[1]郜庆林,简 单.基于卷积神经网络算法的金华地区高山气象观测站逐时气温预报订正[J].山地学报,2024,(2):278-286.[doi:10.16089/j.cnki.1008-2786.000822]
　GAO Qinglin,JIAN Dan.Hourly Temperature Forecast Revision Based on Convolutional Neural Network Algorithm on Observations at Alpine Meteorological Stations in Jinhua Area of China[J].Mountain Research,2024,(2):278-286.[doi:10.16089/j.cnki.1008-2786.000822]
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基于卷积神经网络算法的金华地区高山气象观测站逐时气温预报订正
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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2024年第2期
页码:
278-286
栏目:
山地技术
出版日期:
2024-06-15

文章信息/Info

Title:
Hourly Temperature Forecast Revision Based on Convolutional Neural Network Algorithm on Observations at Alpine Meteorological Stations in Jinhua Area of China
文章编号:
1008-2786-(2024)2-278-9
作者:
郜庆林1; 简 单2
(1.金华市气象局,浙江 金华 321000; 2.兰溪市气象局,浙江 兰溪 321100)
Author(s):
GAO Qinglin1; JIAN Dan2
(1. Jinhua Meteorological Bureau, Jinhua 321000, Zhejiang, China; 2. Lanxi Meteorological Bureau, Lanxi 321100, Zhejiang, China)
关键词:
卷积神经网络;  高山气象观测站;  精细气温;  订正预报;  智能网格;  金华
Keywords:
convolutional neural network;  alpine meteorological observatory;  fine temperature;  forecast revision;  intelligent grid;  Jinhua
分类号:
P423
DOI:
10.16089/j.cnki.1008-2786.000822
文献标志码:
A
摘要:
受地貌和气候条件影响,智能网格气温预报产品的预报结果,在复杂地形山区易出现误差。通过人工订正关键点降低误差,存在较大主观性,预报精度有限,难以满足精细化气象服务需求。神经网络算法能够大幅提升预报精度,然而这种方法在气温逐时波动小的平原地区应用较多,在气温波动大的山区鲜有应用。本文以浙江金华山区为研究区域,基于浙江省智能网格温度预报产品以及同时段高山气象观测站逐时气温观测数据,采用卷积神经网络算法,实现关键点气温逐时预报订正。研究结果表明:(1)小时尺度上,订正后各站点的气温均方根误差均显著减小,由订正前3 ℃～7 ℃减小至订正后2 ℃～3 ℃,订正后的预测结果更加准确,订正效果符合预期。(2)月尺度上,相较智能网格原始气温预报数据,该模型预报结果准确率也明显提升,订正后月平均气温准确率提高了33.18%～46.86%,其中准确率6月最高。(3)相较人工订正的方式,该模型对山地气温预报的订正能力更稳定,模型的两项关键指标(平均绝对误差和2 ℃预报准确率)均接近或超过同时段浙江省天气预报质量检验平台气温业务指标。该研究结果满足金华地区高山气象观测站对于气温预报产品的业务可用性需求,可为精细化山区气象服务提供数据支撑。
Abstract:
Smart grid temperature forecast products are subjected to geomorphology and climatic conditions, and the temperature forecast results are prone to errors in mountainous areas with complex terrain. Reducing the errors by manually revising observations at some key points of an alpine meteorological observatory network had a large subjectivity and limited forecast accuracy, making it difficult to meet the demand for refined meteorological services.
Neural network algorithms offer an objective computational approach to greatly improving forecast accuracy by learning from historical temperature data to regulate current data; however, this method was more commonly applied in plains with small hourly temperature fluctuations, and seldom used in mountainous areas where temperature fluctuations are large.
In this paper, the Jinhua mountainous area of Zhejiang province, China was taken as research target. The intelligent grid temperature forecast product of Zhejiang province were calibrated with simultaneous hourly temperature observations collected at alpine meteorological observation stations by convolutional neural network(CNN)algorithm for proper revisions on hourly temperature prediction at key points/stations.
(1)On an hourly scale, the root mean square error of temperatures at each station significantly decreased after the CNN revision, from 3 ℃-7 ℃ before the revision to 2 ℃-3 ℃ after the revision, satisfying expectation of accuracy.
(2)On a monthly scale, temperature prediction accuracy improved notably as compared with product by the intelligent grid. After the revision, the accuracy of the monthly average temperature increased by 33.18% to 46.86%, with the highest accuracy in June.
(3)The CNN model was more stable in revising mountain temperature forecasts than the manual revision method. It justified that two key indicators(average absolute error and 2 ℃ forecast accuracy)were close to or exceeded the concurrent acceptance check standards of Zhejiang Provincial Weather Forecast Quality Inspection Platform.
This study improves the operational availability of temperature forecast products in alpine meteorological observatory in Jinhua area, which can provide data support for refined mountain weather services in mountainous areas.

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备注/Memo

备注/Memo:
收稿日期(Received date): 2023-11- 09; 改回日期(Accepted date):2024- 04-10
基金项目(Foundation item): 金华市科学技术局计划项目(2022-4-082)。[Jinhua Science and Technology Bureau Project(2022-4-082)]
作者简介(Biography): 郜庆林(1977-),男,浙江金华人,本科,高级工程师,主要研究方向:气象信息技术、大气探测、气象算法。[GAO Qinglin(1977-), male, born in Jinhua, Zhejiang province, B.S., senior engineer, research on meteorological information technology, atmospheric detection and meteorological algorithm]E-mail: 26250447@qq.com
更新日期/Last Update: 2024-03-30