干旱区生态环境知识资源中心(Arid): Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran

Arid

DOI	10.3390/rs12132102
	Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran
	Katiraie-Boroujerdy, Pari-Sima; Naeini, Matin Rahnamay; Asanjan, Ata Akbari; Chavoshian, Ali; Hsu, Kuo-lin; Sorooshian, Soroosh
通讯作者	Katiraie-Boroujerdy, PS
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:13
英文摘要	High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantile mapping 0.6 degrees x 0.6 degrees filter to adjust the values of PERSIANN-CCS. We use eight years (2009-2016) of historical data to calibrate our method, generating nonparametric cumulative distribution functions of ground-based measurements and satellite estimations for each climate region, as well as two years (2017-2018) of additional data to validate our approach. The results show that the bias correction approach improves PERSIANN-CCS data at aggregated to monthly, seasonal and annual scales for both the calibration and validation periods. The areal average of the annual bias and annual root mean square errors are reduced by 98% and 56% during the calibration and validation periods, respectively. Furthermore, the averages of the bias and root mean square error of the monthly time series decrease by 96% and 26% during the calibration and validation periods, respectively. There are some limitations in bias correction in the Southern region of the Caspian Sea because of shortcomings of the satellite-based products in recognizing orographic clouds.
英文关键词	satellite-based precipitation PERSIANN-CCS bias correction quantile mapping extreme events Iran
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000550823100001
WOS关键词	HIGH-RESOLUTION SATELLITE ; ANALYSIS TMPA ; PERSIANN ; PRODUCTS ; GAUGE ; VALIDATION ; REGIMES
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/325124
作者单位	[Katiraie-Boroujerdy, Pari-Sima] Islamic Azad Univ, Fac Marine Sci & Technol, Dept Meteorol, Tehran North Branch, Tehran 1651153311, Iran; [Naeini, Matin Rahnamay; Asanjan, Ata Akbari; Hsu, Kuo-lin; Sorooshian, Soroosh] Henry Samueli Sch Engn, Ctr Hydrometeorol & Remote Sensing CHRS, Irvine, CA 92617 USA; [Naeini, Matin Rahnamay; Asanjan, Ata Akbari; Chavoshian, Ali; Hsu, Kuo-lin; Sorooshian, Soroosh] Univ Calif Irvine, Dept Civil & Environm Engn, Irvine, CA 92697 USA; [Asanjan, Ata Akbari] Univ Space Res Assoc, Mountain View, CA 94043 USA; [Chavoshian, Ali] UNESCO, Int Drought Initiat IDI, Reg Ctr Urban Water Management RCUWM Tehran, Irvine, CA 92697 USA; [Hsu, Kuo-lin; Sorooshian, Soroosh] Univ Calif Irvine, Dept Earth Syst Sci, 3200 Croul Hall, Irvine, CA 92697 USA
推荐引用方式 GB/T 7714	Katiraie-Boroujerdy, Pari-Sima,Naeini, Matin Rahnamay,Asanjan, Ata Akbari,et al. Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran[J],2020,12(13).
APA	Katiraie-Boroujerdy, Pari-Sima,Naeini, Matin Rahnamay,Asanjan, Ata Akbari,Chavoshian, Ali,Hsu, Kuo-lin,&Sorooshian, Soroosh.(2020).Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran.REMOTE SENSING,12(13).
MLA	Katiraie-Boroujerdy, Pari-Sima,et al."Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran".REMOTE SENSING 12.13(2020).