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

Abstract

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

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.

Figures/Tables 11

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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

Precipitation retrieval for Xinjiang region based on radar and remote sensing satellites

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