Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulation Article Swipe
Wang Bo
,
Xiaolin Liu
,
Hua Shenbing
,
Shuanglong Jin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s42452-025-07160-5
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s42452-025-07160-5
S-band radar beams are easily obstructed by terrain during propagation. After the beam is blocked, the radar cannot receive the echo signal of the target area, forming a data blind spot. Traditional methods cannot obtain a complete precipitation` dataset, which increases the difficulty of precipitation estimation and leads to errors in precipitation estimation results, resulting in lower scores. This study conducted quantitative precipitation estimation using S-band dual-polarization radar under a convective scale ensemble simulation. Firstly, a certain region in Hebei Province was selected as the research object to conduct convective scale ensemble simulation to obtain more precipitation datasets. Then, the precipitation data was smoothed and used to invert the radar precipitation intensity every 6 min. The estimated hourly rainfall of the radar was matched with the hourly rainfall measurement of a single-point rainfall station. Finally, based on deep learning theory, a quantitative precipitation estimation model for S-band dual-polarization radar was constructed. The experimental results show that using the proposed method, the root mean square error (RMSE) value is less than 0.372, the mean absolute error (MAE) is less than 0.247, the correlation coefficient (CC) value is higher than 94.7%, the TS score is higher than 95.1%, and the quantitative precipitation estimation effect is good.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s42452-025-07160-5
- https://link.springer.com/content/pdf/10.1007/s42452-025-07160-5.pdf
- OA Status
- diamond
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411075023
All OpenAlex metadata
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- OpenAlex ID
-
https://openalex.org/W4411075023Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s42452-025-07160-5Digital Object Identifier
- Title
-
Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-05Full publication date if available
- Authors
-
Wang Bo, Xiaolin Liu, Hua Shenbing, Shuanglong JinList of authors in order
- Landing page
-
https://doi.org/10.1007/s42452-025-07160-5Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s42452-025-07160-5.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s42452-025-07160-5.pdfDirect OA link when available
- Concepts
-
Radar, Precipitation, Dual (grammatical number), Convection, Weather radar, Scale (ratio), Meteorology, Polarization (electrochemistry), Remote sensing, Environmental science, Computer science, Geology, Physics, Telecommunications, Chemistry, Literature, Physical chemistry, Quantum mechanics, ArtTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.spot. | 31 |
| abstract_inverted_index.study | 60 |
| abstract_inverted_index.under | 69 |
| abstract_inverted_index.using | 65, 157 |
| abstract_inverted_index.value | 167, 185 |
| abstract_inverted_index.which | 40 |
| abstract_inverted_index.(RMSE) | 166 |
| abstract_inverted_index.0.247, | 180 |
| abstract_inverted_index.0.372, | 171 |
| abstract_inverted_index.94.7%, | 189 |
| abstract_inverted_index.95.1%, | 196 |
| abstract_inverted_index.S-band | 1, 66, 147 |
| abstract_inverted_index.cannot | 18, 34 |
| abstract_inverted_index.during | 9 |
| abstract_inverted_index.easily | 5 |
| abstract_inverted_index.effect | 202 |
| abstract_inverted_index.errors | 50 |
| abstract_inverted_index.higher | 187, 194 |
| abstract_inverted_index.hourly | 118, 127 |
| abstract_inverted_index.invert | 108 |
| abstract_inverted_index.object | 87 |
| abstract_inverted_index.obtain | 35, 95 |
| abstract_inverted_index.region | 78 |
| abstract_inverted_index.signal | 22 |
| abstract_inverted_index.square | 164 |
| abstract_inverted_index.target | 25 |
| abstract_inverted_index.certain | 77 |
| abstract_inverted_index.conduct | 89 |
| abstract_inverted_index.forming | 27 |
| abstract_inverted_index.matched | 124 |
| abstract_inverted_index.method, | 160 |
| abstract_inverted_index.methods | 33 |
| abstract_inverted_index.receive | 19 |
| abstract_inverted_index.results | 154 |
| abstract_inverted_index.scores. | 58 |
| abstract_inverted_index.terrain | 8 |
| abstract_inverted_index.theory, | 140 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Finally, | 135 |
| abstract_inverted_index.Firstly, | 75 |
| abstract_inverted_index.Province | 81 |
| abstract_inverted_index.absolute | 174 |
| abstract_inverted_index.blocked, | 15 |
| abstract_inverted_index.complete | 37 |
| abstract_inverted_index.dataset, | 39 |
| abstract_inverted_index.ensemble | 73, 92 |
| abstract_inverted_index.learning | 139 |
| abstract_inverted_index.proposed | 159 |
| abstract_inverted_index.rainfall | 119, 128, 133 |
| abstract_inverted_index.research | 86 |
| abstract_inverted_index.results, | 54 |
| abstract_inverted_index.selected | 83 |
| abstract_inverted_index.smoothed | 104 |
| abstract_inverted_index.station. | 134 |
| abstract_inverted_index.conducted | 61 |
| abstract_inverted_index.datasets. | 98 |
| abstract_inverted_index.estimated | 117 |
| abstract_inverted_index.increases | 41 |
| abstract_inverted_index.intensity | 112 |
| abstract_inverted_index.resulting | 55 |
| abstract_inverted_index.convective | 71, 90 |
| abstract_inverted_index.difficulty | 43 |
| abstract_inverted_index.estimation | 46, 53, 64, 144, 201 |
| abstract_inverted_index.obstructed | 6 |
| abstract_inverted_index.simulation | 93 |
| abstract_inverted_index.Traditional | 32 |
| abstract_inverted_index.coefficient | 183 |
| abstract_inverted_index.correlation | 182 |
| abstract_inverted_index.measurement | 129 |
| abstract_inverted_index.simulation. | 74 |
| abstract_inverted_index.constructed. | 151 |
| abstract_inverted_index.experimental | 153 |
| abstract_inverted_index.propagation. | 10 |
| abstract_inverted_index.quantitative | 62, 142, 199 |
| abstract_inverted_index.single-point | 132 |
| abstract_inverted_index.precipitation | 45, 52, 63, 97, 101, 111, 143, 200 |
| abstract_inverted_index.precipitation` | 38 |
| abstract_inverted_index.dual-polarization | 67, 148 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.8899999856948853 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.27649343 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |