基于雷达和遥感卫星的新疆区域降水反演

doi:10.13866/j.azr.2025.06.01

摘要/Abstract

摘要：

为获取偏远地区较为准确的降水分布情况，本文结合雷达高分辨率和卫星大范围探测优势，通过融合雷达和卫星反演的降水，获取高精度的定量降水估计分析产品（QPE）。以新疆地区2023年8月12日、13日两天强对流过程为例，采用云分类、Z-R关系的方法，利用雷达反射率进行雷达降水反演；将Himawari 9卫星亮温和IMERG降水输入BP神经网络模型来建立平均亮温和平均雨强关系，随后将Himawari 9卫星瞬时亮温输入BP神经网络模型来反演时刻降水；同时提出方案一利用统一修正值来融合雷达和卫星反演降水，在此基础上进一步考虑雨强等级融合的方案二进行对比，最后得到新疆地区高精度降水反演产品。结果表明：（1）通过亮温划分云类型可以对雷达范围内降水进行细致估算，利用亮温差异在一定程度上则可以降低非降水云的影响。（2）卫星反演降水的均方根误差为1.793 mm·h^-1，决定系数为0.572，模型精度较为合理，二分类评分结果说明模型可以较为准确地反演超过70%的降水区域。（3）两种方案融合降水对短时小雨精度提升有限，短时中雨上方案二优于方案一，短时大雨上方案二比方案一略有下降。说明高空卫星观测和近地面降水不同步有一定影响。（4）在95%置信区间下，两种方案较卫星反演的均方根误差和决定系数差异显著性检验P值均小于0.005，方案二较方案一的P值大于0.05。两种融合降水的方案均显著改善了卫星降水精度，但考虑雨强等级融合的方案二对统一修正值融合的方案一精度提升较小。

关键词: 多雷达, Himawari 9卫星, 亮温, 降水反演, BP神经网络, 降水数据融合, 新疆

Abstract:

To more accurately obtain precipitation distributions in remote areas, this study combined the high-resolution advantages of radar and the wide-coverage detection of satellites. By integrating radar and satellite-derived precipitation, we generated high-precision quantitative precipitation estimation products. Using the strong convective events in Xinjiang on August 12 and 13, 2023, as an example, we used radar reflectivity for precipitation inversion based on cloud classification and Z-R relationships. We fed the Himawari 9 satellite brightness temperature and IMERG precipitation into a BP neural network model to establish the relationship between the average brightness temperature and the average rainfall intensity. Subsequently, we used the instantaneous brightness temperature of the Himawari 9 satellite to invert the momentary precipitation through the BP neural network model. We also proposed two precipitation data fusion schemes: Scheme I uses a uniform correction value to integrate radar and satellite precipitation, whereas Scheme Ⅱ further considers the precipitation intensity levels for comparison. Finally, we obtained high-precision precipitation inversion products for Xinjiang. The results showed that: (1) Cloud classification based on brightness temperature can finely estimate precipitation within the radar range, and brightness temperature differences can reduce the impact of non-precipitating clouds to some extent. (2) The root mean square error (RMSE) of the satellite precipitation inversion was 1.793 mm·h^-1, with a coefficient of determination (R²) of 0.572, indicating reasonable model accuracy. The binary classification score indicated that the model can accurately invert precipitation in over 70% of the areas. (3) The fusion of precipitation by the two schemes slightly improved the accuracy of short-duration light rain distributions. Scheme Ⅱ outperformed Scheme I for short-duration moderate rain but showed a slight decline for short-duration heavy rain compared with Scheme I, indicating that the asynchrony between satellite observation and near-surface precipitation had some impact. (4) Under a 95% confidence interval, the P-values for the RMSE and R² differences between the two schemes and satellite inversion were all less than 0.005, while the P-value for Scheme Ⅱ compared with Scheme I was greater than 0.05. Both fusion schemes significantly improved the accuracy of the satellite precipitation; however, the improvement of Scheme Ⅱ, which considers the precipitation intensity levels, over Scheme I was minimal.

