Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used. 相似文献
By using the hourly data from surface meteorological stations in China, the 3-hour precipitation data from
CRA-Interim (Chinese Reanalysis-Interim), ERA5 (ECMWF Reanalysis 5) and JRA-55 (Japanese Reanalysis-55) are
compared, both on the spatial-temporal distributions and on bias with observation precipitation in China. The results
show that: (1) The three sets of reanalysis datasets can all reflect the basic spatial distribution characteristics of annual
average precipitation in China. The simulation of topographic forced precipitation in complex terrain by CRA-interim is
more detailed, while CRA-interim has larger negative bias in central and East China, and larger positive bias in
southwest China. (2) In terms of seasonal precipitation, the three sets of reanalysis datasets overestimate the precipitation
in the heavy rainfall zone of spring and summer, especially in southwest China. CRA interim’s location of the rain belt
in the First Rainy Season in South China is west by south, the summer precipitation has positive bias in southwest and
South China. (3) All of the reanalysis datasets can basically reflect the distribution difference of inter-annual variation of
drought and flood, but the overall the CRA-Interim generally shows negative bias, while the ERA5 and JRA-55 exhibit
positive bias. (4) For the diurnal variation of precipitation in summer, all the reanalysis datasets perform better in
simulating the daytime precipitation than in the night, and bias of CRA-interim is less in southeast and northeast than
elsewhere. (5) ERA5 generally performs the best on the evaluation of quantitative precipitation forecast, the JRA-55 is
the next, followed by the CRA-Interim. CRA-Interim has higher missing rate and lower threat score for heavy rains;
however, at the level of downpour, the CRA-Interim performs slightly better. 相似文献
以广州市为例,基于城市感知数据、遥感影像等多源数据,采用卷积神经网络对遥感影像进行了语义提取,将提取结果与兴趣点（Points of Interest）样方密度的功能用地识别结果进行补充校验,根据政策和规划文件建立功能用地与主体功能区之间的关联,利用信息熵分析广州市功能用地混合程度,以辅助判别主体功能区,最终得到广州市主体功能区划分结果。将划分结果与《广州市主体功能区规划（2008—2020年）》和《广州市城市总体规划（2017—2035年）》草案对比验证,结果表明文章所提出的方法精准度较高,并能体现广州市空间格局形态,反映主体功能区实际分布情况。 相似文献
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.
