全球降水计划IMERG和GSMaP反演降水在四川地区的精度评估

IMERG和GSMaP是全球降水计划（GPM）时代最主要的高分辨率降水产品。为研究其在中国四川地区的适用性,以中国气象局提供的自动气象站融合降水数据为参考基准,采用6种统计指数分析了IMERG（IMERG_Uncal, IMERG_Cal）和GSMaP（GSMaP_MVK, GSMaP_Gauge）系列产品在四川的误差特征。结果表明：① 在日和小时尺度上,GSMaP系列产品均高估地面降水观测,GSMaP_MVK高估最显著,校正产品GSMaP_Gauge的相关系数（CC）、相对偏差（BIAS）和均方根误差（RMSE）较GSMaP_MVK均有较大提高,尤其对川西高原降水的高估现象改善明显,而IMERG_Uncal存在低估川西高原降水、轻微高估四川盆地降水的问题,校正产品IMERG_Cal一定程度上降低了对川西高原降水的低估现象,但整体精度（CC, RMSE）提高不明显。② IMERG系列产品对降水事件的探测准确性更好,GSMaP_Gauge虽然在四川表现出较高的命中率（POD）,但存在较多的误报降水,在盆地和四川南部各产品均表现出较高的POD和关键成功指数（CSI）以及低误报率（FAR）,而四川西北部表现最差,尤其是在无自动站分布地区。③ 4套降水产品中,IMERG_Cal表现出最好的探测强降水和弱降水的能力,具有一定的监测极端降水的潜力。总体上,IMERG和GSMaP在盆地的反演精度优于高原山区,校正产品精度优于纯卫星产品,不同地形地区精度差异明显,表明对卫星降水产品进行不同地形误差订正仍是未来降水反演工作的重点和难点。     

Accuracy evaluation of two precipitation datasets over upper reach of Heihe River Basin, northwestern China

As an important forcing data for hydrologic models, precipitation has significant effects on model simulation. The China Meteorological Forcing Dataset (ITP) and Global Land Data Assimilation System (G...     

陕西年降水量变化特征及周期分析

以1959-2009年陕西省各气象站逐旬降水资料为基础,采用墨西哥帽小波函数,线性趋势分析以及Mann-Kendall检验法,对陕西省51 a降水的时空分布特征及趋势进行分析,揭示了陕西省降水变化的多时间尺度的复杂结构,分析了不同时间尺度下降水序列变化的周期和突变点,并确定了主要周期。结果表明：（1）陕西省年降水量年际变化大且时空分布极不均匀,降水量从北部向南部递增,呈南多北少特征,大致上为纬向分布。陕北、关中、陕南年降水量多年平均值依次为279 mm、563 mm、840 mm。三大区域年均降水变化相对较为平稳,近51 a来,研究区内年均降水总体呈北部和南部略微下降,中部微弱上升的变化格局,线性倾向率分别为-5.110 mm/10 a、-3.758 mm/10 a、1.908 mm/10 a;（2）陕西省年均降水量有3～7 a,10～17 a,17～30 a周期,以中时间尺度10～17 a的少-多交替最为明显。在微观尺度上,陕北、关中、陕南地区的周期均表现得零乱且不显著。中观尺度,关中和陕南的降水周期比较显著,为10～17 a,陕北的降水周期表现得不十分规律。宏观尺度,陕北的降水周期为23～30 a,关中和陕南较为相似,为18～25 a;（3）Mann-Kendall检验发现1993年是陕西省年均降水量增加的一个显著突变点,2009年以后陕西省将处于多雨期。     

卫星降水产品评价研究的演进脉络与前沿进展

卫星遥感观测技术的不断发展,为全球降水准确定量观测提供新的手段,应用卫星降水产品的前提是进行地区的适用性评价.为了解卫星降水产品评价的研究现状和热点,以Web of Sci-ence数据库核心合集中1998—2020年752篇文献作为研究对象,利用文献计量和网络分析的方法剖析卫星降水产品评价研究的演变趋势、合作关系和研...     

