Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere Article Swipe
Jingye Ren
,
Shichun Zou
,
Honghao Xu
,
Guiquan Liu
,
Zhe Wang
,
Anran Zhang
,
Chuanfeng Zhao
,
Min Hu
,
Dongjie Shang
,
Lizi Tang
,
Ru‐Jin Huang
,
Yele Sun
,
Fang Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2025-1483
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2025-1483
The accurate prediction of cloud condensation nuclei (CCN) number concentration (NCCN) on a large spatiotemporal scale is challenging but critical to evaluate the aerosol cloud interaction (ACI) effect. Combining multi-source dataset and the NCCN simulated by the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model, we have developed a new machine learning-based model which predicts well both regional and hourly-to-yearly scale NCCN at typical supersaturations in the North China Plain (NCP). We show that the prediction bias of NCCN compared to observations is reduced from -39 % with the WRF-Chem model to approximately -8 % with the new model. The greatest improvement is seen in polluted cases. The new model captures well the spatial variation and better describes long-term trends of NCCN than the WRF-Chem. More importantly, the study reveals a significant long-term decreasing trend of NCCN in NCP due to a rapid reduction in aerosol concentrations from 2014 to 2018, during which a series of strict emission reduction measures were implemented by the Chinese government. This reflects the climate benefit of pollution control. Our study further illustrates that the new model reduces the uncertainty in simulating cloud radiative forcing from an overestimation of 1.07±0.76 W m-2 to only 0.18±0.65 W m-2, illustrating the high sensitivity of climate forcing to changes in NCCN. This work offers a new modeling framework that has the potential to greatly improve the assessment of the ACI effect in current models, and guides the way to simulate CCN in other regions around the world.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-2025-1483
- https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1483/egusphere-2025-1483.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410998041
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410998041Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/egusphere-2025-1483Digital Object Identifier
- Title
-
Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphereWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-03Full publication date if available
- Authors
-
Jingye Ren, Shichun Zou, Honghao Xu, Guiquan Liu, Zhe Wang, Anran Zhang, Chuanfeng Zhao, Min Hu, Dongjie Shang, Lizi Tang, Ru‐Jin Huang, Yele Sun, Fang ZhangList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-2025-1483Publisher landing page
- PDF URL
-
https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1483/egusphere-2025-1483.pdfDirect link to full text PDF
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-
YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
- OA URL
-
https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1483/egusphere-2025-1483.pdfDirect OA link when available
- Concepts
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Radiative forcing, Atmosphere (unit), Radiative transfer, Cloud computing, Scale (ratio), Environmental science, Forcing (mathematics), Meteorology, Atmospheric sciences, Cloud forcing, Climatology, Computer science, Geography, Geology, Physics, Cartography, Aerosol, Operating system, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 4, 79, 122, 137, 157, 173, 195, 208, 231 |
| abstract_inverted_index.on | 12 |
| abstract_inverted_index.to | 21, 82, 93, 142, 151, 199, 211, 226, 242 |
| abstract_inverted_index.we | 47 |
| abstract_inverted_index.-39 | 87 |
| abstract_inverted_index.ACI | 233 |
| abstract_inverted_index.CCN | 244 |
| abstract_inverted_index.NCP | 140 |
| abstract_inverted_index.Our | 176 |
| abstract_inverted_index.The | 1, 101, 109 |
| abstract_inverted_index.and | 32, 40, 60, 117, 238 |
| abstract_inverted_index.but | 19 |
| abstract_inverted_index.due | 141 |
| abstract_inverted_index.has | 223 |
| abstract_inverted_index.m-2 | 198 |
| abstract_inverted_index.new | 51, 99, 110, 182, 219 |
| abstract_inverted_index.the | 23, 33, 37, 68, 76, 90, 98, 114, 125, 129, 165, 170, 181, 185, 205, 224, 229, 232, 240, 249 |
| abstract_inverted_index.way | 241 |
| abstract_inverted_index.2014 | 150 |
| abstract_inverted_index.More | 127 |
| abstract_inverted_index.NCCN | 34, 63, 80, 123, 138 |
| abstract_inverted_index.This | 168, 215 |
| abstract_inverted_index.bias | 78 |
| abstract_inverted_index.both | 58 |
| abstract_inverted_index.from | 86, 149, 192 |
| abstract_inverted_index.have | 48 |
| abstract_inverted_index.high | 206 |
| abstract_inverted_index.m-2, | 203 |
| abstract_inverted_index.only | 200 |
| abstract_inverted_index.seen | 105 |
| abstract_inverted_index.show | 74 |
| abstract_inverted_index.than | 124 |
| abstract_inverted_index.that | 75, 180, 222 |
| abstract_inverted_index.well | 57, 113 |
| abstract_inverted_index.were | 162 |
| abstract_inverted_index.with | 43, 89, 97 |
| abstract_inverted_index.work | 216 |
