北斗卫星类型间偏差快速改正分析     

贾春  崔鹏  李慧  赵琳  李亮 《测绘通报》2020,(12):42-45

针对北斗卫星类型间偏差（ISTB）导致载波相位差分高精度定位（RTK）的整周模糊度无法固定问题，本文提出了改进ISTB快速改正方法，在观测量层面仅利用1组不同卫星类型的载波相位差分观测量即可实现ISTB估计与改正，实际应用中较传统方法更具普适性与简易性。该方法的测试结果表明，ISTB值由NH码调制不同引起，不同频点的ISTB值具有不一致性；此外，有无改正ISTB的北斗RTK性能对比反映了ISTB会严重降低整周模糊度固定与RTK定位性能。由此可知，本文方法能够保障现阶段北斗接收机更新换代过程中北斗RTK的高精度与高可靠性能。  相似文献   

基于核函数NPSSB模型的HY-2A卫星雷达高度计海况偏差研究     

张洁  田杰  王兆徽 《海洋预报》2020,37(1):1-10

利用机器学习的方法,对14个周期HY-2A卫星高度计数据:风速、有效波高和海面高度差值进行训练,探究海况偏差和风速、有效波高之间的关系,创建海况偏差核函数非参数模型(NPSSB),并与参数模型中具有代表性的BM3、BM4模型进行对比。研究表明:(1)核函数NPSSB模型能够很好的反映SSB与U、SWH之间的关系,SSB与U呈二次函数关系,SSB与SWH呈反比例函数关系;(2)核函数NPSSB模型对SSB的模拟能力与训练数据集相关,数据量越多,模拟能力越好;(3)核函数NPSSB模型与BM3、BM4模型都存在0^-0.03 m的差值,随着风速和有效波高的增加,差值的绝对值越大。  相似文献   

基于“灰箱”模型的海洋公共治理责任结构论证分析     

吕承文 《海洋通报》2018,(3):280-286

海洋公共治理是我国政府治理现代化体系建设的重要组成内容。作为适应新时期"海洋强国"战略实施的海洋公共治理被认作一种异于传统陆地政府治理的新型地公共治理活动,存在着自身一定的责任结构体系。收集了2012-2016年的六大类海洋责任案例(主要是海洋执法案例),以时间(t)、案例数量(q)、制度(I)、人(H)来构建"灰箱"模型,通过考察问责复发率(P)分析海洋公共责任(R)的问题及原因。研究结果表明:(1)我国海洋公共治理过程中应亟须重视"海洋政府"理念,问责事件也从样本数量上得到了体现;(2)海洋公共治理的目标应是防范于未然,确保责任事件的可预期化,推进政府治理能力现代化,应当基于法治的导向来重新塑造海洋政府的责任体系;(3)我国未来海洋公共治理的责任体系建构应通过立法的方式实现责任人和责任形式的可预期化,最终实现对海洋公共治理责任法治监督的应有之义。  相似文献   

基于无人机多源遥感数据海岛植被叶面积指数协同反演研究     

吴剑  陈鹏  傅世锋  陈庆辉  潘翔  宋志晓 《海洋技术学报》2019,38(6):1-8

无人机多源遥感数据的获取、融合以及应用是当今研究的热点和难点。文中以城洲岛为例,针对海岛特殊的地理生态环境,获取无人机多源遥感数据。结合无人机多光谱遥感数据定量分析各遥感植被指数与植被叶面积指数(Leaf Area Index, LAI)的响应关系,构建单因子遥感反演模型;基于无人机激光LiDAR点云提取海岛植被冠层高度模型(Canopy Height Model,CHM),并将其作为自变量引入到多源统计回归分析中,从而构建多源遥感数据协同反演模型,对区域尺度下海岛叶面积指数(LAI)进行估算,开展验证和精度评价。结果显示,加入植被冠层高度因子的协同反演模型的判定系数R2为0.92,绝对平均误差系数为12.29%,预测精度要优于单因子反演模型(判定次数R2为0.86,绝对平均误差系数19.95%)。研究表明,加入了植被冠层高度因子的协同反演模型能在一定程度上提高乔木植被LAI的预测精度。实践证明,无人机多源遥感技术在生态学定量研究中具有巨大的潜力和广阔的应用前景。  相似文献   

