Implications of low-pass filtering on power spectra and autocorrelation functions of turbulent velocity signals

Filtering either through the electronics of an instrument or through digital procedure is performed routinely on geophysical data. When velocity fluctuations are measured in turbulent flows using electromagnetic current meters (ECMs), a builtin lowpass Butterworth filter of order n usually attenuates fluctuations at high frequencies. However, the effects of this filter may not be acknowledged in turbulence studies, thus impeding comparisons between data collected with different ECMs. This paper explores the implications of the filters on the characteristics of velocity signals, mainly on variance, power spectra, and correlation analyses. Variance losses resulting from filtering can be important but will vary with the order n of the Butterworth filter, decreasing as n increases. Knowing the filter response, it is possible to reconstruct the original signal spectrum to evaluate the effect of filtering on variance and to allow comparisons between data collected with different instruments. The autocorrelation function also is affected by filtering which increases the value of the coefficients in the first lags, resulting in an overestimation of the integral length scale of coherent structures. These important effects add to those related to size and shape differences in ECM sensors and must be taken into account in comparative studies.     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

Noise reduction and detection of weak, coherent signals through phase-weighted stacks

We present a new tool for efficient incoherent noise reduction for array data employing complex trace analysis. An amplitude-unbiased coherency measure is designed based on the instantaneous phase, which is used to weight the samples of an ordinary, linear stack. The result is called the phase-weighted stack (PWS) and is cleaned from incoherent noise. PWS thus permits detection of weak but coherent arrivals. The method presented can easily be extended to phase-weighted cross-correlations or be applied in the τ p domain. We illustrate and discuss the advantages and disadvantages of PWS in comparison with other coherency measures and present examples. We further show that our non-linear stacking technique enables us to detect a weak lower-mantle P -to- S conversion from a depth of approximately 840 km on array data. Hints of an 840 km discontinuity have been reported; however, such a discontinuity is not yet established due to the lack of further evidence.     

Population abundance and seasonal migration patterns indicated by commercial catch-per-unit-effort of hakes (Merluccius capensis and M. paradoxus) in the northern Benguela Current Large Marine Ecosystem

We developed generalised additive models (GAMs) to estimate standardised time-series of population abundance indices for assessment purposes and to infer ecological and behavioural information on northern Benguela hakes, Merluccius capensis and M. paradoxus, using haul-by-haul commercial trawl catch-rate data as proxies for hake densities. The modelling indicated that individual ship identifiers should be used rather than general vessel characteristics, such as vessel size. The final models explained 79% and 68% of the variability in the commercial catch rates of M. capensis and M. paradoxus, respectively. The spatial density patterns were consistent and confirmed existing knowledge about these species in the northern Benguela system. Furthermore, seasonal migration patterns were described for the first time and were found to correspond to the known spawning areas and seasons for M. capensis and M. paradoxus. Spatial density patterns were validated using the geostatistical modelling results of fisheries-independent trawl survey data. Improved understanding of the relationships between fleet dynamics and fish movement can be achieved by taking into consideration the present catch-rate model and spatial and seasonal distribution maps. We conclude that the yearly standardised CPUE time-series are problematic as proxies for total stock abundance because of spatial coverage issues. Consequently, such CPUE data should not be used for stock-size assessments and fisheries advice concerning northern Benguela hakes until this is solved. We generally recommend the exclusion of standardised CPUE time-series from stock assessments when important and changing parts of the stock distribution cannot be targeted by the fishery, such as due to closed areas or seasons.     

Landsat长时间序列的阳澄湖湖面围网时空变化

准确提取湖泊围网区域的时空分布信息对湖泊的保护和可持续发展具有重要意义。本文以阳澄湖为研究区域,收集该地区1984年—2017年所有的Landsat 5和Landsat 8影像(共计396景),提出了结合光谱和纹理特征的围网提取新算法,同时利用时间序列滤波消除年际间因数据不一致造成的偏差。以高清影像人工解译作为参考,阳澄湖围网提取结果的生产者精度在72.57%—88.53%,用户者精度在79.79%—98.10%,围网面积变化与文献记录吻合。结果表明,阳澄湖围网经历了"无围网期"(1984年—1994年)、"快速增长期"(1994年—1998年)、"巅峰期"(1999年—2002年)、"快速下降期"(2003年—2006年)和"稳定期"(2007年—2017年)5个阶段,最高达到100 km2,目前稳定在30 km2;通过研究围网区植被指数发现,2002年之后围网区浮水植物的种植面积增大;通过对比水质数据发现,2002年至今持续15年的围网拆除并未使阳澄湖恢复到80年代无围网养殖时期的II类水,其水质依然处于Ⅲ—Ⅳ类。因此在湖泊养殖开发过程中,政府应该坚持可持续发展道路,在不破坏湖泊水质的基础上发展湖泊经济。