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Title: Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR

Authors:
 [1];  [2];  [2];  [3];  [2];  [2];  [4];  [2];  [2]
  1. Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, Nong Lam University, Ho Chi Minh City, Vietnam
  2. Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
  3. Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, Department of Hydraulic Engineering, Civil Engineering College, Zhejiang University, Hangzhou, China
  4. State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, and Joint Center for Global Change Studies, Beijing, China
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1375072
Grant/Contract Number:
IA0000018
Resource Type:
Journal Article: Published Article
Journal Name:
Journal of Hydrometeorology
Additional Journal Information:
Journal Volume: 18; Journal Issue: 9; Related Information: CHORUS Timestamp: 2017-08-15 10:16:40; Journal ID: ISSN 1525-755X
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English

Citation Formats

Nguyen, Phu, Thorstensen, Andrea, Sorooshian, Soroosh, Zhu, Qian, Tran, Hoang, Ashouri, Hamed, Miao, Chiyuan, Hsu, KuoLin, and Gao, Xiaogang. Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR. United States: N. p., 2017. Web. doi:10.1175/JHM-D-16-0201.1.
Nguyen, Phu, Thorstensen, Andrea, Sorooshian, Soroosh, Zhu, Qian, Tran, Hoang, Ashouri, Hamed, Miao, Chiyuan, Hsu, KuoLin, & Gao, Xiaogang. Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR. United States. doi:10.1175/JHM-D-16-0201.1.
Nguyen, Phu, Thorstensen, Andrea, Sorooshian, Soroosh, Zhu, Qian, Tran, Hoang, Ashouri, Hamed, Miao, Chiyuan, Hsu, KuoLin, and Gao, Xiaogang. 2017. "Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR". United States. doi:10.1175/JHM-D-16-0201.1.
@article{osti_1375072,
title = {Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR},
author = {Nguyen, Phu and Thorstensen, Andrea and Sorooshian, Soroosh and Zhu, Qian and Tran, Hoang and Ashouri, Hamed and Miao, Chiyuan and Hsu, KuoLin and Gao, Xiaogang},
abstractNote = {},
doi = {10.1175/JHM-D-16-0201.1},
journal = {Journal of Hydrometeorology},
number = 9,
volume = 18,
place = {United States},
year = 2017,
month = 9
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on August 15, 2018
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  • Numerous studies have emphasized that climate simulations are subject to various biases and uncertainties. The objective of this study is to cross-validate 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations of precipitation against the Global Precipitation Climatology Project (GPCP) data, quantifying model pattern discrepancies and biases for both entire data distributions and their upper tails. The results of the Volumetric Hit Index (VHI) analysis of the total monthly precipitation amounts show that most CMIP5 simulations are in good agreement with GPCP patterns in many areas, but that their replication of observed precipitation over arid regions and certain sub-continentalmore » regions (e.g., northern Eurasia, eastern Russia, central Australia) is problematical. Overall, the VHI of the multi-model ensemble mean and median also are superior to that of the individual CMIP5 models. However, at high quantiles of reference data (e.g., the 75th and 90th percentiles), all climate models display low skill in simulating precipitation, except over North America, the Amazon, and central Africa. Analyses of total bias (B) in CMIP5 simulations reveal that most models overestimate precipitation over regions of complex topography (e.g. western North and South America and southern Africa and Asia), while underestimating it over arid regions. Also, while most climate model simulations show low biases over Europe, inter-model variations in bias over Australia and Amazonia are considerable. The Quantile Bias (QB) analyses indicate that CMIP5 simulations are even more biased at high quantiles of precipitation. Lastly, we found that a simple mean-field bias removal improves the overall B and VHI values, but does not make a significant improvement in these model performance metrics at high quantiles of precipitation.« less
  • By employing wavelet and selected features (WSF), median merging (MM), and selected curve-fitting (SCF) techniques, the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) has been improved. The PERSIANN-CCS methodology includes the following four main steps: 1) segmentation of satellite cloud images into cloud patches, 2) feature extraction, 3) classification of cloud patches, and 4) derivation of the temperature rain-rate (T R) relationship for every cluster. The enhancements help improve step 2 by employing WSF, and step 4 by employing MM and SCF. For the study area herein, the results show that themore » enhanced methodology improves the equitable threat score (ETS) of the daily and hourly rainfall estimates mostly in the winter and fall. The ETS percentage improvement is about 20% for the daily (10% for hourly) estimates in the winter, 10% for the daily (8% for hourly) estimates in the fall, and at most 5% for the daily estimates in the summer at some rainfall thresholds. In the winter and fall, the area bias is improved almost at all rainfall thresholds for daily and hourly estimates. However, no significant improvement is obtained in the spring, and the area bias in the summer is also greater than that of the implemented PERSIANN-CCS algorithm.« less