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首页> 外文期刊>International Journal of Climatology: A Journal of the Royal Meteorological Society >Evaluation of a new satellite-based precipitation data set for climate studies in the Xiang River basin, southern China
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Evaluation of a new satellite-based precipitation data set for climate studies in the Xiang River basin, southern China

机译：南方湘河流域气候研究新卫星沉淀数据集评价

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摘要

A new satellite-based precipitation data set, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with long-term time series dating back to 1983, can be one valuable data set for climate studies. This study investigates the feasibility of using PERSIANN-CDR as a reference data set for climate studies. Sixteen Coupled Model Intercomparison Projection Phase 5 (CMIP5) models are evaluated over the Xiang River basin, southern China, by comparing their performance on precipitation projection and streamflow simulation, particularly on extreme precipitation and streamflow events. The results show PERSIANN-CDR is a valuable data set for climate studies, even on extreme precipitation events. The precipitation estimates and their extreme events from CMIP5 models are improved significantly compared with rain gauge observations after bias correction by the PERSIANN-CDR precipitation estimates. Given streamflows simulated with raw and bias-corrected precipitation estimates from 16 CMIP5 models, 10 out of 16 are improved after bias correction. The impact of bias correction on extreme events for streamflow simulations are unstable, with 8 out of 16 models can be clearly claimed they are improved after the bias correction. Concerning the performance of raw CMIP5 models on precipitation, IPSL-CM5A-MR excels the other CMIP5 models, while MRI-CGCM3 outperforms on extreme events with its better performance on six extreme precipitation metrics. Case studies also show that raw CCSM4, CESM1-CAM5, and MRI-CGCM3 outperform other models on streamflow simulation, while MIROC5-ESM-CHEM, MIROC5-ESM, and IPSL-CM5A-MR behave better than the other models after bias correction.
机译：一种新的卫星沉淀数据集，使用人工神经网络 - 气候数据记录（Persiann-CDR）的远程感测信息的降水估计，长期时间序列追溯到1983年，可以是用于气候研究的一个有价值的数据集。本研究调查了使用Persiann-CDR作为气候研究的参考数据集的可行性。通过比较其对降水投影和流流模拟的性能，尤其是在极端降水和流流程中的表现上，评估了十六次耦合型号的型号型号（CMIP5）模型。结果显示Persiann-CDR是一种有价值的数据库，即使在极端降水事件上也是如此。通过Persiann-CDR降水估算偏差后，与CMIP5模型的降水估算和其极端事件与雨量尺寸观测相比有显着改善。给定由16个CMIP5型号的原始和偏置校正降水估计模拟的流式流出，偏压后16个中的10个在16个中得到改善。偏差校正对流仿真的极端事件的影响是不稳定的，可以清楚地声称在偏置校正后改善了超过16个型号。关于Regip5在降水上的性能上的性能，IPSL-CM5A-MR擅长其他CMIP5型号，而MRI-CGCM3在极端事件上占有较好的六个极端降水度量。案例研究还表明，原始CCSM4，CESM1-CAM5和MRI-CGCM3优于Stream流模拟的其他模型，而Miroc5-ESM-Chem，MiroC5-ESM和IPSL-CM5A-MR在偏差校正后的其他模型比其他模型更好。

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