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High-resolution hydrometeorological forecast in Southwest China based on a multi-layer nested WRF model
被引:1
作者:
Chen, Z. N.
[1
]
Li, J.
[1
]
Zhu, Y.
[1
]
Hu, L. C.
[1
]
Wen, X.
[2
,3
]
Lei, X. H.
[2
,3
]
机构:
[1] CHN ENERGY DaDu River Hydropower Dev Co Ltd, 7 Tianyun Rd, Chengdu, Peoples R China
[2] Hohai Univ, 1 Xikang Rd, Nanjing 210098, Peoples R China
[3] China Inst Water Resources & Hydropower Res, 1 Fuxing Rd, Beijing 100038, Peoples R China
来源:
6TH INTERNATIONAL CONFERENCE ON WATER RESOURCE AND ENVIRONMENT
|
2020年
/
612卷
基金:
国家重点研发计划;
中国国家自然科学基金;
关键词:
WINTER PRECIPITATION;
SURFACE-TEMPERATURE;
CLIMATE MODELS;
SIMULATION;
EXTREMES;
RAINFALL;
SCHEMES;
SYSTEM;
IMPACT;
INDIA;
D O I:
10.1088/1755-1315/612/1/012062
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
In this study, a high-resolution (5km:1km) regional hydrometeorological simulation (Weather Research and Forecasting, WRF) in Southwest China was evaluated by comparisons with the multiple General Circulation Model (multi-GCM) ensemble mean from Coupled Model Intercomparison Project phase 5 (CMIP5) and in-situ observation data, to prove its advantage to precisely delineate the regional complex topographical and climatic conditions. The temperature and precipitation were selected to evaluate the model performance skills. Simulations of the spatiotemporal rainfall and near-surface air temperature distribution across the entire research area and at four specific sites (Ganzi, Daofu, Jiulong Huili) were analyzed based on observational data from 2007-2010. Overall, both the WRF and multi-GCM demonstrated satisfactory capabilities in representing seasonal variation, but systematic biases remained. The regional average near-surface air temperature of WRF outputs had cold biases of -4.91, -1.96, -3.92 and -8.17 degrees C in spring, summer, autumn and winter, respectively, and wet biases of 40.5-428.5 mm in cumulative precipitation over the four seasons. Overall, the multi-GCM means had consistent bias, but were closer to regional averages derived from in-situ data. At the four validation stations, the WRF outputs consistently performed better for temperature and precipitation according to the correlation coefficient, root-mean-square error, and index of agreement. The simulation capabilities identified herein can serve as a foundation for addressing WRF model biases and improving projection accuracy in the future.
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