The data‐driven approach as an operational real‐time flood forecasting model     

Phuoc Khac‐Tien Nguyen  Lloyd Hock‐Chye Chua 《水文研究》2012,26(19):2878-2893

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

Study on the negative bias temperature instability effect under dynamic stress     

马晓华  曹艳荣  郝跃 《海洋学报(英文版)》2010,29(11)

This paper studies negative bias temperature instability (NBTI) under alternant and alternating current (AC) stress. Under alternant stress, the degradation smaller than that of single negative stress is obtained. The smaller degradation is resulted from the recovery of positive stress. There are two reasons for the recovery. One is the passivation of H dangling bonds, and another is the detrapping of charges trapped in the oxide. Under different frequencies of AC stress, the parameters all show regular degradation, and also smaller than that of the direct current stress. The higher the frequency is, the smaller the degradation becomes. As the negative stress time is too small under higher frequency, the deeper defects are hard to be filled in. Therefore, the detrapping of oxide charges is easy to occur under positive bias and the degradation is smaller with higher frequency.  相似文献   

基于高速公路气象监测数据分析的浓雾预报指标     

赵娜  孟宪罗  马翠平  曲晓黎  张金满 《气象科技》2015,43(1):145-150

利用京秦高速公路沿线交通气象监测站实况资料,通过对84个站次的浓雾雾生和雾消各气象要素变化特征进行分析,归纳出高速公路沿线浓雾和强浓雾天气雾生雾消的预报指标。爆发性强浓雾期间能见度少波动,在能见度爆发下降前,温度下降过程中的小幅上升对能见度突然下降有很好的指示作用;相对湿度在能见度爆发下降前1 h内达到80%以上。一般性强浓雾大多数出现在温度波动之后继续直线下降期间;在500 m浓雾出现15 h之前空气相对湿度达90%以上,能见度达50 m之前相对湿度基本达饱和状态。浓雾消散主要有两个方面,因冷空气造成的雾消,预报应着眼于冷空气前锋影响高速公路所在区域的时间;而由辐射升温造成的雾消,预报应着眼于对天空状况和升温速度的判断。  相似文献   

基于空间自回归和空间聚类的渔情预报模型     

袁红春  谭明星  顾怡婷  陈新军 《海洋科学》2015,39(12):165-172

作者针对远洋渔场渔情预报精度偏低的问题,提出一种基于空间自回归和空间聚类的渔情预报模型。该模型利用空间自回归对收集到的渔业历史数据进行预处理,然后通过空间聚类将所有数据样本根据地理位置分划成若干个区域,最后研究每个区域中环境数据与渔获数据之间的数学关系,各自建立栖息地适宜性指数模型(Habitat Suitability Index,HSI),并以印度洋大眼金枪鱼(Thunnus obesus)为例进行验证。结果表明,本模型的均方差为0.1742,与传统线性回归方法的均方差0.2363相比,能更好地表达海洋环境数据与渔获量之间的关系,预测精度显著提高。  相似文献   

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China     

Tuanjie Hou  Fanyou Kong  Xunlai Chen  Hengchi Lei  Zhaoxia Hu 《大气科学进展》2015,32(7):967-978

To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.  相似文献   

Seasonal prediction of June rainfall over South China: Model assessment and statistical downscaling     

Kun-Hui Ye  Chi-Yung Tam  Wen Zhou  Soo-Jin Sohn 《大气科学进展》2015,32(5):680-689

The performances of various dynamical models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) in predicting station-scale rainfall in South China(SC) in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME's ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output(DMO) over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.  相似文献