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1.
Sci Total Environ ; 933: 173129, 2024 Jul 10.
Artículo en Inglés | MEDLINE | ID: mdl-38734104

RESUMEN

The vertical structure and microphysical characteristics of stratiform precipitation (SP) and convective precipitation (CP) in North China are revealed based on the GPM-DPR product during boreal summer of 2014-2021 in this study. Additionally, the differences in precipitation features between the mountain and the plain are investigated. Under the combined influence of climatic factors and local topography, the precipitation amount is larger in the plain than in the mountain while precipitation frequency exhibits an opposite pattern. The proportions of the two precipitation types are similar in the mountain and the plain, with CP contributing to approximately a quarter of total precipitation frequency. In terms of mean intensity, both SP and CP are roughly 20 % more intense in the plain than in the mountain. The greater number of light SPs is a major contributor to higher precipitation frequency in the mountain, while more intense CPs result in larger precipitation amount in the plain. Compared to the mountain, the precipitation system is deeper in the plain, where higher storm top altitudes (STAs) and larger freezing level heights contribute to more intense CPs. Besides, it is observed that for the STA, more intense CPs occur in the plain compared to the mountain. In both the mountain and the plain, the coalescence process is dominant in the low-level layers for heavy (8-20 mm/h) to storm-level (>20 mm/h) CPs. Compared to the mountain, the low-level growth of hydrometeor size and radar reflectivity is more significant in the plain. These findings are important to quantitative precipitation estimation and precipitation prediction in the mountainous region, and can help understand the influence of local topography on precipitation.

2.
Sci Total Environ ; 933: 173022, 2024 Jul 10.
Artículo en Inglés | MEDLINE | ID: mdl-38719049

RESUMEN

Urban areas are increasingly vulnerable to sudden flooding disasters caused by intense rainfall and high imperviousness degree, resulting in great economic losses and human casualties. Interactions between rainfall data and urban catchment characteristics highlight the urgent need of accurate and effective precipitation data to apply in reliable hydrological simulations. However, it remains a challenge to obtain accurate rainfall datasets on such small scales in urban areas. As satellite remote sensing is the only method that can achieve global observation, it is important to evaluate satellite precipitation products in their ability to accurately capture intense precipitation on urban flood scales. This study evaluates the performance of the latest version 06B (V06B) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) in North China Plain, with using the Radar-Gauge merged precipitation estimates as reference data. First, it could be concluded that IMERG fails to accurately estimate precipitation in the whole study area, having the problem of overestimating light precipitation and underestimating heavy precipitation. Second, results show that IMERG has poor ability to capture heavy precipitation on small scales, with the percentage of Hit nearly 0 and the percentage of Miss higher than 40 % for all the precipitation cases. Third, with the expansion of heavy precipitation centers' coverage, the problem of IMERG not to detect heavy precipitation gets mitigated, with the percentage of Miss decreasing by 14 % (19 %). However, the ability to capture both spatial location and precipitation intensity is still not good, the percentage of Hit ranging from 0.05 % to 7 %, without obvious improvement. When IMERG is able to capture the center of strong precipitation, it also tends to overestimate the weak precipitation around the center of strong precipitation. Results of this study provide an improved understanding of how well the V06B IMERG products capture the heavy precipitation center at small scales in urban areas, which will be useful for both developers and users of IMERG.

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