基于实测波浪的单桩式海上风电基础波浪力计算研究

基于实测波面的波浪力获取作为结构动力响应分析以及数字孪生模型建立的必备环节,对海上风电数字化运维至关重要。为了满足更大的装机容量需求,单桩式海上风电基础趋于大型化,其尺度因子D/L也随之增大;并且实际海域均为非规则波,以尺度因子划分波浪力计算理论的方法对非规则波的适用性尚不明确。通过建立数值水槽,依据实际工况对不规则波与桩基的作用进行数值模拟,得到入射波浪场与桩基所受波浪力,在此基础上,基于入射波浪场分别采用Morison方程以及绕射理论求解波浪力并将之与数值模拟结果进行对比,分析了不同波浪力计算理论关于尺度因子的适用性,同时探究了波浪要素对计算精度的影响。结果表明：Morison方程在波高较大时精度下降;相对于Morison方程,绕射理论在该尺度下的精度更高。最后,通过分析实测数据进一步探讨了典型工况下的波浪力特征,以期通过实测波面计算波浪力的方法为实际服役风机波浪力计算提供技术支持。     

西北太平洋九州-帕劳海脊北部KPR12岩芯40万年来有机碳来源、埋藏特征及其环境响应

西北太平洋海陆相互作用强烈,受黑潮和亚洲大陆风尘输入影响显著,是研究海洋沉积物中有机碳埋藏与陆源输入、海洋初级生产力和海洋环境演化关系的理想靶区。本研究基于采自九州-帕劳海脊北部的重力活塞647 cm长的KPR12岩芯,分析了总有机碳（TOC）、总氮（TN）、有机碳稳定碳同位素组成（δ¹³C_TOC）、蛋白石（Opal）以及粒度等指标,初步探讨了近40万年来轨道尺度沉积有机碳的来源、埋藏特征及其影响机制。岩芯的TOC含量平均值为0.214%,呈冰期较高、间冰期较低的旋回特征。岩芯沉积物粒度的不同端元反映了研究区风尘输入大致呈冰期较高的趋势。岩芯的δ¹³C_TOC（-25.24‰~-21.04‰,平均值为-22.59‰）显示有机质以海源为主（约62%）,海源有机碳含量趋势与TOC的变化一致。研究区不同时期陆源物质的输入受风尘输送和黑潮的综合控制,黑潮增强可能对该区间冰期陆源有机碳贡献的升高有一定影响。岩芯中TOC与Opal含量的相关性分析表明,海洋初级生产力可能不是控制该岩芯有机碳含量变化的主要原因。冰期-间冰期旋回中的深层水通风条件、黑潮和北太平洋中层水的发育以及陆源细颗粒物质输入可能共同影响着本区沉积有机碳的降解与埋藏保存。

     

Size and spatial distribution of loess slides on the Chinese Loess Plateau

ABSTRACT

The size and spatial distribution of loess slides are important for estimating the yield of eroded materials and determining the landslide risk. While previous studies have investigated landslide size distributions, the spatial distribution pattern of landslides at different spatial scales is poorly understood. The results indicate that the loess slide distribution exhibits a power-law scaling across a range of the size distribution. The mean landslide size and size distribution in the different geomorphic types are different. The double Pareto and inverse gamma functions can coincide well with the empirical probability distribution of the loess slide areas and can quantitatively reveal the rollover location, maximum probability, and scaling exponents. The frequency of loess slides increases with mean monthly precipitation. Moreover, point distance analysis showed that > 80% of landslides are located < 3 km from other loess slides. We found that the loess slides at the two study sites (Zhidan and Luochuan County) in northern Shaanxi Province, China show a significant clustered distribution. Furthermore, analysis results of the correlated fractal dimension show that the landslides exhibit a dispersed distribution at smaller spatial scales and a clustered distribution at larger spatial scales.     

Consistent seasonal snow cover depth and duration variability over the Western Himalayas (WH)

Precipitation in solid form, i.e., snow, during winter season over the Western Himalayas (WH) leads to the build-up of seasonal snow cover. Seasonal snow cover build-up (snow cover depth and duration) largely depends on atmospheric variables such as temperature, precipitation, radiation, wind, etc. Integrated (combined) influence of atmospheric variables on seasonal snow cover gets reflected in terms of spatial and temporal variability in seasonal snow cover build-up pattern. Hence spatial and temporal variability of seasonal snow cover build-up can serve as a good indicator of climate change in high altitude mountainous regions like the WH. Consistent seasonal snow cover depth and duration, delay days and early melt days of consistent seasonal snow cover at 11 stations spread across different mountain ranges over the WH were analyzed. Mean, maximum and percentiles (25th, 50th, 75th, 90th and 95th) of consistent seasonal snow cover depth and duration show decline over the WH in the recent past 2–3 decades. Consistent seasonal snow cover is found to melt early and snow cover build-up pattern is found to show changes over the WH. Decline in consistent seasonal snow cover depth, duration and changing snow cover build-up pattern over the WH in recent decades indicate that WH has undergone considerable climate change and winter weather patterns are changing in the WH.     

Characteristics of the Asian–Pacific Oscillation in Boreal Summer Simulated by BCC_CSM with Different Horizontal Resolutions

The summer Asian–Pacific Oscillation(APO) is a major teleconnection pattern that reflects the zonal thermal contrast between East Asia and the North Pacific in the upper troposphere. The performance of Beijing Climate Center Climate System Models(BCC CSMs) with different horizontal resolutions, i.e., BCC CSM1.1 and BCC CSM1.1(m), in reproducing APO interannual variability, APO-related precipitation anomalies, and associated atmospheric circulation anomalies, is evaluated.The results show that BCC CSM1.1(m) can successfully capture the interannual variability of the summer APO index. It is also more capable in reproducing the APO's spatial pattern, compared to BCC CSM1.1, due to its higher horizontal resolution. Associated with a positive APO index, the northward-shifted and intensified South Asian high, strengthened extratropical westerly jet, and tropical easterly jet in the upper troposphere, as well as the southwesterly monsoonal flow over North Africa and the Indian Ocean in the lower troposphere, are realistically represented by BCC CSM1.1(m), leading to an improvement in reproducing the increased precipitation over tropical North Africa, South Asia, and East Asia, as well as the decreased precipitation over subtropical North Africa, Japan, and North America. In contrast, these features are less consistent with observations when simulated by BCC CSM1.1. Regression analysis further indicates that surface temperature anomalies over the North Pacific and the southern and western flanks of the Tibetan Plateau are reasonably reproduced by BCC CSM1.1(m), which contributes to the substantial improvement in the simulation of the characteristics of summer APO compared to that of BCC CSM1.1.     

An algorithm to retrieve precipitation with synthetic aperture radar

This paper presents a new type of rainfall retrieval algorithm, called the model-oriented statistical and Volterra integration. It is a combination of the model-oriented statistical (MOS) and Volterra integral equation (VIE) approaches. The steps involved in this new algorithm can be briefly illustrated as follows. Firstly, information such as the start point and width of the rain is obtained through pre-analysis of the data received by synthetic aperture radar (SAR). Secondly, the VIE retrieval algorithm is employed over a short distance to obtain information on the shape of the rain. Finally, the rain rate can be calculated by using the MOS retrieval algorithm. Simulation results show that the proposed algorithm is effective and simple, and can lead to time savings of nearly 50% compared with MOS. An example of application of SAR data is also discussed, involving the retrieval of precipitation information over the South China Sea.     

Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data

Reliable and accurate estimates of tropical forest above ground biomass (AGB) are important to reduce uncertainties in carbon budgeting. In the present study we estimated AGB of central Indian deciduous forests of Madhya Pradesh (M.P.) state, India, using Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) L-band data of year 2010 in conjunction with field based AGB estimates using empirical models. Digital numbers of gridded 1?×?1° dual polarization (HH & HV) PALSAR mosaics for the study area were converted to normalized radar cross section (sigma naught - σ⁰). A total of 415 sampling plots (0.1 ha) data collected over the study area during 2009–10 was used in the present study. Plot-level AGB estimates using volume equations representative to the study area were computed using field inventory data. The plot-level AGB estimates were empirically modeled with the PALSAR backscatter information in HH, HV and their ratios from different forest types of the study area. The HV backscatter information showed better relation with field based AGB estimates with a coefficient of determination (R²) of 0.509 which was used to estimate spatial AGB of the study area. Results suggested a total AGB of 367.4 Mt for forests of M.P. state. Further, validation of the model was carried out using observed vs. predicted AGB estimates, which suggested a root mean square error (RMSE) of ±19.32 t/ha. The model reported robust and defensible relation for observed vs. predicted AGB values of the study area.     

