectral shape is carefully taken into consideration when selecting ground motions. Several single-degree-of-freedom and multiple-degree-of-freedom systems are analyzed when subjected to unscaled and scaled ground motions selected to approximately match the mean and the variance of the conditional spectrum at the target level of intensity. Results show that an explicit consideration of spectral shape is not enough to avoid a systematic overestimation of lateral displacement demands and collapse probabilities as the scale factor increases. Moreover, the bias is observed in practically all cases for systems with strength degradation and it increases with decreasing period and decreasing lateral strength relative to the strength required to remain elastic. Key reasons behind the bias are presented by evaluating input energy, causal parameters, and damaging pulse distributions in unscaled and scaled ground motion sets.     

Improving global rainfall forecasting with a weather type approach in Japan

An automated version of the weather type classification scheme was performed over Japan to characterize daily circulation conditions. A daily gridded field of mean sea-level pressure (MSLP) from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis dataset (ERA-interim) and the THORPEX Interactive Grand Global Ensemble (TIGGE) daily forecast dataset were used. The weather type is advantageous as it provides an opportunity to improve global rainfall prediction by refining statistical bias correction. We distinguished 11 weather types: anticyclone, cyclone, hybrid and eight purely wind directions. The results indicate that the main weather types contributing to the total volume of rainfall are cyclone, hybrid, purely westerly and northwest winds. A gamma-based bias correction decreases the global rainfall forecast root mean square by 10%, while specific weather type gamma bias correction accounts for 5–10% root mean square error reduction, with a total decrease of errors up to a maximum of 20%. Both global and weather type bias corrections improve the extreme dependency scores (EDS), but for different extreme rainfall thresholds. The study advocates the use of weather type bias-correction methods for extreme event rainfall intensity corrections higher than 100 mm/d.

EDITOR

A. Castellarin

ASSOCIATE EDITOR

A. Jain     

一种卫星钟差精度评估方法

GNSS观测量测量的是测站与卫星间的相对时间延迟,卫星钟差求解的是相对于基准钟的相对钟差,因此卫星钟差的精度评估方法不同于其他GNSS产品。分析了卫星钟差的精度评估方法,采用消除卫星钟系统偏差的方法进行钟差精度的评估,得到的各产品的精度评定结果与各产品公布的钟差精度基本一致,证明了此方法的有效性。     

Correcting bias in rainfall inputs to a semi-distributed hydrological model using downstream flow simulation errors

The focus of this study is on bias correcting semi-distributed rainfall inputs into a hydrological model applied in the Okavango River basin in southern Africa, where there are very few local observations and heavy reliance is placed on global rainfall datasets. While the hydrological model, before rainfall bias correction, is able to represent the broad characteristics of the sub-basin streamflow responses, as demonstrated by good agreement between observed and simulated flow duration curves, there are many years where the annual volumes are over- or underestimated. The long records of observed flow at downstream stations are successfully used to bias correct the rainfall inputs to the upstream sub-basins using an analysis of their individual contributions to downstream flow and their annual rainfall–runoff response ratios. The results show improved simulations for the relatively shorter observation periods at the upstream gauging stations.     

High Precision Geoid Models HKGEOID-2000 for Hong Kong and SZGEOID-2000 for Shenzhen,China

High precision geoid models HKGEOID-2000 for Hong Kong and SZGEOID-2000 for Shenzhen, China, have been developed with a hybrid approach of so-called sequential processing, using high precision GPS/leveling data, land and sea gravity anomalies, and digital terrain models. These two local geoid models have the same 1-km resolution. The estimated accuracy (external accuracy) is better than 1.7 cm for HKGEOID-2000 and 1.4 cm for SZGEOID-2000. Some common areas are covered by HKGEOID-2000 and SZGEOID-2000. So these two geoid models, along with high quality GPS/leveling data collected on the overlapping areas, can be used to detect the systematic bias between HKGEOID-2000 and SZGEOID-2000, as well as the difference between Hong Kong Principal Datum and 1956 yellow sea height datum of China, yielding RMS errors of 1.011 m and 1,003 m, respectively. Moreover, HKGEOID-2000, along with GPS ellipsoidal heights, is employed to determine the errors of the “orthometric heights” from purely trigonometric heighting, yielding an RMS error of 0.102 m. The combination of SZGEOID-2000 and GPS ellipsoidal heights has been used to replace the traditional spirit leveling and mapping, called GPS mapping.