Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
Shuzhen Hu
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, 210044, China
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Total article views: 4,132 (including HTML, PDF, and XML)
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Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on Tibetan Plateau snow cover (TPSC) is the first step to applying the S2S model to hydrological forecasts over the Tibetan Plateau. This study conducted a multimodel comparison of the TPSC prediction skill to learn about their performance in capturing TPSC variability. S2S models can skillfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Systematic biases of TPSC were found.
Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on Tibetan...