Using a regional numerical weather prediction model for GNSS positioning over Brazil

The global navigation satellite system (GNSS) can provide centimeter positioning accuracy at low costs. However, in order to obtain the desired high accuracy, it is necessary to use high-quality atmospheric models. We focus on the troposphere, which is an important topic of research in Brazil where the tropospheric characteristics are unique, both spatially and temporally. There are dry regions, which lie mainly in the central part of the country. However, the most interesting area for the investigation of tropospheric models is the wet region which is located in the Amazon forest. This region substantially affects the variability of humidity over other regions of Brazil. It provides a large quantity of water vapor through the humidity convergence zone, especially for the southeast region. The interconnection and large fluxes of water vapor can generate serious deficiencies in tropospheric modeling. The CPTEC/INPE (Center for Weather Forecasting and Climate Studies/Brazilian Institute for Space Research) has been providing since July 2012 a numerical weather prediction (NWP) model for South America, known as Eta. It has yield excellent results in weather prediction but has not been used in GNSS positioning. This NWP model was evaluated in precise point positioning (PPP) and network-based positioning. Concerning PPP, the best positioning results were obtained for the station SAGA, located in Amazon region. Using the NWP model, the 3D RMS are less than 10 cm for all 24 h of data, whereas the values reach approximately 60 cm for the Hopfield model. For network-based positioning, the best results were obtained mainly when the tropospheric characteristics are critical, in which case an improvement of up to 7.2 % was obtained in 3D RMS using NWP models.     

Is the long-term variation of the estimated GPS differential code biases associated with ionospheric variability?

The global positioning system (GPS) differential code biases (DCB) provided by the International GNSS Service (IGS) show solar-cycle-like variation during 2002–2013. This study is to examine whether this variation of the GPS DCBs is associated with ionospheric variability. The GPS observations from low earth orbit (LEO) satellites including CHAMP, GRACE and Jason-1 are used to address this issue. The GPS DCBs estimated from the LEO-based observations at different orbit altitudes show a similar tendency as the IGS DCBs. However, this solar-cycle-like dependency is eliminated when the DCBs of 13 continuously operating GPS satellites are constrained to zero-mean. Our results thus revealed that ionospheric variation is not responsible for the long-term variation of the GPS DCBs. Instead, it is attributed to the GPS satellite replacement with different satellite types and the zero-mean condition imposed on all satellite DCBs.     

Testing block subdivision algorithms on block designs

Integrated land use–transportation models predict future transportation demand taking into account how households and firms arrange themselves partly as a function of the transportation system. Recent integrated models require parcels as inputs and produce household and employment predictions at the parcel scale. Block subdivision algorithms automatically generate parcel patterns within blocks. Evaluating block subdivision algorithms is done by way of generating parcels and comparing them to those in a parcel database. Three block subdivision algorithms are evaluated on how closely they reproduce parcels of different block types found in a parcel database from Montreal, Canada. While the authors who developed each of the algorithms have evaluated them, they have used their own metrics and block types to evaluate their own algorithms. This makes it difficult to compare their strengths and weaknesses. The contribution of this paper is in resolving this difficulty with the aim of finding a better algorithm suited to subdividing each block type. The proposed hypothesis is that given the different approaches that block subdivision algorithms take, it’s likely that different algorithms are better adapted to subdividing different block types. To test this, a standardized block type classification is used that consists of mutually exclusive and comprehensive categories. A statistical method is used for finding a better algorithm and the probability it will perform well for a given block type. Results suggest the oriented bounding box algorithm performs better for warped non-uniform sites, as well as gridiron and fragmented uniform sites. It also produces more similar parcel areas and widths. The Generalized Parcel Divider 1 algorithm performs better for gridiron non-uniform sites. The Straight Skeleton algorithm performs better for loop and lollipop networks as well as fragmented non-uniform and warped uniform sites. It also produces more similar parcel shapes and patterns.