ABSTRACT Due to high spatial and temporal resolution and near real-time accessibility of satellite precipitation data, the necessity of using these data in the hydrological application seems to be more pressing than ever. In this study, the skill of six post real-time (Climate Hazards Group Infrared Precipitation with Station data (CHIRPS); CPC MORPHing technique (CMORPH); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); PERSIANN Climate Data Record (PERSIANN-CDR); precipitation produced from the inversion of the satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (SM2RAIN); Tropical Rainfall Measuring Mission (TRMM 3B42-V7)) and two near real-time (PERSIANN Cloud Classification System (PERSIANN-CCS); TRMM real-time (TRMM 3B42-RT)) satellite daily precipitation products are evaluated by comparing with 28 rain gauges in Karkheh River Basin, located in the semi-arid region of Iran. The evaluation is performed for two types of quantiles (lower quantile (< Q10 and < Q25) and upper quantile (> Q50, > Q75, and > Q95)) and rainy seasons using categorical and quantitative metrics for the period March 2003 to December 2014. The spatial analysis indicated that there is not remarkable variation in the skill of satellite precipitation products across the study area. Results showed that the satellite precipitation estimates are more accurate in lower than upper quantile. The seasonal analysis presented that the skill of satellite precipitation products for fall and spring is slightly higher than winter. For post real-time satellite, in terms of POD (VHI), PERSIANN-CDR in spring (winter and spring), SM2RAIN in winter and spring (fall) shows the best skill, and according to FAR and CSI, CMORPH is the best in all seasons. In addition, VHI and POD of PERSIANN-CCS have better skill than 3B42-RT for near real-time satellite for all seasons. Generally, PERSIANN-CCS (PERSIANN-CDR and SM2RAIN) shows the best skill for near (post) real-time satellite precipitation estimations when whole data are included in the analysis.

中文翻译：

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伊朗卡尔赫河流域的近实时卫星降水产品技术

摘要 由于高空间和时间分辨率以及卫星降水数据的近实时可访问性，在水文应用中使用这些数据的必要性似乎比以往任何时候都更加紧迫。在本研究中，六后实时技术（Climate Hazards Group Infrared Precipitation with Station data (CHIRPS); CPC MORPHing technology (CMORPH); Precipitation Estimation from Remote Sensed Sensed Estimation from Remote Sensed Estimation from Remote Sensed Estimation Estimation from Remote Sensed Estimation of Remote Sensed Estimation Estimation from Remote Sensed Estimation Estimation from Remote Sensed Estimation using Artist (PERSIANN-CDR)；来自欧洲航天局 (ESA) 气候变化倡议 (SM2RAIN) 的卫星土壤水分 (SM) 观测值反演产生的降水；热带降雨测量任务 (TRMM 3B42-V7)）和两个近实时（PERSIANN 云分类系统（PERSIANN-CCS）；TRMM 实时 (TRMM 3B42-RT)) 卫星日降水产品通过与位于伊朗半干旱地区的 Karkheh 河流域的 28 个雨量计进行比较来评估。使用 2003 年 3 月至 2014 年 12 月期间的分类和定量指标对两种类型的分位数（下分位数（< Q10 和 < Q25）和上分位数（> Q50、> Q75 和 > Q95））和雨季进行评估. 空间分析表明，整个研究区卫星降水产品的技能没有显着变化。结果表明，卫星降水估计在下分位数比上分位数更准确。季节性分析表明，秋季和春季卫星降水产品的技能略高于冬季。对于后实时卫星，在POD（VHI）方面，春季（冬季和春季）的PERSIANN-CDR，冬季和春季（秋季）的SM2RAIN表现最好，根据FAR和CSI，CMORPH在所有季节都是最好的。此外，PERSIANN-CCS 的VHI 和POD 比3B42-RT 具有更好的全天候近实时卫星技能。一般来说，当整个数据都包含在分析中时，PERSIANN-CCS（PERSIANN-CDR 和 SM2RAIN）显示了近（后）实时卫星降水估计的最佳技能。