| abstract_inverted_index.(ACI) | 27 |
| abstract_inverted_index.(CCN) | 8 |
| abstract_inverted_index.2018, | 152 |
| abstract_inverted_index.China | 70 |
| abstract_inverted_index.NCCN. | 214 |
| abstract_inverted_index.North | 69 |
| abstract_inverted_index.Plain | 71 |
| abstract_inverted_index.cloud | 5, 25, 189 |
| abstract_inverted_index.large | 14 |
| abstract_inverted_index.model | 54, 92, 111, 183 |
| abstract_inverted_index.other | 246 |
| abstract_inverted_index.rapid | 144 |
| abstract_inverted_index.scale | 16, 62 |
| abstract_inverted_index.study | 130, 177 |
| abstract_inverted_index.trend | 136 |
| abstract_inverted_index.which | 55, 154 |
| abstract_inverted_index.(NCCN) | 11 |
| abstract_inverted_index.(NCP). | 72 |
| abstract_inverted_index.around | 248 |
| abstract_inverted_index.better | 118 |
| abstract_inverted_index.cases. | 108 |
| abstract_inverted_index.during | 153 |
| abstract_inverted_index.effect | 234 |
| abstract_inverted_index.guides | 239 |
| abstract_inverted_index.model, | 46 |
| abstract_inverted_index.model. | 100 |
| abstract_inverted_index.nuclei | 7 |
| abstract_inverted_index.number | 9 |
| abstract_inverted_index.offers | 217 |
| abstract_inverted_index.series | 156 |
| abstract_inverted_index.strict | 158 |
| abstract_inverted_index.trends | 121 |
| abstract_inverted_index.world. | 250 |
| abstract_inverted_index.Chinese | 166 |
| abstract_inverted_index.Weather | 38 |
| abstract_inverted_index.aerosol | 24, 147 |
| abstract_inverted_index.benefit | 172 |
| abstract_inverted_index.changes | 212 |
| abstract_inverted_index.climate | 171, 209 |
| abstract_inverted_index.coupled | 42 |
| abstract_inverted_index.current | 236 |
| abstract_inverted_index.dataset | 31 |
| abstract_inverted_index.effect. | 28 |
| abstract_inverted_index.forcing | 191, 210 |
| abstract_inverted_index.further | 178 |
| abstract_inverted_index.greatly | 227 |
| abstract_inverted_index.improve | 228 |
| abstract_inverted_index.machine | 52 |
| abstract_inverted_index.models, | 237 |
| abstract_inverted_index.reduced | 85 |
| abstract_inverted_index.reduces | 184 |
| abstract_inverted_index.regions | 247 |
| abstract_inverted_index.reveals | 131 |
| abstract_inverted_index.spatial | 115 |
| abstract_inverted_index.typical | 65 |
| abstract_inverted_index.Research | 39 |
| abstract_inverted_index.WRF-Chem | 91 |
| abstract_inverted_index.accurate | 2 |
| abstract_inverted_index.captures | 112 |
| abstract_inverted_index.compared | 81 |
| abstract_inverted_index.control. | 175 |
| abstract_inverted_index.critical | 20 |
| abstract_inverted_index.emission | 159 |
| abstract_inverted_index.evaluate | 22 |
| abstract_inverted_index.greatest | 102 |
| abstract_inverted_index.measures | 161 |
| abstract_inverted_index.modeling | 220 |
| abstract_inverted_index.polluted | 107 |
| abstract_inverted_index.predicts | 56 |
| abstract_inverted_index.reflects | 169 |
| abstract_inverted_index.regional | 59 |
| abstract_inverted_index.simulate | 243 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.Chemistry | 44 |
| abstract_inverted_index.Combining | 29 |
| abstract_inverted_index.WRF-Chem. | 126 |
| abstract_inverted_index.describes | 119 |
| abstract_inverted_index.developed | 49 |
| abstract_inverted_index.framework | 221 |
| abstract_inverted_index.long-term | 120, 134 |
| abstract_inverted_index.pollution | 174 |
| abstract_inverted_index.potential | 225 |
| abstract_inverted_index.radiative | 190 |
| abstract_inverted_index.reduction | 145, 160 |
| abstract_inverted_index.simulated | 35 |
| abstract_inverted_index.variation | 116 |
| abstract_inverted_index.(WRF-Chem) | 45 |
| abstract_inverted_index.0.18±0.65 | 201 |
| abstract_inverted_index.1.07±0.76 | 196 |
| abstract_inverted_index.assessment | 230 |
| abstract_inverted_index.decreasing | 135 |
| abstract_inverted_index.prediction | 3, 77 |
| abstract_inverted_index.simulating | 188 |
| abstract_inverted_index.Forecasting | 41 |
| abstract_inverted_index.challenging | 18 |
| abstract_inverted_index.government. | 167 |
| abstract_inverted_index.illustrates | 179 |
| abstract_inverted_index.implemented | 163 |
| abstract_inverted_index.improvement | 103 |
| abstract_inverted_index.interaction | 26 |
| abstract_inverted_index.sensitivity | 207 |
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| abstract_inverted_index.uncertainty | 186 |
| abstract_inverted_index.condensation | 6 |
| abstract_inverted_index.illustrating | 204 |
| abstract_inverted_index.importantly, | 128 |
| abstract_inverted_index.multi-source | 30 |
| abstract_inverted_index.observations | 83 |
| abstract_inverted_index.approximately | 94 |
| abstract_inverted_index.concentration | 10 |
| abstract_inverted_index.concentrations | 148 |
| abstract_inverted_index.learning-based | 53 |
| abstract_inverted_index.overestimation | 194 |
| abstract_inverted_index.spatiotemporal | 15 |
| abstract_inverted_index.hourly-to-yearly | 61 |
| abstract_inverted_index.supersaturations | 66 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 13 |
| citation_normalized_percentile.value | 0.21063159 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |