A New X-band Weather Radar System with Distributed Phased-Array Front-ends: Development and Preliminary Observation Results

A novel weather radar system with distributed phased-array front-ends was developed. The specifications and preliminary data synthesis of this system are presented, which comprises one back-end and three or more front-ends. Each front-end, which utilizes a phased-array digital beamforming technology, sequentially transmits four 22.5°-width beams to cover the 0°–90° elevational scan within about 0.05 s. The azimuthal detection is completed by one mechanical scan of0°–360° azimuths within about 12 s volume-scan update time. In the case of three front-ends, they are deployed according to an acute triangle to form a fine detection area(FDA). Because of the triangular deployment of multiple phased-array front-ends and a unique synchronized azimuthal scanning(SAS) rule, this new radar system is named Array Weather Radar(AWR). The back-end controls the front-ends to scan strictly in accordance with the SAS rule that assures the data time differences(DTD) among the three front-ends are less than 2 s for the same detection point in the FDA. The SAS can maintain DTD < 2 s for an expanded seven-front-end AWR. With the smallest DTD, gridded wind fields are derived from AWR data, by sampling of the interpolated grid, onto a rectangular grid of 100 m ×100 m ×100 m at a 12 s temporal resolution in the FDA. The first X-band single-polarized three-front-end AWR was deployed in field experiments in 2018 at Huanghua International Airport, China. Having completed the data synthesis and processing, the preliminary observation results of the first AWR are described herein.     

广西茅尾海牡蛎天然种苗种类鉴定和群体组成的初步分析

了解广西茅尾海牡蛎种质资源现状,有助于提高牡蛎采苗效果。通过在茅尾海采集常见5种牡蛎成体,并在牡蛎不同繁殖高峰期分别采集牡蛎D型幼虫DⅠ、DⅡ,在不同附着期分别采集牡蛎幼苗MⅠ、MⅡ、MⅢ,采用多重PCR技术对牡蛎成体、稚贝及幼虫进行种类鉴定。香港巨牡蛎(Crassostrea hongkongensis)在茅尾海牡蛎稚贝和幼虫高峰期均属于优势种;稚贝中香港巨牡蛎、有明巨牡蛎(Crassostrea ariakensis)和熊本牡蛎(Crassostrea sikamea)3个种类的平均比例分别为88.3%、6.7%和5.0%,香港巨牡蛎与熊本牡蛎、有名巨牡蛎相比差异显著(P0.05),熊本牡蛎与有明巨牡蛎差异不显著(P0.05)。香港巨牡蛎是茅尾海牡蛎优势种,7月12日之前投放采苗器可采集到数量多、种类单一的香港巨牡蛎。7月12日之后投放采苗器,附着的稚贝中有明巨牡蛎和熊本牡蛎比例增加,影响采苗效果及养殖生产。     

机载LiDAR数据的多回波信息分析及滤波方案

本文通过分析机载LiDAR系统获取的激光数据的多回波特性,阐述了多回波信息对地物类型信息的揭示作用,并将多回波特性用于减少参与滤波的激光脚点数量。实验证明,本文提出的滤波方案,可以预先剔除掉大部分的植被激光脚点和部分的建筑物激光脚点,这既减少了参与滤波的数据量,又可以改善滤波算法对建筑物和植被的滤除效果。     

基于多核学习的高分辨率遥感图像目标检测方法

为更有效地实现复杂场景中的多类目标同时检测,本文提出了一种基于多核学习算法进行目标检测的框架。该方法由特征提取和模型训练2个阶段组成。特征提取阶段,引入了多尺度下的点特征、表观特征同时对多类目标进行综合描述;模型训练阶段,分别采用加权相加和相乘2种方法将提取的各个基础特征组合起来,在支持向量机的框架下对各特征所代表的基础核权重进行学习。将训练所得的分类器结合滑动窗搜索技术对遥感图像进行目标检测实验,结果表明,与传统单核支持向量机相比,准确率更高。     

Using in situ hyperspectral reflectance data to distinguish nine aquatic plant species

In situ hyperspectral reflectance data were studied at 50 bands (10 nm bandwidth) over the 400–900 nm spectral range to determine their potential for distinguishing among nine aquatic plant species: American lotus [Nelumbo lutea (Willd.) Pers.], American pondweed (Potamogeton nodusus Poir.), giant duckweed [Spirodela polyrrhiza (L.) Schleid.], Mexican waterlily (Nymphaea mexicana Zucc.), white waterlily (Nymphaea odorata Aiton), spatterdock [Nuphar lutea (L.) Sm.], giant salvinia (Salvinia molesta Mitchell), waterhyacinth [Eichhornia crassipes (Mart.) Solms] and waterlettuce (Pistia stratiotes L.). The species were studied on three dates: 30 May, 1 July and 3 August 2009. All nine species were studied in July and August, while only eight species were studied in May; giant duckweed was not studied in May due to insufficient availability. Two procedures were used to determine the optimum bands for discriminating among species: multiple comparison range tests and stepwise discriminant analysis. Multiple comparison range tests results for May showed that most separations among species occurred at bands 795–865 nm in the near-infrared (NIR) spectral region where up to six species could be distinguished. For July, few species could be distinguished amongthe 50 bands; most separations occurred at the 715 nm red-NIR edge band where four species could be differentiated. The optimum bands in August occurred in the green (525–595 nm), red (605–635 nm) and red-NIR edge (695–705 nm) spectral regions where up to six species could be distinguished. Stepwise discriminant analysis identified 11 bands in the blue, green, red-NIR edge and NIR spectral regions to be significant to discriminate among the eight species in May. For July and August, stepwise discriminant analysis identified 15bands and 13 bands, respectively, from the blue to NIR regions to be significant for discriminating among the nine species.     

