| 多模式温度集成预报 |
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| 引用本文： | 赵声蓉.多模式温度集成预报[J].应用气象学报,2006,17(1):52-58. |
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| 作者姓名： | 赵声蓉 |
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| 作者单位： | 国家气象中心, 北京 100081 |
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| 基金项目： | 国家科技攻关项目;中国气象局科技攻关项目 |
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| 摘 要： | 基于中国国家气象中心T213模式、德国气象局业务模式和日本气象厅业务模式2 m高温度预报, 利用神经网络方法中的BP网络建立了我国600多个站的温度集成预报系统, 该预报系统的预报时效为72 h, 间隔为3 h。通过对2004年1—5月的预报结果检验, 表明:集成的温度预报结果明显优于3个模式单独的预报结果, 72 h内预报的平均绝对误差在3 ℃以内, 并且不存在明显的系统误差, 预报达到了一定的精度, 可以为预报员提供定时、定点精细的客观温度预报参考。分区的检验结果表明:不同区域预报误差存在差别, 新疆和西藏误差比较大, 而长江流域和华南地区误差很小, 并且不同区域系统误差的情况也不相同。从总体情况看, 预报误差还存在日变化, 一般来讲, 夜间的预报误差小于白天。
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| 关 键 词： | 神经网络 BP网络 温度预报 集成预报 |
| 收稿时间： | 2004-11-26 |
| 修稿时间： | 2005-08-16 |
Multi-model Consensus Forecast for Temperature |
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| Zhao Shengrong.Multi-model Consensus Forecast for Temperature[J].Quarterly Journal of Applied Meteorology,2006,17(1):52-58. |
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| Authors: | Zhao Shengrong |
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| Affiliation: | National Meteorological Center, Beijing 100081 |
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| Abstract: | Based on temperature forecast of operational middle-range model of China, operational model of German meteorological administration, operational model of Japan meteorological agency and temperature observations of China, a temperature consensus forecast system is developed through method of artificial neural network. Product of the system is station temperature forecast of China with 3-hour interval within 72 hours.Forecast modes of summer half year and winter half year are established separately. In order to include most recent impact of data, the process of developing forecast mode runs once a week under the condition of absorbing new data as much as possible.The system has been running stably from 1st of January in 2004. Testing of forecast result from January to May in 2004 indicates that consensus forecast is better than single model forecast. Absolute forecast error of consensus is less than 3.0 ℃ within 72 hours, and it has no systematical error. That means consensus can provide objective forecast support for forecast people. And it also indicates that artificial neural network is a kind of effective method to temperature consensus forecast.Consensus forecast error is different according to different area. It is bigger over Xinjiang and Xizang and smaller over south China and the reaches of the Ynagtze. The cause of this phenomenon is possibly that temperature variability is bigger over Xizang than that over the reaches of the Yangtze. Forecast error of consensus has obvious daily variation. It is always bigger during daytime than in night. On average, the consensus forecast also has forecast ability for temperature changing process with much more argument through contrasting between observation and corresponding forecast result of June in 2004 at partial stations. But the ability of forecast temperature with big argument is poor over the Tibetan area.In order to investigate single forecast impact to consensus forecast, different consensus schemes are developed with forecast results of different schemes checked. Contrasting between different schemes shows that every single forecast with good impacts is important for consensus forecasts results. |
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| Keywords: | neural network BP network temperature forecast consensus forecast |
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