Surface temperature forecast skill comparison for the west coast of Saudi Arabia     

《Atmósfera》2014,27(3):287-303

Given the growing interest of the general public in accessing commercial weather forecasts through various media outlets and the available impetuses for promoting tourism in Saudi Arabia (SA), a first attempt is made to present a forecast skill comparison for surface temperature in four cities (Wejh, Yenbo, Jeddah, and Gizan) along the west coast of SA, for the 61-day transitional period (from January 16 to March 16) between the December-January-February (DJF) and the March-April-May (MAM) seasons. A simple skill score comparison method is used to assess the next-day city forecasts for surface temperature from six commercial weather forecast providers based on the operational numerical weather prediction (NWP) model outputs. All the NWP model forecast providers performed better than the respective daily climatology (Clm) for each station. Depending upon the station and the provider, the absolute average maximum daily surface temperature difference between the forecasts and the observations was less than 2 °C. Daily surface temperature forecasts from two versions of an atmospheric-ocean general circulation model are also compared to assess their performance for these coastal locations.  相似文献   

Biological assessment of rivers in the Manawatu‐Wanganui region of New Zealand using a predictive macroinvertebrate model     

Michael K. Joy  Russell G. Death 《新西兰海洋与淡水研究杂志》2013,47(2):367-379

This study presents a river invertebrate and classification system (RIVPACS) type bioassessment methodology for the Manawatu‐Wanganui region of New Zealand. Aquatic macroinvertebrates and related physico‐chemical data were collected at 127 sites, with minimal human impacts (reference sites) in 2000. The reference sites were classified into five groups based on their macroinvertebrate data using TWINSPAN. These biotic groupings were then applied to their corresponding physico‐chemical data and discriminant functions were obtained to assign sites into the biotic groups using the physico‐chemical data. The discriminant functions correctly allocated 72% of the sites to the correct classification group using a jack‐knife validation. The probabilities from the discriminant functions were used to predict macroinvertebrate assemblages and these were compared with observed macroinvertebrate assemblages. The model was then used to assess the health of 29 test sites with known impacts. All test sites were assessed as impacted based on the 10th percentile of the reference data. To evaluate the temporal reliability of the model, data available for 11 sites sampled in 1997 and 2000 were run through the model. The results of this comparison showed little variation in O/E ratios over time and the two sites classed as impacted in 1997 were also classed as impacted in 2000.  相似文献   

相控阵天气雷达数据压缩技术     

胡林宏  周红根  沈邦跃  曾强宇 《气象科技》2023,51(3):356-365

相控阵天气雷达较新一代天气雷达在时空分辨率上有明显的优势。随着探测能力的提高,相控阵雷达数据量急剧增长,数据传输和存储问题凸显。现有天气雷达数据压缩算法可以减少传输和存储的数据量,但现有算法并未充分考虑相控阵雷达特点,数据压缩率有较大提升空间。本文提出时空预测的相控阵雷达数据压缩算法(PARDC),使用径向预测压缩数据相关冗余。使用CHATOR雷达数据对PARDC的算法性能进行评估,试验结果表明PARDC较通用的压缩算法压缩率性能大约提升了24%。  相似文献   

新型局地增长模培育法对两次飑线个例的对流尺度集合预报试验          下载免费PDF全文

李坤  陈超辉  何宏让  马申佳  姜勇强 《大气科学学报》2021,44(4):518-528

利用局地增长模培育法对两次典型飑线过程进行了对流尺度集合预报试验,通过与传统增长模培育法对比,检验了局地增长模培育法的实际预报效果.通过概率匹配平均处理后,将降水预报结果与实况资料进行对比分析,并用分数技巧评分来代替传统公平技巧评分实现对降水结果的合理检验,得出结论:1)在飑线降水预报上,局地增长模培育法优于增长模培育...  相似文献   

基于Local-BGM法的冷涡暴雨集合预报试验及评估检验北大核心          下载免费PDF全文

张海鹏  智协飞  李昊  黎振宇  张志强  黄晶 《气象科学》2023,43(1):36-45

基于传统增长模繁殖法(Breeding Growing Mode,BGM)和局地增长模繁殖法(Local Breeding Growing Mode,Local-BGM)生成初始扰动成员,对一次冷涡暴雨过程进行集合预报试验,从多方面比较两种方案的预报效果,并且在邻域概率法(Neighborhood Probability,NP)中引入时间邻域,评估概率预报结果。结果表明,引入局地化思想的Local-BGM方案能够生成比传统BGM方案更合理的初始扰动,具有很明显的局地特征。对于扰动变量的预报,Local-BGM方案在均方根误差和离散度等方面均表现更好,同时能够提高各量级降水的预报技巧。邻域集合概率法能够综合各个集合成员预报的降水信息得到优于集合平均的概率预报,分数技巧评分更高。并且在考虑时间不确定性后,无论是控制预报、集合平均还是邻域集合概率法,分数技巧评分均有很大改善,并且降水阈值越大改善效果越明显,能够为极端强降水天气提供较为客观的概率预报信息。  相似文献   

结合长时序遥感监测和机器学习算法预测藻类增殖风险时空变化          下载免费PDF全文

时晨燚  刘凤  祝铠  张媛  刘海 《湖泊科学》2024,36(3):670-684

饮用水源地藻类增殖监测和预测对于改善水生态系统环境和保护人类健康具有重要意义。利用多源遥感数据能够获取高时空分辨率的藻类动态信息,结合长时序遥感监测与机器学习算法能够适应藻类生长复杂的影响机制和非线性特征,实现藻类增殖风险时空变化的预测。本文利用Landsat与MODIS长时间序列卫星遥感数据,采用FAI与NDVI两种方法提取2000—2020年丹江口水库藻类浓度的时空变化信息,在此基础上分析藻类增殖对气象因子(气温、气压、相对湿度、风速和累计日照时间)的时间滞后效应。利用支持向量机、朴素贝叶斯与随机森林3种机器学习算法预测藻类增殖风险,并对3种算法的预测性能进行评价和对比。结果表明:丹江口水库藻类浓度年际变化呈现出先增后降的趋势,呈现出明显的季节性周期变化,春末夏初是藻类快速增殖时期。空间上入库支流和库湾等局部地区藻类浓度相对较高,为藻类增殖高风险区,丹江口水库藻类增殖风险预测模型能够较为准确地确定藻类增殖高风险区位置并反映短期内的空间变化情况,3种算法的预测结果呈现出整体上的一致性,其中支持向量机与朴素贝叶斯算法表现出更高的精度,提前4～5天是最佳预测时间。  相似文献