Key words: multi-radar, Himawari 9 satellite, brightness temperature, precipitation retrieval, BP neural network, precipitation data fusion, Xinjiang

郭建茂, 吴登国, 韩金龙, 张茹水, 王勇. 基于雷达和遥感卫星的新疆区域降水反演[J]. 干旱区研究, 2025, 42(6): 957-969.

GUO Jianmao, WU Dengguo, HAN Jinlong, ZHANG Rushui, WANG Yong. Precipitation retrieval for Xinjiang region based on radar and remote sensing satellites[J]. Arid Zone Research, 2025, 42(6): 957-969.

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云类型	雨强等级 /（mm·h^-1）	亮温阈值（T）/K	回波强度（Z）/dBZ	a	b
无雨	0	T≥266	Z≤5	0	0
层云	≤0.1	240≤T<266	Z≤5	130	2.00
厚层云	≤2.6	T≥273 266≤T<273 233≤T<240	5<Z≤35 5<Z≤35 Z≤5	200	1.60
混合云	≤8.1	197≤T<233	Z≤5	262	1.33
弱对流	≤16.0	T≥266 233≤T<266 197≤T<233 T<197 233≤T<266	35<Z≤50 5<Z≤35 5<Z≤35 Z≤5 35<Z≤50	300	1.40
对流云	>16.0	T<197	5<Z≤35	357	1.34
深对流	>16.0	197≤T<233 T<197 -	35<Z≤50 35<Z≤50 Z>50	250	1.20

组号	FAR	POD	HSS	RMSE/（mm·h^-1）	R²	组号	FAR	POD	HSS	RMSE/（mm·h^-1）	R²
1	0.422	0.757	0.471	1.651	0.612	9	0.491	0.763	0.383	2.059	0.471
2	0.254	0.854	0564	1.805	0.554	10	0.315	0.796	0.534	1.551	0.504
3	0.348	0.736	0.446	1.951	0.586	11	0.262	0.808	0.467	2.006	0.635
4	0.293	0.618	0.373	1.454	0.492	12	0.227	0.721	0.598	1.903	0.617
5	0.386	0.781	0.491	1.758	0.528	13	0.435	0.676	0.399	1.407	0.563
6	0.429	0.703	0.487	2.043	0.594	14	0.371	0.753	0.572	1.651	0.638
7	0.273	0.825	0.548	2.054	0.637	15	0.449	0.637	0.434	1.734	0.585
8	0.391	0.690	0.381	1.857	0.452	16	0.322	0.719	0.457	1.807	0.514

雨强等级/（mm·h^-1）	卫星降水		方案一融合降水		方案二融合降水
雨强等级/（mm·h^-1）	RMSE/（mm·h^-1）	R²	RMSE/（mm·h^-1）	R²	RMSE/（mm·h^-1）	R²
<0.1（无雨）	1.618	0.547	1.624	0.539	1.632	0.533
>0.1（有雨）	1.741	0.572	1.505	0.688	1.471	0.694
0.1~2.0（短时小雨）	1.785	0.514	1.732	0.541	1.713	0.552
2.0~4.0（短时中雨）	1.594	0.601	1.369	0.665	1.288	0.698
4.0~8.0（短时大雨）	1.556	0.617	1.318	0.697	1.322	0.691

精度指标	反演方法	均值	标准差	t	P
RMSE	方案一	1.556	0.284	-2.946	P<0.005
	卫星反演	1.687	0.331	-2.946	P<0.005
	方案二	1.541	0.276	-3.322	P<0.002
	方案一和方案二	-	-	-0.371	P>0.05
R²	方案一	0.625	0.068	6.086	P<0.001
	卫星反演	0.564	0.071	6.086	P<0.001
	方案二	0.637	0.065	7.431	P<0.001
	方案一和方案二	-	-	1.255	P>0.05