One decade of multi-objective calibration approaches in hydrological modelling: a review

Abstract

One decade after the first publications on multi-objective calibration of hydrological models, we summarize the experience gained so far by underlining the key perspectives offered by such approaches to improve parameter identification. After reviewing the fundamentals of vector optimization theory and the algorithmic issues, we link the multi-criteria calibration approach with the concepts of uncertainty and equifinality. Specifically, the multi-criteria framework enables recognition and handling of errors and uncertainties, and detection of prominent behavioural solutions with acceptable trade-offs. Particularly in models of complex parameterization, a multi-objective approach becomes essential for improving the identifiability of parameters and augmenting the information contained in calibration by means of both multi-response measurements and empirical metrics (“soft” data), which account for the hydrological expertise. Based on the literature review, we also provide alternative techniques for dealing with conflicting and non-commeasurable criteria, and hybrid strategies to utilize the information gained towards identifying promising compromise solutions that ensure consistent and reliable calibrations.

Citation Efstratiadis, A. & Koutsoyiannis, D. (2010) One decade of multi-objective calibration approaches in hydrological modelling: a review. Hydrol. Sci. J. 55(1), 58–78.     

Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20 cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R² for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected.     

Integration of genetic algorithms and GIS for optimal location search

Optimal location search is frequently required in many urban applications for siting one or more facilities. However, the search may become very complex when it involves multiple sites, various constraints and multiple‐objectives. The exhaustive blind (brute‐force) search with high‐dimensional spatial data is infeasible in solving optimization problems because of a huge combinatorial solution space. Intelligent search algorithms can help to improve the performance of spatial search. This study will demonstrate that genetic algorithms can be used with Geographical Information systems (GIS) to effectively solve the spatial decision problems for optimally sitting n sites of a facility. Detailed population and transportation data from GIS are used to facilitate the calculation of fitness functions. Multiple planning objectives are also incorporated in the GA program. Experiments indicate that the proposed method has much better performance than simulated annealing and GIS neighborhood search methods. The GA method is very convenient in finding the solution with the highest utility value.     

Disconnected Cartographies and Immappancy

Abstract

Manual shading, traditionally produced manually by specifically trained cartographers, is still considered superior to automatic methods, particularly for mountainous landscapes. However, manual shading is time-consuming and its results depend on the cartographer and as such difficult to replicate consistently. For this reason there is a need to create an automatic method to standardize its results. A crucial aspect of manual shading is the continuous change of light direction (azimuth) and angle (zenith) in order to better highlight discrete landforms. Automatic hillshading algorithms, widely available in many geographic information systems (GIS) applications, do not provide this feature. This may cause the resulting shaded relief to appear flat in some areas, particularly in areas where the light source is parallel to the mountain ridge. In this work we present a GIS tool to enhance the visual quality of hillshading. We developed a technique based on clustering aspect to provide a seamless change of lighting throughout the scene. We also provide tools to change the light zenith according to either elevation or slope. This way the cartographer has more room for customizing the shaded relief representation. Moreover, the method is completely automatic and this guarantees consistent and reproducible results. This method has been embedded into an ArcGIS toolbox.     

Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables

Abstract

The development of statistical relationships between local hydroclimates and large-scale atmospheric variables enhances the understanding of hydroclimate variability. The rainfall in the study basin (the Upper Chao Phraya River Basin, Thailand) is influenced by the Indian Ocean and tropical Pacific Ocean atmospheric circulation. Using correlation analysis and cross-validated multiple regression, the large-scale atmospheric variables, such as temperature, pressure and wind, over given regions are identified. The forecasting models using atmospheric predictors show the capability of long-lead forecasting. The modified k-nearest neighbour (k-nn) model, which is developed using the identified predictors to forecast rainfall, and evaluated by likelihood function, shows a long-lead forecast of monsoon rainfall at 7–9 months. The decreasing performance in forecasting dry-season rainfall is found for both short and long lead times. The developed model also presents better performance in forecasting pre-monsoon season rainfall in dry years compared to wet years, and vice versa for monsoon season rainfall.

Editor Z.W. Kundzewicz

Citation Singhrattna, N., Babel, M.S. and Perret, S.R., 2012. Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables. Hydrological Sciences Journal, 57 (1), 26–41.