Climatic trends of different intensity heavy precipitation events concentration in China

Based on 740 stations of daily precipitation datasets in China, the precipitation- concentration degree (PCD) and precipitation-concentration period (PCP) of different intensity durative precipitation events were calculated to analyze their statistical characteristics, mainly including spatial and temporal distributions, variations and climatic trends of the two parameters of the durative heavy precipitation events in China. It is proved that these two parameters of heavy rainfall can display the temporal inhomogeneity in the precipitation field. And it is also found that there is a good positive relationship between the precipitation-concentration degree and annual rainfall amount in the Eastern and Central China. This method can be applied in flood assessment and climate change fields.     

中国强降水过程时空集中度气候趋势

1 Introduction Changes in climate variability and extrem es of weather and clim ate events have received increasing attention in the pastyears since these events have the strongestimpacton society. There hasbeen a widespread increase in heavy and extrem e…     

SRTM DEM高程精度评价

为了全面认识SRTM DEM数据精度特征并完善SRTM DEM数据精度评定方法,该文以我国1∶5万比例尺DEM为参考数据,以具有多种地貌类型的陕西省为实验样区,利用高程中误差模型及空间插值方法对SRTMDEM进行高程精度分析。结果表明:陕西省的SRTM DEM高程中误差在3.5~60.7 m,呈现出较为显著的空间分异特征;并且高程中误差与实验样区平均坡度有较强的指数相关性,拟合的指数函数具有较高的模拟精度。     

An analysis of seasonal biases in satellite and reanalysis rainfall products in the Savannah River basin

Satellite-based precipitation data are a viable source of information in data-sparse regions and are particularly valuable for hydrological modelling applications. Several studies of satellite-based precipitation focus on monthly or greater timescales, but a relatively fewer number have been done on the daily or sub-daily scales. Also, biases in satellite-based precipitation data are often region-specific and such information is important for quantifying input errors in hydrological models. Our study builds upon these needs by examining biases in daily precipitation data for a watershed in the southeastern United States. We observed biases that occur seasonally and by magnitude. Seasonally, precipitation correlates well in most seasons but summer, likely due to the sporadic nature of convective precipitation that is a common precipitation mechanism in this region during the summer. Daily precipitation biases are around 5?mm, but the sign of the bias varies by season, with positive biases in all seasons but fall. Additionally, we found that satellite-based data tend to overestimate light precipitation relative to the multi-gauge average, which more often records no precipitation.     

辽宁省大雨以上降水日数分布与趋势分析

大雨以上降水日数反映了降水的非平均状况,对于气候异常变化和预报预测都有非常重要的意义。以辽宁省作为研究区域,根据1957-2013年辽宁省内23个和省外4个雨量站点的逐日降水数据,利用趋势系数法计算大雨以上降水日数的变化趋势,通过Mann-Kendall检验法检测突变。采用Morlet小波技术分析年际与各季节的变化周期;借助ArcGIS软件工具的Kriging插值和IDW插值模块对研究区进行空间分析。结果表明：辽宁省大雨以上降水日数的空间变化表现为由东南向西北递减;年际以及春、夏两季的周期大约为38年,秋季周期约为15年;大雨以上降水日数的年际变化呈现微弱的减少趋势,不同季节略有差异。57年来,辽宁省大雨以上降水日数变化总体呈现下降趋势,辽中地区下降趋势略有滞后,辽西地区变化情况较其他区域相对复杂。     

Meteorological inventory of rain-on-snow events in the Canadian Arctic Archipelago and satellite detection assessment using passive microwave data

The spatial and temporal distributions of rain-on-snow (ROS) events across the Canadian Arctic Archipelago (CAA) remain poorly understood owing to their sporadic nature in time and space. This situation motivated the development of remote sensing detection algorithms. This paper uses a large meteorological dataset across the CAA to adapt an existing ROS-detection algorithm developed in a previous study by our group. Results highlight the spatial distribution and evolution of ROS occurrences reported since 1985 at 14 weather stations across the CAA. Results show that >600 ROS events were inventoried since 1985, for which >70% were classified as pure rain (liquid form) and 30% as mixed precipitation (solid/liquid). Of the pure rain events, 75% occurred during spring, 14% during fall, 8% during summer and <1% during winter. Such events can have significant impacts on ungulate grazing conditions through the creation of ice layers, causing serious problems for caribou calf survival, especially during the migration period. This paper introduces an adaptation for larger scale Arctic application of a detection algorithm (sensitivity analysis on the detection threshold) with an error of ~5%. The validation, however, remains limited due to a short study period and limited number of sites.