Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Pao-Liang Chang"'
Autor:
Cheng-Chin Liu, Kathryn Hsu, Melinda S. Peng, Der-Song Chen, Pao-Liang Chang, Ling-Feng Hsiao, Chin-Tzu Fong, Jing-Shan Hong, Chia-Ping Cheng, Kuo-Chen Lu, Chia-Rong Chen, Hung-Chi Kuo
Publikováno v:
npj Climate and Atmospheric Science, Vol 7, Iss 1, Pp 1-12 (2024)
Abstract Recent development of artificial intelligence (AI) technology has resulted in the fruition of machine learning-based weather prediction (MLWP) systems. Five prominent global MLWP model, Pangu-Weather, FourCastNet v2 (FCN2), GraphCast, FuXi,
Externí odkaz:
https://doaj.org/article/d9679c76540347f6851eb45ec81c9459
Publikováno v:
Atmosphere, Vol 13, Iss 2, p 331 (2022)
On 2 April 2007, a strong bow echo struck southern Taiwan, with a peak surface wind speed of 26 m s−1. On observation, the rear inflow jet (RIJ) was located at the northern flank and only one anticyclonic vortex dominated behind the bow structure.
Externí odkaz:
https://doaj.org/article/573da4567d37430da7eddb4587dfada3
Publikováno v:
Atmosphere, Vol 12, Iss 12, p 1688 (2021)
Surface wind speed forecast from an operational WRF Ensemble Prediction System (WEPS) was verified, and the system-bias representations of the WEPS were investigated. Results indicated that error characteristics of the ensemble 10-m wind speed foreca
Externí odkaz:
https://doaj.org/article/43085e4b3e144e1ea87ae7045283b46d
Publikováno v:
Remote Sensing, Vol 13, Iss 1, p 154 (2021)
The key factors, namely, the radar data quality, raindrop size distribution (RSD) variability, and the data integration method, which significantly affect radar-based quantitative precipitation estimation (QPE) are investigated using the RCWF (S-band
Externí odkaz:
https://doaj.org/article/e8d1abbfa3fc49f690665f1ca9e66b3f
Publikováno v:
Advances in Meteorology, Vol 2016 (2016)
Complex terrain poses significant challenges to the radar based quantitative precipitation estimation (QPE) because of blockages to the lower tilts of radar observations. The blockages often force the use of higher tilts data to estimate precipitatio
Externí odkaz:
https://doaj.org/article/cf847d915534460c96a5fa520a251d33
Autor:
I-Han Chen, Yi-Jui Su, Hsiao-Wei Lai, Jing-Shan Hong, Chih-Hsin Li, Pao-Liang Chang, Ying-Jhang Wu
Publikováno v:
Weather and Forecasting. 38:517-538
A 16-member convective-scale ensemble prediction system (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective s
Autor:
Jui Le Loh, Wei-Yu Chang, Hsiu-Wei Hsu, Pin-Fang Lin, Pao-Liang Chang, Yung-Lin Teng, Yu-Chieng Liou
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-17
Autor:
Pao-Liang Chang1, Jian Zhang2 jian.zhang@noaa.gov, Yu-Shuang Tang1, Lin Tang3, Pin-Fang Lin1, Langston, Carrie3, Kaney, Brian3, Chia-Rong Chen1, Howard, Kenneth2
Publikováno v:
Bulletin of the American Meteorological Society. Mar2021, Vol. 102 Issue 3, pE555-E577. 23p.
Publikováno v:
Weather & Forecasting. Dec2020, Vol. 35 Issue 6, p2235-2254. 20p.
Autor:
Kenneth W. Howard, Pin-Fang Lin, Yu-Shuang Tang, Chia-Rong Chen, Jian Zhang, Lin Tang, Carrie Langston, Brian Kaney, Pao-Liang Chang
Publikováno v:
Bulletin of the American Meteorological Society. 102:E555-E577
Over the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring and prediction of river flooding, flash floods, de