一种基于地转风模型的平流层风场插值方法

胡康; 周江华; 张晓军

doi:10.13700/j.bh.1001-5965.2020.0625

一种基于地转风模型的平流层风场插值方法

doi: 10.13700/j.bh.1001-5965.2020.0625

胡康^{1, 2},
周江华^{1, 2, ,},
张晓军¹

1.
中国科学院空天信息创新研究院, 北京 100094
2.
中国科学院大学光电学院, 北京 100049

基金项目:

国家自然科学基金 61733017

中国科学院战略性先导科技专项 XDA170400

详细信息

通讯作者:
周江华, E-mail: zhoufma@aoe.ac.cn

中图分类号: V321.2
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- 文章访问数: 526
- HTML全文浏览量: 61
- PDF下载量: 90
- 被引次数: 0
出版历程
- 收稿日期: 2020-11-09
- 录用日期: 2020-12-25
- 网络出版日期: 2022-04-20

A stratospheric wind field interpolation method based on geostrophic wind model

HU Kang^{1, 2},
ZHOU Jianghua^{1, 2
, ,},
ZHANG Xiaojun¹

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China

Funds:

National Natural Science Foundation of China 61733017

Strategic Priority Research Program of Chinese Academy of Sciences XDA170400

More Information

Corresponding author: ZHOU Jianghua, E-mail: zhoufma@aoe.ac.cn

摘要

摘要:
平流层风场水平分量的数据精度和密度对高空气球实验的轨迹预测精度和飞行控制精度有着直接的影响。面向平流层风场，提出一种基于地转风模型的插值方法。该方法通过改进科里奥利频率公式，并用二维卷积计算地转风模型，提升了地转风模型的计算效率和低纬度地区风场精度，再用迭代二元线性回归和改进的自适应观测场偏差权重矩阵减小了观测误差对插值结果的影响。实验结果表明：所提插值方法能有效提升平流层风场插值计算速度，并对平流层风场水平分量的插值精度有显著提升。
- 风场插值 /
- 平流层风场 /
- 地转风 /
- 二元线性回归 /
- 二维卷积
Abstract:
The data accuracy and density of the stratospheric horizontal wind field have direct influence on the trajectory prediction accuracy and flight control accuracy of the high-altitude balloon experiment. Focusing on stratospheric wind field, this paper puts forward an interpolation method based on geostrophic wind model. The method improves the wind field accuracy in low-latitude area and calculation speed of the geostrophic wind model by improving the formula of Coriolis frequency and using two-dimensional convolution. Meanwhile, the method reduces the influence of observation error on interpolation result by using iterative binary linear regression and an improved adaptive bias weight matrix for the observed field. The experimental results show that the proposed interpolation method can effectively improve the calculation speed of the interpolation of stratospheric wind field and the interpolation accuracy of stratospheric horizontal wind field.
- wind field interpolation /
- stratospheric wind field /
- geostrophic wind /
- binary linear regression /
- two-dimensional convolution

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图 1 风速背景场和实际风场数据

Figure 1. Background field and actual field of wind speed data

下载: 全尺寸图片幻灯片

表 1 相关系数矩阵中位数

Table 1. Median of correlation coefficient matrix

风速分量	7月1日	7月2日	7月3日	7月4日	7月5日	7月6日	7月7日
西风	0.715 669	0.879 563	0.666 926	0.705 835	0.826 480	0.630 954	0.694 991
南风	0.627 580	0.708 596	0.517 900	0.453 280	0.649 182	0.630 954	0.478 832
合风	0.715 380	0.783 561	0.617 456	0.651 150	0.718 303	0.650 856	0.665 432

下载: 导出CSV

表 2 不同插值方法的R²检验结果

Table 2. R² square test results of different interpolation methods

方法	R²
方法	西风	南风	合风
BGW(30)	0.999 937	0.997 788	0.999 770
BGW(60)	0.999 804	0.995 244	0.999 441
BGW(90)	0.999 726	0.993 489	0.999 138
NN	0.999 782	0.996 398	0.999 522
IDW	0.999 202	0.998 867	0.998 437

下载: 导出CSV

表 3 不同插值方法的平均绝对误差

Table 3. Mean absolute error of different interpolation methods

方法	MAE/(m·s^-1)
方法	西风	南风	合风
BGW	0.082 857	0.115 629	0.125 175
NN	0.195 772	0.238 833	0.211 241
IDW	0.398 948	0.134 958	0.429 27

下载: 导出CSV

参考文献(15)

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