Probabilistic 2-meter surface temperature forecasting over Xinjiang based on Bayesian model averaging
Probabilistic 2-meter surface temperature forecasting over Xinjiang based on Bayesian model averaging
复制标题
基于贝叶斯模型平均的新疆2米地表温度概率预报
DOI：
10.3389/feart.2022.960156
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发表时间：
2022-08
影响因子：
2.9
通讯作者：
Ali Mamtimin
中科院分区：
文献类型：
--
作者：
Ailiyaer Aihaiti;Yu Wang;Ali Mamtimin
利用中国气象局乌鲁木齐沙漠气象研究所开发的沙漠绿洲戈壁区域分析预报系统（DOGRAFS）和快速更新多尺度分析预报系统（RMAPS）的预报结果，基于贝叶斯模型平均（BMA），分析了BMA模型在新疆2 m气温预报中的适用性和特点。中国气象局-全球预报系统（CMA-GFS）由中国气象局开发，欧洲中期天气预报中心（ECMWF）由欧洲中心开发。结果表明：（1）ECMWF对2 m温度预报的权重在不同训练时间长度下均保持在0.6-0.7左右，其他模式产品的权重均在0.15以下。(2)各模型在4个代表站的预报差异较大，最大预报误差达到6.9 ℃不过，BMA预报的最大误差只有2 ℃左右此外，南疆预报不确定性大于北方。(3)与多模式集成相比，BMA方法的整体预报性能在空间分布上更加一致。此外，BMA预报与观测值之间的标准差和相关系数均大于0.98，RMSE显著降低。用BMA方法对新疆2 m气温预报的精度进行订正是可行的。
Based on Bayesian model averaging (BMA), the suitability and characteristics of the BMA model for forecasting 2-m temperature in Xinjiang of China were analyzed by using the forecast results of the Desert Oasis Gobi Regional Analysis Forecast System (DOGRAFS) and Rapid-refresh Multiscale Analysis and Prediction System (RMAPS) developed by the Urumqi Institute of Desert Meteorology of the China Meteorological Administration, China Meteorological Administration–Global Forecast System (CMA-GFS) developed by the China Meteorological Administration, and the European Center for Medium-Range Weather Forecasts (ECMWF) developed by the European Center. The results showed that (1) the weight of ECMWF to the 2-m temperature forecast is maintained at about 0.6–0.7 under different lengths of training periods, and the weight of other model products is below 0.15. (2) The forecasts of each model at the four representative stations are quite different, and the maximum forecast error reaches 6.9°C. However, the maximum error of the BMA forecast is only about 2°C. In addition, the forecast uncertainty in southern Xinjiang is greater than that in northern Xinjiang. (3) Compared with multi-model ensembles, the overall prediction performance of the BMA method is more consistent in spatial distribution. Additionally, the standard deviation and correlation coefficient between the BMA forecast and observation were greater than 0.98, and the RMSE decreased significantly. It is feasible to use the BMA method to correct the accuracy of the 2-m temperature forecast in Xinjiang.
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影响因子：
4.9
作者：
M. Fang;X. Li
通讯作者：
M. Fang;X. Li
光梯度增强机：一种高效的软计算模型，用于利用本地和外部气象数据估算每日参考蒸散量
影响因子：
6.7
作者：
Fan, Junliang;Ma, Xin;Zeng, Wenzhi
通讯作者：
Zeng, Wenzhi
DOI：
--
发表时间：
2012
期刊：
Journal of Chengdu University of Information Technology
影响因子：
--
作者：
Long Ke-ji
通讯作者：
Long Ke-ji
DOI：
10.1002/joc.6510
发表时间：
2020-02
期刊：
International Journal of Climatology
影响因子：
--
作者：
Cuiping Zhao;Jiaguo Gong;Hao Wang;Su-hong Wei;Qianggong Song;Yuyan Zhou
通讯作者：
Cuiping Zhao;Jiaguo Gong;Hao Wang;Su-hong Wei;Qianggong Song;Yuyan Zhou
影响因子：
2.7
作者：
Qadeer, Khaula;Rehman, Wajih Ur;Jeon, Moongu
通讯作者：
Jeon, Moongu