Parameter sensitivity of automated baseflow separation for snowmelt‐dominated watersheds and new filtering procedure for determining end of snowmelt period

Automation in baseflow separation procedures allowed fast and convenient baseflow and baseflow index (BF and BFI) estimation for studies including multiple watersheds and covering large spatio‐temporal scales. While most of the existing algorithms are developed and tested extensively for rainfall‐ and baseflow‐dominated systems, little attention is paid on their suitability for snowmelt‐dominated systems. Current publishing practice in regional‐scale studies is to omit BF and BFI uncertainty evaluation or sensitivity analysis. Instead, “standard” and “previously recommended” parameterizations are transferred from rainfall/BF to snowmelt‐dominated systems. We believe that this practice should be abandoned. First, we demonstrate explicitly that the three most popular heuristic automated BF separation methods—Lyne–Hollick and Eckhardt recursive digital filters, and the U.K. Institute of Hydrology smoothed minima method—produce a wide range of annual BF and BFI estimates due to parameter sensitivity during the annual snowmelt period. Then, we propose a solution for cases when BF and BFI calibration is not possible, namely excluding the snowmelt‐dominated period from the analysis. We developed an automated filtering procedure, which divides the hydrograph into pre‐snowbelt, post‐snowmelt, and snowmelt periods. The filter was tested successfully on 218 continuous water years of daily streamflow data for four snowmelt‐dominated headwater watersheds located in Wyoming (60–837 km²). The post‐snowmelt BF and BFI metric can be used for characterizing summer low‐flows for snowmelt‐dominated systems. Our results show that post‐snowmelt BF and BFI sensitivity to filter parameterization is reduced compared with the sensitivity of annual BF and BFI and is similar to the sensitivity levels for rainfall/baseflow systems.     

Effect of causal and acausal filters on elastic and inelastic response spectra

With increasing interest in displacement spectra and long‐period motions, it is important to check the sensitivity of both elastic and inelastic response spectra to the filtering that is often necessary to remove long period artifacts, even from many modern digital recordings. Using two records of very different character from the M=7.1, 1999 Hector Mine, California, earthquake, we find that the response spectra can be sensitive to the corner periods used in causal filtering, even for oscillator periods much less than the filter corner periods. The effect is most pronounced for inelastic response spectra, where the ratio of response spectra computed from accelerations filtered at 25 and 200 sec can be close to a factor of 2 for oscillator periods less than 5 sec. Published in 2003 by John Wiley Sons, Ltd.     

Influence of parameters selection in Chebyshev filters on the strong motion data processing

This contribution discusses the application of Chebyshev Type I filter for processing real earthquake records. Consideration is given to the effects of filtering parameters (passband amplitude ripple and order of the filter) on the time series, strong-motion parameters, Fourier Amplitude Spectrum of acceleration, and elastic displacement response spectra. Time histories of five earthquakes with different moment magnitudes have been examined (from stations located close to the epicenters). Data processing is based on application of bandpass Chebyshev filtering over frequency range with substantial signal to noise ratio (level of 3 or approximately 3 dB). Applying different filters, we have monitored several important strong-motion parameters: peak values of acceleration, velocity, and displacement; Arias intensity, acceleration/velocity spectrum intensity, significant duration, etc. Some new results and conclusions concerning the influence of Chebyshev filter in data processing of records have been summarized. The graphical and numerical outcomes obtained, as well as the comparison with a Butterworth causal filter, are included in the work. The results could be potentially useful to engineering seismologists who need to evaluate and better understand the merits of this type of filtering for strong-motion data processing.     

贺兰山地区大气冰核浓度的测量及初步分析

根据 1 994年在银川和阿拉善左旗观测得到的冰核资料 ,给出了两地不同天气条件下的大气冰核浓度 ,分析并讨论了冰核浓度与风、降水、天空状况等的关系 ,给出了飞机在空中测量冰核的结果。     

基于DotLucene的全文检索及其在GIS中的应用

DotLucene是一个开源的、可扩展的、高性能的全文检索工具包,它可以方便的嵌入到各种应用系统中实现全文索引和查询功能.在研究DotLucene的体系结构和主要功能模块的基础上,针对传统GIS查询功能中全文检索能力较弱的现状,将全文检索引擎DotLucene引入到GIS中,实现根据GIS文档资料的内容而不是其外部特征...     

Spectral sensitivity of ALOS,ASTER, IKONOS,LANDSAT and SPOT satellite imagery intended for the detection of archaeological crop marks

This study compares the spectral sensitivity of remotely sensed satellite images, used for the detection of archaeological remains. This comparison was based on the relative spectral response (RSR) Filters of each sensor. Spectral signatures profiles were obtained using the GER-1500 field spectroradiometer under clear sky conditions for eight different targets. These field spectral signature curves were simulated to ALOS, ASTER, IKONOS, Landsat 7-ETM+, Landsat 4-TM, Landsat 5-TM and SPOT 5. Red and near infrared (NIR) bandwidth reflectance were re-calculated to each one of these sensors using appropriate RSR Filters. Moreover, the normalised difference vegetation index (NDVI) and simple ratio (SR) vegetation profiles were analysed in order to evaluate their sensitivity to sensors spectral filters. The results have shown that IKONOS RSR filters can better distinguish buried archaeological remains as a result of difference in healthy and stress vegetation (approximately 1–8% difference in reflectance of the red and NIR band and nearly 0.07 to the NDVI profile). In comparison, all the other sensors showed similar results and sensitivities. This difference of IKONOS sensor might be a result of its spectral characteristics (bandwidths and RSR filters) since they are different from the rest of sensors compared in this study.     

Seamount detection and size estimation using filtered geosat altimetry data

Local changes in the marine geoid (<100 nm in size) correspond well with bathymetric features such as seamounts. Thus the marine geoid height may be used to verify existing features, predict the bathymetry of unsurveyed areas, and fill gaps in existing data. The application of matching high‐pass filters to both the geoid and bathymetry data of an area allows the regional trends to be removed so that only the features remain. Filter values that begin to pass data with wavelengths less than 125 miles and all data with wave lengths less than 70 miles were selected. The high‐frequency variations of the geoid can then be correlated to the bathymetry and a scaling factor between the two calculated. The highest correlations (.81) were achieved using a cut‐off value for the filtered geoid data. A gridded synthetic bathymetry file was created by scaling the filtered geoid to the filtered bathymetry and adding the low pass background bathymetry. The gridded historical bathymetry could then be subtracted from the synthetic bathymetry in an automated method to display probable new features. A final selection of 458 previously unreported major features was then made.     

A new methodology for the extension of the impact of data assimilation on ocean wave prediction

It is a common fact that the majority of today's wave assimilation platforms have a limited, in time, ability of affecting the final wave prediction, especially that of long-period forecasting systems. This is mainly due to the fact that after “closing” the assimilation window, i.e., the time that the available observations are assimilated into the wave model, the latter continues to run without any external information. Therefore, if a systematic divergence from the observations occurs, only a limited portion of the forecasting period will be improved. A way of dealing with this drawback is proposed in this study: A combination of two different statistical tools—Kolmogorov–Zurbenko and Kalman filters—is employed so as to eliminate any systematic error of (a first run of) the wave model results. Then, the obtained forecasts are used as artificial observations that can be assimilated to a follow-up model simulation inside the forecasting period. The method was successfully applied to an open sea area (Pacific Ocean) for significant wave height forecasts using the wave model WAM and six different buoys as observational stations. The results were encouraging and led to the extension of the assimilation impact to the entire forecasting period as well as to a significant reduction of the forecast bias.     

Biases and accuracy of, and an alternative to, discrete nonlinear filters

The biases and accuracy of the extended Kalman filter (EKF) and a second-order nonlinear filter (SONF) are discussed from the point of view of a frequentist; these are often derived by applying the relevant conditional quantities to the linear Kalman algorithm under the Bayesian framework. The EKF and the SONF are biased, although the SONF has been derived in the hope of improving first-order filters. Unfortunately the biases of the SONF may be magnified further, because the second-order terms of the relevant Bayesian conditional quantities have never been properly used to derive the SONF from the frequentist point of view. The variance–covariance matrix of the SONF given in the literature is proven to be incorrect up to the second-order approximation, and the correct one is derived. Finally, also from the point of view of a frequentist, an alternative, almost unbiased SONF is proposed, if the randomness of partials is neglected. Received: 12 July 1997 / Accepted: 5 October 1998     

GRAPES-3DVar高阶递归滤波方案及其初步试验

背景误差协方差矩阵B及其逆的求解是三维变分同化研究的核心问题之一.在GRAPES区域三维变分同化系统(GRAPES-3Dvar)中背景误差协方差矩阵的水平变换部分,假定各向同性并进行递归滤波运算.原有方案中采用一阶递归滤波器,但收敛不够迅速,每次循环同化时需滤波10次才能使目标函数收敛.根据Purser等2003年的研...