摘要:
|
介绍了一种智能网格订正释用技术,该技术对EC细网格风力资料进行线性加密,对比其结果和站点平均风实况,采用逐时加权滚动更新的方法,利用站点平均风和极大风关系,进行订正释用。检验浙江省气象局指定的12个考核关键点0~6 h、0~12 h、0~24 h和0~48 h的平均极大风绝对误差分别为1.14 m/s、1.23 m/s、1.48 m/s和1.72 m/s。开发的舟山风力智能网格精细化订正释用平台对2020年台风“美莎克”的风力预报较为精准,4个县区局预报过程极大风力绝对误差平均仅为1.25 m/s,对9月11日小尺度低压风力预报的修正效果显著,嵊泗站点极大风力由9~10级修正到7级以下。 |
An intelligent grid correction and interpretation technology is developed and applied in this paper based on the ECMWF gridded wind data and station observations. The ECMWF data is refined linearly and is compared with station observations. The correction and interpretation is conducted based on the hourly weighted rolling update method and the relation between the average wind and the maximum wind of the observations. The absolute error of 0~6 h, 0~12 h, 0~24 h, 0~48 h average maximum wind forecasts for 12 key assessment points designated by the Zhejiang Meteorological Bureau is 1.14 m/s, 1.23 m/s, 1.48 m/s and 1.72 m/s, respectively. Meanwhile, a refined intelligent grid correction and interpretation platform is developed for the wind fields near Zhoushan area, which produced accurate forecasts for the wind force of the typhoon"Maysak"in year 2020. The absolute error of the extreme wind force forecasted by four county bureaus is only 1.25 m/s on average. The correction effect is remarkable in forecasting the small scale low-pressure wind force on 11st September, and the maximum wind force level at Shengsi station was revised from 9~10 to below 7. |
参考文献:
|
[1] 李晓丽, 唐跃, 范其平, 等. PPM方法在马迹山港船舶靠离泊临界值预报中的应用[J]. 浙江气象, 2010, 31(4):23-27, 34. [2] 黄辉, 陈淑琴. MM5数值预报产品在舟山海域风力分区预报中的释用[J]. 海洋预报, 2006, 23(2):67-71. [3] Glahn H R, Ruth D P. The new digital forecast database of the national weather service[J]. Bulletin of the American Meteorological Society, 2003, 84(2):195-202. [4] Kann A, Wang Y, Atencia A, et al. Seamless probabilistic analysis and forecasting:from minutes to days ahead[C]//Proceedings of the 18th Conference on Mountain Meteorology. Vienna:EGU, 2018. [5] Engel C, Ebert E E. Gridded operational consensus forecasts of 2m temperature over Australia[J]. Weather and Forecasting, 2012, 27(2):301-322. [6] 潘旸, 谷军霞, 徐宾, 等. 多源降水数据融合研究及应用进展[J]. 气象科技进展, 2018, 8(1):143-152. [7] 师春香, 姜立鹏, 朱智, 等. 基于CLDAS2.0驱动数据的中国区域土壤湿度模拟与评估[J]. 江苏农业科学, 2018, 46(4):231-236. [8] 韩帅, 师春香, 姜志伟, 等. CMA高分辨率陆面数据同化系统(HRCLDAS-V1.0)研发及进展[J]. 气象科技进展, 2018, 8(1):102-108, 116. [9] 刘艳, 薛纪善, 张林, 等. GRAPES全球三维变分同化系统的检验与诊断[J]. 应用气象学报, 2016, 27(1):1-15. [10] 沈学顺, 苏勇, 胡江林, 等. GRAPES_GFS全球中期预报系统的研发和业务化[J]. 应用气象学报, 2017, 28(1):1-10. [11] 张进, 麻素红, 陈德辉, 等. GRAPES_TYM改进及其在2013年西北太平洋和南海台风预报的表现[J]. 热带气象学报, 2017, 33(1):64-73. [12] 黄丽萍, 陈德辉, 邓莲堂, 等. GRAPES_Meso V4.0主要技术改进和预报效果检验[J]. 应用气象学报, 2017, 28(1):25-37. [13] 朱立娟, 龚建东, 黄丽萍, 等. GRAPES三维云初始场形成及在短临预报中的应用[J]. 应用气象学报, 2017, 28(1):38-51. [14] 万子为, 王建捷, 黄丽萍, 等. GRAPES-MESO模式浅对流参数化的改进与试验[J]. 气象学报, 2015, 73(6):1066-1079. [15] 金荣花, 代刊, 赵瑞霞, 等. 我国无缝隙精细化网格天气预报技术进展与挑战[J]. 气象, 2019, 45(4):445-457. [16] 陈锦冠, 林少冰. 10分钟平均最大风速与极大风速评估方程的建立[J]. 气象, 2016, 27(10):38-41. [17] 汪宏宇, 龚强, 杨洪斌. 基于测风塔数据的最大风速与极大风速关系研究[J]. 气象与环境科学, 2019, 42(3):110-117. [18] 王慧, 马学款, 赵伟. 人工神经网络在成山头风预报中的应用[J]. 海洋预报, 2013, 30(1):20-24. [19] 张娟, 周水华, 黄宝霞, 等. 人工神经网络在台风风暴潮模拟中的解释应用[J]. 海洋预报,2016, 33(2):60-65. [20] 潘微, 邢建勇, 万莉颖. 一种基于BP神经网络方法的HY-2A散射计反演风场偏差订正方案[J]. 海洋预报, 2018, 35(2):8-18. |
服务与反馈:
|
【文章下载】【发表评论】【查看评论】【加入收藏】
|
|
|